Perspective: Advances, challenges, and insight for predictive coarse-grained models
WG Noid - The Journal of Physical Chemistry B, 2023 - ACS Publications
By averaging over atomic details, coarse-grained (CG) models provide profound
computational and conceptual advantages for studying soft materials. In particular, bottom …
computational and conceptual advantages for studying soft materials. In particular, bottom …
Materials 4.0: Materials big data enabled materials discovery
R Jose, S Ramakrishna - Applied Materials Today, 2018 - Elsevier
Materials discovery is an incessant process and has been the landmark of human progress.
This article sees the evolution of materials discovery in generations, its current generation as …
This article sees the evolution of materials discovery in generations, its current generation as …
Machine-learning interatomic potentials for materials science
Y Mishin - Acta Materialia, 2021 - Elsevier
Large-scale atomistic computer simulations of materials rely on interatomic potentials
providing computationally efficient predictions of energy and Newtonian forces. Traditional …
providing computationally efficient predictions of energy and Newtonian forces. Traditional …
Roadmap on multiscale materials modeling
Modeling and simulation is transforming modern materials science, becoming an important
tool for the discovery of new materials and material phenomena, for gaining insight into the …
tool for the discovery of new materials and material phenomena, for gaining insight into the …
Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases
Materials innovations enable new technological capabilities and drive major societal
advancements but have historically required long and costly development cycles. The …
advancements but have historically required long and costly development cycles. The …
Transferability in interatomic potentials for carbon
C de Tomas, A Aghajamali, JL Jones, DJ Lim… - Carbon, 2019 - Elsevier
Interatomic potentials underpin many atomistic simulations and great effort is devoted to
develop and benchmark potentials. In 2016 [Carbon 109, 681–693], we tested six common …
develop and benchmark potentials. In 2016 [Carbon 109, 681–693], we tested six common …
Classical interaction potentials for diverse materials from ab initio data: a review of potfit
P Brommer, A Kiselev, D Schopf, P Beck… - … and Simulation in …, 2015 - iopscience.iop.org
Force matching is an established technique to generate effective potentials for molecular
dynamics simulations from first-principles data. This method has been implemented in the …
dynamics simulations from first-principles data. This method has been implemented in the …
Stratified construction of neural network based interatomic models for multicomponent materials
Recent application of neural networks (NNs) to modeling interatomic interactions has shown
the learning machines' encouragingly accurate performance for select elemental and …
the learning machines' encouragingly accurate performance for select elemental and …
Dihedral-angle-corrected registry-dependent interlayer potential for multilayer graphene structures
The structural relaxation of multilayer graphene is essential in describing the interesting
electronic properties induced by intentional misalignment of successive layers, including the …
electronic properties induced by intentional misalignment of successive layers, including the …
Cross-scale covariance for material property prediction
A simulation can stand its ground against an experiment only if its prediction uncertainty is
known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction …
known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction …