Machine learning surrogate models for quantum mechanical simulations have 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 (AL) and uncertainty estimates. When starting with small datasets, convergence of AL approaches is a major outstanding challenge which has limited most demonstrations to online AL. In this work we demonstrate a Δ-machine learning (ML) approach that enables stable convergence in offline AL strategies by avoiding unphysical configurations with initial datasets as little as a single data point. 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 ML potential package, along with interactive Google Colab notebook examples.

用于量子力学模拟的机器学习替代模型使该领域能够高效，准确地研究材料和分子系统。已开发的模型通常依赖大量数据对势能格局进行可靠的预测，或者进行认真的主动学习（AL）和不确定性估计。当从小型数据集开始时，AL方法的收敛是一个重大的挑战，将大多数演示限于在线AL。在这项工作中，我们演示了一种Δ机器学习（ML）方法，该方法通过避免使用初始数据集少至单个数据点的非物理配置来实现离线AL策略中的稳定收敛。我们展示了我们的框架在结构弛豫，过渡态计算和分子动力学模拟方面的功能，第一性原理的计算数量减少了70％–90％。该方法与AMP一起开发火炬，一个开源的ML潜在软件包，以及交互式Google Colab笔记本示例。