Search Results for author: Markus J. Buehler

Found 24 papers, 13 papers with code

Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model

no code implementations14 Feb 2025 Bo Ni, Markus J. Buehler

Here, we introduce VibeGen, a generative AI framework that enables end-to-end de novo protein design conditioned on normal mode vibrations.

In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR

1 code implementation14 Jan 2025 Markus J. Buehler

The pursuit of automated scientific discovery has fueled progress from symbolic logic to modern AI, forging new frontiers in reasoning and pattern recognition.

Knowledge Graphs Language Modeling +2

Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers

1 code implementation4 Jan 2025 Markus J. Buehler

We present an approach to modifying Transformer architectures by integrating graph-aware relational reasoning into the attention mechanism, merging concepts from graph neural networks and language modeling.

Language Modeling Language Modelling +1

Learning the rules of peptide self-assembly through data mining with large language models

1 code implementation8 Nov 2024 Zhenze Yang, Sarah K. Yorke, Tuomas P. J. Knowles, Markus J. Buehler

Peptides are ubiquitous and important biologically derived molecules, that have been found to self-assemble to form a wide array of structures.

Large Language Model Literature Mining

Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems

no code implementations17 Oct 2024 Alireza Ghafarollahi, Markus J. Buehler

A set of LLM-driven AI agents collaborate to automate the exploration of the vast design space of MPEAs, guided by predictions from the GNN.

Graph Neural Network Retrieval

PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking

1 code implementation16 Oct 2024 Markus J. Buehler

We propose a recursive learning approach that engages the model in multi-step reasoning, revisiting, and refining intermediate steps before producing a final output in training and inference phases.

Language Modeling Language Modelling +1

SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning

2 code implementations9 Sep 2024 Alireza Ghafarollahi, Markus J. Buehler

A key challenge in artificial intelligence is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data.

AI Agent Knowledge Graphs +1

Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities

7 code implementations5 Sep 2024 Wei Lu, Rachel K. Luu, Markus J. Buehler

The advancement of Large Language Models (LLMs) for domain applications in fields such as materials science and engineering depends on the development of fine-tuning strategies that adapt models for specialized, technical capabilities.

Domain Adaptation Image Generation

LifeGPT: Topology-Agnostic Generative Pretrained Transformer Model for Cellular Automata

1 code implementation3 Sep 2024 Jaime A. Berkovich, Markus J. Buehler

Conway's Game of Life (Life), a well known algorithm within the broader class of cellular automata (CA), exhibits complex emergent dynamics, with extreme sensitivity to initial conditions.

Large Language Model

AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence

no code implementations13 Jul 2024 Alireza Ghafarollahi, Markus J. Buehler

The design of alloys is a multi-scale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically reserved for human experts.

Data Integration

Multicell-Fold: geometric learning in folding multicellular life

no code implementations9 Jul 2024 Haiqian Yang, Anh Q. Nguyen, Dapeng Bi, Markus J. Buehler, Ming Guo

We demonstrate that multicellular data can be represented with both granular and foam-like physical pictures through a unified graph data structure, considering both cellular interactions and cell junction networks.

Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design

3 code implementations29 May 2024 Markus J. Buehler

We present Cephalo, a series of multimodal vision large language models (V-LLMs) designed for materials science applications, integrating visual and linguistic data for enhanced understanding.

Dataset Generation Image to text +2

Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning

1 code implementation18 Mar 2024 Markus J. Buehler

Leveraging generative Artificial Intelligence (AI), we have transformed a dataset comprising 1, 000 scientific papers into an ontological knowledge graph.

Graph Sampling Knowledge Graphs +2

X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Molecular Design

3 code implementations11 Feb 2024 Eric L. Buehler, Markus J. Buehler

Starting with a set of pre-trained LoRA adapters, our gating strategy uses the hidden states to dynamically mix adapted layers, allowing the resulting X-LoRA model to draw upon different capabilities and create never-before-used deep layer-wise combinations to solve tasks.

graph construction Knowledge Graphs +2

Learning Dynamics from Multicellular Graphs with Deep Neural Networks

1 code implementation22 Jan 2024 Haiqian Yang, Florian Meyer, Shaoxun Huang, Liu Yang, Cristiana Lungu, Monilola A. Olayioye, Markus J. Buehler, Ming Guo

Multicellular self-assembly into functional structures is a dynamic process that is critical in the development and diseases, including embryo development, organ formation, tumor invasion, and others.

Graph Neural Network

MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

1 code implementation14 Nov 2023 Bo Ni, Markus J. Buehler

Solving mechanics problems using numerical methods requires comprehensive intelligent capability of retrieving relevant knowledge and theory, constructing and executing codes, analyzing the results, a task that has thus far mainly been reserved for humans.

Language Modeling Language Modelling +2

Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design

no code implementations30 Oct 2023 Markus J. Buehler

Here we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths.

Code Generation Language Modeling +4

MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities

no code implementations16 Oct 2023 Markus J. Buehler

The resulting MechGPT LLM foundation model is used in a series of computational experiments to explore its capacity for knowledge retrieval, various language tasks, hypothesis generation, and connecting knowledge across disparate areas.

Knowledge Graphs Language Modelling +1

ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model

no code implementations16 Oct 2023 Bo Ni, David L. Kaplan, Markus J. Buehler

Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology.

Language Modeling Protein Language Model

BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-inspired Materials

no code implementations15 Sep 2023 Rachel K. Luu, Markus J. Buehler

The model has proven that it is able to accurately recall information about biological materials and is further enhanced with enhanced reasoning ability, as well as with retrieval-augmented generation to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains.

Language Modeling Language Modelling +1

Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteins

1 code implementation7 May 2023 Markus J. Buehler

In a broader sense, this work illustrates a form of multiscale modeling that relates a set of ultimate building blocks (here, byte-level utf8 characters that define the nature of the physical system at hand) to complex output.

Graph Neural Network Language Modelling

Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing

no code implementations11 Apr 2023 Wei Lu, Nic A. Lee, Markus J. Buehler

Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e. g., lightweight but high strength, achieving diverse mechanical responses).

graph construction

Wave propagation and energy dissipation of collagen molecules

no code implementations7 Sep 2020 Mario Milazzo, Gang Seob Jung, Serena Danti, Markus J. Buehler

Using a one-dimensional string model as a model system, we investigate the roles of hydration and load direction on wave propagation along the collagen peptide and the related energy dissipation.

Applied Physics

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