一种基于遗传算法和机器学习的方法,用于通过优化量子能量表面来研究合金和分子
Umar Lucio Rezende1, Leonardo A De Souza2, Jadson C Belchior1
1Departamento de Química, ICEx, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Journal of computational chemistry
|June 12, 2023
概括
一个新的遗传算法使用量子力学有效地找到原子集群的全球最小值. 这种方法改善了集群生成,并使用机器学习来实现更快的优化,为计算化学提供了无偏差的方法.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 量子力学就是量子力学.
背景情况:
- 在潜在能量表面 (PES) 中准确确定全球最小值对于理解分子和集群行为至关重要.
- 传统方法通常依赖于经典近似,可能引入偏差并限制复杂系统的准确性.
- 开发高效的算法直接 ab initio PES 全球最小值的搜索是计算化学的一个持续的挑战.
研究的目的:
- 引入一种新的基因算法 (NQGA),用于直接初始潜在能量表面 (PES) 全球最小值的搜索.
- 通过改进集群生成,分类和基于机器学习的PES建模来增强算法,以实现并行优化.
- 通过将该方法应用于已知的系统并确定新的全球最小值来验证该方法.
主要方法:
- 一个新的遗传算法,包含用于初始集群生成改进,集群分类和基于机器学习的量子 PES 建模的运算符.
- 使用高层次的初始方法 (MP2,DFT,DLPNO-CCSD) 进行并行优化.
- 在包括纳米集群在内的系统上进行验证.
主要成果:
- 对于各种原子集群,NQGA成功地以高效率定位了先前报告的全球最小值.
- 该方法与现有文献价值观达成了公平一致.
- 为了确定了一个新的全球最小值.
结论:
- 拟议的遗传算法提供了一种灵活和高效的方法,用于直接初始优化集群几何.
- 它有效地克服了古典方法带来的偏差,使得直接使用高级量子化学计算成为可能.
- 根据NQGA的研究,在计算化学和材料科学中,NQGA在识别全球最小值方面具有很大的应用潜力.
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