科学工具
维思纳为研究智能体提供29 个专用工具构建于29 个科学库、数据库和模型覆盖文献检索、实验方案工作、化学、工作区文件和云计算。
智能体会为每一步选择工具，报告假设和缺口，并保留工具输出供复查。你也可以固定特定工具，或让智能体决定。在应用中，目录按可展开的概念分组，并与文件和 Computer/JupyterLab 一起作为右侧面板标签显示。免费试用。
如果你关注化学，请查看我们的专门页面真正面向化学的 AI，包含实验方案审计、反应预测和计算化学工作流。
在进实验室前设计实验？请查看实验方案设计，了解有来源依据的草稿、待验证项和审计证书。
需要持久项目记忆？请查看工作区文件，保存实验方案、审计、上传论文、图表、CSV 和可复用笔记。
需要用于建模、计算或模拟的笔记本？请查看科学模拟，了解维思纳的持久化云端 JupyterLab 和科学计算机功能。
我们持续改进并扩展维思纳，以服务你的研究和工程工作。告诉我们你需要什么。
工作区
1 个工具用于保存研究文档、上传文件、生成输出和可复用科学工作的持久文件与文件夹。
工作区文件
持久化研究文件系统
创建、读取、编辑、整理和搜索维思纳文件侧栏中显示的持久文件。它适用于持久研究文档、保存的实验方案审计、上传的 PDF/图像/CSV、Markdown 笔记、生成图表和项目文件夹。不要用它保存临时 Jupyter 或 Linux 计算机文件；快速执行工作请用计算命令行，想保留的文件请用工作区文件。
工作方式
智能体把工作区文件当作一个小型私有研究硬盘。它解析或创建普通正斜杠路径，按需读取已有文件，写入持久 Markdown 或数据文件，更新文件侧栏，并返回可点击工作区链接。当任务需要计算时，智能体应单独使用科学计算机，并且不能声称文件已在两个系统间移动；直接桥接尚未可用。
工作区文件流程
智能体把工作区文件作为面向用户文档的持久事实来源。它可以从空工作区开始，创建文件夹、保存研究输出或编辑已有文件，而不涉及计算沙箱。
空工作区
起始路径当用户还没有文件，并希望维思纳创建第一个项目文件夹或文档时使用。
- 1 创建项目文件夹 选择清晰路径，例如 /Projects/Battery Literature 或 /Notes。
- 2 写入第一个文件 创建 README、文献综述笔记、检查清单、实验方案草稿或审计证书。
- 3 返回工作区链接 显示保存的文件路径，方便用户在右侧面板打开。
已有工作区
维护路径当用户已有上传文件、保存的审计或项目文件夹时使用。
- 1 搜索或列出文件 读取或编辑前，按路径、名称或文件夹找到候选文件。
- 2 编辑前先读取 修补目标段落前检查当前文件内容。
- 3 只更新被请求的文件 保持编辑范围明确，并保留无关工作区文件。
计算机边界
独立卷当任务同时涉及持久工作区文件和临时科学计算机执行文件时使用。
- 1 使用正确的卷 工作区文件是持久文档；计算机文件用于执行和笔记本。
- 2 不要声称已转移 工作区到计算机的直接文件传输尚不可用；智能体应明确说明，而不是暗示已经复制。
持久结果
用户会得到保存的文件、更新的文件夹、渲染预览，或未找到匹配文件的清晰报告。
输入
自然语言指令，例如“创建项目文件夹”、“把这个保存为 /Notes/summary.md”、“做文献综述并写入文件”、“读取我上传的 PDF”或“把这个审计移动到项目文件夹”。
输出
目录列表、文件内容、渲染预览、保存的文档，或带可点击工作区路径的确认信息，可在右侧面板打开。
限制
工作区文件独立于科学计算机/Jupyter 文件系统。它本身不运行代码，也不会在没有明确请求时自动整理或删除文件。工作区文件和科学计算机之间尚不能直接复制、导入、导出或移动文件。免费账号存储为 0 GB；Plus 为 2 GB；Ultra 为 10 GB。
提示
当结果需要在聊天之后继续保留时使用它：保存笔记、文献摘要、实验方案草稿、审计证书、上传文件和最终图表。Compute Shell 或 Jupyter 仅用于执行、分析和临时计算机文件。
Example prompts
- › 工作区文件工具能做什么？我应该如何使用？
- › 创建 /Projects/Graphene Membranes/literature-review.md，写一段关于氧化石墨烯膜水净化的简短文献综述
- › 创建 /Notes 文件夹，并写一个入门 README，说明我应该如何组织研究文件
研究与发现
6 个工具检索科学文献、专利和网页。AI 智能体会在多个数据库中迭代优化查询，直到得到全面且带引用的结果。
文献综述
带最新文献补充检索的索引论文综述
检索 Vicena 广泛的科学论文索引，覆盖主要期刊、预印本、生物医学记录、摘要和可用全文。它提取紧凑证据包，并提供稳定论文/摘录指针。当问题需要比索引可能包含内容更新的出版物时，它会在使用网页研究前明确说明需要最新文献补充检索。
工作方式
该工具从请求中提取约束，在索引科学语料中生成目标查询，选择相关论文，并返回标记为 P1、P2 和 P1-E1 形式摘录指针的紧凑证据包。对于当前、近期、最先进或索引为空的问题，它会显示文献时效性检查，解释为什么需要额外一轮，然后添加限定在科学文献来源内的网页研究补充。
文献综述流程
2 种模式该工具先检索索引论文语料。当需要最新文献时，它会明确解释原因，然后用限定科学来源的网页补充填补当前文献缺口。
索引论文轮次
快速路径用于在维思纳索引科学语料上进行主要文献综述。
- 1 生成查询 为索引语料创建有针对性的科学关键词检索。
- 2 选择论文 返回证据前，根据用户请求和约束对索引命中排序。
- 3 提取指针 返回带 P1/P2 标签和 P1-E1 摘录指针的紧凑论文包。
- 4 证据边界 报告选中文章数量、证据指针数量、文献时效性状态和回答规则。
最新文献补充检索
契约路径Used for current, recent, state-of-the-art, or empty-index requests.
- 1 Explain freshness check Tell the user why an extra pass is running: the request is current/recent/state-of-the-art or the index returned no papers.
- 2 Scientific source gate Restrict Web Research to papers, preprints, publisher pages, PubMed/PMC/arXiv/Crossref-style records, and academic pages.
- 3 Reject general web Exclude news, marketing, vendor, consulting, patent analytics, product pages, and general blogs.
- 4 Separate evidence Treat supplement evidence as current-literature coverage distinct from indexed paper evidence.
Grounded synthesis
The chat agent answers from paper evidence pointers and accepted scientific supplement evidence only.
输入
A natural language research question or topic. Be as specific as you would in a database search. You can include constraints like date ranges, organisms, or techniques.
输出
A bounded literature context with selected papers, source URLs, compact excerpts, evidence pointers, a paper evidence boundary, and a visible Freshness Check when a scientific web supplement is needed.
限制
The local paper index may lag very recent papers. The automatic supplement is limited to scientific literature sources; paywalled full text and newly posted papers may still be unavailable. Date filters are honored only when the request explicitly asks for a date range or recent/current literature; inferred filters are reported and ignored.
提示
Use this for broad research questions and trend analysis. For operational protocol drafts, use Protocol Builder instead. For patents, use Patent Research.
