AI for Catalyst Discovery and Optimization

Low-Carbon Chemical Catalysts

Design catalyst portfolios for lower-carbon chemical routes by optimizing molecular performance together with process energy, feedstock, durability, and lifecycle constraints.

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Overview

Accelerate Practical Catalysts for Lower-Carbon Chemical Routes

A catalyst is not automatically low-carbon because it activates CO2, biomass, waste carbon or green hydrogen. Climate value depends on the full route: feed carbon origin, hydrogen and electricity intensity, conversion, recycle, separations, catalyst lifetime, product displacement and regional energy assumptions.

CD ComputaBio helps teams evaluate catalyst candidates in the context of route-level decisions. We couple AI-guided catalyst screening with process constraints and transparent carbon assumptions, so shortlists are ranked by practical performance rather than isolated activity metrics. The deliverable is a defensible catalyst and validation roadmap for lower-carbon chemical manufacturing.

How we avoid over-claiming

Define functional unit, baseline route and system boundary before ranking candidates
Separate catalytic feasibility from carbon-intensity assumptions
Report break-even requirements for energy, selectivity, lifetime and feedstock
Flag critical-material, durability and separation risks early

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.

Route and baseline definition

Map incumbent and proposed pathways, functional unit, boundary, carbon source, energy scenario and decision thresholds.

Catalyst-process co-optimization

Connect activity, selectivity and stability predictions with conversion, recycle, separations, heat integration and utility demand.

CO2 conversion catalyst screening

Screen thermo-, electro- or photocatalytic routes for CO, formate, methanol, hydrocarbons and oxygenates under product-specific constraints.

Sustainable feedstock upgrading

Evaluate biomass, waste-derived, plastic-derived or captured-carbon routes where impurities and product distributions drive feasibility.

Lifecycle-aware sensitivity analysis

Estimate break-even selectivity, catalyst lifetime, energy intensity and hydrogen or electricity carbon intensity.

Validation roadmap

Prioritize calculations and experiments by expected decision value, with clear thresholds for advancing or stopping a route.

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. Counterfactual setup	Define incumbent route, geography, time horizon, functional unit, energy scenario and evidence standard.	Transparent baseline and system boundary.
2. Coupled data build	Harmonize catalyst results with process conditions, preparation, deactivation and energy/carbon factors.	Catalyst-process dataset and assumption register.
3. Catalytic feasibility model	Predict activity, selectivity, stability, impurity tolerance and uncertainty; verify critical chemistry with DFT or kinetics.	Candidate feasibility map.
4. Process consequence model	Translate catalyst performance into conversion, recycle, separation load, utility demand and carbon-intensity sensitivity.	Scenario-resolved carbon decision map.
5. Validation planning	Rank tests by expected decision value and define break-even performance thresholds.	Practical validation roadmap.

Deliverables

Files your scientists can inspect and reuse

Deliverable	What is included
Assumption register	Functional unit, system boundary, baseline, regional energy scenario, feedstock assumptions and cited sources.
Catalyst shortlist	Ranked candidates with performance, uncertainty, feasibility, critical-material and synthesis-risk flags.
Process-coupled model	Catalyst-to-process response model, mass/energy assumptions, separation burden and sensitivity analysis.
Carbon decision map	Scenario-resolved carbon intensity and break-even requirements, avoiding unqualified green claims.
Validation roadmap	Experiments and calculations prioritized by expected decision value, with go/no-go thresholds.

Applications

Representative project contexts

CO2-to-CO, formate, methanol and C2+ productsLow-carbon ammonia and methanolSustainable aviation fuel intermediatesBiomass upgrading and selective deoxygenationPlastic and waste feedstock upcyclingElectrified and modular chemical manufacturing

Scientific Evidence

Evidence for data-driven low-carbon catalyst design

Open-access CO2 reduction studies show how active-motif representations, selectivity maps and text-mined electrocatalyst corpora can connect broad catalyst search spaces to product-level decisions.

High-throughput virtual screening workflow for CO2 reduction catalysts — High-throughput virtual screening strategy and ML binding-energy model performance for CO2 reduction catalysts.¹

CO2 reduction selectivity map — Potential-dependent 3D selectivity map for CO2 reduction product prediction.¹

References

Mok, D. H. et al. Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning. Nature Communications 14, 7303 (2023). doi:10.1038/s41467-023-43118-0. Open access, CC BY 4.0.
Chen, X. et al. Large language model enhanced corpus of CO2 reduction electrocatalysts and synthesis procedures. Scientific Data 11, 347 (2024). doi:10.1038/s41597-024-03180-9. Open access, CC BY 4.0.

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