The ABINIT project: Impact, environment and recent developments

X Gonze, B Amadon, G Antonius, F Arnardi… - Computer Physics …, 2020 - Elsevier
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 …

Data‐Driven Materials Innovation and Applications

Z Wang, Z Sun, H Yin, X Liu, J Wang, H Zhao… - Advanced …, 2022 - Wiley Online Library
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …

A universal graph deep learning interatomic potential for the periodic table

C Chen, SP Ong - Nature Computational Science, 2022 - nature.com
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 …

[HTML][HTML] The application of data-driven methods and physics-based learning for improving battery safety

DP Finegan, J Zhu, X Feng, M Keyser, M Ulmefors… - Joule, 2021 - cell.com
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 …

ABINIT: Overview and focus on selected capabilities

AH Romero, DC Allan, B Amadon, G Antonius… - The Journal of …, 2020 - pubs.aip.org
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 …

[HTML][HTML] Electron–phonon physics from first principles using the EPW code

H Lee, S Poncé, K Bushick, S Hajinazar… - npj Computational …, 2023 - nature.com
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 …

A data-science approach to predict the heat capacity of nanoporous materials

SM Moosavi, BÁ Novotny, D Ongari, E Moubarak… - Nature materials, 2022 - nature.com
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 …

[HTML][HTML] Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

A Dunn, Q Wang, A Ganose, D Dopp… - npj Computational …, 2020 - nature.com
We present a benchmark test suite and an automated machine learning procedure for
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 …

[HTML][HTML] Compositionally restricted attention-based network for materials property predictions

AYT Wang, SK Kauwe, RJ Murdock… - Npj Computational …, 2021 - nature.com
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 …