经典的两体电位与初始计算有多接近? 基于线性机器学习的力量匹配的洞察力
Zheng Yu1, Ajay Annamareddy2, Dane Morgan2
1Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
我们开发了一种机器学习方法,从无形材料的初始计算中提取原子相互作用. 这种新方法为等材料创造了更准确的力场,挑战了现有的模型.
科学领域:
- 计算材料科学科学 计算材料科学
- 机器学习在物理学中的应用
- 凝聚物质理论 凝聚物质理论
背景情况:
- 经典力场对于模拟材料至关重要,但往往缺乏准确性.
- 准确的原子相互作用对于预测材料特性至关重要.
研究的目的:
- 开发一种机器学习方法,从初始计算中提取精确的对原子相互作用.
- 为了创建一个更可靠的古典力场无形.
主要方法:
- 采用了线性机器学习力匹配方法.
- 使用局部特征表示来定义作为原子间距离函数的电位.
- 该方法使用对无形二氧化的ab initio计算进行了验证.
主要成果:
- 对Si-Si,Si-O和O-O相互作用的推导力场潜力与现有的潜力有显著差异.
- 使用新力场的模拟结果是更低的玻璃过渡温度 (Tg ~ 1800 K) 和正的液体热膨胀.
- 现有的经典力场可能会产生模拟的属性的工件,例如异常高的Tg和负热膨胀.
结论:
- 拟议的方法提供了一个基本的方法来评估两体潜力与ab initio数据对比.
- 这种方法有效地指导了对无形材料的准确经典力场的开发.
- 通常用于二氧化的经典力场可能不能准确地代表原子相互作用,可能导致模拟性质的工件.
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