Example prompts
- › What can the Literature Review tool do, and how should I use it?
- › Find recent papers on CRISPR delivery mechanisms in solid tumors
- › Review the literature on metal-organic frameworks for CO2 capture
专利研究
Indexed patent search with freshness supplement
Searches Vicena’s indexed patent corpus for patent records, assignees, inventors, publication dates, and legal technical context. When the question needs newer patent activity than the index may contain, it announces a patent-only freshness check before using Web Research.
工作方式
The tool extracts constraints from the request, generates targeted queries over the indexed patent corpus, applies date and inventor filters when explicitly requested, requires indexed assignee/applicant/owner evidence for assignee-specific requests, selects relevant patent records, and returns compact evidence packets labeled with source-stable publication-number pointers such as US10932928B2-E1. For current, recent, state-of-the-art, empty-index, or unverifiable-assignee patent requests, it shows a Freshness Check and adds a Web Research supplement restricted to patent sources.
Patent Research Flow
2 种模式The tool first searches the indexed patent corpus. When freshness is needed, it explains the reason, then fills current patent-record gaps with a patent-only web supplement.
Indexed patent pass
快速路径Used for the primary patent search over Vicena’s indexed patent corpus.
- 1 生成查询 Create targeted patent keyword searches for the indexed corpus.
- 2 Apply filters Honor explicit publication-date and inventor filters, require matching assignee metadata for assignee-specific requests, and report ignored inferred date filters.
- 3 提取指针 Return compact patent packets with source-stable publication-number labels and excerpt pointers such as US10932928B2-E1.
- 4 证据边界 Report selected patent count, evidence pointer count, freshness status, and answering rules.
Patent freshness supplement
契约路径Used for current, recent, state-of-the-art, or empty-index patent requests.
- 1 Explain freshness check Tell the user why an extra pass is running: the request is current/recent/state-of-the-art or the index returned no patents.
- 2 Patent source gate Restrict Web Research to patent-office records, Google Patents-style records, patent families, and assignee patent pages.
- 3 Reject general web Exclude scientific papers, blogs, news, marketing, vendor pages, consulting pages, product pages, and general web pages.
- 4 Separate evidence Treat supplement evidence as current patent-record coverage distinct from indexed patent evidence.
Grounded synthesis
The chat agent answers from patent evidence pointers and accepted patent supplement evidence only.
输入
A natural language description of the invention or technology. You can include specific assignees, date ranges, or patent classification codes.
输出
A bounded patent context with selected patent records, source URLs, compact excerpts, source-stable evidence pointers, date/inventor/assignee filter reporting, a patent evidence boundary, and a visible Freshness Check when a patent-only web supplement is needed.
限制
The local patent index may lag recently published applications and may not expose assignee metadata for every indexed patent. Assignee-specific answers require positive assignee evidence from indexed metadata or the patent-only supplement. The automatic supplement is limited to patent sources such as patent-office records, Google Patents-style pages, patent families, and assignee patent pages. It does not provide legal opinions on patentability or freedom to operate.
提示
Use this for patent landscape analysis and patent-record discovery. For scientific papers, use Literature Review. For broad non-patent prior art, use Web Research.
Example prompts
- › What can the Patent Research tool do, and how should I use it?
- › Find patents on electrochemical CO2 reduction catalysts filed after 2020
- › Search patents for a biodegradable polymer stent with drug elution
实验方案生成器
Evidence-grounded operational protocol drafts
Turns a scientific objective into a structured lab protocol draft: checklist, reagents, equipment, chronological steps, data analysis, troubleshooting, assumptions, gaps, and an AI-led sanity review. Helper chemistry and thermo tools provide context when the draft has enough structured inputs.
工作方式
Searches indexed papers and scientific web evidence, drafts a protocol, records source-backed details separately from assumptions, and runs a sanity review. Existing chemistry helpers are used as advisory context, not automatic verdicts; the AI decides whether a tool result applies and preserves unresolved concerns.
Protocol Builder Flow
3 种模式The tool turns a scientific objective into a Nature/protocols.io-style operational draft. It separates source-backed details from assumptions and then runs an AI-led sanity review before presenting the final protocol.
Build draft protocol
快速路径Used to assemble the first structured protocol from the evidence and explicit assumptions.
- 1 Create protocol sections Produce objective, background, scope, safety, materials, equipment, before-you-begin notes, procedure, timing, QC, expected results, data analysis, troubleshooting, and evidence.
- 2 Label assumptions Record inferred values as assumptions instead of presenting them as extracted facts.
- 3 Track missing details Keep unresolved variables visible when the evidence does not support a concrete choice.
- 4 Reduce critical gaps Run one bounded V1 gap search for the most important missing detail, then preserve anything still unresolved.
Sanity review path
契约路径Used to check whether the draft is internally coherent before the final answer is shown.
- 1 Extract check targets Identify explicit solvent/temperature, reagent-vessel, reagent-reagent, yield, stoichiometry, and biological constraint targets when the draft provides enough structure.
- 2 Add helper context Call chemistry or thermo helpers only when required inputs are present; otherwise record the missing input as a gap.
- 3 AI scientific review Use evidence, assumptions, and helper context to flag blockers, warnings, gaps, or notes.
- 4 Preserve concerns Surface unresolved issues in the final sanity review instead of letting the draft hide them.
Operational draft for review
The chat agent presents a scientist-facing protocol in the format: Brief, Materials, Equipment, Safety, Procedure, Timing, QC, Expected Results, Troubleshooting, and Evidence.
输入
A scientific objective or experimental goal. The more specific you are about the target material, organism, technique, scale, constraints, and available equipment, the better the draft.
输出
An operational protocol draft with checklist, reagents, equipment, steps, analysis, troubleshooting, source-backed details, assumptions, missing critical details, and sanity findings labeled as blockers, warnings, gaps, or notes.
限制
This is a draft builder, not a validated executable protocol. Helper checks have limited coverage and may miss context. A scientist must review unresolved gaps, assumptions, safety, and feasibility before any lab use.
提示
Use this when you need to build, draft, create, write, design, or assemble a first operational protocol draft. Use Protocol Audit only after a protocol exists.
Example prompts
- › What can the Protocol Builder tool do, and how should I use it?
- › Build a protocol for synthesizing gold nanoparticles via citrate reduction
- › Draft an operational Suzuki coupling protocol for aryl bromides
网页研究
Verified web resources and sourced facts
Searches the open web for practical lab information: technical specifications, manuals, chemical pricing, supplier pages, safety data sheets, and other sources outside papers and patents. It verifies source text before the agent answers.
工作方式
Routes each request to the smallest workflow that can answer it. Resource lookups build explicit required resources, verify candidate URLs against source text, and return only verified resources. Comparisons, pricing, specifications, and broader questions use a deeper evidence contract that tracks coverage and missing fields.
Adaptive Web Research Flow
6 种模式The tool first decides whether the user needs verified resource URLs or researched facts, then runs only the path needed for that request.
Resource lookup path
快速路径Used for manuals, SDS documents, datasheets, and other requests where the answer should be verified URLs.
- 1 Required resources Create one required resource per requested vendor or entity, such as Sigma-Aldrich SDS for DMSO, CAS 67-68-5.
- 2 Search candidates Run targeted queries and read likely product, SDS, PDF, or manual pages.
