AI 中文总结
本文基于CECAM会议讨论，提出原子级机器学习生态系统的战略路线图，涵盖算法、模型、软件硬件及科学应用，旨在协调社区努力，实现长期可持续发展。
AI 中文摘要
数据驱动的机器学习（ML）技术已成为科学许多领域的重要工具。它们在物质原子级模拟中的应用尤为广泛且影响深远。这一成功很大程度上归功于一个成熟且完善的基于物理的建模框架的存在，从第一性原理电子结构计算到分子动力学和统计采样，机器学习被自然地整合其中，以重塑精度、效率和规模之间长期存在的权衡关系。然而，这种整合也带来了概念和实践上的挑战，从选择以数据为中心还是基于物理的建模方法，到使现有软件栈适应现代硬件加速器和机器学习库。随着该领域快速发展，部分受到广泛热情但也受到实际影响的推动，现在似乎是时候停下来审视当前的技术水平和面临的挑战，并思考如何更好地协调社区间的努力。为此，该社区的几位成员于2026年1月在洛桑的CECAM举行会议，讨论算法、模型、软件和硬件基础设施，以及由于在原子级模拟中使用人工智能而成为可能的最有前景的科学应用。这份战略路线图论文总结了这些讨论的成果，提出了一些长期目标和具体行动，以建立一个健康、可持续且有影响力的原子级机器学习生态系统。
英文摘要
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.
