AI for Catalyst Discovery and Optimization

Industrial Catalyst Optimization

Improve catalyst formulation and operating windows using plant-relevant data, design of experiments, Bayesian optimization, kinetic modeling, and multiscale validation.

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Overview

Turn Process Data into Higher Catalyst Performance

Industrial catalyst optimization has to separate catalyst chemistry from feed variation, reactor history, analytical drift, heat and mass transfer, regeneration cycles and campaign effects. A model that ignores these factors may fit historical data but fail when used for plant decisions.

Our service combines mechanistic catalysis knowledge, design of experiments, Bayesian optimization, kinetic modeling and uncertainty-aware analytics to improve catalyst formulation, preparation parameters and operating windows. We emphasize robust design spaces rather than fragile single-point optima, helping teams decide what to test next, what to avoid, and what evidence is needed before scale-up.

Typical decision questions

Which promoter/support/preparation variables most affect yield, selectivity or lifetime?
Where is the robust operating envelope under realistic feed and impurity variation?
Which pilot runs will most reduce uncertainty before plant implementation?
Which deactivation signal should be monitored during extended campaigns?

Core Services

From fragmented evidence to testable decisions

Each module can be commissioned independently or integrated into a staged program, from rapid feasibility assessment to closed-loop optimization.

Historical data audit

Align batch, analytical, reactor, feed, regeneration and campaign records; identify censoring, drift, confounding, missingness and untested regions.

Formulation optimization

Optimize active phase, promoter, support, loading, preparation, calcination, shaping and binder variables under manufacturability constraints.

Operating-window optimization

Model temperature, pressure, space velocity, feed ratio, impurity tolerance and regeneration strategy to define robust regions.

Hybrid kinetic and ML modeling

Combine mechanistic rate expressions, microkinetic insight, response surfaces and Bayesian models when data are sparse or non-stationary.

Experiment design

Plan factorial, response-surface, adaptive or Bayesian optimization runs with blocking, randomization and stopping rules.

Scale-up risk assessment

Evaluate transfer assumptions, transport limitations, monitoring variables and sensitivity to feed, reactor and analytical uncertainty.

Workflow

A gated, uncertainty-aware project plan

The workflow is organized as a decision table so scope, evidence quality and next actions remain clear throughout the project.

Project stage	Key activities	Decision output
1. Objective and constraints	Define yield, selectivity, lifetime, raw-material cost, utilities, safety limits and baseline economics.	Optimization target and no-go constraints.
2. Data diagnosis	Map data lineage, sensors, assay delays, campaigns, interventions, catalyst lots and reactor regimes.	Data readiness report and confounder map.
3. Experiment strategy	Select DOE, response-surface or adaptive Bayesian design to resolve interactions efficiently.	Prioritized run plan with controls and blocking.
4. Model and stress test	Validate by time, reactor, campaign or composition holdouts; test uncertainty and sensitivity.	Robust predictive model and risk register.
5. Implementation support	Translate findings into operating envelope, formulation recommendations and monitoring plan.	Pilot or plant decision package.

Deliverables

Files your scientists can inspect and reuse

Deliverable	What is included
Data readiness report	Aligned dataset, lineage, missingness and outlier map, confounder assessment and recommended data fixes.
Optimization model	Validated response surfaces or hybrid kinetic/ML model with uncertainty, sensitivity and applicability domain.
Robust design space	Feasible operating or formulation envelope, Pareto front, constraint margins and failure-region warnings.
Experiment plan	Prioritized runs, randomization/blocking, sampling plan, analytical methods and stopping rules.
Scale-up brief	Transfer assumptions, kinetic/transport checks, monitoring variables, deactivation hypotheses and risk register.

Applications

Representative project contexts

Hydrogenation and dehydrogenationOxidation and ammoxidationReforming and syngas conversionAmmonia and methanol synthesisRefining and emissions controlPolymerization and fine-chemical catalysis

Scientific Evidence

Evidence for closed-loop and data-efficient catalyst optimization

Recent open-access work supports active learning, interpretable descriptors and machine-learning potentials as practical ways to reduce the number of costly catalyst tests while preserving mechanistic interpretability.

Active learning and enhanced sampling workflow for catalytic reactivity — Active-learning workflow for data-efficient machine-learning potentials in catalytic reactivity modeling.¹

Interpretable descriptor workflow for high-throughput catalyst screening across multiple reactions.²

References

Perego, S. & Bonati, L. Data efficient machine learning potentials for modeling catalytic reactivity via active learning and enhanced sampling. npj Computational Materials 10, 291 (2024). doi:10.1038/s41524-024-01481-6. Open access, CC BY 4.0.
Lin, X. et al. Machine learning-assisted dual-atom sites design with interpretable descriptors unifying electrocatalytic reactions. Nature Communications 15, 8169 (2024). doi:10.1038/s41467-024-52519-8. Open access, CC BY 4.0.

Online Inquiry

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