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Machine Learning (ML) for Simulating Complex Energy Materials with Non-Crystalline Structures

Many materials with applications in energy materials, e.g., catalysis or batteries are non-crystalline with amorphous structures, chemical disorder, and complex compositions, which makes the direct modelling with first principles methods challenging. To address this challenge, we developed accelerated sampling strategies based on ML potentials, genetic algorithms, and molecular-dynamics simulations. [more]
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