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 …
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 …
Unified graph neural network force-field for the periodic table: solid state applications
Classical force fields (FFs) based on machine learning (ML) methods show great potential
for large scale simulations of solids. MLFFs have hitherto largely been designed and fitted …
for large scale simulations of solids. MLFFs have hitherto largely been designed and fitted …
High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses
Many technological applications depend on the response of materials to electric fields, but
available databases of such responses are limited. Here, we explore the infrared …
available databases of such responses are limited. Here, we explore the infrared …
[HTML][HTML] pyiron: An integrated development environment for computational materials science
To support and accelerate the development of simulation protocols in atomistic modelling,
we introduce an integrated development environment (IDE) for computational materials …
we introduce an integrated development environment (IDE) for computational materials …
JARVIS-Leaderboard: a large scale benchmark of materials design methods
Lack of rigorous reproducibility and validation are significant hurdles for scientific
development across many fields. Materials science, in particular, encompasses a variety of …
development across many fields. Materials science, in particular, encompasses a variety of …
[HTML][HTML] The potential for machine learning in hybrid QM/MM calculations
Hybrid quantum-mechanics/molecular-mechanics (QM/MM) simulations are popular tools for
the simulation of extended atomistic systems, in which the atoms in a core region of interest …
the simulation of extended atomistic systems, in which the atoms in a core region of interest …
[HTML][HTML] Database of Wannier tight-binding Hamiltonians using high-throughput density functional theory
KF Garrity, K Choudhary - Scientific data, 2021 - nature.com
Wannier tight-binding Hamiltonians (WTBH) provide a computationally efficient way to
predict electronic properties of materials. In this work, we develop a computational workflow …
predict electronic properties of materials. In this work, we develop a computational workflow …
Adapting UFF4MOF for heterometallic Rare-Earth Metal–Organic Frameworks
Y Yang, IA Ibikunle, DF Sava Gallis… - ACS Applied Materials & …, 2022 - ACS Publications
Heterometallic metal–organic frameworks based on rare-earth metals (RE-MOFs) have
potential in a number of applications where energy transfer between nearby metal atoms is …
potential in a number of applications where energy transfer between nearby metal atoms is …
Computational investigation of a promising Si–Cu anode material
AY Galashev, KA Ivanichkina - Physical Chemistry Chemical Physics, 2019 - pubs.rsc.org
The lack of suitable anode materials is a limiting factor in the creation of a new generation of
lithium-ion batteries. We use the molecular dynamics method to explore the processes of …
lithium-ion batteries. We use the molecular dynamics method to explore the processes of …