Strategies to build a sustainable machine learning ecosystem for atomic simulations
A strategic roadmap for an atomistic machine-learning ecosystem
Machine Learning
Summary
Machine learning helps scientists simulate atoms and materials more quickly and accurately. The authors summarize a community meeting that discussed how to overcome challenges in combining physics-based methods with machine learning. They suggest ways to improve software, hardware use, and collaboration to make these tools more effective and long-lasting. Their roadmap aims to guide future work to get the most from AI in atomic-scale research.
What this means in practice
- •For computational chemists: Coordinate development of integrated software and hardware tools to simulate materials more efficiently and accurately using AI methods tailored for atomic-scale problems.
- •For materials engineering teams: Develop workflows that combine machine learning models with physics-based simulations to accelerate the design of new materials and predict their properties.
A position paper. It proposes an approach and reports no results.
Authors
Jörg Behler, Michele Ceriotti, Cecilia Clementi, Gábor Csányi, Alin-Marin Elena, Aditi Krishnapriyan, Joseph W. Abbott, Fabio Affinito, Albert P. Bartók, Ilyes Batatia, Filippo Bigi, Florian N. Brünig, Yannick Calvino Alonso, Giuseppe Carleo, Aurélie Champagne, Stefan Chmiela, Marc L. Descoteaux, Ralf Drautz, Alexandra Farcas, Meng Gao, Rohit Goswami, Michael F. Herbst, Christian Holm, James R. Kermode, Alexander L. M. Knoll, Tobias Kreiman, Hoang-Thien Luu, Yury Lysogorskiy, Mihai-Cosmin Marinica, Rocco Meli, Klaus-Robert Müller, Frank Noé, Mohamadhosein Nosratjoo, Simon Olsson, Christoph Ortner, Aldo S. Pasos-Trejo, Anyang Peng, Eric Qu, Andrea Rizzi, Mariana Rossi, Bassem Sboui, Gregor N. C. Simm, Alexandre Tkatchenko, Jacopo Venturin, O. Anatole von Lilienfeld, William C. Witt, Brandon M. Wood, Tigany Zarrouk, Fabian Zills
Abstract
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 adapting established software stacks to modern hardware accelerators and ML libraries. As the field evolves rapidly, fueled in part by widespread enthusiasm but also by tangible impact, it seems appropriate to take a moment to consider the current state of the art and open challenges, and reflect on what can be done to better coordinate efforts across the community. With this goal in mind, several members of this community met in Lausanne in January 2026 at CECAM to discuss algorithms, models, software and hardware infrastructure, and the most promising scientific applications that have become possible thanks to the use of artificial intelligence in atomic-scale simulations. This strategic roadmap paper summarizes the outcomes of these discussions, suggesting some long-term goals, and some concrete actions, to establish a healthy, sustainable and impactful atomistic ML ecosystem.