Case Study

AI for Catalyst Discovery and Optimization

Inquiry

Home
Services
AI for Materials
AI for Catalyst Discovery and Optimization

AI for Materials

AI for Catalyst Discovery and Optimization

Connect catalyst data, atomic-scale simulation and uncertainty-aware machine learning to prioritize active, selective and durable candidates—and define the next experiment with confidence.

Discuss Your Catalyst Program

Catalysis informatics

Turn heterogeneous evidence into testable catalyst decisions

Catalyst performance emerges from composition, active-site structure, support, synthesis, operating conditions and time on stream. Our project-specific workflows organize these variables and connect machine learning with physics-based simulation to support candidate selection, operating-window optimization and mechanism-focused validation.

Physics-informed modelingUncertainty-aware rankingConfidential data workflows

DiscoverScreen compositions, surfaces, ligands and supports against defined activity and selectivity targets.

OptimizeLearn robust formulation and operating windows under process, cost and safety constraints.

ExplainPrioritize pathways, rate-controlling steps and degradation hypotheses for validation.

Scope My Catalyst Project

Scientific visualization of electrocatalysts, porous industrial catalysts, molecular catalysts and catalyst aging

Specialized services

Four routes from catalyst concept to decision-ready evidence

Choose a focused work package or combine modules within a staged discovery and optimization program.

Electrocatalyst Discovery

Prioritize surfaces, dopants, defects and multimetallic motifs for reactions such as hydrogen evolution, oxygen evolution, oxygen reduction, CO₂ reduction and nitrogen-cycle electrochemistry.

Explore service

Industrial Catalyst Optimization

Model formulation, preparation and plant operating variables to improve conversion, selectivity, catalyst utilization and robustness within equipment and feed constraints.

Explore service

Low-Carbon Chemical Catalysts

Assess catalysts for CO₂ utilization, green hydrogen, ammonia, biomass upgrading and lower-temperature routes using performance, energy and resource criteria.

Explore service

Catalyst Stability and Reaction Mechanism Modeling

Investigate elementary steps, kinetic bottlenecks and deactivation modes including poisoning, coking, sintering, dissolution and phase reconstruction.

Explore service

Select a Service Track

Closed-loop catalyst research workflow connecting data, atomistic simulation, AI ranking and experimental validation

Integrated approach

AI grounded in catalysis physics and validation

The model is only one component. Each program is built around traceable evidence, chemically meaningful representations and an explicit plan to test the highest-value uncertainty.

✓

Data curation and context

Harmonize catalyst identity, preparation, activation, feed, reactor configuration, test protocol and time-on-stream metadata.

✓

Mechanism-aware descriptors

Use adsorption and activation energies, coordination environments, electronic descriptors, pore/support features and kinetic variables where relevant.

✓

Hybrid prediction and search

Combine DFT or molecular simulation, microkinetics, interpretable ML, graph models, surrogates and constrained Bayesian optimization.

✓

Applicability and uncertainty

Separate interpolation from extrapolation, assess domain distance and model disagreement, and propose informative validation points.

Method selection

Fit-for-purpose computational methods

Methods are selected from the decision endpoint backward, based on system scale, data maturity, accessible validation and required fidelity.

Project need	Typical methods	Key inputs	Decision output
Candidate and active-site screening	Descriptor ML, graph models, transfer learning, high-throughput DFT	Structures, compositions, adsorption/activation data, measured performance	Ranked catalysts or sites with uncertainty and constraint flags
Reaction pathway analysis	DFT, transition-state search, free-energy analysis, automated reaction networks	Surface or molecular structures, intermediates, solvent/potential conditions	Feasible pathways, barriers and selectivity hypotheses
Kinetics and operating conditions	Microkinetic modeling, reactor models, sensitivity analysis, CFD surrogates	Elementary kinetics, transport, feed, pressure, temperature and reactor data	Rate-controlling steps and operating-window recommendations
Formulation and process optimization	Gaussian processes, Bayesian optimization, design of experiments, multi-objective search	Composition, preparation, process variables, cost and safety constraints	Pareto-ranked formulations and next-best experiments
Stability and deactivation	Atomistic simulation, time-series ML, survival/degradation models, anomaly detection	Time-on-stream, regeneration cycles, characterization and impurity data	Lifetime risk factors, deactivation hypotheses and monitoring indicators
Low-carbon pathway assessment	Multi-criteria optimization, process surrogates, sensitivity and scenario analysis	Yield/selectivity, energy demand, feedstock, critical materials and process assumptions	Trade-off map and validation-ready shortlist; not a substitute for full LCA

Match Methods to My Data

Project workflow

From target reaction to validation-ready recommendations

Define the decision

Specify reaction, catalyst class, baseline, metrics, conditions and non-negotiable constraints.

