Recent advances and applications of machine learning in experimental solid mechanics: A review
For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …
and understanding the mechanical properties of natural and novel artificial materials …
Multiscale modeling at the interface of molecular mechanics and natural language through attention neural networks
MJ Buehler - Accounts of Chemical Research, 2022 - ACS Publications
Conspectus Humans are continually bombarded with massive amounts of data. To deal with
this influx of information, we use the concept of attention in order to perceive the most …
this influx of information, we use the concept of attention in order to perceive the most …
Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model
We report two generative deep-learning models that predict amino acid sequences and 3D
protein structures on the basis of secondary-structure design objectives via either the overall …
protein structures on the basis of secondary-structure design objectives via either the overall …
MeLM, a generative pretrained language modeling framework that solves forward and inverse mechanics problems
MJ Buehler - Journal of the Mechanics and Physics of Solids, 2023 - Elsevier
We report a flexible multi-modal mechanics language model, MeLM, applied to solve
various nonlinear forward and inverse problems, that can deal with a set of instructions …
various nonlinear forward and inverse problems, that can deal with a set of instructions …
Inverse design of shell-based mechanical metamaterial with customized loading curves based on machine learning and genetic algorithm
Y Wang, Q Zeng, J Wang, Y Li, D Fang - Computer Methods in Applied …, 2022 - Elsevier
Triply periodic minimal surfaces (TPMSs) have attracted great attention due to their distinct
advantages such as high strength and light weight compared to traditional lattice structures …
advantages such as high strength and light weight compared to traditional lattice structures …
[HTML][HTML] Deep language models for interpretative and predictive materials science
Y Hu, MJ Buehler - APL Machine Learning, 2023 - pubs.aip.org
Machine learning (ML) has emerged as an indispensable methodology to describe,
discover, and predict complex physical phenomena that efficiently help us learn underlying …
discover, and predict complex physical phenomena that efficiently help us learn underlying …
Single-shot forward and inverse hierarchical architected materials design for nonlinear mechanical properties using an Attention-Diffusion model
AJ Lew, MJ Buehler - Materials Today, 2023 - Elsevier
Inspired by natural materials, hierarchical architected materials can achieve enhanced
properties including achieving tailored mechanical responses. However, the design space …
properties including achieving tailored mechanical responses. However, the design space …
Predicting stress, strain and deformation fields in materials and structures with graph neural networks
Developing accurate yet fast computational tools to simulate complex physical phenomena
is a long-standing problem. Recent advances in machine learning have revolutionized the …
is a long-standing problem. Recent advances in machine learning have revolutionized the …
ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model
Through evolution, nature has presented a set of remarkable protein materials, including
elastins, silks, keratins and collagens with superior mechanical performances that play …
elastins, silks, keratins and collagens with superior mechanical performances that play …
Deep learning accelerated design of mechanically efficient architected materials
Lattice structures are known to have high performance-to-weight ratios because of their
highly efficient material distribution in a given volume. However, their inherently large void …
highly efficient material distribution in a given volume. However, their inherently large void …