PAL 2.0:一个物理驱动的贝叶斯优化框架,用于材料发现
Maitreyee Sharma Priyadarshini1, Oluwaseun Romiluyi1, Yiran Wang1
1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, 21218, Maryland, USA. pclancy3@jhu.edu.
Materials horizons
|November 24, 2023
概括
高效地发现新材料对于能源和健康技术至关重要. PAL 2.0是一种新的计算方法,使用基于物理的模型和贝叶斯优化来快速搜索庞大的物质空间,超越现有技术.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 高效地发现先进的功能性材料受到庞大的组合空间和高性价比的高成本的阻碍.
- 当前的搜索方法通常依赖于专家知识,可能会忽视未经探索的地区的高性能材料.
- 对于能够高效地导航大参数空间的计算算法,对于材料发现有着至关重要的需求.
研究的目的:
- 推出PAL 2.0,一个用于高效材料发现的新型计算框架.
- 将基于物理的代用模型与贝叶斯优化相结合,用于加速材料设计.
- 为了证明PAL 2.0在各种材料系统中的有效性.
主要方法:
- PAL 2.0 集成了 XGBoost 和神经网络,以生成基于物理学的假设,作为高斯过程模型的先验.
- 贝叶斯优化被用来高效地搜索由基于物理学的先验指导的材料设计空间.
- 该方法在三个不同的材料测试案例中得到了验证:光伏矿,用于溶液处理的矿溶剂系统和有机热电半导体.
主要成果:
- 与最先进的方法相比,PAL 2.0在搜索材料设计空间方面表现出更高的效率.
- 在PAL 2.0中,基于物理的替代模型对未见材料组成的预测误差较低.
- 该框架在经过测试的材料发现场景中成功确定了最佳候选者.
结论:
- 在计算材料发现方面,PAL 2.0提供了显著的进步,特别是在数据稀缺的情况下.
- 基于物理学的先验和贝叶斯优化的结合为材料设计提供了强大而高效的方法.
- 这种方法加速了下一代功能性材料的发现,用于能源,健康和可持续性应用.
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