ASE with ML Potentials: Units, Optimization, and Validation
Understand ASE calculators, fixed and variable cells, force convergence, and the validation needed beyond a successful ML-potential relaxation.
Guides
Learn core chemistry AI concepts, tool selection, deployment, and evaluation. Looking for concrete tool combinations and setup steps? Explore Recipes.
AI for Drug Discovery: Agents, MCP Servers and Platforms
Choose tools by workflow stage: target evidence, protein structures, known binding sites, molecular generation, property analysis and screening. Compare research agents, MCP integrations, hosted APIs and platforms, with a proposed end-to-end example and practical evaluation gates.
AI for Materials Science: Tools, Agents and Skills
A practical guide to materials databases, research agents and scientific Skills: how to organize crystal screening, computation and analysis while keeping convex-hull, equilibrium, phonon and experimental-stability claims distinct.
Protein & Structural Biology AI Workflow
Build an evidence-led protein workflow from UniProt identity and sequences to RCSB structure metadata, observed ligand contacts, and proposed downstream candidate evaluation—with explicit checks for species, isoforms, chains, missing data, and experimental validation.
Building an AI Cheminformatics Workflow with RDKit
Design a traceable RDKit workflow for molecular validation, standardization, descriptors, fingerprints and chemical searches. Compare Python, MCP and Skill access, then add bounded PubChem enrichment without confusing tool execution with scientific validation.
AI for Computational Chemistry Workflows
Build evidence-aware computational chemistry workflows with AI Skills for structure preparation, phonons, gas-phase ignition, CALPHAD equilibrium, and reactive-MD analysis—while keeping real inputs, execution dependencies, and scientific validation explicit.
AI Literature Research for Chemistry
Build a traceable chemistry literature workflow with PubMed MCP: refine searches, inspect metadata and abstracts, synthesize multiple papers, follow citations, and separate retrieved evidence from agent-generated hypotheses.
Evaluating AI workflows for chemical engineering
Plan evaluations that separate process modelling, reaction optimisation and control. Define inputs, operating limits, time-aware validation and approval gates before considering operational use.
Reproducibility for Chemistry AI Workflows
Build a compact evaluation record that connects scientific questions to data provenance, transformations, model configurations, outputs and failures, with practical planning examples for chemistry AI.