A critical review of machine learning of energy materials
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 …
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
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 …
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
Characterizing internal structures and defects in materials is a challenging task, often
requiring solutions to inverse problems with unknown topology, geometry, material …
requiring solutions to inverse problems with unknown topology, geometry, material …
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
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 …
Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how …
Deep learning predicts path-dependent plasticity
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 …
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
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 …
versatile and practical means of extracting mechanical properties of materials. This method …
Achieving large uniform tensile elasticity in microfabricated diamond
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 …
with an ultrawide bandgap, exceptional carrier mobilities, and thermal conductivity. Straining …
Topology optimization of 2D structures with nonlinearities using deep learning
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 …
resulted in new architected materials and structural designs. With the availability of cloud …
Machine learning in nuclear materials research
Nuclear materials are often demanded to function for extended time in extreme
environments, including high radiation fluxes with associated transmutations, high …
environments, including high radiation fluxes with associated transmutations, high …
Locally strained 2D materials: Preparation, properties, and applications
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 …
exceptional mechanical properties. In particular, non‐uniform and localized strain can be …