GPTFF:一个高精度的开箱即用的通用人工智能力场,用于任意的无机材料
Fankai Xie1, Tenglong Lu1, Sheng Meng1
1Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; Songshan Lake Materials Laboratory, Dongguan 523808, China.
Science bulletin
|September 15, 2024
概括
一个新的人工智能 (AI) 力场,基于图形的预训练变压器力场 (GPTFF),准确模拟无机材料. 这种多功能的人工智能模型预测了各种物理系统的能量,力和压力.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 准确模拟无机系统对于材料发现至关重要.
- 现有的力场往往在各种材料类型中缺乏通用性和精度.
- 开发强大的AI模型用于原子级模拟是一个持续的挑战.
研究的目的:
- 引入一种新型的人工智能力场,GPTFF,用于模拟任意无机系统.
- 在预测材料性质方面实现高精度和普遍性.
- 为材料模拟提供一种普遍适用的工具.
主要方法:
- 开发了一个基于图形的预训练变压器力场 (GPTFF).
- 在一个大数据集上训练模型,其中包括3780万个单点能,1170亿个力对和3402万个应力.
- 利用变压器算法的注意力机制进行准确的预测.
主要成果:
- 获得的平均绝对误差 (MAE) 为能量为32 meV/原子,原子力为71 meV/Å,应力为0.365 GPa.
- 证明了GPTFF在模拟晶体结构优化,相位过渡和质量传输方面的能力.
- 在各种物理系统中验证了模型的精度和通用性.
结论:
- 对于无机材料,GPTFF提供了一个高度准确和可泛化的AI力场.
- 该模型的性能使得可用于各种应用的可靠模拟.
- GPTFF是公开发布的,这便于在不需要再培训的情况下立即用于科学研究.
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