多体模拟的有限尺寸外推的高斯过程
Edgar Josué Landinez Borda1, Kenneth O Berard1, Annette Lopez2
1Department of Chemistry, Brown University, Providence, Rhode Island 02912, USA. edgar_landinez_borda@brown.edu.
Faraday discussions
|September 16, 2024
概括
机器学习,特别是高斯过程,在热力学极限中准确预测材料特性. 这种方法有效地从较小的模拟中推断出数据,克服了材料建模中的计算挑战.
科学领域:
- 计算材料科学 计算材料科学
- 量子化学是一种量子化学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 精确的材料建模需要预测热力学极限内的特性.
- 对于大型系统,多体电子结构方法在计算上是昂贵的.
- 从较小的系统进行外推是常用的,但依赖于简化的缩放表达式.
研究的目的:
- 开发一种更准确,更有效的方法来推断多体模拟到热力学极限.
- 利用机器学习来纠正模拟中的有限大小效应.
主要方法:
- 使用高斯过程 (GPR) 来进行外推.
- 在平滑重叠的原子位置 (SOAP) 描述器上训练了GPR模型.
- 模拟的1D链的外推能量与合集群理论和辅助场量子蒙特卡洛 (AFQMC).
主要成果:
- 斯过程准确地预测了链的热力学极限能量,精度低于毫哈特树.
- 该方法对均和不均的链有效.
- 使用相对较小的10-30个原子链的训练数据实现了高精度.
结论:
- 高斯过程提供了一个可概括和准确的方法来推断多体模拟到热力学极限.
- 这种基于机器学习的方法独立于系统几何和维度.
- 突出了机器学习的潜力,以提高对有限大小模拟的解释.
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