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Machine learning for condensed matter physics
E Bedolla, LC Padierna… - Journal of Physics …, 2020 - iopscience.iop.org
Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter
at the quantum and atomistic levels, and describes how these interactions result in both …
at the quantum and atomistic levels, and describes how these interactions result in both …
Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review
C ** distinct phase transitions to a neural network
We demonstrate, by means of a convolutional neural network, that the features learned in
the two-dimensional Ising model are sufficiently universal to predict the structure of …
the two-dimensional Ising model are sufficiently universal to predict the structure of …
A cautionary tale for machine learning generated configurations in presence of a conserved quantity
We investigate the performance of machine learning algorithms trained exclusively with
configurations obtained from importance sampling Monte Carlo simulations of the two …
configurations obtained from importance sampling Monte Carlo simulations of the two …
Learning by confusion approach to identification of discontinuous phase transitions
Recently, the learning by confusion (LbC) approach has been proposed as a machine
learning tool to determine the critical temperature T c of phase transitions without any prior …
learning tool to determine the critical temperature T c of phase transitions without any prior …
Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
The transfer learning of a neural network is one of its most outstanding aspects and has
given supervised learning with neural networks a prominent place in data science. Here we …
given supervised learning with neural networks a prominent place in data science. Here we …