论文标题
使用简单的基于物理学的先验，为机器学习潜力启用强大的离线学习
Enabling robust offline active learning for machine learning potentials using simple physics-based priors
论文作者
论文摘要
用于量子机械模拟的机器学习替代模型使该领域能够有效，准确地研究材料和分子系统。开发的模型通常依靠大量数据来对势能景观进行可靠的预测，或仔细的积极学习和不确定性估计。从小型数据集开始时，主动学习方法的融合是一个主要的挑战，大多数演示都将大多数演示限制在在线积极学习中。在这项工作中，我们展示了一种$δ$ - 机器学习方法，该方法可以通过避免非物理配置来使离线活跃学习策略的稳定收敛。我们在结构放松，过渡状态计算和分子动力学模拟上演示了框架的能力，首先原理计算的数量从70-90％降低了。该方法与Amptorch一起合并并开发，Amptorch是一种开源机器学习潜在的软件包，以及交互式Google COLAB笔记本示例。
Machine learning surrogate models for quantum mechanical simulations has enabled the field to efficiently and accurately study material and molecular systems. Developed models typically rely on a substantial amount of data to make reliable predictions of the potential energy landscape or careful active learning and uncertainty estimates. When starting with small datasets, convergence of active learning approaches is a major outstanding challenge which limited most demonstrations to online active learning. In this work we demonstrate a $Δ$-machine learning approach that enables stable convergence in offline active learning strategies by avoiding unphysical configurations. We demonstrate our framework's capabilities on a structural relaxation, transition state calculation, and molecular dynamics simulation, with the number of first principle calculations being cut down anywhere from 70-90%. The approach is incorporated and developed alongside AMPtorch, an open-source machine learning potential package, along with interactive Google Colab notebook examples.