- 3 Verify identity Accept a candidate only when source text supports the requested resource, source, subject, and CAS or model identity when requested.
- 4 Lookup boundary Report Required resources, Verified resources, Missing resources, and Lookup complete: Yes or No.
- 5 Verified URLs only The agent may list only verified resource URLs. Related but unverified pages remain discovery metadata.
Evidence research path
契约路径Used when the answer needs facts, tables, ranges, comparisons, or coverage across several entities and fields.
- 1 Research contract Define coverage targets, minimum evidence, comparison fields, and quality preferences.
- 2 Coverage-aware search Search and select useful sources across targets before spending reads on duplicates.
- 3 Extract evidence Read candidates and return exact paragraph IDs, source URLs, and supporting text.
- 4 证据边界 Report covered targets, missing targets, supported evidence, and whether another pass is needed.
Grounded answer
The chat agent answers from accepted evidence only. If required resources or coverage are missing, it runs another focused pass instead of filling gaps.
输入
Any question about equipment, pricing, suppliers, technical specifications, or practical lab information that is not covered by scientific papers or patents.
输出
Verified resource links for lookup requests, or a structured answer with extracted facts, prices, or specifications. Every claim is grounded in accepted source text and cited by URL.
限制
Results depend on what is publicly available on the web. Paywalled content, internal company documents, and very recent pages may not be accessible.
提示
Use this for practical lab questions: equipment specs, reagent pricing, supplier comparisons, SDS sheets. For scientific literature, use Literature Review.
Example prompts
- › What can the Web Research tool do, and how should I use it?
- › What is the price range of 99.9% pure titanium dioxide from lab suppliers?
- › Compare specifications of benchtop centrifuges rated for 15,000 RPM
数据集检索
Public repository dataset discovery
Searches public dataset repositories through official APIs and returns normalized candidates with source links, IDs, DOIs, public status, file counts, and direct file candidates when an API provides them. It separates fuzzy discovery from deterministic download so the agent can ask for clarification when results are ambiguous.
工作方式
The agent sends a focused query to supported repository adapters such as BioStudies, Zenodo, and Figshare. Each adapter uses the repository API, applies conservative per-host pacing, parses only documented JSON fields, and returns ranked candidates without saving bytes. Follow-up download requires an explicit selected candidate, dataset_resolve result, or direct file URL.
Repository Discovery Flow
3 种模式Search official APIs first, then resolve or download only the selected dataset candidate.
Ready to resolve
The selected result can be passed to Dataset Resolve or Dataset Download depending on whether it is a repository page or direct file URL.
输入
A dataset query, title, DOI, accession, organism, topic, or repository hint. Optional inputs select repositories, result limit, and BioStudies collection search such as transqst.
输出
A candidate list with repository, title, landing URL, API URL, DOI or accession, release date, public status, file count, snippets, and available direct file links.
限制
This is not a crawler and does not scrape arbitrary websites. It does not download files, bypass login walls, or silently choose among ambiguous datasets. Repository failures and rate limits are returned as warnings so the agent can continue with other sources or ask the user for a direct link.
提示
Use Dataset Search when the user describes a dataset but does not provide a direct file URL. Use Dataset Download only after a specific candidate or file link is selected.
Example prompts
- › What can the Dataset Search tool do, and how should I use it?
- › Find the S-TQST115 BioStudies dataset
- › Search Zenodo for the Clotho dataset
数据集下载
Direct source files saved to Workspace
Downloads public dataset files, article supplements, repository exports, PDFs, CSVs, ZIP archives, and other HTTP(S) file URLs into Vicena Workspace Files. Supported repository adapters resolve official BioStudies, Zenodo, and Figshare metadata into file candidates or single-file downloads. It keeps provenance with source URL, final redirected URL, MIME type, size, and sha256 checksum.
工作方式
The agent first uses Dataset Search, Literature Review, Patent Research, Web Research, or a repository page to identify a direct file URL or supported repository record. Repository adapters resolve BioStudies, Zenodo, and Figshare metadata into single-file downloads or multiple candidate files. The downloader then starts a background MCP job with redirects, a clear Vicena user agent, retries for transient failures, size limits, optional sha256 verification, and a durable Workspace save. It refuses relative Workspace paths, path traversal, local hosts, oversize files, and accidental overwrites unless overwrite is explicit.
Dataset Acquisition Flow
3 种模式The tool separates discovery from byte transfer: research tools find and verify the source, then Dataset Download saves exactly one file with provenance.
Reusable Workspace dataset
The saved file can be opened from Files, cited in the chat answer, or moved into Compute with Workspace Compute Transfer.
输入
A direct public HTTP(S) file URL plus an absolute Workspace destination path, such as /Datasets/study.csv or /Datasets/. Optional inputs include max_bytes, overwrite, and expected_sha256.
输出
A download job card that updates until the file is saved, fails, or asks for manual user upload. Successful jobs include a Workspace file link with source URL, final URL after redirects, MIME type, size in bytes, sha256 checksum, and a next-action field for the agent.
限制
This is a downloader plus explicit repository adapters, not a crawler. Unsupported HTML landing pages are rejected. It cannot bypass paywalls, login walls, robots restrictions, or rate limits. Current Workspace ingestion is capped at 50 MiB, so larger files return a manual-upload state. For multi-file repository pages, the resolver returns candidate URLs and a choose-candidate next action instead of auto-downloading.
提示
Use Dataset Search for supported repositories, or Web Research for publisher supplements and ordinary websites, then use Dataset Download to save the verified file into Workspace.
Example prompts
- › What can the Dataset Download tool do, and how should I use it?
- › Download https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv into /Datasets/iris.csv
- › Download https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv into /Datasets/penguins.csv and report the sha256
实验室模拟与验证
5 个工具Simulate your protocol in a virtual lab before going to the bench. The AI validates each step against thermodynamic, kinetic, and chemical constraints.
实验方案审计
Safety, yield, and feasibility check
Reviews an already described synthesis protocol or explicit procedure-safety question and flags physical impossibilities, proof gaps, and safety risks. It does not build or draft new protocols; use Protocol Builder for that.
工作方式
The agent decomposes your protocol and runs deterministic checks on each step: yield validation (reactant MW vs product MW via RDKit), atom balance (conservation of mass from SMILES), chemical compatibility (curated rules + SMARTS-based classification, e.g. HF+glass, alkali+protic, chlorinated+polycarbonate), and boiling point lookup from the Thermo library. Safety data is pulled live from PubChem. Every finding cites its source. Tools return facts, not verdicts — the LLM synthesizes the final judgment.
输入
An existing protocol, procedure excerpt, or concrete safety/feasibility question with the relevant reagents, quantities, temperatures, vessels, and claimed yields where available.
输出
A report flagging impossibilities (yield > 100%, atoms from nowhere), incompatibilities (with cited rules), GHS hazards, and coverage gaps where the audit could not verify and you must apply your own judgment.
限制
~20 hardcoded compatibility rules (more coming via RHEACT/CAMEO). Thermo library covers ~70,000 compounds; unusual or proprietary molecules may hit coverage gaps. The audit complements your expertise, it does not replace it.
提示
Use Protocol Builder to draft a protocol, then audit it here for physical issues before refining the procedure.
Example prompts
- › What can the Protocol Audit tool do, and how should I use it?
