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Efficient and insightful descriptors for representing molecular and material space

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Efficient and insightful descriptors for representing molecular and material space

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Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Thermal transport of glasses via machine learning driven simulations

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

A recipe for cracking the quantum scaling limit with machine learned electron densities

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Machine-learned interatomic potentials: Recent developments and prospective applications

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

Beyond potentials: Integrated machine learning models for materials

Assessing the persistence of chalcogen bonds in solution with neural network potentials

Unified theory of atom-centered representations and message-passing machine-learning schemes