加速异质催化剂的明确溶剂模型,使用机器学习的原子间潜力
Benjamin W J Chen1, Xinglong Zhang1, Jia Zhang1
1Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) 1 Fusionopolis Way, #16-16 Connexis Singapore 138632 Singapore benjamin_chen@ihpc.a-star.edu.sg zhang_xinglong@ihpc.a-star.edu.sg.
机器学习潜力 (MLIP) 将催化模拟的溶剂建模加速四个数量级. 这使得对异质催化剂的吸附和反应能量的准确预测成为可能.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 化学动力学 化学动力学
背景情况:
- 模拟溶剂对催化反应的影响在计算上是昂贵的.
- 显式溶剂处理需要分子动力学 (MD) 和增强的采样方法.
研究的目的:
- 为了证明机器学习原子间潜力 (MLIPs) 快速和准确的异质催化剂的显式溶剂建模.
- 为了实现大规模的,真实的模拟化催化剂.
主要方法:
- 开发并使用飞行训练的MLIP与积极学习相结合.
- 在初始的MD模拟中加速了多达4个数量级.
- 经过验证的MLIP与初始计算对准.
主要成果:
- MLIPs准确地复制了水的结构在散装和金属-水接口.
- 对关键物种 (CO*,OH*,等) 的预测吸附能量. 在Cu表面上.
- 计算了Cu和Pd表面上乙烯基醇C-H裂变的自由能量障碍.
结论:
- MLIPs为催化过程中的显式溶剂建模提供了一个计算效率高的方法.
- 这种方法可以在现实的溶解环境中准确预测催化性能.
- 在前所未有的规模上对溶解催化剂进行详细研究.
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