吸附能计算：AdsorbML实现效率飞跃

Fig. 1 An overview of the steps involved in identifying the adsorption energy for an adsorbate-surface combination. 

计算催化具有筛选大量材料的潜力，能够与耗时且昂贵的实验研究形成互补。使用第一性原理方法寻找异相催化剂的一项关键任务是吸附能的计算。吸附能是通过计算吸附质–表面所有可能构型中的最小值来确定的，它是计算自由能图并确定催化剂表面最佳反应途径的起点。

传统上，确定全局最优的吸附质–表面构型依赖于启发式方法和研究者的直觉。随着高通量筛选需求的日益增加，仅仅使用启发式和直觉变得极具挑战。

来自美国加州人工智能基础研究所的Janice Lan等，证明了使用机器学习势能够更加准确、高效地识别低能吸附质–表面构型。他们使用启发式和随机的策略对大量可能的吸附质构型进行采样，并使用机器学习势对结构进行弛豫。对于最佳的k个能量，作者进一步使用单点密度泛函理论（DFT）或完全DFT弛豫以改善计算结果。通过这种方法，可以在精度和效率之间取得适当的权衡，其中一种选择能够在87.36%的时间内找到能量最低的构型，同时在计算上实现约2000倍的加速。

Fig. 4 Illustration of the lowest energy configurations as found by DFT-Heur+Rand, SchNet, GemNet-OC, and SCN-MD-Large on the OC20-Dense validation set.

在作者之前的工作中，使用机器学习模型加速寻找低能吸附质–表面构型的过程曾一度依赖于针对特定吸附质/催化剂组合的定制模型，这限制了更加广泛的应用。作者现在的工作所使用的可泛化机器学习势，有望大大扩展以往方法的多功能性，并同时继续降低人力和计算成本。该文近期发布于npj Computational Materials 9: 172 (2023).

Fig. 5 ML+SP success rate at k = 5 across the different subsplits of the OC20-Dense test set and several baseline models. 

Editorial Summary

The design of novel heterogeneous catalysts plays an essential role in the synthesis of everyday fuels and chemicals. To accommodate the growing demand for energy while combating climate change, efficient, low-cost catalysts are critical to the utilization of renewable energy. Given the enormity of the material design space, efficient screening methods are highly sought after. Computational catalysis offers the potential to screen vast numbers of materials to complement more time- and cost-intensive experimental studies. A critical task for first-principles approaches to heterogeneous catalyst discovery is the calculation of adsorption energies. The adsorption energy is the global minimum energy across all potential adsorbate placements and configurations, and is the starting point for the calculation of the free energy diagrams to determine the most favorable reaction pathways on a catalyst surface. Traditionally, the identification of the globally optimal adsorbate-surface configuration 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. 

Janice Lan et al. from the Fundamental AI Research, CA, USA, demonstrated that machine learning (ML) potentials can be leveraged to identify low-energy adsorbate-surface configurations more accurately and efficiently. The authors sampled a large number of potential adsorbate configurations using both heuristic and random strategies and perform relaxations using ML potentials. The best k-relaxed energies can then be refined using single-point density functional theory (DFT) calculations or with full DFT relaxations. Using this approach, the appropriate trade-offs can be made between accuracy and efficiency, with one balanced option finding the lowest energy configuration 87.36% of the time, while achieving a ~2000× speedup in computation. Prior attempts to use machine learning models to accelerate the search for low-energy adsorbate-surface configurations have typically relied on bespoke models for each adsorbate/catalyst combination, which limits broader applicability. In this work, the generalizable machine learning potentials are promising to greatly expand the versatility of these methods while continuing to reduce the human and computational cost. This article was recently published in npj Computational Materials 9: 172 (2023).

原文Abstract及其翻译

Janice Lan,Aini Palizhati, Muhammed Shuaibi, Brandon M. Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C. Lawrence Zitnick & Zachary W. Ulissi 

Abstract

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 ~2000× speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1000 diverse surfaces and ~100,000 unique configurations.

摘要

计算催化在广泛应用的催化剂设计中发挥着越来越重要的作用。许多计算方法的一项共同任务是需要精确计算吸附质在催化剂表面的吸附能。传统上，低能吸附质–表面构型的确定依赖于启发式方法和研究者的直觉。随着高通量筛选需求的日益增加，仅仅使用启发式和直觉变得极具挑战。在本文中，我们证明了使用机器学习势能够更加准确、高效地识别低能吸附质–表面构型。我们的算法提供了精度和效率之间的权衡范围，其中一种选择能够在87.36%的时间内找到能量最低的构型，同时在计算上实现约2000倍的加速。为了标准化基准测试，我们引入了开源催化剂密集型（Open Catalyst Dense）数据集，其中包含近1000个不同的表面和约100000万种不同的构型。