ACCELERATING SCIENTIFIC DISCOVERY WITH GENERATIVE KNOWLEDGE EXTRACTION, GRAPH-BASED REPRESENTATION, AND MULTIMODAL INTELLIGENT GRAPH REASONING

The paper addresses how generative AI and graph representations can accelerate knowledge discovery across disciplines, particularly in bioinspired materials. The central hypothesis is that large language models (LLMs), when provided with structured, graph-based context distilled from literature, can reason beyond retrieval to generate novel connections, hypotheses, and designs. The work situates itself in the context of in-context learning and emergent capabilities of LLMs, arguing that providing proper graph-derived context (subgraphs capturing relationships) can trigger complex inference. It proposes a pipeline to construct an ontological knowledge graph from heterogeneous sources and to exploit graph-theoretic properties (e.g., transitivity, centralities) for discovery, path-finding between disparate concepts, and multimodal reasoning that integrates text, images, and numerical data for materials design and hypothesis generation.

Prior work used category theory and ontological graphs to represent knowledge and analogies across domains, including biological materials and music. The authors build on these ideas but move from static ontologies to a generative AI-driven pipeline that discovers and utilizes graphs from text and other modalities. Related strands include in-context learning with modern LLMs; the concept of augmented thinking emphasizing cross-disciplinary interfaces; and graph theory tools (degree/centrality, community structure, clustering, giant components) for extracting critical nodes and pathways. The study also relates to scale-free network research and robustness properties, and to prior demonstrations of cross-domain structural mappings (e.g., protein materials to music) via rigorous formalisms (ologs), here generalized through autonomous isomorphism discovery using generative AI.

Corpus and distillation: ~1,000 bioinspired materials papers (per prior compilation) are converted from PDF to markup using Nougat, split into text chunks (mean ~772 words), then distilled by Mistral-7B-OpenOrca into (i) heading, (ii) summary, and (iii) bulleted key facts/reasoning (raw structured context). Knowledge graph generation: Using Zephyr-7B-8, the system extracts triples (node_1, edge, node_2) from each distilled chunk, iteratively revising labels for ontology consistency (LlamaIndex-style graph generation). Global graph assembly: Local graphs are concatenated via NetworkX compose; node embeddings are computed with BAAI-bge-large-en-v1.5 (1024-d vectors; up to 512-token inputs). Nodes with cosine similarity >0.95 are merged (retaining the label with highest degree). Small components (<10 nodes) are pruned to focus on salient structure (optionally considering the giant component). Community detection uses Girvan–Newman; additional statistics include degree distributions, clustering, and centralities (betweenness, bridging centrality for subgraph analyses). Graph reasoning: Node embeddings enable cosine similarity ranking to identify top-k similar nodes, multi-hop neighborhoods, and multi-path sampling between search terms (including terms absent verbatim but mapped via nearest nodes in embedding space). Paths are labeled by edge semantics, enabling explanatory traversals. Isomorphism analysis: Independent corpora (e.g., Beethoven’s 9th symphony analysis) are processed into graphs; computational isomorphism analysis (and model-assisted semantic mapping) identifies structurally isomorphic subgraphs with no shared nodes, enabling cross-domain transfer of patterns. Graph augmentation: New knowledge is generated and integrated via (i) conversations with a specialized LLM (X-LORA) that produces technical datasets (e.g., protein unfolding energies) and (ii) adversarial multi-agent modeling (chef/inventor X-LORA agents) to explore topics (e.g., synthetic proteins in food). Triples from the generated text are merged into the global graph and analyzed for new reasoning paths. Multimodal reasoning: GPT-4V interprets graphs and artistic images (e.g., Kandinsky’s Composition VII), jointly reasoning with graph context to propose materials designs. DALL·E 3 is prompted (via GPT-4) to synthesize microstructure visualizations, and GPT-4V generates stepwise manufacturing plans. Models used: X-LORA (dynamic dense MoE, ~7B), BioinspiredLLM-Mixtral (MoE), Mistral-7B-OpenOrca and Zephyr-7B-8 (distillation/triples), GPT‑4/GPT‑3.5 (select tasks, integration), GPT‑4V (image reasoning), Claude‑3 (long-context synthesis). Tools: NetworkX, Pyvis, Gephi, Graphviz; multi-GPU parallelism for throughput.

