通过机器学习有效优化巨型和结构上有序晶体中的原子装饰
Frank T Cerasoli1, Davide Donadio1
1Department of Chemistry, University of California, Davis, California 95616, USA.
The Journal of chemical physics
|July 22, 2024
概括
现在可以更快地预测复杂晶体中的原子排列. 结晶图卷积神经网络 (CGCNN) 结合位置顺位搜索 (SPS) 准确地确定混合或无序晶体结构的最佳原子装饰.
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
- 晶体学 晶体学是指结晶学.
背景情况:
- 复杂的晶体结构通常在Wyckoff网站上表现出混合化学占用,使精确的原子建模复杂化.
- 无序或混合的占用可以导致超结构的排序,显著增加单元细胞大小和计算成本.
- 预测这些系统中的原子排列对于理解材料特性和设计新材料至关重要.
研究的目的:
- 开发和验证一种计算方法,用于预测具有复杂几何和混合化学占用度的晶体中最佳的原子装饰.
- 利用晶体图卷积神经网络 (CGCNN) 和位点顺位搜索 (SPS) 来高效准确地预测原子排列.
- 证明该方法在处理无序/混合占用和超结构订单方面的能力.
主要方法:
- 利用晶体图卷积神经网络 (CGCNN) 预测给定化学成分的不同原子装饰的能量排序.
- 实现了一个网站排列搜索 (SPS) 优化算法,结合蒙特卡洛移动,模拟化和盆地跳跃技术.
- 采用CGCNN的能源景观来指导SPS在固定晶体几何上找到最稳定的原子配置.
主要成果:
- 该CGCNN-SPS方法准确地预测了已知化合物如Rb8Ga27Sb16和Cs2SnI6的原子装饰,这些化合物不是训练集的一部分.
- 该方法成功地确定了混合或无序占用和超结构排序的晶体中有利的原子装饰.
- 在CuZn中,通过分析位点配置轨迹,准确地探测了秩序-混乱阶段过渡的临界温度.
结论:
- 开发的CGCNN-SPS策略为预测复杂晶体结构中有利的原子装饰提供了一个强大而有效的工具.
- 这种方法显著加快了对混合或无序占用和超结构排序的材料的场地占用分析.
- 该方法提供了准确的预测,使新的晶体材料的更快发现和设计成为可能.
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