Tang Deqi, Ketkaew Rangsiman, Luber Sandra
Department of Chemistry, University of Zurich, Zurich, Switzerland.
Chemistry. 2024 Oct 28;30(60):e202401148. doi: 10.1002/chem.202401148. Epub 2024 Oct 16.
Atomistic modeling can provide valuable insights into the design of novel heterogeneous catalysts as needed nowadays in the areas of, e. g., chemistry, materials science, and biology. Classical force fields and ab initio calculations have been widely adopted in molecular simulations. However, these methods usually suffer from the drawbacks of either low accuracy or high cost. Recently, the development of machine learning interatomic potentials (MLIPs) has become more and more popular as they can tackle the problems in question and can deliver rather accurate results at significantly lower computational cost. In this review, the atomistic modeling of catalytic systems with the aid of MLIPs is discussed, showcasing recently developed MLIP models and selected applications for the modeling of heterogeneous catalytic systems. We also highlight the best practices and challenges for MLIPs and give an outlook for future works on MLIPs in the field of heterogeneous catalysis.
如今，在化学、材料科学和生物学等领域，原子模型可以为新型多相催化剂的设计提供有价值的见解。经典力场和从头算计算已在分子模拟中被广泛采用。然而，这些方法通常存在精度低或成本高的缺点。近年来，机器学习原子间势（MLIPs）的发展越来越流行，因为它们可以解决上述问题，并能以显著更低的计算成本提供相当准确的结果。在这篇综述中，我们讨论了借助MLIPs对催化体系进行的原子模型研究，展示了最近开发的MLIP模型以及多相催化体系建模的选定应用。我们还强调了MLIPs的最佳实践和挑战，并对多相催化领域中MLIPs的未来工作进行了展望。
