计算框架结合量子力学,分子动力学和深度神经网络来评估材料的内在特性
Amirmasoud Lanjan1, Zahra Moradi2, Seshasai Srinivasan1,2
1Department of Mechanical Engineering, McMaster University, Hamilton, Ontario L8S 4K1, Canada.
The journal of physical chemistry. A
|July 27, 2023
概括
这项研究引入了一种新的多尺度框架,将量子力学和深度神经网络结合起来,以准确确定分子动力学潜在参数. 这种方法克服了设计先进纳米材料的传统方法的局限性.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 传统的纳米材料设计依赖于耗时的试错方法.
- 分子动力学 (MD) 模拟需要精确的非结合潜能参数,这些参数通常无法用于新材料.
- 现有的从量子力学 (QM) 计算中推导参数的方法在计算上是不可行的,而且缺乏准确性.
研究的目的:
- 开发一个准确和高效的计算框架来确定非绑定潜在参数.
- 为了使具有特定性质的新型纳米材料的设计和评估.
- 克服计算化学中传统参数化方法的局限性.
主要方法:
- 一个连接QM计算和MD模拟的多尺度框架.
- 集成先进的深度神经网络 (DNN) 用于参数拟合.
- 使用各种分子进行验证:H2O,LiPF6,乙醇,C8H18和乙烯碳酸盐.
主要成果:
- 拟议的框架成功地确定了MD模拟的潜在参数.
- 准确预测材料的特性,如密度,沸点和点.
- 从简单到复杂的系统,在各种分子类型中证明了适用性.
结论:
- 新的QM-MD-DNN框架为准确的潜在参数确定提供了强大的解决方案.
- 这种方法显著提高了计算材料设计的效率和可靠性.
- 经过验证的框架为加速发现下一代纳米材料铺平了道路.
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