Name: LAMM: MIT Laboratory for Atomistic and Molecular Mechanics
Type: User
Company: MIT
Bio: PI: Markus J. Buehler, MIT.
Our research focus on developing a new paradigm that designs materials from the molecular scale, using MD, ML and other methods.
Twitter: lamm_mit
Location: Cambridge, MA
Blog: http://lamm.mit.edu/
LAMM: MIT Laboratory for Atomistic and Molecular Mechanics's Projects
Codes for translating structural defects to atomic properties
Unsupervised cross-domain translation via deep learning and adversarial attention neural networks and application to music-inspired protein designs
CollagenTransformer: End-to-End Transformer Model to Predict Thermal Stability of Collagen Triple Helices Using an NLP Approach
The DynaGen model allows the prediction of dynamical field data based on microstructure input. The model is exemplified to dynamic fracture problems in brittle materials.
The Falcon Programming Language.
Fast and memory efficient PyTorch implementation of the Perceiver with FlashAttention.
GAN/convolutional and Transformer models to predict missing mechanical information given limited known data in part of the domain, and further characterize the composite geometries from the recovered mechanical fields for 2D and 3D complex microstructures
Deep learning model to predict complex stress and strain fields in hierarchical composites
GraphGeneration: Modeling and design of hierarchical bio-inspired de novo spider web structures using deep learning and additive manufacturing
A computational building block approach towards multiscale architected materials analysis and design with application to hierarchical metal metamaterials
Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Materials
Agent-based LLM modeling of mechanics problems
Tools for merging pretrained large language models.
Molecular generation using diffusion models and autoregressive transformer models
Generative method to design novel proteins using a diffusion model
Rapid Prediction of Protein Natural Frequencies using Graph Neural Networks
Generative strategies for modeling, design and analysis of silk protein sequences for enhanced mechanical properties
PDB files of spider silk structures