Components from open-source chemistry
FormulaIQ Layer 1 is fed by a multi-source open materials ETL — not a single vendor dump. Components stay CAS-keyed into inverse formulation.
Formula · open data
Open chemistry sources
Technology stack
Every layer chemists need — from open components to plant agents.
Formula components from open-source chemistry data, ML/DL models we train, LLM evaluation, regulation RAG, workflow simulation & digital twins, and Chemical Engineering Agents — a growing fleet for chemistry.
prediction = first-principles baseline + ML residual ± uncertainty
Open components9 sources
ML / DL + LLMtrain · eval
Twin · agentsHITL
Full chemistry stackinspectable
The full technology map
One inspectable stack. Each layer is purpose-built for chemistry — not a generic LLM wrapper.
- 01
Formula components · open-source data
Component-level properties and structures from multiple open chemistry databases — QM9, Materials Project, OQMD, AFLOW, NOMAD, PoLyInfo, Polymer Genome, PubChem, ZINC — normalised onto a CAS-keyed backbone for FormulaIQ Layer 1.
- 02
ML / DL models
In-house machine learning and deep learning: molecular descriptors & GNNs, mixture models, physics-informed residuals, inverse search (Bayesian / multi-objective). Trained by Lavoisier — not rented property APIs.
- 03
LLM evaluation
Chemistry LLMs are evaluated on formulation, process, and regulation tasks before they orchestrate agents — grounded answers, cited sources, and failure modes measured — not vibes.
- 04
RAG for regulations
Local LLM + RAG over REACH, RoHS, market rules, and customer policy docs. Compliance answers cite clauses; inference can stay private.
- 05
Workflow · simulation · digital twin
Studio Pro workflow tools: full-metric process simulation, thermodynamic packages (Ideal, NRTL, Peng-Robinson, UNIQUAC), Monte Carlo risk, Bayesian optimization, and digital twin profiles ready for scale-up.
- 06
Chemical Engineering Agents
The same twin moves into operations. Agents keep plants on-spec under chemistry constraints, incomplete data, and quantified uncertainty — with humans in the loop, and more agent roles ahead.
- 07
Growing agent fleet
A fleet of chemistry AI agents — plant ops today; formulation, regulation, simulation, and scale-up roles expanding on one backbone.
From open components to models you can trust.
FormulaIQ starts with real components drawn from open-source chemistry corpora, then runs ML/DL forward models and inverse search so recipes stay physically plausible. LLM evaluation gates what agents are allowed to say; regulation RAG constrains what they are allowed to recommend.
See how FormulaIQ uses this stack.
Explore FormulaIQHybrid prediction
First-principles baseline plus ML residual — confidence and out-of-domain flags on every answer.
Physics + ML control
Confidence80
Mixture level+ High
Uncertainty
Data flywheel
Validated experiments
25→200
Simulation, digital twins — and Chemical Engineering Agents.
Studio Pro runs the workflow: simulate the process, extract a digital twin profile, then hand the same twin to Chemical Engineering Agents — plant ops today, a growing fleet tomorrow.
Formula confidence
+7.8%80.5%
Twin stability
+4.5%94%
Model readiness
72
Eval score
LLM evaluation72%
Chemical Engineering Agents
- Plant operations agents
- Formulation advisor agent
- Regulation agent
- Simulation agent
- Scale-up agent
Diligence detail by layer
Open components, ML/DL + LLM evaluation, digital twin workflows, and chemistry agents — the stack investors and PhDs both ask about.
Formula · open data
9
Open chemistry sources
Questions
- Where do FormulaIQ components come from?
- Component-level properties and structures are built from multiple open chemistry databases — including QM9, Materials Project, OQMD, AFLOW, NOMAD, PoLyInfo, Polymer Genome, PubChem, and ZINC — normalised onto a CAS-keyed backbone. Customer formulation data never trains shared models without a written agreement.
- How do ML/DL models, LLM evaluation, and RAG fit together?
- Lavoisier trains ML/DL property and mixture models in-house. Chemistry LLMs are evaluated on formulation, process, and compliance tasks before they orchestrate agents. Regulation answers use a local RAG pipeline over REACH, market rules, and customer policy — with cited clauses.
- What about digital twins and Chemical Engineering Agents?
- Studio Pro runs workflow simulation and digital twin profiles with real thermodynamic packages. Chemical Engineering Agents — plant control today, a growing fleet tomorrow — use evaluated LLMs with human-in-the-loop for safety-critical acts.
Walk the full stack on your chemistry.
Open components, ML/DL, LLM evaluation, RAG, digital twins, process control, and chemistry agents — in one technical session.