From DFT to machine learning: recent approaches to materials science–a review
Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …
and complexity of generated data. This massive amount of raw data needs to be stored and …
Atomistic line graph neural network for improved materials property predictions
Graph neural networks (GNN) have been shown to provide substantial performance
improvements for atomistic material representation and modeling compared with descriptor …
improvements for atomistic material representation and modeling compared with descriptor …
Rational Design of Earth‐Abundant Catalysts toward Sustainability
Catalysis is crucial for clean energy, green chemistry, and environmental remediation, but
traditional methods rely on expensive and scarce precious metals. This review addresses …
traditional methods rely on expensive and scarce precious metals. This review addresses …
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an
integrated infrastructure to accelerate materials discovery and design using density …
integrated infrastructure to accelerate materials discovery and design using density …
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
The current predictive modeling techniques applied to Density Functional Theory (DFT)
computations have helped accelerate the process of materials discovery by providing …
computations have helped accelerate the process of materials discovery by providing …
Group-IV (A) Janus dichalcogenide monolayers and their interfaces straddle gigantic shear and in-plane piezoelectricity
Inversion symmetry in the 1T-phase of pristine dichalcogenide monolayer MX2 (M= Ge, Sn;
X= S, Se) is broken in their Janus structures, MXY (M= Ge, Sn; X≠ Y= S, Se), which induces …
X= S, Se) is broken in their Janus structures, MXY (M= Ge, Sn; X≠ Y= S, Se), which induces …
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
The use of statistical/machine learning (ML) approaches to materials science is
experiencing explosive growth. Here, we review recent work focusing on the generation and …
experiencing explosive growth. Here, we review recent work focusing on the generation and …
MechElastic: A Python library for analysis of mechanical and elastic properties of bulk and 2D materials
Abstract The MechElastic Python package evaluates the mechanical and elastic properties
of bulk and 2D materials using the elastic coefficient matrix (C ij) obtained from any ab-initio …
of bulk and 2D materials using the elastic coefficient matrix (C ij) obtained from any ab-initio …
Designing high-TC superconductors with BCS-inspired screening, density functional theory, and deep-learning
We develop a multi-step workflow for the discovery of conventional superconductors, starting
with a Bardeen–Cooper–Schrieffer inspired pre-screening of 1736 materials with high …
with a Bardeen–Cooper–Schrieffer inspired pre-screening of 1736 materials with high …
Nanoscale interfaces of Janus monolayers of transition metal dichalcogenides for 2D photovoltaic and piezoelectric applications
Using first-principles calculations, we demonstrate a combination of two emergent fields,
type II van der Waals heterostructures and Janus structures, for the purpose of optimizing the …
type II van der Waals heterostructures and Janus structures, for the purpose of optimizing the …