AI for Catalyst Discovery and Optimization

Catalyst Stability and Reaction Mechanism Modeling

Resolve plausible pathways and degradation drivers under operating conditions using DFT, enhanced sampling, machine-learning potentials, microkinetics, and uncertainty-aware validation.

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Overview

Reveal What Controls Catalyst Lifetime, Activity, and Selectivity

Catalysts can reconstruct, change oxidation state, sinter, dissolve, coke, leach, become poisoned, or switch active sites under reaction conditions. A static ideal surface and one minimum-energy pathway are often insufficient for explaining lifetime, selectivity or scale-up behavior.

Our stability and mechanism modeling service uses the least expensive model that can resolve the decision at hand, then escalates only when uncertainty affects the conclusion. Depending on the system, the workflow may combine DFT, transition-state searches, ab initio molecular dynamics, machine-learning potentials, enhanced sampling, thermodynamic stability maps, microkinetics and experiment-facing mechanism discrimination.

Questions this service answers

Which active state is plausible under operating potential, temperature, pressure or coverage?
Which elementary step or surface state controls rate and selectivity?
What degradation route is most consistent with the observed lifetime loss?
Which isotope, transient or operando test can falsify a proposed mechanism?

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.

Active-state hypothesis generation

Enumerate facets, defects, coverages, oxidation states, supports, solvent/electrolyte and adsorbate-induced structures.

Reaction-pathway mapping

Locate intermediates and transition states; compare pathways, spin/charge states, coverage effects and competing products.

Operando-condition stability

Compute thermodynamic or electrochemical phase stability and use ab initio or ML-potential dynamics for reconstruction and diffusion.

Deactivation mechanism modeling

Assess sintering, dissolution, poisoning, coking, leaching, phase segregation and support interactions.

Microkinetic and reactor linkage

Convert energetics into rates, selectivity, degree of rate control and operating-condition response; test transport sensitivity.

Mechanism discrimination

Design isotope, transient, kinetic or operando-characterization tests that distinguish competing models.

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. Hypothesis framing	Define candidate active sites, reaction pathways, degradation routes, observables and operating state.	Mechanistic question and falsifiable hypotheses.
2. Model construction	Converge cell, slab/cluster, coverage, solvation, charge, spin and electronic-structure settings.	Traceable model set and method sensitivity.
3. Event exploration	Use DFT, transition-state search, AIMD, enhanced sampling or active-learned potentials according to timescale.	Pathway and stability event map.
4. Kinetic interpretation	Propagate energetic uncertainty, calculate rates/selectivity and identify rate- or stability-controlling states.	Microkinetic or mechanism dossier.
5. Experimental linkage	Recommend isotope, transient, kinetic or operando tests that can reject or refine competing mechanisms.	Validation plan with measurable observables.

Deliverables

Files your scientists can inspect and reuse

Deliverable	What is included
Model provenance	Structures, assumptions, convergence tests, electronic-structure settings and reproducible input files.
Mechanism dossier	Intermediates, transition states, energy/free-energy profiles and competing pathway analysis.
Stability assessment	Phase/coverage maps, reconstruction or degradation events, diffusion barriers and lifetime hypotheses.
Kinetic model	Rate/selectivity predictions, sensitivity, degree of rate control and uncertainty intervals.
Experimental test plan	Discriminating observables, conditions, isotope/transient tests and operando characterization targets.

Applications

Representative project contexts

Heterogeneous and single-atom catalystsElectrocatalyst reconstruction and dissolutionHomogeneous and organometallic cyclesEnzyme and bioinspired catalysisThermal aging, sintering, poisoning and cokingSelectivity and rate-determining-state analysis

Scientific Evidence

Evidence for data-efficient mechanism and stability modeling

Open-access studies show that active learning, enhanced sampling and first-principles reinforcement learning can expand mechanism exploration beyond a small set of hand-selected structures.

Active learning workflow for catalytic machine-learning potentials — Active-learning and enhanced-sampling workflow for training catalytic machine-learning potentials.¹

Descriptor relationship for multiple small-molecule activation reactions — Descriptor-based unification of multiple small-molecule activation reactions on dual-atom sites.³

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.
Lan, T., Wang, H. & An, Q. Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms. Nature Communications 15, 6281 (2024). doi:10.1038/s41467-024-50531-6. 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.

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