  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 $\Delta$-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.
