Graph neural networks for materials science and chemistry
Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …
and materials science, being used to predict materials properties, accelerate simulations …
Applications of artificial intelligence and machine learning algorithms to crystallization
Artificial intelligence and specifically machine learning applications are nowadays used in a
variety of scientific applications and cutting-edge technologies, where they have a …
variety of scientific applications and cutting-edge technologies, where they have a …
Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation
The marriage of density functional theory (DFT) and deep-learning methods has the
potential to revolutionize modern computational materials science. Here we develop a deep …
potential to revolutionize modern computational materials science. Here we develop a deep …
Electronic Transport Properties and Nanodevice Designs for Monolayer
Y Gao, J Liao, H Wang, Y Wu, Y Li, K Wang, C Ma… - Physical Review …, 2022 - APS
A family of MA 2 Z 4 materials has recently inspired great interest due to its exotic geometry
and intriguing electronic properties. Here we investigate the electronic transport and …
and intriguing electronic properties. Here we investigate the electronic transport and …
Heterogeneous relational message passing networks for molecular dynamics simulations
With many frameworks based on message passing neural networks proposed to predict
molecular and bulk properties, machine learning methods have tremendously shifted the …
molecular and bulk properties, machine learning methods have tremendously shifted the …
Equivariant neural network force fields for magnetic materials
Neural network force fields have significantly advanced ab initio atomistic simulations across
diverse fields. However, their application in the realm of magnetic materials is still in its early …
diverse fields. However, their application in the realm of magnetic materials is still in its early …
Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab initio simulations
NM Chtchelkatchev, RE Ryltsev… - The Journal of …, 2023 - pubs.aip.org
Boron phosphide (BP) is a (super) hard semiconductor constituted of light elements, which is
promising for high demand applications at extreme conditions. The behavior of BP at high …
promising for high demand applications at extreme conditions. The behavior of BP at high …
Second-order topological insulator in two-dimensional and its derivatives
The high-order topological phase exhibits nontrivial gapless states at the boundaries whose
dimension is lower than bulk by two. However, this phase has not been observed …
dimension is lower than bulk by two. However, this phase has not been observed …
Prediction of COVID-19 Cases Using Constructed Features by Grammatical Evolution
A widely used method that constructs features with the incorporation of so-called
grammatical evolution is proposed here to predict the COVID-19 cases as well as the …
grammatical evolution is proposed here to predict the COVID-19 cases as well as the …
[HTML][HTML] Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations
Machine learning interatomic potential (MLIP) overcomes the challenges of high
computational costs in density-functional theory and the relatively low accuracy in classical …
computational costs in density-functional theory and the relatively low accuracy in classical …