Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …

Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

In this work we demonstrate that nonrandom mechanisms that lead to single-particle
localization may also lead to many-body localization, even in the absence of disorder. In …

We theoretically study the quench dynamics for an isolated Heisenberg spin chain with a
random on-site magnetic field, which is one of the paradigmatic models of a many-body …

In recent years materials informatics, which is the application of data science to problems in
materials science and engineering, has emerged as a powerful tool for materials discovery …

The asymmetry in the flow of events that is expressed by the phrase 'time's arrow'traces back
to the second law of thermodynamics. In the microscopic regime, fluctuations prevent us …

In a many-body quantum system, local operators in the Heisenberg picture O (t)= ei H t O ei
H t spread as time increases. Recent studies have attempted to find features of that …

We present a deep neural network representation of the AdS/CFT correspondence, and
demonstrate the emergence of the bulk metric function via the learning process for given …