Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly.

The field of machine learning potentials has experienced a rapid surge in progress, thanks to advances in machine learning theory, algorithms, and hardware capabilities. While the underlying methods are continuously evolving, the infrastructure for their deployment has lagged. The community, due to these rapid developments, frequently finds itself split into groups built around different implementations of machine-learned potentials. In this work, we introduce IPSuite, a Python-driven software package designed to connect different methods and algorithms from the comprehensive field of machine-learned potentials into a single platform while also providing a collaborative infrastructure, helping ensure reproducibility. Furthermore, the data management infrastructure of the IPSuite code enables simple model sharing and deployment in simulations. Currently, IPSuite supports six state-of-the-art machine learning approaches for the fitting of interatomic potentials as well as a variety of methods for the selection of training data, running of ab initio calculations, learning-on-the-fly strategies, model evaluation, and simulation deployment.

Understanding the Underlying Field Evaporation Mechanism of Pure Water Tips in High Electrical Fields.

We investigated the field evaporation process of frozen water in atom probe tomography (APT) by density functional simulations. In previous experiments, a strong tailing effect was observed for peaks caused by the molecular structure (H2O)nH+, in contrast to other peaks. In purely field-induced and thermally assisted evaporation simulations, we found that chains of protonated water molecules were pulled out of the dielectric surface by up to 6 Å, which are stable over a wide range of field strengths. Therefore, the resulting water clusters experience only part of the acceleration after evaporation compared to molecules evaporating directly from the surface and, thus, exhibit an energy deficit, which explains the tailing effect. Our simulations provide new insight into the complex evaporation behavior of water in high electrical fields and reveal possibilities for adapting the existing reconstruction algorithms.