Advanced Energy Materials· In Kee Park,
Saeed Pourasad,
Jinhong Mun,
Arunkumar Chitteth Rajan,
Tae Hyeon Park,
Anand Rohit,
Mohammad Zafari,
Tan‐Lien Pham,
Eunjae Jung,
Bonseung Koo,
Miran Ha,
Gi Beom Sim,
Hyun Gyu Park,
Won Woong Choi,
Kwanghyeon Jo,
Soohaeng Yoo Willow,
David ChangMo Yang,
Chang Woo Myung,
Geunsik Lee,
Kwang S. Kim·· 9 天前AI 评分38
机器学习原子间势在能源材料中的应用：架构、训练策略与验证
Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications
模型导读
这篇综述系统梳理机器学习原子间势（MLIP）从描述符模型、带不确定性量化的高斯过程到等变图神经网络和大规模数据集训练的基础模型，指出 MLIP 可基于 DFT 数据实现 10^4–10^6 原子、纳秒尺度的原子模拟。
来源：Advanced Energy Materials · advanced.onlinelibrary.wiley.com