Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy

Mingzi Sun, Alan William Dougherty, Bolong Huang, Yuliang Li, Chun‐Hua Yan

Advanced Energy Materials 2020 article cited by 165

Source Open-access PDF More in Engineering & Materials

importance 5/10 ai assisted Electrocatalysts for Energy Conversion energy materials stage: research

Problem

The lack of a full picture of the hydrogen evolution reaction impeded development of potential electrocatalysts, motivating systematic screening of graphdiyne-based atomic catalysts.

Why it matters

Better HER electrocatalysts are important for sustainable energy applications.

What the AI did

DFT calculations systematically screened HER performance across all transition and lanthanide metals, and a machine-learning bag-tree technique was used as part of the discovery strategy.

Results

Screened catalyst candidates matched experimental results; no numbers stated in abstract.

Impact

Enabled screening of potential catalyst candidates across all transition and lanthanide metals.

Record

Venue	Advanced Energy Materials
Published	2020
Work type	article
Authors	Mingzi Sun, Alan William Dougherty, Bolong Huang, Yuliang Li, Chun‐Hua Yan
Citations	165 (OpenAlex, at import time)
References	63
Model or method	bag-tree machine learning approach
Organization	not stated in source
Primary source	https://doi.org/10.1002/aenm.201903949
Open access	green
Keywords	atomic catalysts, hydrogen evolution, machine learning, DFT, graphdiyne

Importance rating

5 / 10

real but narrower advance

Systematic screening of graphdiyne-based atomic catalysts for hydrogen evolution supports sustainable energy; abstract claims broad potential but demonstrates screening guidelines, not deployed impact.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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