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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
Published on: April 12, 2019
Smarter Data: Rethinking Data Generation for Machine Learning Potentials in Heterogeneous Catalysis
Wenbo Xie1, Yixiao Han1, Chenyu Wu1
1School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China.
JACS Au
|June 26, 2026
Summary
Generating the right data is crucial for reliable machine-learning potentials (MLPs) in heterogeneous catalysis. This work reinterprets data generation strategies, focusing on scope, relevance, and coverage for improved simulations.
Area of Science:
- Computational Chemistry
- Materials Science
- Chemical Engineering
Background:
- Machine-learning potentials (MLPs) offer high accuracy for simulating catalytic systems.
- The reliability of MLPs is critically dependent on the quality and representativeness of training data.
- Heterogeneous catalysis involves complex, dynamic systems requiring diverse structural and chemical information.
Purpose of the Study:
- To re-evaluate strategies for constructing training datasets for MLPs in heterogeneous catalysis.
- To address the challenge of generating 'right' data, not just 'more' data.
- To propose a hybrid data generation approach for enhanced MLP accuracy.
Main Methods:
- Analysis of existing MLP training set construction strategies (scope, relevance, coverage).
- Examination of structure-based and interaction-based sampling methods.
- Conceptualization of a hybrid data generation workflow.
Main Results:
- Existing structure-based sampling methods prioritize system relevance and targeted coverage.
- Emerging interaction-based strategies expand local interaction support beyond predefined systems.
- A hybrid approach combining structure-based and interaction-based sampling is proposed.
Conclusions:
- The focus must shift from data quantity to data quality for reliable MLPs.
- Hybrid data generation workflows can leverage the strengths of different sampling strategies.
- This approach advances the simulation accuracy of complex heterogeneous catalytic systems using MLPs.
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