化晶体从融中生长的可转移力场,使用飞行式主动学习
Xiangyu Chen1, William Shao1, Nam Q Le2
1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.
我们开发了一个用于原子模拟的机器学习力场,以经典分子动力学速度实现密度函数理论的准确性. 这使得高效的模拟化 (GaN) 添加剂制造工艺.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 反应过程的原子尺度模拟在计算上受到缺乏准确的半经验力场和初始方法的高成本的限制.
- 模拟复杂的过程,如化 (GaN) 薄膜增长通过增材制造需要高效和准确的建模工具.
研究的目的:
- 开发一种非参数化,基于机器学习的力场,用于原子规模的模拟.
- 为了使化 (GaN) 结晶过程在增材制造中的准确和高效的建模.
主要方法:
- 利用"即时"主动学习技术创建了一个新的机器学习力场.
- 开发了一种单一的力场,能够模拟参与GaN合成的固体,液体和气体相.
- 与实验测量和ab initio计算对比验证的计算预测.
主要成果:
- 机器学习力场的准确性与密度函数理论相提并论,其速度与古典分子动力学的速度相当.
- 成功模拟了从液态和气态前体中化 (GaN) 的结晶.
- 证明了力场模拟固体-液体接口和来自化的GaN结晶的能力.
结论:
- 开发的可转移,非参数化的力场显著提高了模拟反应过程的准确性和效率.
- 这一进步为精确建模液相表轴增长和为GaN薄膜建立强大的增材制造模型开辟了新的途径.
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