Global graph statistics: Global graph has 12,319 nodes and 15,752 edges; giant component has 11,878 nodes and 15,396 edges. Average node degree ≈2.56 (global) and 2.59 (giant); max degree 171; min degree 1; median degree 1; densities ≈0.00021 (global) and 0.00022 (giant). Communities: 109 (global) vs 80 (giant), indicating a connected, information-rich core. Degree distribution exhibits a heavy tail consistent with scale-free behavior; fitted power-law exponent α=2.8786 (SE=0.0698), log-likelihood ratio R=4.1526 favoring power-law over exponential, p≈3.29×10^-5. Community analyses show heterogeneous hub nodes (e.g., nacre, collagen, strength, mechanical properties) and variable clustering/betweenness profiles; smaller communities tend to have higher average betweenness centrality per node. Graph reasoning demonstrations: • Paths link disparate concepts via labeled edges, e.g., graphene → improves → strength → … → biological materials → provide functionalities → silk; and inkjet-based bioprinting → … → functional silk fibers → … → spider silk protein. • Multi-path sampling and merged-path subgraphs reveal richer topologies and new connectivities (e.g., relating “a flower” to “nacre-inspired cement”). LLM responses (X-LORA, BioinspiredLLM-Mixtral, GPT‑4) consolidate insights into nacre-inspired cement designs (e.g., chitosan–PEGDMA layered structures, hydrogen/covalent bonding, superhydrophobic surface engineering). Isomorphism: Structurally isomorphic subgraphs identified between bioinspired materials and Beethoven’s 9th symphony corpora enable cross-domain analogical reasoning, with semantically coherent node/edge mappings (e.g., adhesive force↔tonality, protein unfolding↔deafness). Graph augmentation: Agentic generation (X‑LORA) of protein unfolding datasets (energies ~0.288, 0.248, 0.121) and adversarial dialogues on food-focused synthetic proteins produced new subgraphs linking flavor↔sustainability via synthetic proteins and green flexible electronics. Literature augmentation: Adding three mycelium papers yielded a 501-node/375-edge new graph (giant component 155 nodes/162 edges) and, when merged, enabled shortest-path and bridging centrality analyses (top bridging nodes include mechanical properties, mycelium, porosity, collagen). Design outcomes: Proposed sustainable mycelium composites integrating nanocellulose and graphene oxide, supramolecular chemistries (e.g., cyclodextrins), and hierarchical architectures; identified mycelium–collagen hybrids with predicted hydrogen bonding (chitin–hydroxyproline), improved stiffness/toughness, tunable porosity, and potential self-healing. Multimodal synthesis: GPT‑4V combined graph context with art (Kandinsky’s Composition VII; Delaunay’s Le Premier Disque) to propose hierarchical, responsive mycelium-based composites; DALL·E 3 generated microstructure images; GPT‑4V produced detailed manufacturing steps.

The findings validate the hypothesis that LLM-guided knowledge graphs provide a powerful scaffold for discovery beyond retrieval. The scale-free, highly connected graph structure facilitates efficient navigation via hubs and multiple alternative paths, enabling connection of disparate concepts (e.g., bioprinting and silk proteomics) and surfacing hidden relationships. Labeled edges support mechanistic interpretability along traversed paths. Isomorphism analysis demonstrates that structural patterns in one domain (music) can map onto another (materials), enabling transfer of analytical frameworks and hypothesis generation across non-overlapping ontologies. Augmenting the graph with AI-generated or newly published data expands reachable paths and enriches reasoning outcomes (e.g., flavor–sustainability connections, food-focused materials). Multimodal reasoning further enriches design by incorporating visual abstractions into hierarchical material architectures and manufacturing workflows. Collectively, these results show that generative AI plus graph reasoning can propose technically detailed, novel materials designs with plausible mechanisms, offering a reusable framework for cross-disciplinary innovation.

This work presents an end-to-end, scalable framework to distill large scientific corpora into ontological knowledge graphs, analyze their structure, and exploit graph reasoning with generative AI for hypothesis generation, cross-domain analogy, and materials design. Key contributions include: (i) automated graph construction with ontology consolidation; (ii) demonstration of scale-free structure and community organization supporting discovery; (iii) path-based and multi-path reasoning yielding detailed, novel design proposals (e.g., nacre-inspired cements; mycelium–collagen composites); (iv) autonomous isomorphism mapping across disjoint domains (materials↔music); (v) graph augmentation via agentic generation and literature updates; and (vi) multimodal integration (graphs+images) culminating in microstructure synthesis and stepwise manufacturing plans. Future research should expand the corpus across domains for larger, denser graphs; adopt higher-capability models for triple extraction; couple with physics-based simulators and autonomous labs for closed-loop validation; deepen formal treatments of graph metrics for reasoning strategies; and study manufacturability, costs, and environmental impacts to translate AI-generated designs into practice.

• Corpus scope: Primarily bioinspired materials; broader, denser corpora may change graph topology and enrich reasoning. • Triple extraction models: Open-source, smaller LLMs were used for scalability; higher-capability models (e.g., GPT‑4) could improve precision/recall and ontology consistency but were rate-limited. • Analysis depth: Clustering coefficient analyses and other regional metrics warrant deeper, systematic exploration; robustness/vulnerability studies (e.g., node/edge removal) remain future work. • Manufacturability: Proposed designs are detailed but not experimentally validated here; practical synthesis, cost, and performance need testing. • Multimodal integration: Interpreting heterogeneous data (scientific text, images, music analyses) remains nontrivial and may introduce biases. • Ethics and bias: Data integrity, potential model biases, and AI-driven research directions require governance and transparency as systems scale.