Inverse design of bespoke interatomic potentials via active learning by information-matching

2026-06-06 · Source: Machine Learning · Field: Science & Research — Engineering & Applied Sciences, Mathematics & Computational Sciences, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

The information-matching (IM) approach offers a principled framework for designing interatomic potentials (IPs) specifically tailored for predicting material properties, addressing a key limitation in active learning (AL) strategies. Unlike most AL methods that reduce parameter uncertainty without considering specific quantities of interest (QoIs), IM ensures training data provides sufficient parameter space information to meet prescribed uncertainty targets for selected QoIs. Applied to developing bespoke IPs for plastic strength in metals, the method employs an indirect IM strategy, targeting inexpensive intermediate QoIs that correlate with strength. This approach achieves precise parameter constraints with minimal training data, yielding accurate predictions for both intermediate QoIs and plastic strength. While model error remains a limitation, a post hoc uncertainty inflation correction effectively mitigates this issue, highlighting the potential and boundaries of uncertainty-aware AL for complex material property prediction.

Key takeaway

For research scientists designing interatomic potentials for material simulations, consider adopting the information-matching (IM) approach to precisely tailor IPs for specific material properties like plastic strength. This method allows you to achieve targeted uncertainty levels with minimal training data by focusing on relevant quantities of interest. Be prepared to implement a post hoc uncertainty inflation correction to mitigate inherent model error, ensuring robust and reliable predictions in your large-scale atomistic simulations.

Key insights

The information-matching (IM) approach tailors interatomic potentials by linking training data selection to specific material property uncertainty targets.

Principles

• IP reliability hinges on training data.
• IM links training data to QoI uncertainty.

Method

The IM method uses an indirect strategy for plastic strength prediction, targeting inexpensive intermediate QoIs that correlate with strength to achieve precise parameter constraints with minimal data.

In practice

• Develop bespoke IPs for specific properties.
• Mitigate model error with uncertainty correction.

Topics

• Interatomic Potentials
• Active Learning
• Information-Matching
• Plastic Strength Prediction
• Atomistic Simulations
• Uncertainty Quantification

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.