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使用神经网络波函数对固体进行力和应力计算
Yubing Qian1,2, Xiang Li2, Ji Chen1,3
1School of Physics, Peking University, Beijing 100871, People's Republic of China.
Faraday discussions
|July 26, 2024
概括
一种新的方法改进了使用神经网络变量蒙特卡洛 (VMC) 对真实固体的原子间力和应力张量计算. 这提高了材料建模的准确性和效率.
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.
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 准确的初始计算对于理解化学,相和材料科学至关重要.
- 基于神经网络 (NN) 的变量蒙特卡洛 (VMC) 提供了一种有希望的方法来克服初始计算中的挑战.
研究的目的:
- 开发和评估一种基于神经网络的新型VMC方法,用于计算实体固体中的原子间力和应力张量.
- 为了提高材料建模的初始计算的准确性,效率和稳定性.
主要方法:
- 基于神经网络的变量蒙特卡洛 (VMC) 框架的实施.
- 开发一种使用空间曲线协调转换计算原子间力量的新方案.
- 为神经网络设计新的周期性特征,以提高不同晶格的稳定性.
主要成果:
- 与现有的力计算技术相比,拟议的空间曲线协调转换方法显示出更高的准确性,效率和稳定性.
- 新的周期性特征提高了对各种格子结构的力计算的可靠性.
- 该研究验证了NN-VMC在实体固体中计算力和应力张量时的有效性.
结论:
- 开发的NN-VMC方法与空间曲线转换和周期性特征显著提高了对真实固体精确的初始计算能力.
- 这项工作促进了机器学习量子蒙特卡罗方法在材料科学和凝聚物质物理学中的更广泛应用.
- 改进的计算效率和稳定性为更复杂的材料建模和发现铺平了道路.
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