- › Check if 6.2g of naproxen methyl ester from 5g naproxen is physically possible
- › Audit a Fischer esterification: refluxing 30 mL acetic acid with 50 mL ethanol and 2 mL conc. H2SO4 in a round-bottom flask at 85°C for 2 hours, expecting 40 g of ethyl acetate
反应能量学
Virtual calorimeter and thermal safety
Calculates reaction enthalpy and the resulting adiabatic temperature rise. Detects thermal runaway risks, solvent boiling hazards, and exothermic safety concerns before you run the reaction.
工作方式
Computes reaction enthalpy from standard formation enthalpies (Thermo database, 70,000+ compounds). Calculates adiabatic temperature rise using heat capacities of the reaction mixture. Compares the predicted temperature against solvent boiling points and known decomposition thresholds to flag runaway or boiling risks.
输入
A chemical reaction with reagent names or formulas. Optionally include quantities and solvent for adiabatic temperature rise calculations.
输出
Reaction enthalpy (kJ/mol), adiabatic temperature rise, and safety flags (exothermic warning, boiling risk, runaway risk).
限制
Relies on standard formation enthalpy data. Compounds not in the Thermo database cannot be analyzed. Does not model heat dissipation or cooling.
提示
Use this before running any exothermic reaction. If the adiabatic temperature rise exceeds the solvent boiling point, you need active cooling or slower addition.
Example prompts
- › What can the Reaction Energetics tool do, and how should I use it?
- › Is the neutralization of concentrated H2SO4 with NaOH safe in a 500 mL flask?
- › Calculate the adiabatic temperature rise for nitration of toluene
反应动力学
Rate constants, half-lives, and timing
Estimates how long a reaction takes and how temperature affects speed. Uses the Arrhenius equation and integrated rate laws to calculate rate constants, half-lives, and time-to-completion.
工作方式
Uses the Arrhenius equation (k = A * exp(-Ea/RT)) to compute or adjust rate constants at different temperatures. Applies integrated rate laws for zero, first, and second-order reactions to calculate half-lives and time to reach a target conversion.
输入
Activation energy (Ea), pre-exponential factor (A), reaction order, and temperature. Or provide a known rate at one temperature to predict rates at another.
输出
Rate constant at the specified temperature, half-life, and estimated time to reach a target conversion percentage.
限制
Requires known kinetic parameters (Ea and A). For reactions where these are unknown, the tool can estimate from two data points at different temperatures.
提示
Use this to plan reaction timing and temperature optimization. Combine with Reaction Energetics to check both speed and safety at your chosen temperature.
Example prompts
- › What can the Reaction Kinetics tool do, and how should I use it?
- › How long does a first-order reaction with Ea=85 kJ/mol take at 60C vs 80C?
- › If a first-order reaction is 50% complete in 2 hours at 25C, how fast is it at 40C with Ea=65 kJ/mol?
溶解性预测器
Solvent compatibility and dissolution
Predicts whether a compound dissolves in a given solvent using polarity matching via LogP. Ranks common lab solvents by compatibility. Use before choosing a reaction or workup solvent.
工作方式
Calculates the octanol-water partition coefficient (LogP) of the solute using RDKit descriptors. Compares it against the polarity profile of common lab solvents (water, ethanol, DCM, hexane, DMSO, etc.) to rank solvents by predicted compatibility. Retrieves additional solubility data from PubChem when available.
输入
A compound name, SMILES, or CAS number. Optionally specify a solvent to check, or ask for a ranked list of common solvents.
输出
A compatibility prediction (soluble/insoluble/partial) with a ranked list of solvents from best to worst match.
限制
Based on LogP polarity matching, which is a heuristic. Does not account for specific solute-solvent interactions, pH effects, or temperature dependence.
提示
Use this before choosing a reaction solvent or planning a liquid-liquid extraction. For safety data on solvents, combine with the Safety Summary tool.
Example prompts
- › What can the Solubility Predictor tool do, and how should I use it?
- › Will ibuprofen dissolve in water or do I need an organic solvent?
- › Rank solvents for dissolving polyethylene glycol 6000
光谱预测器
Predicted IR, NMR, and MS fingerprints
Predicts the expected spectral fingerprint (IR, NMR, MS) of a compound by identifying its functional groups. Verify whether your reaction produced the correct product or determine what peaks to look for.
工作方式
Parses the molecular structure with RDKit and scans it against a library of SMARTS patterns to identify functional groups. Maps each group to its characteristic spectral signatures: IR absorption bands (cm-1), expected 1H/13C NMR chemical shift ranges, and mass fragmentation patterns. Returns a predicted fingerprint you can compare against your experimental spectrum.
输入
A compound name, SMILES string, or CAS number. You can also describe a reaction product and ask what peaks to expect.
输出
Predicted IR bands (cm-1 with assignment), expected NMR chemical shifts (ppm ranges per proton environment), and major mass spec fragments (m/z).
限制
Predictions are based on functional group identification, not full quantum mechanical calculations. Fine structure splitting in NMR and exact fragmentation patterns may differ from experiment.
提示
Use this to verify reaction products: compare the predicted fingerprint against your measured spectrum. For full computational spectra, use the Science Computer with PySCF.
Example prompts
- › What can the Spectroscopy Predictor tool do, and how should I use it?
- › What IR peaks should I expect from the product of Fischer esterification?
- › Predict the NMR spectrum of aspirin
计算化学
13 个工具Analyze molecular structures, predict reactions, calculate properties, and assess safety. Powered by RDKit, PubChem, and neural reaction models.
化学计算器
Quantitative chemistry calculations
Deterministic calculator for stoichiometry, yields, solution preparation, pH, simple equilibria, vapor pressure, and Ideal Gas Law pressure. It returns the quantitative result, assumptions, and model boundary the agent uses when a chemistry question needs numbers and units.
工作方式
The calculator keeps the agent-facing request compact, then converts it into mode-specific internal requests before execution. It preserves valid formulas, resolves common names only when unambiguous, tracks excess reagents separately from measured amounts, validates required fields, and rejects ambiguous or under-specified systems instead of guessing. ChemPy handles equation balancing and stoichiometric math; Thermo provides property correlations for supported pure-substance vapor-pressure calculations.
Chemistry Calculator Flow
6 种模式The tool turns a natural-language chemistry question into a validated calculation plan, runs deterministic calculators, and returns model assumptions and boundaries so the agent can interpret the result without inventing extra chemistry.
Stoichiometry engine
StoichiometryUsed for balancing equations, limiting-reagent analysis, theoretical yield, and combustion-style product calculations.
- 1 Build reaction model Use validated reactants and products; infer only narrowly supported textbook products such as complete combustion products when the request clearly specifies that reaction class.
- 2 Balance with ChemPy Solve the symbolic equation balance. If balancing fails, return a validation error rather than assuming a 1:1 reaction.
- 3 Find limiting reagent Convert provided masses to moles, mark excess reagents as non-limiting, and identify the reagent that constrains reaction extent.
- 4 Calculate target amount Return requested product yields or required reactant amounts with moles, grams, the balanced equation, and gas-generation boundary notes when relevant.
Physical chemistry engines
Phys chemUsed for vapor pressure, ideal-gas pressure, solution preparation, dilution, pH, and simple equilibrium questions.
- 1 Choose calculation mode Dispatch to vapor_pressure, pressure_volume, solution_prep, pH, or equilibrium based on the typed validated request.
