Machine learning for quantum matter
J Carrasquilla - Advances in Physics: X, 2020 - Taylor & Francis
Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …
The role of local structure in dynamical arrest
CP Royall, SR Williams - Physics Reports, 2015 - Elsevier
Amorphous solids, or glasses, are distinguished from crystalline solids by their lack of long-
range structural order. At the level of two-body structural correlations, glassformers show no …
range structural order. At the level of two-body structural correlations, glassformers show no …
Modern applications of machine learning in quantum sciences
In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …
advances in the application of machine learning methods in quantum sciences. We cover …
Mutual information, neural networks and the renormalization group
M Koch-Janusz, Z Ringel - Nature Physics, 2018 - nature.com
Physical systems differing in their microscopic details often display strikingly similar
behaviour when probed at macroscopic scales. Those universal properties, largely …
behaviour when probed at macroscopic scales. Those universal properties, largely …
Machine learning on neutron and x-ray scattering and spectroscopies
Neutron and x-ray scattering represent two classes of state-of-the-art materials
characterization techniques that measure materials structural and dynamical properties with …
characterization techniques that measure materials structural and dynamical properties with …
Network analysis of particles and grains
The arrangements of particles and forces in granular materials have a complex organization
on multiple spatial scales that range from local structures to mesoscale and system-wide …
on multiple spatial scales that range from local structures to mesoscale and system-wide …
Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach
The increasing availability of temporal network data is calling for more research on
extracting and characterizing mesoscopic structures in temporal networks and on relating …
extracting and characterizing mesoscopic structures in temporal networks and on relating …
A structural signature of liquid fragility
NA Mauro, M Blodgett, ML Johnson, AJ Vogt… - Nature …, 2014 - nature.com
Virtually all liquids can be maintained for some time in a supercooled state, that is, at
temperatures below their equilibrium melting temperatures, before eventually crystallizing. If …
temperatures below their equilibrium melting temperatures, before eventually crystallizing. If …
Community detection for correlation matrices
M MacMahon, D Garlaschelli - arXiv preprint arXiv:1311.1924, 2013 - arxiv.org
A challenging problem in the study of complex systems is that of resolving, without prior
information, the emergent, mesoscopic organization determined by groups of units whose …
information, the emergent, mesoscopic organization determined by groups of units whose …
Multiscale event detection in social media
Event detection has been one of the most important research topics in social media analysis.
Most of the traditional approaches detect events based on fixed temporal and spatial …
Most of the traditional approaches detect events based on fixed temporal and spatial …