Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the minimum binding energy - the adsorption energy - for an adsorbate and a catalyst surface of interest. Traditionally, the identification of low energy adsorbate-surface configurations relies on heuristic methods and researcher intuition. As the desire to perform high-throughput screening increases, it becomes challenging to use heuristics and intuition alone. In this paper, we demonstrate machine learning potentials can be leveraged to identify low energy adsorbate-surface configurations more accurately and efficiently. Our algorithm provides a spectrum of trade-offs between accuracy and efficiency, with one balanced option finding the lowest energy configuration, within a 0.1 eV threshold, 86.33% of the time, while achieving a 1331x speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1,000 diverse surfaces and 85,658 unique configurations.
翻译:在设计各种应用的催化剂方面,计算性催化作用正在发挥越来越重要的作用。许多计算方法的共同任务是需要精确地计算一个吸附剂和一种令人感兴趣的催化剂表面的最小装配能量-吸附能量。传统上,低能量吸附剂表面配置的确定取决于超温方法和研究者的直觉。随着进行高通量筛选的愿望增加,单用超量和直觉就变得具有挑战性。在本文中,我们展示了机器学习的潜力,以便更准确、更高效地识别低能量吸附器表面配置。我们的算法提供了精确与效率之间的一系列权衡取舍,有一个平衡的选项在0.1 eV门槛范围内找到最低能源配置,86.33%的时间,同时实现1331x速度的计算。为了标准化,我们引入了包含近1,000个不同表面和85 658个独特的配置的开放热解解解调数据集。