使用Ab Initio和机器学习的原子间潜力的蒙特卡洛模拟的Python库
Woodrow N Wilson1,2, Vivek S Bharadwaj3, Neeraj Rai1
1Dave C. Swalm School of Chemical Engineering and Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, Mississippi 39762, United States.
一个新的Python库,ASE-MC,使用ab initio方法和机器学习原子间潜力 (MLIPs) 实现了透明和可重复的蒙特卡洛 (MC) 模拟. 该框架简化了研究人员的复杂模拟,提高了科学见解的可发现性.
科学领域:
- 计算化学和材料科学.
- 模拟软件和算法的开发.
背景情况:
- 模拟社区需要透明,可复制,可使用和可扩展的 (TRUE) 蒙特卡洛 (MC) 框架.
- 将ab initio方法和机器学习的原子间潜力 (MLIP) 集成到MC模拟中,对于推进计算研究至关重要.
研究的目的:
- 引入ASE-MC,这是一个增强原子模拟环境 (ASE) 的Python库,具有MC模拟功能.
- 为使用各种能源发动机进行多种MC模拟提供灵活和可扩展的框架.
主要方法:
- 开发了ASE-MC,这是一个Python库,将MC算法与ASE集成在一起.
- 通过对液态水,双二面角和在Pt上吸附氨的模拟,证明了灵活性111).
- 整合了ab initio和MLIP发动机,具有空洞偏差的大法典MC,以及自定义MC移动添加.
主要成果:
- 展示了将ASE的系统构建和计算工具与MC算法相结合的能力.
- 在正规,异热-异和大正规合奏中成功执行了模拟.
- 突出了在发动机选择,MC组合和定制移动集成方面的灵活性.
结论:
- ASE-MC为复杂的MC工作流提供了一个简洁的Python脚本方法.
- 该图书馆促进了可重复的MC模拟,使其更容易应用于新的研究系统.
- 该框架支持在材料模拟中对配置空间进行透明和可扩展的采样.
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