基于机器学习的IIB组到VIA半导体的原子间潜力:走向通用模型
Jianchuan Liu1, Xingchen Zhang2, Tao Chen3
1School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, P. R. China.
Journal of chemical theory and computation
|June 20, 2024
概括
我们为19个半导体开发了一种统一的深度学习原子间潜力 (DPA-Semi). 这种模型实现了高精度,为材料科学中的通用原子间潜能铺平了道路.
科学领域:
- 计算材料科学科学 计算材料科学
- 机器学习在物理学中的应用
- 半导体物理 半导体物理
背景情况:
- 机器学习的原子间潜能为大规模模拟提供了初始准确性.
- 需要一种适用于各种半导体而无参数调的通用原子间模型.
研究的目的:
- 为19个半导体开发一个统一的深度学习原子间潜力 (DPA-Semi).
- 将统一模型的性能与每个半导体的单个模型进行比较.
- 评估DPA-Semi模型与已建立的计算方法的准确性.
主要方法:
- 密度函数理论 (DFT) 计算使用数值原子轨道基础集用于训练数据生成.
- 开发一个统一的深度学习原子间潜力 (DPA-Semi) 模型.
- 在不同的机器学习模型中对固态和液态相性质进行系统比较.
主要成果:
- DPA-Semi模型是为19个半导体 (IIB到VIA组) 开发的.
- 独立的深度潜力模型被创建用于详细的比较分析.
- DPA-Semi模型的准确性与GGA交换-关联功能质量相当.
结论:
- DPA-Semi模型作为一个预训练的模型,用于普遍的原子间潜力.
- 它显示了研究广泛的IIB组到VIA半导体的巨大潜力.
- 该模型促进了材料模拟中准确和广泛适用的原子间潜力的发展.
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