- 2 Resolve constants Use property correlations or user-supplied constants when the model is in scope; reject systems where the needed constant or species identity is missing.
- 3 Solve with units Compute the numeric result in chemistry-native units such as grams, moles, molarity, atm, psi, mmHg, kPa, or pH.
- 4 Report boundaries State assumptions such as pure-substance vapor-liquid equilibrium or ideal-gas behavior, and distinguish those from sealed-vessel safety or protocol feasibility.
Agent interpretation boundary
BoundaryThe calculator provides quantitative evidence; the final agent may add qualitative scientific interpretation but must not invent additional quantitative or safety conclusions.
- 1 Allowed interpretation The agent can explain reaction class, excess-reagent assumptions, theoretical-vs-isolated yield, equilibrium partial pressure, exothermic reaction class, and why gas generation may matter.
- 2 Needs another tool Heat released, adiabatic temperature rise, closed-vessel pressure beyond supplied ideal-gas inputs, flammability risk, reaction rate, toxicity, compatibility, and protocol feasibility require a dedicated Vicena tool.
- 3 Next-step routing If the user asks for one of those follow-up analyses, the agent should call or recommend Reaction Energetics, Reaction Kinetics, Safety Summary, Protocol Audit, or a related tool with the missing inputs.
Calculation plus bounded interpretation
The chat agent answers from the calculator result, adds supported qualitative context, and routes unsupported quantitative or safety claims to the right tool instead of treating calculator output as a full simulation.
输入
A quantitative chemistry question, optional reagent amounts, product species, solution targets, equilibrium constants, or temperature and gas conditions.
输出
Balanced equation, limiting reagent, reaction extent, theoretical yield, required solution mass, pH, equilibrium result, vapor pressure, or ideal-gas pressure with units, assumptions, and validation errors for corrected follow-up calls.
限制
Requires calculation-safe species identities and known constants for equilibrium systems. It does not estimate heat release, adiabatic temperature rise, vessel compatibility, reaction timing, toxicity, protocol feasibility, or real experimental yield; those require dedicated tools or additional inputs.
提示
Use this for routine quantitative chemistry. For thermal safety use Reaction Energetics, for timing use Reaction Kinetics, and for protocol or hazard conclusions use the relevant audit or safety tool.
Example prompts
- › What can the Chemistry Calculator tool do, and how should I use it?
- › Balance the combustion of propane and calculate the yield from 50g
- › How many grams of NaCl do I get from 10g NaOH and excess HCl?
化学智能
Compound identity, properties, hazards, and fingerprints
Builds a deterministic compound fact pack from PubChem, RDKit, Thermo, and SMARTS patterns. It resolves one compound, computes descriptors, checks functional groups, summarizes available GHS hazards, estimates solvent compatibility, predicts analytical fingerprints, and compares molecular similarity when references are provided.
工作方式
The MCP tool keeps the public interface compact, then normalizes the request into a single compound identity and optional fact sections. It preserves explicit SMILES, rejects ambiguous bare SMILES-like inputs, resolves common names or CAS numbers through PubChem when available, computes local molecular descriptors with RDKit, uses SMARTS for functional groups and spectroscopy hints, asks PubChem for GHS records, uses Thermo for available pure-compound physical properties, and reports data gaps instead of guessing.
Chemical Intelligence Flow
6 种模式The tool resolves one compound, builds deterministic fact sections, and exposes assumptions and data gaps so the chat agent can explain the chemistry without inventing unsupported facts.
Grounded agent interpretation
The agent uses the fact pack as evidence for qualitative explanation, and routes unsupported quantitative safety or protocol claims to dedicated tools.
输入
A compound-level request with a compound name, CAS number, or explicit SMILES. Optional fields can request sections, solvents for focused solubility guidance, or reference compounds for similarity.
输出
A grounded fact pack covering identity, identifiers, RDKit properties, Thermo physical properties when available, functional groups, PubChem/GHS hazards, LogP-based solubility guidance, SMARTS-based spectroscopy hints, similarity scores, assumptions, provenance, and data gaps.
限制
This is a fact-pack tool, not a protocol audit, toxicity model, measured solubility database, or full spectral simulator. Hazard records may be missing from PubChem, solubility is estimated from polarity/LogP, and spectra are functional-group hints rather than measured or quantum-calculated spectra.
提示
Use this before asking the agent to reason about an unfamiliar compound. For numeric lab math use Chemistry Calculator, for reaction heat use Reaction Energetics, and for kinetics use Reaction Kinetics.
Example prompts
- › What can the Chemical Intelligence tool do, and how should I use it?
- › Build a chemical intelligence fact pack for aspirin
- › Analyze caffeine and show solubility guidance for water and ethanol
反应智能
Reaction prediction, feasibility, compatibility, energetics, and kinetics
Builds a reaction-level fact pack that interprets a reaction, checks balance and atom conservation, predicts products when SMILES inputs are available, suggests retrosynthesis routes for targets, screens compatibility, and runs energetics or kinetics sections when the required inputs are supplied.
工作方式
The MCP tool keeps a compact public schema, normalizes the request into reaction sections, and calls deterministic or model-backed helpers only when the required inputs are present. ChemPy balances formula equations, RDKit checks atom conservation and yield feasibility, SMARTS rules handle simple textbook reactions, ReactionT5v2 handles complex prediction and retrosynthesis, curated compatibility rules screen reagent and vessel pairs, and Thermo/Arrhenius engines are used for thermal or timing sections.
Reaction Intelligence Flow
6 种模式The tool turns a reaction request into bounded evidence sections so the chat agent can interpret the chemistry without treating model suggestions or limited checks as final verdicts.
输入
A reaction-level request with optional reaction equation, reactants, products, target molecule, solvent, vessel, conditions, amounts, and requested sections.
输出
A structured reaction fact pack with interpretation, prediction or route suggestions, balance checks, yield feasibility, compatibility checks, energetics or kinetics output, assumptions, boundaries, data gaps, and provenance.
限制
This is not an executable protocol or proof of safety. Neural predictions and retrosynthesis are suggestions; compatibility rules have limited coverage; energetics is screening-level; kinetics requires explicit rate constants or Arrhenius parameters; yield checks only bound physical feasibility.
提示
Use this for reaction-level grounding before the agent explains feasibility. Use Chemistry Calculator for routine numeric stoichiometry and Protocol Audit for procedure-level review.
Example prompts
- › What can the Reaction Intelligence tool do, and how should I use it?
- › 为乙醇加乙酸生成反应智能事实包
- › Check atom balance and glass compatibility for ethanol plus acetic acid forming ethyl acetate and water
化学分析器
Properties and hazards lookup
Retrieves physical properties and safety data for chemicals from PubChem. Returns boiling points, molecular weights, GHS hazard classifications, and reactive group information.
工作方式
Queries the PubChem REST API (116 million compounds) by name, CAS number, or SMILES. Returns physical constants (melting point, boiling point, density, molecular weight), GHS hazard pictograms, H-statements, and reactive group classifications.
Example prompts
- › What can the Chemical Analyzer tool do, and how should I use it?
- › What are the physical properties and hazards of dimethyl sulfoxide?
- › Look up the boiling point and GHS classification of acetonitrile
反应预测器
Neural network reaction prediction
Predicts products of complex chemical reactions using a trained neural network. Optimized for multi-step and pharmaceutical reactions like Suzuki coupling, Heck reactions, and Sonogashira coupling. ~93% accuracy on complex patent reactions.
