Browsing Physics (Scholarly Publications) by Subject "Machine learning"
Now showing items 1-7 of 7
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Data-driven time propagation of quantum systems with neural networks
(2022)We investigate the potential of supervised machine learning to propagate a quantum system in time. While Markovian dynamics can be learned easily, given a sufficient amount of data, non-Markovian systems are nontrivial and ... -
Data-driven time propagation of quantum systems with neural networks
(2022)We investigate the potential of supervised machine learning to propagate a quantum system in time. While Markovian dynamics can be learned easily, given a sufficient amount of data, non-Markovian systems are nontrivial and ... -
Machine-learning approach for quantified resolvability enhancement of low-dose STEM data
(2023)High-resolution electron microscopy is achievable only when a high electron dose is employed, a practice that may cause damage to the specimen and, in general, affects the observation. This drawback sets some limitations ... -
Machine-learning semilocal density functional theory for many-body lattice models at zero and finite temperature
(2021)We introduce a machine-learning density-functional-theory formalism for the spinless Hubbard model in one dimension at both zero and finite temperature. In the zero-temperature case this establishes a one-to-one ... -
Predicting the Curie temperature of ferromagnets using machine learning
(American Physical Society (APS), 2019)The magnetic properties of a material are determined by a subtle balance between the various interactions at play, a fact that makes the design of new magnets a daunting task. High-throughput electronic structure theory ... -
Spectral neighbor representation for vector fields: Machine learning potentials including spin
(2022)We introduce a translational and rotational invariant local representation for vector fields, which can be employed in the construction of machine learning energy models of solids and molecules. This allows us to describe, ... -
A unified picture of the covalent bond within quantum-accurate force fields: From organic molecules to metallic complexes? reactivity
(American Association for the Advancement of Science (AAAS), 2019)Computational studies of chemical processes taking place over extended size and time scales are inaccessible by electronic structure theories and can be tackled only by atomistic models such as force fields. These have ...