Traditional computational approaches to design chemical species are limited by the need to
compute properties for a vast number of candidates, eg, by discriminative modeling …

Quantum optimal control, a toolbox for devising and implementing the shapes of external
fields that accomplish given tasks in the operation of a quantum device in the best way …

Controlling electromagnetic waves and information simultaneously by information
metasurfaces is of central importance in modern society. Intelligent metasurfaces are smart …

We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

Abstract Machine learning (ML) and deep learning (DL) have enabled algorithms to
autonomously acquire knowledge from data, facilitating predictive and decision-making …

Decision tree classifiers are regarded to be a standout of the most well-known methods to
data classification representation of classifiers. Different researchers from various fields and …

Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

The theory of entanglement provides a fundamentally new language for describing
interactions and correlations in many-body systems. Its vocabulary consists of qubits and …