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Respecting a-priori known symmetries of underlying data is a key principle in designing efficient neural architectures. Convolutional neural networks are a canonical example, encoding translation equivariance. Extending this inductive bias to richer symmetry groups such as $\mathrm{SE}(2)$ in the most effective way pos...
Molecular dynamics simulations provide a computational microscope for atomic scale processes but remain restricted to relatively small spatial and temporal scales. Coarse-grained models extend their reach by representing groups of atoms as effective interaction sites. The number of particles is systematically reduced b...
The Active Learning Framework (ALF), an open-source Python package designed to streamline the design and deployment of MLIP training datasets on High Performance Computing resources, is introduced, illustrating ALF’s effectiveness in compiling datasets that capture essential chemical and structural regimes.
New methods and workflows to overcome the challenges inherent to automating unrestricted coupled cluster calculations are developed and a transferable MLIP for gas-phase reactions, trained on unrestricted CCSD(T) data is developed.
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