Materials & Energy

Catalyst Candidate Screening From Existing Data

Published 2026-08-22 · Updated 2026-08-22

Evidence status: Decision-method guide; not a completed investigation or final validation.

The decision this guide supports

which catalyst families deserve deeper computational or experimental assessment

Why the problem is difficult

The article-specific identification challenge is whether the question “which catalyst families deserve deeper computational or experimental assessment” can be resolved using activity, selectivity, stability, mechanism, conditions, poisoning, cost, and data comparability, rather than merely restated in new language.

A falsifier-first workflow

• Define the decision precisely: which catalyst families deserve deeper computational or experimental assessment.
• Build a source and data ledger around activity, selectivity, stability, mechanism, conditions, poisoning, cost, and data comparability.
• Compare the inherited route with a mechanistically distinct alternative and a constraint-based null.
• Actively search for the strongest counterevidence relevant to this decision, including boundary cases and prior failures.
• Run the lowest-cost discriminating challenge: challenge the ranking under realistic operating conditions and deactivation assumptions.
• Record pursue, reframe, or stop, the confidence level, the evidence ceiling, and who owns downstream validation.

Decision criteria

• Decision impact: would the result materially change the choice about which catalyst families deserve deeper computational or experimental assessment?
• Evidence fit: does the available evidence—activity, selectivity, stability, mechanism, conditions, poisoning, cost, and data comparability—directly address the decision rather than merely correlate with it?
• Discrimination: does the preferred route predict an outcome a credible alternative does not?
• Robustness: does the ranking survive the challenge “challenge the ranking under realistic operating conditions and deactivation assumptions”?
• Validation boundary: is the conclusion no stronger than the available sources, data and computation?

Supporting evidence

activity, selectivity, stability, mechanism, conditions, poisoning, cost, and data comparability

Counterevidence

For this decision, a result from “challenge the ranking under realistic operating conditions and deactivation assumptions” that reverses or flattens the ranking must remain visible even when it is commercially inconvenient.

Computation

Here computation earns its place only if it changes the choice about which catalyst families deserve deeper computational or experimental assessment or exposes why the available evidence cannot resolve it.

Fastest falsifier

challenge the ranking under realistic operating conditions and deactivation assumptions

When to stop or reframe

A decision-specific stop trigger is failure of the challenge “challenge the ranking under realistic operating conditions and deactivation assumptions” without an independently supported alternative mechanism.

Evidence ceiling

Computed descriptors and literature data do not establish reactor performance or lifetime.

Sources and starting points

• Materials Project — Open computed materials properties and structures for candidate screening.
• NIST Materials Data Repository — Public materials datasets with provenance and repository records.
• NREL Data Catalog — Additional authoritative starting point selected for this decision area; applicability must be checked against the precise question.
• EPA CompTox Chemicals Dashboard — Additional authoritative starting point selected for this decision area; applicability must be checked against the precise question.

Continue the decision journey

1. Materials R&D Prioritization Before the Next Development Cycle
2. Materials Informatics vs Traditional Screening
3. A Material-Candidate Ranking Framework With Uncertainty
4. Stop Conditions for Materials Development Programs

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Frequently asked questions

What decision does “Catalyst Candidate Screening From Existing Data” help make?

It supports a bounded decision about which catalyst families deserve deeper computational or experimental assessment. The framework keeps alternatives, evidence, counterevidence, uncertainty, and the fastest falsification test visible.

What is the fastest useful test?

challenge the ranking under realistic operating conditions and deactivation assumptions

Can computation validate the final scientific claim?

No. Computed descriptors and literature data do not establish reactor performance or lifetime. Computation can prioritize and eliminate directions; final validation remains with the appropriate domain methods and accountable specialists.

When should the project stop or reframe?

Stop or reframe when the candidate lies outside the model domain, violates a hard physical or process constraint, depends on unavailable validation data, or loses its advantage under realistic uncertainty and degradation.