From prediction to design: recent advances in machine learning for the study of 2D materials
H He, Y Wang, Y Qi, Z Xu, Y Li, Y Wang - Nano Energy, 2023 - Elsevier
Although data-driven approaches have made significant strides in various scientific fields,
there has been a lack of systematic summaries and discussions on their application in 2D …
there has been a lack of systematic summaries and discussions on their application in 2D …
Machine learning paves the way for high entropy compounds exploration: challenges, progress, and outlook
Abstract Machine learning (ML) has emerged as a powerful tool in the research field of high
entropy compounds (HECs), which have gained worldwide attention due to their vast …
entropy compounds (HECs), which have gained worldwide attention due to their vast …
Machine Learning-Aided Band Gap Engineering of BaZrS3 Chalcogenide Perovskite
The non-toxic and stable chalcogenide perovskite BaZrS3 fulfills many key optoelectronic
properties for a high-efficiency photovoltaic material. It has been shown to possess a direct …
properties for a high-efficiency photovoltaic material. It has been shown to possess a direct …
Deep dive into machine learning density functional theory for materials science and chemistry
With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
On-the-fly interpretable machine learning for rapid discovery of two-dimensional ferromagnets with high Curie temperature
Machine learning (ML) techniques have accelerated the discovery of new materials.
However, challenges such as data scarcity, representations without deep physical insights …
However, challenges such as data scarcity, representations without deep physical insights …
The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning
Machine learning (ML) has experienced rapid development in recent years and been widely
applied to assist studies in various research areas. Two-dimensional (2D) materials, due to …
applied to assist studies in various research areas. Two-dimensional (2D) materials, due to …
Comprehensive Insights into the Family of Atomically Thin 2D‐Materials for Diverse Photocatalytic Applications
Abstract 2D materials with their fascinating physiochemical, structural, and electronic
properties have attracted researchers and have been used for a variety of applications such …
properties have attracted researchers and have been used for a variety of applications such …
Property-oriented material design based on a data-driven machine learning technique
Property-oriented material design is a persistent pursuit for material scientists. Recently,
machine learning (ML) as a powerful new tool has attracted worldwide attention in the …
machine learning (ML) as a powerful new tool has attracted worldwide attention in the …
Strain-Induced Room-Temperature Ferromagnetic Semiconductors with Large Anomalous Hall Conductivity in Two-Dimensional
XJ Dong, JY You, B Gu, G Su - Physical Review Applied, 2019 - APS
By density-functional-theory calculations, we predict a stable two-dimensional (2D)
ferromagnetic semiconductor, Cr 2 Ge 2 Se 6, where the Curie temperature TC can be …
ferromagnetic semiconductor, Cr 2 Ge 2 Se 6, where the Curie temperature TC can be …
Physical symmetries embedded in neural networks
Neural networks are a central technique in machine learning. Recent years have seen a
wave of interest in applying neural networks to physical systems for which the governing …
wave of interest in applying neural networks to physical systems for which the governing …