A strategic roadmap for an atomistic machine-learning ecosystem
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespread and impactful. This success is due largely to the existence of a well-developed and established physics-based modeling framework, ranging from first-principles electronic-structure calculations to molecular dynamics and statistical sampling, into which ML was integrated naturally to reshape long-standing trade-offs between accuracy, efficiency, and scale. Nevertheless, this integration raises both conceptual and practical challenges, from choosing between data-centric and physics-based modeling approaches to ...
| Authors | Jörg Behler, Michele Ceriotti, Cecilia Clementi et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 30 September 2026 |
| Last updated at source | 13 October 2026 |
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