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 science: status, challenges, and perspectives
Data‐driven science is heralded as a new paradigm in materials science. In this field, data is
the new resource, and knowledge is extracted from materials datasets that are too big or …
the new resource, and knowledge is extracted from materials datasets that are too big or …
Scaling deep learning for materials discovery
Novel functional materials enable fundamental breakthroughs across technological
applications from clean energy to information processing,,,,,,,,,–. From microchips to batteries …
applications from clean energy to information processing,,,,,,,,,–. From microchips to batteries …
Enabling selective zinc-ion intercalation by a eutectic electrolyte for practical anodeless zinc batteries
Two major challenges hinder the advance of aqueous zinc metal batteries for sustainable
stationary storage:(1) achieving predominant Zn-ion (de) intercalation at the oxide cathode …
stationary storage:(1) achieving predominant Zn-ion (de) intercalation at the oxide cathode …
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 …
Autonomous experimentation systems for materials development: A community perspective
Solutions to many of the world's problems depend upon materials research and
development. However, advanced materials can take decades to discover and decades …
development. However, advanced materials can take decades to discover and decades …
Efficient calculation of carrier scattering rates from first principles
The electronic transport behaviour of materials determines their suitability for technological
applications. We develop a computationally efficient method for calculating carrier scattering …
applications. We develop a computationally efficient method for calculating carrier scattering …
Unsupervised word embeddings capture latent knowledge from materials science literature
The overwhelming majority of scientific knowledge is published as text, which is difficult to
analyse by either traditional statistical analysis or modern machine learning methods. By …
analyse by either traditional statistical analysis or modern machine learning methods. By …
A critical review of machine learning of energy materials
Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …
change landscapes for physics and chemistry. With its ability to solve complex tasks …
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 …