Data Distillation for Neural Network Potentials toward Foundational
  Dataset
Gang Seob Jung Sangkeun Lee Jong Youl Choi
Machine learning (ML) techniques and atomistic modeling have rapidly
transformed materials design and discovery. Specifically, generative models can
swiftly propose promising materials for targeted applications. However, the
predicted properties of materials through the generative models often do not
match with calculated properties through ab initio calculations. This
discrepancy can arise because the generated coordinates are not fully relaxed,
whereas the many properties are derived from relaxed structures. Neural
network-based potentials (NNPs) can expedite the process by providing relaxed
structures from the initially generated ones. Nevertheless, acquiring data to
train NNPs for this purpose can be extremely challenging as it needs to
encompass previously unknown structures. This study utilized extended ensemble
molecular dynamics (MD) to secure a broad range of liquid- and solid-phase
configurations in one of the metallic systems, nickel. Then, we could
significantly reduce them through active learning without losing much accuracy.
We found that the NNP trained from the distilled data could predict different
energy-minimized closed-pack crystal structures even though those structures
were not explicitly part of the initial data. Furthermore, the data can be
translated to other metallic systems (aluminum and niobium), without repeating
the sampling and distillation processes. Our approach to data acquisition and
distillation has demonstrated the potential to expedite NNP development and
enhance materials design and discovery by integrating generative models.