Application of machine learning for advanced material prediction and design
CH Chan, M Sun, B Huang - EcoMat, 2022 - Wiley Online Library
In material science, traditional experimental and computational approaches require
investing enormous time and resources, and the experimental conditions limit the …
investing enormous time and resources, and the experimental conditions limit the …
The 2021 quantum materials roadmap
In recent years, the notion of'Quantum Materials' has emerged as a powerful unifying
concept across diverse fields of science and engineering, from condensed-matter and …
concept across diverse fields of science and engineering, from condensed-matter and …
Ab Initio Simulations and Materials Chemistry in the Age of Big Data
In this perspective, we discuss computational advances in the last decades, both in
algorithms as well as in technologies, that enabled the development, widespread use, and …
algorithms as well as in technologies, that enabled the development, widespread use, and …
Machine learning study of the magnetic ordering in 2D materials
Magnetic materials have been applied in a large variety of technologies, from data storage
to quantum devices. The development of two-dimensional (2D) materials has opened new …
to quantum devices. The development of two-dimensional (2D) materials has opened new …
First-principles investigations of 2D materials: Challenges and best practices
The successful exfoliation of graphene from graphite has brought significant attention to
predicting new two-dimensional (2D) materials that can be realized experimentally. As a …
predicting new two-dimensional (2D) materials that can be realized experimentally. As a …
Topogivity: A machine-learned chemical rule for discovering topological materials
Topological materials present unconventional electronic properties that make them attractive
for both basic science and next-generation technological applications. The majority of …
for both basic science and next-generation technological applications. The majority of …
Artificial intelligence for high-throughput discovery of topological insulators: The example of alloyed tetradymites
Significant advances have been made in predicting new topological materials using high-
throughput empirical descriptors or symmetry-based indicators. To date, these approaches …
throughput empirical descriptors or symmetry-based indicators. To date, these approaches …
Machine learning of octahedral tilting in oxide perovskites by symbolic classification with compressed sensing
The steady growth of online materials databases, coupled with efforts in materials
informatics, has invited the reexamination of existing empirical models through the lens of …
informatics, has invited the reexamination of existing empirical models through the lens of …
A machine learning based classifier for topological quantum materials
Prediction and discovery of new materials with desired properties are at the forefront of
quantum science and technology research. A major bottleneck in this field is the …
quantum science and technology research. A major bottleneck in this field is the …
High-throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds
The development of spintronic devices demands the existence of materials with some kind
of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in …
of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in …