Topology optimization of multi-scale structures: a review
Multi-scale structures, as found in nature (eg, bone and bamboo), hold the promise of
achieving superior performance while being intrinsically lightweight, robust, and multi …
achieving superior performance while being intrinsically lightweight, robust, and multi …
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 …
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 …
Density functional theory for electrocatalysis
It is a considerably promising strategy to produce fuels and high‐value chemicals through
an electrochemical conversion process in the green and sustainable energy systems …
an electrochemical conversion process in the green and sustainable energy systems …
[HTML][HTML] The status, barriers, challenges, and future in design for 4D printing
The combination of scientific advances in additive manufacturing (AM) and smart materials
(SMs) has enabled the development of a new interdisciplinary research area: 4D printing …
(SMs) has enabled the development of a new interdisciplinary research area: 4D printing …
[HTML][HTML] Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing
Artificial intelligence (AI) embedded within digital models of manufacturing processes can be
used to improve process productivity and product quality significantly. The application of …
used to improve process productivity and product quality significantly. The application of …
[HTML][HTML] Scope of machine learning in materials research—A review
This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …
materials research across six key dimensions, redefining the field's boundaries. It explains …
Overview of emerging hybrid and composite materials for space applications
Abstract Space exploration is one of humanity's most challenging and costly activities.
Nevertheless, we continuously strive to venture further and more frequently into space. It is …
Nevertheless, we continuously strive to venture further and more frequently into space. It is …
Elucidation of collagen amino acid interactions with metals (B, Ni) encapsulated graphene/PEDOT material: insight from DFT calculations and MD simulation
Collagen amino acids play a pivotal role as essential building blocks within diverse
biological structures. Exploring their interactions with composite materials holds the potential …
biological structures. Exploring their interactions with composite materials holds the potential …
Quo vadis multiscale modeling in reaction engineering?–A perspective
This work reports the results of a perspective workshop held in summer 2021 discussing the
current status and future needs for multiscale modeling in reaction engineering. This …
current status and future needs for multiscale modeling in reaction engineering. This …