论文标题
AdsorBML：使用可通用的机器学习潜力的吸附能量计算的效率飞跃
AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
论文作者
论文摘要
计算催化在广泛应用的催化剂设计中起着越来越重要的作用。许多计算方法的常见任务是需要准确计算吸附物和感兴趣的催化剂表面的吸附能。传统上，低能吸附面配置的识别取决于启发式方法和研究人员的直觉。随着进行高通量筛查的愿望增加，仅使用启发式方法和直觉就变得具有挑战性。在本文中，我们证明可以利用机器学习电位来更准确，更有效地识别低能吸附面配置。我们的算法在准确性和效率之间提供了一系列权衡，一个平衡的选项找到了最低的能量配置为87.36％的时间，同时实现了2000x的计算加速。为了标准化基准测试，我们介绍了开放的催化剂密集数据集，该数据集包含近1,000个不同的表面和100,000个独特的配置。
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 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 87.36% of the time, while achieving a 2000x speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1,000 diverse surfaces and 100,000 unique configurations.
