The ABINIT project: Impact, environment and recent developments
Abinit is a material-and nanostructure-oriented package that implements density-functional
theory (DFT) and many-body perturbation theory (MBPT) to find, from first principles …
theory (DFT) and many-body perturbation theory (MBPT) to find, from first principles …
Data‐Driven Materials Innovation and Applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
A universal graph deep learning interatomic potential for the periodic table
Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a
fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow …
fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow …
[HTML][HTML] The application of data-driven methods and physics-based learning for improving battery safety
Enabling accurate prediction of battery failure will lead to safer battery systems, as well as
accelerating cell design and manufacturing processes for increased consistency and …
accelerating cell design and manufacturing processes for increased consistency and …
ABINIT: Overview and focus on selected capabilities
ABSTRACT ABINIT is probably the first electronic-structure package to have been released
under an open-source license about 20 years ago. It implements density functional theory …
under an open-source license about 20 years ago. It implements density functional theory …
[HTML][HTML] Electron–phonon physics from first principles using the EPW code
EPW is an open-source software for ab initio calculations of electron–phonon interactions
and related materials properties. The code combines density functional perturbation theory …
and related materials properties. The code combines density functional perturbation theory …
A data-science approach to predict the heat capacity of nanoporous materials
The heat capacity of a material is a fundamental property of great practical importance. For
example, in a carbon capture process, the heat required to regenerate a solid sorbent is …
example, in a carbon capture process, the heat required to regenerate a solid sorbent is …
[HTML][HTML] Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm
We present a benchmark test suite and an automated machine learning procedure for
evaluating supervised machine learning (ML) models for predicting properties of inorganic …
evaluating supervised machine learning (ML) models for predicting properties of inorganic …
Review of computational approaches to predict the thermodynamic stability of inorganic solids
CJ Bartel - Journal of Materials Science, 2022 - Springer
Improvements in the efficiency and availability of quantum chemistry codes, supercomputing
centers, and open materials databases have transformed the accessibility of computational …
centers, and open materials databases have transformed the accessibility of computational …
[HTML][HTML] Compositionally restricted attention-based network for materials property predictions
In this paper, we demonstrate an application of the Transformer self-attention mechanism in
the context of materials science. Our network, the Compositionally Restricted Attention …
the context of materials science. Our network, the Compositionally Restricted Attention …