工作方式
Converts reactants to SMILES notation and feeds them through a Molecular Transformer, a sequence-to-sequence neural network trained on 1.2 million patent reactions. The model treats reactions as translations between molecular languages. Predicted products are validated for chemical validity using RDKit before being returned.
输入
Reactant names, SMILES strings, or a natural language description of the reaction. You can specify catalysts and conditions.
输出
Predicted product(s) as SMILES with names and a confidence indicator. The product is validated for chemical correctness.
限制
Optimized for organic synthesis. Less reliable for inorganic, enzymatic, or radical reactions. Accuracy is ~93% on complex patent-style reactions, lower on unusual chemistries.
提示
Use this for complex, multi-step reactions. For simple textbook reactions (acid-base, esterification), use Simple Reactions instead for higher accuracy.
Example prompts
- › What can the Reaction Predictor tool do, and how should I use it?
- › Predict the product of Suzuki coupling between phenylboronic acid and 4-bromoanisole
- › Predict the product of a Heck reaction with styrene and iodobenzene
逆合成
Work backwards from target molecule
Suggests reactants needed to synthesize a target molecule. Proposes synthetic routes by working backwards from your desired product, identifying feasible starting materials and reaction conditions.
工作方式
Takes a target molecule in SMILES format and runs it through the Molecular Transformer in reverse mode. The model proposes disconnections and suggests commercially available starting materials. Multiple synthetic routes are ranked by feasibility.
输入
A target molecule as a name, SMILES string, or drawn structure.
输出
One or more proposed synthetic routes with starting materials and reaction types. Routes are ranked by feasibility.
限制
Works best for drug-like organic molecules. Very large molecules (polymers) or inorganic compounds may produce unreliable suggestions.
提示
Use this when you know what you want to make but not how to make it. Combine with the Reaction Predictor to validate each proposed step.
Example prompts
- › What can the Retrosynthesis tool do, and how should I use it?
- › How can I synthesize ibuprofen from simple starting materials?
- › Suggest a retrosynthetic route to paracetamol
简单反应
Textbook reaction predictions
Predicts products of common textbook reactions: esterification, acid-base neutralization, SN2 substitution, hydrolysis, and oxidation/reduction of simple substrates.
工作方式
Uses rule-based reaction templates implemented in RDKit for well-characterized reaction types. Unlike the neural predictor, this is deterministic and optimized for simple, well-understood transformations where accuracy is near 100%.
输入
Reactant names or formulas and the reaction type (e.g. "esterification", "neutralization", "SN2").
输出
The predicted product with a balanced equation.
限制
Only covers well-known textbook reaction types. For complex or novel reactions, use the Reaction Predictor.
提示
Use this for common organic and inorganic reactions where accuracy matters more than novelty. The rule-based approach is deterministic, so identical inputs always give identical outputs.
Example prompts
- › What can the Simple Reactions tool do, and how should I use it?
- › What is the product of ethanol and acetic acid with an acid catalyst?
- › Predict the product of SN2 reaction between NaBr and 1-chlorobutane
官能团识别器
SMARTS substructure matching
Identifies functional groups present in a molecule from its SMILES representation. Uses SMARTS pattern matching to scan for common organic functional groups.
工作方式
Parses the input molecule with RDKit and runs it against a curated library of SMARTS patterns covering 60+ functional groups: alcohols, amines, carbonyls, halogens, heterocycles, and more. Reports all matches with their positions in the molecule.
输入
A molecule name, SMILES string, or CAS number.
输出
A list of all functional groups found in the molecule, with their names and positions.
限制
Identifies standard organic functional groups. Unusual or very complex heterocyclic motifs may not be covered.
提示
Use this to understand an unfamiliar molecule before running other tools. Knowing the functional groups helps predict reactivity, solubility, and spectral features.
Example prompts
- › What can the Functional Group Identifier tool do, and how should I use it?
- › What functional groups are present in aspirin?
- › Does SMILES CC(=O)Nc1ccc(O)cc1 contain any amine groups?
分子描述符
Drug-likeness and Lipinski analysis
Calculates molecular descriptors and checks Lipinski's Rule of Five for drug-likeness. Returns LogP, molecular weight, hydrogen bond donors/acceptors, and polar surface area.
工作方式
Computes 2D molecular descriptors using RDKit: LogP (Wildman-Crippen), molecular weight, number of hydrogen bond donors and acceptors, topological polar surface area, and rotatable bond count. Evaluates Lipinski's Rule of Five and Veber's rules to assess oral bioavailability.
输入
A molecule name, SMILES string, or CAS number.
输出
A table of descriptors (LogP, MW, HBD, HBA, TPSA, rotatable bonds) and a pass/fail assessment against Lipinski and Veber rules.
限制
Evaluates drug-likeness based on physicochemical properties only. Does not predict biological activity, toxicity, or metabolic stability.
提示
Use this early in drug design to filter candidates. Molecules that fail Lipinski's rules are unlikely to be orally bioavailable.
Example prompts
- › What can the Molecular Descriptors tool do, and how should I use it?
- › Does caffeine pass Lipinski's Rule of Five?
- › Is aspirin drug-like? CC(=O)Oc1ccccc1C(=O)O
分子相似性
Tanimoto fingerprint comparison
Calculates structural similarity between two molecules using Morgan fingerprints and Tanimoto coefficient. Returns a score between 0 (completely different) and 1 (identical).
工作方式
Generates Morgan circular fingerprints (radius 2, 2048 bits) for each molecule using RDKit. Computes the Tanimoto coefficient (intersection over union of bit vectors) to quantify structural similarity. This is the same method used in pharmaceutical virtual screening.
输入
Two molecule names, SMILES strings, or CAS numbers to compare.
输出
A Tanimoto similarity score between 0.0 (completely different) and 1.0 (identical), with a qualitative assessment.
限制
Measures 2D structural similarity only. Molecules with similar shapes but different connectivity (3D similarity) are not captured.
提示
Scores above 0.85 generally indicate very similar molecules. Use this to find structural analogs or check if two molecules are variants of the same scaffold.
Example prompts
- › What can the Molecular Similarity tool do, and how should I use it?
- › How similar are ibuprofen and naproxen?
- › Compare the structures of caffeine and theobromine
安全摘要
GHS hazard information
Retrieves GHS safety information from PubChem for any chemical. Returns hazard statements, signal words, and pictogram descriptions. Accepts chemical names, SMILES, or CAS numbers.
工作方式
Queries PubChem's GHS classification data. Returns the signal word (Danger/Warning), all H-statements (hazard), P-statements (precaution), and pictogram codes. Accepts input as chemical name, SMILES, or CAS number.
输入
A chemical name, SMILES string, or CAS number.
输出
Signal word (Danger/Warning), GHS pictograms, hazard statements (H-codes), and precautionary statements (P-codes).
限制
Returns the GHS classification from PubChem. Compounds not registered in PubChem will not have data. Does not assess mixture hazards.
提示
Always check safety before handling a new reagent. Combine with the Solubility Predictor to assess both compatibility and safety of your chosen solvents.
Example prompts
- › What can the Safety Summary tool do, and how should I use it?
- › What are the safety hazards of hydrofluoric acid?
- › Give me the GHS classification for sodium cyanide
分子转换器
Name, SMILES, and CAS conversion
Converts between molecule representations: common names to SMILES, SMILES to names, and molecules to CAS registry numbers. Uses PubChem as the reference database.
