开发用于催化剂发现的可推广的大规模机器学习模型面临的挑战
Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery
摘要：
用于催化剂发现的机器学习潜力的开发主要集中在非常特定的化学和材料成分上。虽然这些方法在可用材料之间进行插值是有效的，但它们很难在化学空间中推广。最近对大规模催化剂数据集的管理提供了建立通用机器学习潜力的机会，跨越化学和成分空间。如果实现，所述潜力可以加速各种应用（CO2还原，NH3生产等）的催化剂发现过程。而不需要目前所需的额外专门培训。Open Catalyst 2020（OC20）的发布已经开始，推动异构催化和机器学习社区构建更准确和强大的模型。在这个角度来看，我们讨论了一些挑战和最近的发展结果OC20。我们研究了当前模型在不同材料和吸附物中的性能，以确定表现不佳的子集。然后，我们讨论了一些建模的努力，周围的能量守恒，方法来寻找和评估的局部极小值，并增强非平衡数据。为了补充社区的持续发展，我们最后展望了一些尚未彻底探索大规模催化剂发现的重要挑战。
英文摘要：
The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While effective in interpolating between available materials, these approaches struggle to generalize across chemical space. The recent curation of large-scale catalyst datasets has offered the opportunity to build a universal machine learning potential, spanning chemical and composition space. If accomplished, said potential could accelerate the catalyst discovery process across a variety of applications (CO2 reduction, NH3 production, etc.) without additional specialized training efforts that are currently required. The release of the Open Catalyst 2020 (OC20) has begun just that, pushing the heterogeneous catalysis and machine learning communities towards building more accurate and robust models. In this perspective, we discuss some of the challenges and findings of recent developments on OC20. We examine the performance of current models across different materials and adsorbates to identify notably underperforming subsets. We then discuss some of the modeling efforts surrounding energy-conservation, approaches to finding and evaluating the local minima, and augmentation of off-equilibrium data. To complement the community's ongoing developments, we end with an outlook to some of the important challenges that have yet to be thoroughly explored for large-scale catalyst discovery.
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