通过物理意识模型积极学习原子大小的气体/固体潜在能量表面
Nikolaos Patsalidis1, Mohsen Doust Mohammadi2, Somnath Bhowmick2
1Computation-based Science and Technology Research Center, The Cyprus Institute, Aglantzia 2121, Cyprus.
我们开发了一个积极的学习框架,以创建准确的经典力场,以建模原子尺度的气体/固体相互作用. 这种方法实现了环境传感应用的量子级准确性,使用高效的物理意识潜能.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 环境科学 环境科学
背景情况:
- 对气体/固体接口的准确建模对于传感等环境应用至关重要.
- 为复杂系统开发具有量子级准确性的经典力场 (FFs) 仍然是一个挑战.
- 将主动学习 (AL) 与物理意识潜能相结合,提供了一个有前途的解决方案.
研究的目的:
- 为开发经典力场 (FFs) 提出一个积极的学习框架.
- 准确地建模气体/固体原子级复合体的潜在能量表面 (PES).
- 通过使用高效,物理意识的潜能,为接口系统实现量子级准确性.
主要方法:
- 在积极采样密度函数理论 (DFT) 数据上的训练有素的物理意识潜力.
- 使用了通过帕雷托分析优化的可适应的半实证描述符.
- 使用Metropolis Hastings Monte Carlo (MHMC) 或随机分子动力学 (sMD) 生成的候选结构.
- 选择的DFT候选人使用异常分数 (OS) 进行各种PES探索.
主要成果:
- 开发了能够高精度捕捉凝聚性,物理吸收和化学吸收相互作用的FF.
- 实现了接近ab initio方法的准确性,同时保持了半实证的潜在效率.
- 在气相中的银团分子动力学 (MD) 模拟中证明了FF的实用性.
结论:
- 拟议的AL框架允许开发准确和高效的经典力场.
- 该方法非常通用,可以适应各种描述符,基础集和采样技术.
- 这种方法推进了对环境应用的原子级相互作用的准确建模.
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