Audit the evidence

Review data provenance, protocol comparability, sparsity, censoring, imbalance and mechanistic priors.

Build and validate

Select representations and models; use group-aware splits, calibration and stress tests.

Search under constraints

Rank candidates, formulations or conditions while enforcing chemistry, cost and operability rules.

Recommend validation

Deliver testable candidates, rationale, uncertainty, domain boundaries and an efficient experiment plan.

Start an Evidence Review

Typical inputs

Target reaction, catalyst family and benchmark
Activity, selectivity, yield, Faradaic efficiency or lifetime targets
Catalyst composition, structure, support, synthesis and activation data
Operating conditions, reactor/electrode configuration and test protocols
Characterization, kinetic, spectroscopy and time-on-stream data
Permitted elements, cost, feed impurity, safety and equipment constraints
Existing DFT structures, reaction pathways or process models, if available

Typical deliverables

Curated machine-readable dataset and data-quality assessment
Documented model workflow, validation design and performance metrics
Predictions with uncertainty and applicability-domain indicators
Ranked candidates, conditions or formulations with Pareto trade-offs
Descriptor, sensitivity or mechanism-focused interpretation
Reaction-energy, microkinetic or stability analyses as scoped
Recommended validation experiments or higher-fidelity calculations
Technical report, figures and agreed reusable outputs

Check Project Readiness

Representative engagements

Programs centered on concrete R&D decisions

Electrocatalyst active-site down-selection

Goal: prioritize multimetallic surface motifs under activity, selectivity and stability constraints. Workflow: curated adsorption/experimental evidence, surface descriptors, uncertainty-aware surrogate and a confirmatory DFT/experiment matrix.

Industrial catalyst operating-window optimization

Goal: maintain conversion and selectivity as feed and time-on-stream vary. Workflow: harmonized plant or pilot data, process-aware features, constrained surrogate modeling and robust operating recommendations.

Deactivation mechanism prioritization

Goal: distinguish competing poisoning, coking and sintering hypotheses. Workflow: time-series performance plus characterization, mechanistic features, sensitivity analysis and targeted validation tests.

Request a Case-Matched Plan

FAQ

Frequently asked questions

Can a project start with a small or uneven catalyst dataset?

Often, yes, but the supportable endpoint must be assessed first. We may use physically meaningful descriptors, Gaussian processes, transfer learning, simulation-derived features or active learning. If the evidence is insufficient, we define the minimum additional measurements needed rather than overstate model accuracy.

How do you avoid misleading comparisons across catalyst studies?

We retain preparation, activation, test protocol, reactor or electrode configuration, normalization basis and operating conditions as model context. Incompatible records may be stratified, modeled hierarchically or excluded from direct comparison.

Can you combine DFT, machine learning and microkinetic modeling?

Yes. For example, DFT can supply adsorption and activation energetics, ML can accelerate evaluation across a design space, and microkinetics can translate elementary steps into rate and selectivity trends. The combination depends on pathway coverage and uncertainty in each layer.

Can the service predict catalyst lifetime?

Lifetime modeling is possible when time-resolved performance, regeneration and relevant characterization data are available. Outputs are conditional estimates within the observed operating domain, not unconditional guarantees for unseen feeds or reactor regimes.

Do you provide experiments or only computational recommendations?

The core service is computational design and decision support. Customer-generated validation data can be incorporated iteratively; experimental work or external coordination can be scoped separately where available.

How is confidential project data handled?

Data scope, access, retention and reusable outputs are defined before work begins. Public and proprietary evidence can be separated, and deliverables can be tailored to the agreed confidentiality and deployment requirements.

Ask a Catalyst Modeling Expert