Understanding, discovery, and synthesis of 2D materials enabled by machine learning
Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as
input computed or experimental materials data, ML algorithms predict the structural …
input computed or experimental materials data, ML algorithms predict the structural …
Performance assessment of universal machine learning interatomic potentials: Challenges and directions for materials' surfaces
B Focassio, LP M. Freitas… - ACS Applied Materials & …, 2024 - ACS Publications
Machine learning interatomic potentials (MLIPs) are one of the main techniques in the
materials science toolbox, able to bridge ab initio accuracy with the computational efficiency …
materials science toolbox, able to bridge ab initio accuracy with the computational efficiency …
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 …
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 …
Topological thermal transport
Thermal transport is a fundamental mechanism of energy transfer process quite distinct from
wave propagation phenomena. It can be manipulated well beyond the possibilities offered …
wave propagation phenomena. It can be manipulated well beyond the possibilities offered …
Connecting higher-order topology with the orbital hall effect in monolayers of transition metal dichalcogenides
Monolayers of transition metal dichalcogenides (TMDs) in the 2 H structural phase have
been recently classified as higher-order topological insulators (HOTIs), protected by C 3 …
been recently classified as higher-order topological insulators (HOTIs), protected by C 3 …
High-throughput computational discovery and intelligent design of two-dimensional functional materials for various applications
Conspectus Novel technologies and new materials are in high demand for future various
applications to overcome the fundamental limitations of current techniques. For example, the …
applications to overcome the fundamental limitations of current techniques. For example, the …
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 …
3D Carbon Allotropes: Topological Quantum Materials with Obstructed Atomic Insulating Phases, Multiple Bulk‐Boundary Correspondences, and Real Topology
J Wang, TT Zhang, Q Zhang, X Cheng… - Advanced Functional …, 2024 - Wiley Online Library
The study of topological phases with unconventional bulk‐boundary correspondences and
nontrivial real Chern number has garnered significant attention in the topological states of …
nontrivial real Chern number has garnered significant attention in the topological states of …
Applied machine learning for develo** next‐generation functional materials
Abstract Machine learning (ML) is a versatile technique to rapidly and efficiently generate
insights from multidimensional data. It offers a much‐needed avenue to accelerate the …
insights from multidimensional data. It offers a much‐needed avenue to accelerate the …