A critical review of machine learning of energy materials

C Chen, Y Zuo, W Ye, X Li, Z Deng… - Advanced Energy …, 2020 - Wiley Online Library
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 …

Physics-guided, physics-informed, and physics-encoded neural networks in scientific computing

SA Faroughi, N Pawar, C Fernandes, M Raissi… - arXiv preprint arXiv …, 2022 - arxiv.org
Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …

Analyses of internal structures and defects in materials using physics-informed neural networks

E Zhang, M Dao, GE Karniadakis, S Suresh - Science advances, 2022 - science.org
Characterizing internal structures and defects in materials is a challenging task, often
requiring solutions to inverse problems with unknown topology, geometry, material …

A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

E Haghighat, M Raissi, A Moure, H Gomez… - Computer Methods in …, 2021 - Elsevier
We present the application of a class of deep learning, known as Physics Informed Neural
Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how …

Deep learning predicts path-dependent plasticity

M Mozaffar, R Bostanabad, W Chen… - Proceedings of the …, 2019 - National Acad Sciences
Plasticity theory aims at describing the yield loci and work hardening of a material under
general deformation states. Most of its complexity arises from the nontrivial dependence of …

Extraction of mechanical properties of materials through deep learning from instrumented indentation

L Lu, M Dao, P Kumar, U Ramamurty… - Proceedings of the …, 2020 - National Acad Sciences
Instrumented indentation has been developed and widely utilized as one of the most
versatile and practical means of extracting mechanical properties of materials. This method …

Achieving large uniform tensile elasticity in microfabricated diamond

C Dang, JP Chou, B Dai, CT Chou, Y Yang, R Fan… - Science, 2021 - science.org
Diamond is not only the hardest material in nature, but is also an extreme electronic material
with an ultrawide bandgap, exceptional carrier mobilities, and thermal conductivity. Straining …

Topology optimization of 2D structures with nonlinearities using deep learning

DW Abueidda, S Koric, NA Sobh - Computers & Structures, 2020 - Elsevier
The field of optimal design of linear elastic structures has seen many exciting successes that
resulted in new architected materials and structural designs. With the availability of cloud …

Machine learning in nuclear materials research

D Morgan, G Pilania, A Couet, BP Uberuaga… - Current Opinion in Solid …, 2022 - Elsevier
Nuclear materials are often demanded to function for extended time in extreme
environments, including high radiation fluxes with associated transmutations, high …

Locally strained 2D materials: Preparation, properties, and applications

J Wang, L He, Y Zhang, H Nong, S Li, Q Wu… - Advanced …, 2024 - Wiley Online Library
Abstract 2D materials are promising for strain engineering due to their atomic thickness and
exceptional mechanical properties. In particular, non‐uniform and localized strain can be …