工作方式
Resolves chemical identifiers through PubChem's standardization pipeline. Converts between IUPAC names, common names, CAS registry numbers, and SMILES/InChI representations. Handles synonyms and trade names.
输入
A molecule in any format: common name, IUPAC name, CAS number, or SMILES string.
输出
The molecule in the requested format (SMILES, name, or CAS), with the PubChem CID for reference.
限制
Relies on PubChem's compound registry. Very new or proprietary compounds may not be found.
提示
Use this to translate between formats when other tools require SMILES input. Most chemistry tools in Vicena also accept names directly, but SMILES is unambiguous.
Example prompts
- › What can the Molecule Converters tool do, and how should I use it?
- › What is the SMILES for ibuprofen?
- › Convert CAS 50-78-2 to a chemical name
计算化学
Advanced molecular modeling and notebook workflows
Runs computational chemistry work in Vicena Compute: descriptors, molecular comparisons, conformer setup, docking preparation, first-principles calculations, and result analysis in notebooks. Selected advanced molecular modeling workflows are powered by Rowan.
工作方式
Vicena uses Deep Work with the Science Computer to prepare a bounded notebook workflow, keep inputs and workflow state visible, route eligible advanced jobs through Vicena Compute, and turn returned files into tables, plots, structures, and summaries.
输入
A scientific goal with a molecule, SMILES, protein, paper, assay condition, or property you want to compare or compute.
输出
A notebook and persisted outputs such as raw results, CSV/JSON files, plots, molecular structures, scores, and interpretation.
限制
Compute credits, per-job limits, available Rowan workflows, sandbox packages, and account permissions apply. Start with lightweight descriptors or setup notebooks before expensive docking, MD, or batch runs.
提示
Start from the scientific decision you need to make. Vicena can prepare the notebook, keep workflow UUIDs and raw outputs, and help iterate without you having to babysit infrastructure.
Example prompts
- › What can Computational Chemistry do, and how should I use it?
- › Compare aspirin, ibuprofen, and naproxen using descriptors and explain which differences matter
- › Prepare a conformer search for aspirin from SMILES CC(=O)Oc1ccccc1C(=O)O and summarize the lowest-energy structures
科学计算机
4 个工具A persistent cloud computer with JupyterLab where you and the AI agent collaborate in real notebooks. Write code, run simulations, and visualize results together.
计算命令行
Terminal for packages, quick commands, and inline calculations
Run shell commands in an isolated Linux environment. Use it to install packages, inspect the compute environment, manipulate sandbox files, run quick calculations, and execute small scripts with inline output.
工作方式
Each user gets a dedicated Linux container with sandbox storage. The agent runs shell commands directly and reads stdout/stderr. Packages installed via pip or apt persist inside the Science Computer. The container is isolated from other users and separate from Workspace Files.
输入
Natural language instructions for what you want to install, inspect, calculate, or run quickly. You can also paste shell commands directly.
输出
The command output (stdout/stderr) displayed inline in chat. Files created by commands remain in the Science Computer filesystem, not Workspace Files.
限制
CPU-only environment (no GPU). Long-running computations may time out after 2 minutes per command. Network access is available for downloading packages and data. Direct transfer between Science Computer files and Workspace Files is not available yet.
提示
Use this for package installation, quick one-off commands, and short inline computations. For iterative computation with plots and tables, use Jupyter instead.
Example prompts
- › What can the Compute Shell tool do, and how should I use it?
- › Run Python to generate a sine-wave plot from synthetic data and display the image
- › Use RDKit to calculate the molecular weight and LogP of caffeine from SMILES Cn1cnc2c1c(=O)n(C)c(=O)n2C
工作区状态
Read-only visible UI state
Read the current known user-facing workspace state without changing anything. It reports which Jupyter notebook the UI is showing, which notebook the notebook tool last touched, and the previous visible notebook when known.
工作方式
The tool reads canonical state recorded by the app backend from JupyterLab UI events and notebook-tool actions. It never opens notebooks, switches tabs, lists files, runs code, or mutates workspace state.
输入
Ask about the current visible notebook, previous visible notebook, or ambiguous phrases like current notebook and active tab.
输出
A compact JSON state snapshot with known surfaces such as Jupyter visible_notebook, previous_visible_notebook, and tool_active_notebook.
限制
Returns only state the app has observed from the browser and tool calls. Unknown fields mean that surface has not reported state yet.
提示
Use this before acting on ambiguous UI-relative requests. Use Jupyter for actual notebook edits or execution.
Example prompts
- › What can the Workspace State tool do, and how should I use it?
- › Which notebook am I looking at right now?
- › Before appending to a notebook, check the current visible workspace state
Jupyter
Visible Python notebooks for simulations, analysis, and plots
Work in a shared JupyterLab notebook when the code, plots, and outputs should be visible, editable, and reusable. The agent can create, inspect, edit, and run cells while returning compact transcripts and displayed images.
工作方式
Runs a real JupyterLab instance in the Science Computer. The agent uses a small tool family: Jupyter Manager for notebook lifecycle and whole-notebook reads, Jupyter Editor for cell edits, and Jupyter Runner for execution and output inspection. Named notebooks become tool-active for later calls. If the browser reports a newer UI-active notebook, pathless writes fail as ambiguous instead of guessing; choose notebook_path, notebook_target="tool_active", or notebook_target="ui_active".
Notebook Workflow
6 种模式Use notebooks for visible computational work. Use Compute Shell only for support tasks such as package installs, file checks, or short one-off commands.
New notebook
create_notebookBest when the user asks for a fresh notebook with a known structure. It never overwrites an existing file.
- 1 Provide cells Pass notebook_path and an ordered cells list. Then run the saved code cells when outputs are needed.
- 2 Return the transcript Report the path, cell counts, source, execution counts, outputs, and any displayed images.
Continue a notebook
active targetBest when the user says continue, append, current notebook, or active notebook.
- 1 Resolve the target Omit notebook_path only when there is no newer UI-active notebook. Otherwise choose tool_active, ui_active, or a named path.
- 2 Append or edit directly Use append_cell for new work, replace_cell for a known bad cell, or edit_cell for a small source change, then run the relevant cell.
Inspect existing work
read actionsBest when the user asks what is in the notebook, what changed, or what the last output shows.
- 1 Read without running Use read_notebook or read_cell. Set include_outputs=true when outputs or images matter.
- 2 Answer from state Use the returned cell source, text output, errors, tables, and displayed images. Do not infer from filenames.
Visible computational record
The user sees a live notebook with cells and outputs inside the Science Computer session folder. Notebook files are separate from Workspace Files.
输入
Requests for visible Python work: create a notebook, build a simulation, analyze data, plot results, append a cell, replace a known cell, run all cells, or inspect the last displayed notebook image.
输出
A live Jupyter notebook plus a concise transcript with the action, path, cell counts, changed or read source, execution counts, text/error output, and image markers. Displayed notebook images are attached to the tool result.
限制
CPU-only environment. Very large datasets (>1 GB) and GPU training are not supported. Notebook files live in the Science Computer session folder, not Workspace Files. Direct transfer between notebooks and Workspace Files is not available yet. Destructive changes must be explicit: create_notebook never overwrites, delete_cells requires indices, and replace_notebook requires cells.
