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 …
Emerging materials intelligence ecosystems propelled by machine learning
The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …
successes and promises, several AI ecosystems are blossoming, many of them within the …
Quantum chemical accuracy from density functional approximations via machine learning
M Bogojeski, L Vogt-Maranto, ME Tuckerman… - Nature …, 2020 - nature.com
Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry,
but accuracies for many molecules are limited to 2-3 kcal⋅ mol− 1 with presently-available …
but accuracies for many molecules are limited to 2-3 kcal⋅ mol− 1 with presently-available …
Big-data science in porous materials: materials genomics and machine learning
By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
Quantum chemistry in the age of machine learning
PO Dral - The journal of physical chemistry letters, 2020 - ACS Publications
As the quantum chemistry (QC) community embraces machine learning (ML), the number of
new methods and applications based on the combination of QC and ML is surging. In this …
new methods and applications based on the combination of QC and ML is surging. In this …
A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …
computational technology as well as engineering tools in material modeling and material …
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Recent developments in many-body potential energy representation via deep learning have
brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular …
brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular …
Atomic cluster expansion for accurate and transferable interatomic potentials
R Drautz - Physical Review B, 2019 - APS
The atomic cluster expansion is developed as a complete descriptor of the local atomic
environment, including multicomponent materials, and its relation to a number of other …
environment, including multicomponent materials, and its relation to a number of other …
Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics
We introduce a scheme for molecular simulations, the deep potential molecular dynamics
(DPMD) method, based on a many-body potential and interatomic forces generated by a …
(DPMD) method, based on a many-body potential and interatomic forces generated by a …
Opportunities and challenges for machine learning in materials science
Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …
the discovery of novel materials and the improvement of molecular simulations, with likely …