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Found 11 papers, 4 papers with code
Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning
Leveraging generative Artificial Intelligence (AI), we have transformed a dataset comprising 1, 000 scientific papers into an ontological knowledge graph.
X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Molecular Design
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.
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge
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.
Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design
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.
ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein 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 crucial roles in mechanobiology.
MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities
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.
BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-inspired Materials
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.
Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteins
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.
Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing
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).
Diatom-inspired architected materials using language-based deep learning: Perception, transformation and manufacturing
Learning from nature has been a quest of humanity for millennia.
Wave propagation and energy dissipation of collagen molecules
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
