论文标题
从第一原理和主动学习的贝叶斯力场的微米级异质催化
Micron-scale heterogeneous catalysis with Bayesian force fields from first principles and active learning
论文作者
论文摘要
使用火炬贝叶斯力场的H $ _2 $/pt（111）的异质催化系统实现了数十亿个原子的量子机械精确的反应性分子动力学（MD）。这项成就从第一原则中提供了加速的时间，贝叶斯积极的学习实现了机器学习模型的有效和自主培训。然后，使用Kokkos Performance Portaryable库将所得模型部署在GPU上的lammps中。贝叶斯力场在每个原子环境上提供了预测的定量不确定性，这对于检测训练集外的大反应性模拟中的构型至关重要。使用H $ _2 $/pt（111）峰值超级计算机的H $ _2 $/pt（111）异质催化的Realplication MD进行缩放基准，模拟在4556 GPU节点上达到0.5万亿原子。
Quantum-mechanically accurate reactive molecular dynamics (MD) at the scale of billions of atoms has been achieved for the heterogeneous catalytic system of H$_2$/Pt(111) using the FLARE Bayesian force field. This achievement provides accelerated time-to-solution from first principles, with Bayesian active learning enabling efficient and autonomous training of the machine learning model. The resulting model is then deployed in LAMMPS on GPUs using the Kokkos performance portability library. The Bayesian force field provides quantitative uncertainty of predictions on every atomic environment, critical for detecting configurations in large reactive simulations that are outside of the training set. Scaling benchmarks were performed using real-application MD of the H$_2$/Pt(111) heterogeneous catalysis on the Summit supercomputer, with simulations reaching 0.5 trillion atoms on 4556 GPU nodes.
