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Universal machine learning for the response of atomistic systems to external fields

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Universal machine learning for the response of atomistic systems to external fields

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Thermal transport of glasses via machine learning driven simulations

Machine-learned interatomic potentials: Recent developments and prospective applications

Beyond potentials: Integrated machine learning models for materials

Transferable machine-learning models of complex materials: the case of GaAs

Fluctuation Relations for Dissipative Systems in Constant External Magnetic Field: Theory and Molecular Dynamics Simulations

In-silico behavior of dissolved prolamins under electric field effect applied by electrospinning process using molecular dynamics simulation

Thermomechanical properties of honeycomb lattices from internal-coordinates potentials: the case of graphene and hexagonal boron nitride

Bias free multiobjective active learning for materials design and discovery