49 Researchers Publish Strategic Roadmap for Atomistic Machine Learning Ecosystem
FrankNoeBerlin · x · 2026-10-01
A community-driven strategic roadmap paper, A strategic roadmap for an atomistic machine-learning ecosystem (arXiv:2609.39090), has been released by 49 authors led by Jörg Behler, Michele Ceriotti, Cecilia Clementi, Gábor Csányi and others.
The paper argues that data-driven ML has become essential across science, with its widest and most transformative impact in atomistic simulations of matter—largely because ML integrated naturally into a well-established physics-based modeling stack, from first-principles electronic-structure calculations to molecular dynamics and statistical sampling, reshaping long-standing trade-offs between accuracy, efficiency and scale. It also lays out the conceptual and practical challenges of this integration, such as choosing between data-centric and physics-based modeling, and offers a strategic plan for the future of the atomistic ML ecosystem.
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