2 citations · 2 across the 4 of their papers we have counts for
5 papers
Overcoming the limitations of body-ordered potentials for atomistic machine learning
Filippo Bigi, Michelangelo Domina, Maximilian Lloyd Ach +5
Machine-learning interatomic potentials have become indispensable tools in atomistic simulations. The most popular models rely heavily on physical priors, such as locality, smoothn…
Learning to Converge: Warm-Starting DFTB Self-Consistent Charges with Machine Learning
Maximilian L. Ach, Karsten Reuter, Chiara Panosetti
Semiempirical electronic structure methods such as Density-Functional Tight-Binding (DFTB) offer a computationally efficient approach to molecular and materials simulations, bridgi…
General-purpose LLMs as Constrained Crystal Composition Generators
Hedda Oschinski, Maximilian L. Ach, Konstantin S. Jakob +2
The targeted discovery of inorganic materials remains challenging due to the vastness of compositional design spaces and the high cost of exhaustive screening. Task-specific genera…
Adaptive atomic basis sets
Danish Khan, Maximilian L. Ach, O. Anatole von Lilienfeld
Atomic basis sets are widely employed within quantum mechanics based simulations of matter. We introduce a machine learning model that adapts the basis set to the local chemical en…
Adaptive hybrid density functionals
Danish Khan, Alastair James Arthur Price, Maximilian L. Ach +1
Exact exchange contributions are known to crucially affect electronic states, which in turn govern covalent bond formation and breaking in chemical species. Empirically averaging t…