提示
Use this for reusable multi-step computation with plots, tables, and code the user may edit. Use read_notebook or read_cell before modifying user-side edits. Use Compute Shell for package installs, quick file checks, and support commands that should not become notebook cells.
Example prompts
- › What can the Jupyter tool do, and how should I use it?
- › [USE JUPYTER] Build a notebook that fits this Michaelis-Menten dataset and reports Km, Vmax, residuals, and a plot: substrate_uM=[5,10,20,50,100,200], rate=[0.08,0.15,0.27,0.50,0.72,0.88].
- › [USE JUPYTER] Create a notebook that analyzes this calibration curve, fits a linear model, reports R^2 and LOD, and plots the fit with residuals: concentration=[0,1,2,5,10], signal=[0.03,0.18,0.35,0.82,1.61].
计算机文件系统
Temporary sandbox file operations
Read, write, and manage files inside the active compute sandbox or Jupyter-backed working directory. This is separate from the Files sidebar and Workspace Files.
工作方式
Provides structured filesystem access within the compute environment. The agent can read, write, move, delete, and search sandbox files when a task explicitly involves a filesystem path, uploaded dataset, notebook output, or project source file.
输入
Natural language instructions for sandbox file operations, or specific computer/Jupyter file paths to read or write.
输出
File contents, directory listings, or confirmation of write/move/delete operations.
限制
This is not the Vicena Workspace Files filesystem. Direct copy/import/export/move between the Science Computer and Workspace Files is not available yet. For notes, protocol certificates, saved canvas files, or vague requests like “create a new file,” use the Files workspace instead.
提示
Use this for data files used by computation. Use workspace files for documents you want to manage from the Vicena Files sidebar.
Example prompts
- › What can the Computer File System tool do, and how should I use it?
- › Create /tmp/example.csv with columns time,temperature and three rows, then read it back and summarize the columns
- › List files in the current Jupyter working directory
科学技术栈
29 databases, libraries & modelsThe databases, libraries, and models that power Vicena's tools. All are available in the Scientific Simulations notebook environment for direct use in your notebooks.
科学论文索引
Databaseby Vicena
Vicena’s indexed scientific corpus spanning major journal articles, preprints, biomedical records, abstracts, and available full text. Used for fast evidence retrieval before freshness checks on the open web.
by National Library of Medicine (NIH)
The US National Library of Medicine database with over 36 million biomedical citations. The primary source for life sciences and biomedical literature worldwide.
by Cornell University
Cornell University's open-access repository hosting over 2.4 million preprints in physics, mathematics, computer science, and quantitative biology. Covers cutting-edge research before peer review.
by Google
Google's academic search engine indexing the full text of scholarly literature across publishers, disciplines, and formats. Covers papers, theses, books, and conference proceedings.
by Google
Google's patent search covering over 120 million patent documents from 100+ patent offices worldwide, including the USPTO, EPO, and WIPO.
开放网页和代码库 API
Databaseby Vicena
Public web sources and official repository APIs used for dataset discovery, source lookup, and freshness checks when a task needs information outside Vicena’s indexed corpus.
by National Center for Biotechnology Information (NIH)
The world's largest open chemistry database, maintained by the NIH. Contains data on 116 million compounds including structures, properties, biological activities, safety information, and patent references.
by Greg Landrum and contributors
The industry-standard open-source cheminformatics toolkit used by Pfizer, Novartis, and Merck. Handles molecular representation, substructure search, fingerprinting, and property calculation.
by Bjoern Dahlgren
A Python library for physical chemistry. Solves stoichiometry, balances equations, computes equilibrium constants, and models chemical kinetics from first principles.
by Caleb Bell and contributors
An open-source thermodynamic properties library covering 70,000+ chemicals. Calculates vapor pressure, heat capacity, enthalpy, and phase equilibria using validated correlations from the DIPPR database.
by Qiming Sun et al.
A quantum chemistry package for Hartree-Fock, DFT, and post-Hartree-Fock calculations. Used in academic research for electronic structure simulations of molecules and materials.
by Technical University of Denmark
The Atomic Simulation Environment, a set of tools for setting up, running, and analyzing atomistic simulations. Interfaces with dozens of quantum chemistry and molecular dynamics codes.
by NumPy community
The fundamental package for numerical computing in Python. Provides N-dimensional arrays, linear algebra, Fourier transforms, and random number generators. The foundation of nearly all scientific Python.
by SciPy community
Built on NumPy, SciPy adds optimization, integration, interpolation, signal processing, and statistical functions. The go-to library for scientific and engineering computation.
by Wes McKinney and contributors
The standard library for data manipulation in Python. DataFrames make it easy to clean, transform, and analyze tabular data from experiments, simulations, and databases.
by John D. Hunter and contributors
The most widely used plotting library in science. Produces publication-quality figures, histograms, spectra, and scatter plots. Used in thousands of peer-reviewed papers every year.
by Plotly Technologies Inc.
An interactive visualization library for 3D plots, dashboards, and dynamic charts. Particularly useful for exploring molecular structures, reaction landscapes, and multi-dimensional data.
by Meta AI (FAIR)
Meta's open-source deep learning framework, the most popular in academic research. Powers neural networks for reaction prediction, molecular property estimation, and scientific data analysis.
by Hugging Face
Hugging Face's library providing access to thousands of pre-trained models for NLP, computer vision, and scientific applications. Used for text analysis of papers and chemical language models.
by SymPy community
A symbolic mathematics library for Python. Solves equations algebraically, computes integrals and derivatives, and simplifies expressions. Useful for deriving analytical solutions to scientific problems.
by INRIA and contributors
The most widely used machine learning library in Python. Provides classification, regression, clustering, and dimensionality reduction algorithms for analyzing scientific datasets.
by Intel, Willow Garage, and contributors
The standard computer vision library with tools for image processing, feature detection, and analysis. Used in microscopy, materials characterization, and automated lab image analysis.
by Caleb Bell
A Python library for fluid mechanics calculations. Computes pressure drops, pipe friction factors, and hydraulic properties for chemical engineering and process design.
by Project Jupyter
The open-source interactive development environment used by millions of scientists. Originally developed at UC Berkeley, Jupyter is the standard for reproducible computational research across all scientific disciplines.
Molecular Transformer
建模by Philippe Schwaller et al. (IBM Research / EPFL)
A sequence-to-sequence neural network that treats chemical reactions as translations between molecular languages (SMILES). Trained on millions of patent reactions, it predicts products with ~93% accuracy on complex organic synthesis.
Morgan fingerprints
建模by H. L. Morgan (1965), extended by RDKit
A circular fingerprinting algorithm that encodes the local chemical environment around each atom. Widely used in drug discovery for virtual screening and similarity searching because it captures both topology and atom types.
SMARTS
Standardby Daylight Chemical Information Systems
A pattern language for describing molecular substructures. Used to identify functional groups, pharmacophores, and reactive sites by matching atoms and bonds in molecular graphs.
Arrhenius equation
建模by Svante Arrhenius (1889)
The foundational model in chemical kinetics describing how reaction rate constants depend on temperature. Developed by Svante Arrhenius in 1889, it remains the standard for predicting reaction speed.
维思纳工作区文件
Standardby Vicena
The durable per-user file system behind Vicena’s Files tab. It stores research documents, uploads, protocol audit certificates, images, PDFs, CSVs, and reusable notes separately from the execution computer.
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