热化学预测的进步:一个多输出热力学信息的神经网络方法
Raheel Hammad1, Sownyak Mondal2
1Tata Institute of Fundamental Research Hyderabad, Hyderabad, Telangana, 500046, India. raheelhammad@tifrh.res.in.
Journal of cheminformatics
|June 16, 2025
概括
一个新的物理信息神经网络模型准确地预测了材料的热力学特性,如吉布斯自由能量. 这种方法显示了改进的性能,特别是在有限的数据和分布外预测的情况下.
科学领域:
- 计算材料科学科学 计算材料科学
- 热力学是一种热力学.
- 机器学习 机器学习
背景情况:
- 吉布斯自由能量对于预测材料稳定性和化学行为至关重要.
- 精确计算热力学性能指导材料设计,用于催化和储能等应用.
- 在具有挑战性的条件下 (小型数据集,分布之外) 评估机器学习模型越来越重要.
研究的目的:
- 开发一个物理信息神经网络 (PINN),用于同时预测多个热力学量.
- 为了将吉布斯自由能量方程整合到PINN的损失函数中.
- 在标准和具有挑战性的数据场景中评估模型的稳定性和准确性.
主要方法:
- 开发了一个多输出物理信息神经网络模型.
- 将吉布斯的自由能量方程纳入模型的损失函数.
- 将模型与使用小数据集和分布外预测的现有方法进行了对比.
主要成果:
- PINN模型同时预测吉布斯的自由能量,总能量和.
- 与下一个最佳模型相比,在正常条件下,预测准确度提高了43%.
- 在分布外和低数据制度中表现出卓越的稳定性和准确性.
结论:
- 基于物理学的神经网络为计算多个热力学性质提供了一种强大的方法.
- 开发的模型表现出更高的准确性和稳定性,特别是在数据稀缺的环境中.
- 这项工作推动了人工智能在材料科学中的应用,用于预测热力学行为.
关键词:
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