We present a transferable MACE interatomic potential that is applicable to open-and closed-
shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an …

We introduce a training protocol for developing machine learning force fields (MLFFs),
capable of accurately determining energy barriers in catalytic reaction pathways. The …

The future of computational heterogeneous catalysis is shaped by machine learning in two
different but equally important areas:(i) development of atomistic potentials that closely …

Direct access to transition state energies at low computational cost unlocks the possibility of
accelerating catalyst discovery. We show that the top performing graph neural network …

The advent of machine learning potentials (MLPs) provides a unique opportunity to access
simulation time scales and to directly compute physicochemical properties that are typically …

SnOx has received great attention as an electrocatalyst for CO2 reduction reaction
(CO2RR), however; it still suffers from low activity. Moreover, the atomic‐level SnOx structure …

We introduce a data-driven potential aimed at the investigation of pressure-dependent
phase transitions in bulk germanium, including the estimate of kinetic barriers. This is …

Process optimization in heterogeneous catalysis relies on the control of competing
reactions. The reaction mechanisms based on chemical knowledge can be evaluated via …

Platinum nanoclusters (PtNCs) are promising in catalysis due to their large specific surface
area and unique physicochemical properties. Here, ultrasmall and uniform PtNCs are …

Empirical force fields are valuable tools in computational chemistry, however, they suffer
from limitations in terms of accuracy, transferability and their lack of applicability to open …