Efficient exploration of reaction pathways using reaction databases and active learning.

IF 3.1 2区 化学 Q3 CHEMISTRY, PHYSICAL
Domantas Kuryla, Gábor Csányi, Adri C T van Duin, Angelos Michaelides
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引用次数: 0

Abstract

The fast and accurate simulation of chemical reactions is a major goal of computational chemistry. Recently, the pursuit of this goal has been aided by machine learning interatomic potentials (MLIPs), which provide energies and forces at quantum mechanical accuracy but at a fraction of the cost of the reference quantum mechanical calculations. Assembling the training set of relevant configurations is key to building the MLIP. Here, we demonstrate two approaches to training reactive MLIPs based on reaction pathway information. One approach exploits reaction datasets containing reactant, product, and transition state structures. Using an SN2 reaction dataset, we accurately locate reaction pathways and transition state geometries of up to 170 unseen reactions. In another approach, which does not depend on data availability, we present an efficient active learning procedure that yields an accurate MLIP and converged minimum energy path given only the reaction end point structures, avoiding quantum mechanics driven reaction pathway search at any stage of training set construction. We demonstrate this procedure on an SN2 reaction in the gas phase and with a small number of solvating water molecules, predicting reaction barriers within 20 meV of the reference quantum chemistry method. We then apply the active learning procedure on a more complex reaction involving a nucleophilic aromatic substitution and proton transfer, comparing the results against the reactive ReaxFF force field. Our active learning procedure, in addition to rapidly finding reaction paths for individual reactions, provides an approach to building large reaction path databases for training transferable reactive machine learning potentials.

快速准确地模拟化学反应是计算化学的一个主要目标。最近,机器学习原子间势(MLIP)为实现这一目标提供了帮助,它能提供量子力学精度的能量和力,但成本仅为参考量子力学计算的一小部分。组建相关构型的训练集是构建 MLIP 的关键。在这里,我们展示了两种基于反应路径信息训练反应式 MLIP 的方法。一种方法是利用包含反应物、生成物和过渡态结构的反应数据集。利用 SN2 反应数据集,我们准确定位了多达 170 个未见反应的反应路径和过渡态几何结构。在另一种不依赖数据可用性的方法中,我们提出了一种高效的主动学习程序,只需给出反应终点结构,就能得到精确的 MLIP 和收敛的最小能量路径,避免了在训练集构建的任何阶段进行量子力学驱动的反应路径搜索。我们在气相和少量溶解水分子的 SN2 反应中演示了这一程序,预测的反应壁垒比参考量子化学方法低 20 meV。然后,我们将主动学习程序应用于涉及亲核芳香取代和质子转移的更复杂反应,并将结果与反应 ReaxFF 力场进行比较。我们的主动学习程序除了能快速找到单个反应的反应路径外,还提供了一种建立大型反应路径数据库的方法,用于训练可迁移的反应机器学习势能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Chemical Physics
Journal of Chemical Physics 物理-物理:原子、分子和化学物理
CiteScore
7.40
自引率
15.90%
发文量
1615
审稿时长
2 months
期刊介绍: The Journal of Chemical Physics publishes quantitative and rigorous science of long-lasting value in methods and applications of chemical physics. The Journal also publishes brief Communications of significant new findings, Perspectives on the latest advances in the field, and Special Topic issues. The Journal focuses on innovative research in experimental and theoretical areas of chemical physics, including spectroscopy, dynamics, kinetics, statistical mechanics, and quantum mechanics. In addition, topical areas such as polymers, soft matter, materials, surfaces/interfaces, and systems of biological relevance are of increasing importance. Topical coverage includes: Theoretical Methods and Algorithms Advanced Experimental Techniques Atoms, Molecules, and Clusters Liquids, Glasses, and Crystals Surfaces, Interfaces, and Materials Polymers and Soft Matter Biological Molecules and Networks.
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