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Markus J. Buehler — most-cited papers & profile · AI for Science
← authors
·
overview
Markus J. Buehler
22
papers ·
162
citations ·
119
h-index
Institute of Computing Technology · Department of Physics, Mathematics and Informatics · Computing Center · Atlantic Institute of Oriental Medicine · MIT-Harvard Center for Ultracold Atoms · Massachusetts Institute of Technology
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Most-cited papers
X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Molecular Design
2024 · 39 citations
Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning
2024 · 39 citations
Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
2024 · 27 citations
Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteins
2023 · 26 citations
Sparks: Multi-Agent Artificial Intelligence Model Discovers Protein Design Principles
2025 · 8 citations
SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning
2024 · 8 citations
Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model
2025 · 5 citations
Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems
2024 · 4 citations
BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-inspired Materials
2023 · 2 citations
ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model
2023 · 1 citations
GraphAgents: Knowledge Graph-Guided Agentic AI for Cross-Domain Materials Design
2026
Higher-Order Knowledge Representations for Agentic Scientific Reasoning
2026
Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation
2025
Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials
2025
Learning the rules of peptide self-assembly through data mining with large language models
2024
Top co-authors
Alireza Ghafarollahi
· 3
Bo Ni
· 2
David L. Kaplan
· 2
Isabella Stewart
· 2
Rachel K. Luu
· 2
Di Sheng Lee
· 1
Eric L. Buehler
· 1
Fiona Y. Wang
· 1
Jingyu Deng
· 1
Ming Dao
· 1
Mohammed Shahrudin Ibrahim
· 1
Nam‐Joon Cho
· 1
Topics
Materials
Chemistry
Protein Science
Drug Discovery
Medical AI
Genomics
Physics ML