AI & ML interests
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.
Recent Activity
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PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking
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PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking
Paper • 2410.12375 • Published • 4 -
In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR
Paper • 2501.08120 • Published • 5 -
lamm-mit/PRefLexOR_ORPO_DPO_EXO_10242024
Text Generation • 4B • Updated -
lamm-mit/PRefLexOR_ORPO_DPO_EXO_REFLECT_10222024
Text Generation • 4B • Updated • 1 • 3
Cephalo is a series of multimodal vision large language models (V-LLMs) designed to integrate visual and linguistic reasoning in materials science.
Collection of fine-tuned Stable Diffusion models (SD2.x, SD-XL, SD3) to incorporate biological design cues, here exemplified for leaf microstructures.
We present an approach to modifying Transformer architectures by integrating graph-aware relational reasoning into the attention mechanism.
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lamm-mit/Llama-3.2-3B-Instruct-Sparse-GIN-orca-math-word-problems
Updated • 10 • 1 -
lamm-mit/Llama-3.2-3B-Instruct-Sparse-GIN-logic
2B • Updated • 2 • 1 -
lamm-mit/Llama-3.2-3B-Instruct-Sparse-GIN-bio
Updated • 1 -
Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers
Paper • 2501.02393 • Published • 7
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Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
Paper • 2405.19076 • Published • 2 -
X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Design
Paper • 2402.07148 • Published • 4 -
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning
Paper • 2402.04268 • Published -
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge
Paper • 2311.08166 • Published • 2
A set of LLMs fine-tuned on biological materials, mechanics, and materials science applications.
We present an approach to modifying Transformer architectures by integrating graph-aware relational reasoning into the attention mechanism.
-
lamm-mit/Llama-3.2-3B-Instruct-Sparse-GIN-orca-math-word-problems
Updated • 10 • 1 -
lamm-mit/Llama-3.2-3B-Instruct-Sparse-GIN-logic
2B • Updated • 2 • 1 -
lamm-mit/Llama-3.2-3B-Instruct-Sparse-GIN-bio
Updated • 1 -
Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers
Paper • 2501.02393 • Published • 7
PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking
-
PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking
Paper • 2410.12375 • Published • 4 -
In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR
Paper • 2501.08120 • Published • 5 -
lamm-mit/PRefLexOR_ORPO_DPO_EXO_10242024
Text Generation • 4B • Updated -
lamm-mit/PRefLexOR_ORPO_DPO_EXO_REFLECT_10222024
Text Generation • 4B • Updated • 1 • 3
-
Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
Paper • 2405.19076 • Published • 2 -
X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Design
Paper • 2402.07148 • Published • 4 -
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning
Paper • 2402.04268 • Published -
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge
Paper • 2311.08166 • Published • 2
Cephalo is a series of multimodal vision large language models (V-LLMs) designed to integrate visual and linguistic reasoning in materials science.
Collection of fine-tuned Stable Diffusion models (SD2.x, SD-XL, SD3) to incorporate biological design cues, here exemplified for leaf microstructures.
A set of LLMs fine-tuned on biological materials, mechanics, and materials science applications.