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Machine Learning in Condensed Matter Physics

Möller, Gunnar and Quintanilla, Jorge, eds. (2021) Machine Learning in Condensed Matter Physics. Journal of Physics: Condensed Matter, . ISSN 0953-8984. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:91651)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://iopscience.iop.org/journal/0953-8984/page/...

Abstract

Owing to the recent advances in machine learning and artificial intelligence, applications of these techniques are becoming increasingly important in the field of condensed matter physics, often surpassing existing approaches in terms of accuracy or computational efficiency. Underlying these successes is a commonality between questions asked in data science and condensed matter physics, both of which aim to extract simple rules from the complexity of either data or physical systems. Hence, the field of machine learning has given rise to many tools that can be directed at the understanding of the properties arising from the complexity of the many-body physical systems studied in condensed matter physics, including both quantum and classical systems. Most prominently, this includes the successful applications of neural networks to a spectrum of questions in condensed matter physics ranging from microscopic approaches such as modelling quantum many-body wave functions to more phenomenological approaches like understanding the landscape of materials properties over material composition and processes.

This special issue aims to survey applications of machine learning as well as inviting contributions of original new work focusing on three areas of particular interest:

1. Machine Learning for Theory of Quantum Materials

Including uses of machine learning for modelling quantum matter from microscopic considerations, and helping to build and analyse theoretical models of matter.

2. Machine Learning for Experimental Materials Research

Focusing on how machine learning can open novel paths for enhancing experimental data acquisition, analysis or interpretation in condensed matter.

3. Machine Learning for Materials Discovery

Highlighting uses of machine learning to synthesise understanding of the complex landscape of materials properties by amalgamating information from empirical measurements, modelling results, or both.

Item Type: Edited Journal
Subjects: Q Science > QC Physics > QC173.45 Condensed Matter
Q Science > QC Physics > QC176 Solid state physics
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Divisions > Division of Natural Sciences > Physics and Astronomy
Depositing User: Jorge Quintanilla Tizon
Date Deposited: 18 Nov 2021 22:49 UTC
Last Modified: 19 Nov 2021 11:02 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/91651 (The current URI for this page, for reference purposes)
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