Artificial intelligence and machine learning in design of mechanical materials
Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …
is becoming an important tool in the fields of materials and mechanical engineering …
Deep learning model to predict complex stress and strain fields in hierarchical composites
Materials-by-design is a paradigm to develop previously unknown high-performance
materials. However, finding materials with superior properties is often computationally or …
materials. However, finding materials with superior properties is often computationally or …
Mechanisms of Biomolecular Self‐Assembly Investigated Through In Situ Observations of Structures and Dynamics
Biomolecular self‐assembly of hierarchical materials is a precise and adaptable bottom‐up
approach to synthesizing across scales with considerable energy, health, environment …
approach to synthesizing across scales with considerable energy, health, environment …
End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures
Due to the high demand for materials with superior mechanical properties and diverse
functions, designing composite materials is an integral part in materials development …
functions, designing composite materials is an integral part in materials development …
Hierarchical multiresolution design of bioinspired structural composites using progressive reinforcement learning
A new method using reinforcement learning for designing bioinspired composite materials is
proposed. While bioinspired design of materials is a promising avenue, the possible …
proposed. While bioinspired design of materials is a promising avenue, the possible …
Deep learning model to predict fracture mechanisms of graphene
Understanding fracture is critical to the design of resilient nanomaterials. Molecular
dynamics offers a way to study fracture at an atomistic level, but is computationally …
dynamics offers a way to study fracture at an atomistic level, but is computationally …
A semi-supervised approach to architected materials design using graph neural networks
K Guo, MJ Buehler - Extreme Mechanics Letters, 2020 - Elsevier
Recent breakthroughs in artificial intelligence (AI) afford opportunities for new paradigms for
material design and optimization. For modeling-driven design approaches, the optimization …
material design and optimization. For modeling-driven design approaches, the optimization …
Predicting output performance of triboelectric nanogenerators using deep learning model
M Jiang, B Li, W Jia, Z Zhu - Nano Energy, 2022 - Elsevier
With the development of artificial intelligence (AI), the use of AI algorithms for processing
experimental data concerning engineering and physics has attracted broad attention, so the …
experimental data concerning engineering and physics has attracted broad attention, so the …
End-to-end protein normal mode frequency predictions using language and graph models and application to sonification
Y Hu, MJ Buehler - ACS nano, 2022 - ACS Publications
The prediction of mechanical and dynamical properties of proteins is an important frontier,
especially given the greater availability of proteins structures. Here we report a series of …
especially given the greater availability of proteins structures. Here we report a series of …
Nanoengineering in biomedicine: current development and future perspectives
Recent advances in biomedicine largely rely on the development in nanoengineering. As
the access to unique properties in biomaterials is not readily available from traditional …
the access to unique properties in biomaterials is not readily available from traditional …