Machine learning phases of matter

By Juan Felipe Carrasquilla, Roger G. Melko. Nature Physics, 2016

1,406 citations recorded by the source corpus.

The success of machine learning techniques in handling big data sets proves ideal for classifying condensed-matter phases and phase transitions. The technique is even amenable to detecting non-trivial states lacking in conventional order. Condensed-matter physics is the study of the collective behaviour of infinitely complex assemblies of electrons, nuclei, magnetic moments, atoms or qubits1. This complexity is reflected in the size of the state space, which grows exponentially with the number of particles, reminiscent of the ‘curse of dimensionality’ commonly encountered in machine learning2. Despite this curse, the machine learning community has developed techniques with remarkable abilities to recognize, classify, and characterize complex sets of data. Here, we show that modern machine learning architectures, such as fully connected and convolutional neural networks3, can identify phases and phase transitions in a variety of condensed-matter Hamiltonians. Readily programmable through modern software libraries4,5, neural networks can be trained to detect multiple types of order parameter, as well as highly non-trivial states with no conventional order, directly from raw state configurations sampled with Monte Carlo6,7.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dd308eb0d7be24e593fe355476057fc37ab5bf0e

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Citations in the corpus

Machine learning for molecular and materials science (2018, background): “A different example of ML opening new avenues in an area of complicated characterisation is phase transitions of highly-correlated systems; neural networks have been trained to encode phases of matter and thus identify transitions.(52)”
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, background): “Extending this to new, not yet completely understood, systems, could provide an improved way to characterise phases in materials such as topological superconductors (Melko, 2017).”
Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe (2022, background): “For example, in quantum physics [299] it has been applied with success in many-body physics [116, 119] and quantum computing [115, 205].”
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale (2018, background): “The associated technique of deep learning is thus considered a promising candidate for the inverse design of new materials [16-18], drugs [19], and nanophotonic devices”
Machine Learning Phases of Strongly Correlated Fermions (2016, background): “Two of the authors found that by using a simple network consisting of only one hidden layer, one can predict the transition temperature with up to 99% accuracy for two-dimensional (2D) Ising models, solely based on spin configurations generated by Monte Carlo simulations and in the absence of any in”
A Generative Model for Inverse Design of Metamaterials (2018, neutral): “Thanks to rapid developments in artificial intelligence (AI), some scientific problems that classically required human perception or intricate mechanisms have recently been solved by AI 13-17 .”