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

This research addresses the challenge of extrapolating from existing scientific knowledge to generate novel ideas and behaviors. The sheer volume of data makes this difficult, especially when integrating diverse sources. Large language models (LLMs) offer a potential solution by enabling in-context learning, adapting responses based on provided context (data, examples, etc.). Recent LLMs like Gemini 1.5 demonstrate the capacity to learn and apply new knowledge from contextual data, even translating languages not explicitly included in their training. This study postulates that providing structured contextual information, specifically sub-graphs extracted from larger ontological knowledge graphs, is essential for LLMs to effectively generate complex, novel responses and facilitate scientific discovery. Prior research used category theory to build ontological graph-based knowledge representations; this work expands on this by using a generative AI framework to discover and utilize the graphs. The goal is to leverage generative AI to connect disparate areas of knowledge by finding analogies and identifying relationships between seemingly unrelated concepts. This is viewed as a graph traversal process, where nodes and edges represent concepts and their relationships, respectively. Rigorous graph construction methodologies (data mining, embedding models, etc.) allow an autonomous system to explore new connections and possibilities, representing a model of 'thinking' and forming a basis for innovation. This approach aligns with 'augmented thinking,' emphasizing interdisciplinary interfaces to generate new ideas and advancements. The hypothesis is that generative AI, when applied to structured graph representations, can effectively solve complex tasks. Multimodal AI systems, like LLMs, can ingest diverse data types, and graph theory provides tools to analyze and quantify the importance of nodes (betweenness centrality, degree centrality, etc.). High betweenness centrality nodes, which lie on many shortest paths between other nodes, are crucial as they represent pivotal intersections of key concepts. Transitive relationships in graphs (if A→B and B→C, then A→C) are also leveraged to uncover direct and indirect interactions between concepts. This study uses a computational scheme to convert information into knowledge, constructing a graph representation that underpins downstream analyses (queries, connecting concepts, adding new knowledge). Several LLMs are employed, including open source and proprietary models, to leverage their respective strengths for different tasks. X-LORA, a biologically inspired LLM, dynamically reconfigures its structure before responding, enhancing its reasoning capacity across diverse scientific domains.

The study builds upon previous research utilizing category theory to develop ontological graph-based representations of knowledge. Unlike previous work, which relied on pre-defined ontologies, this study utilizes a generative AI framework to autonomously discover and utilize the graphs. This approach leverages the power of large language models (LLMs) to extract knowledge from a corpus of scientific papers and construct a comprehensive knowledge graph. The authors cite several works that have explored the use of category theory and graph-based representations in knowledge representation and reasoning, providing a context for their novel generative approach.

The methodology involves several steps (Figure 1): 1. Identifying a corpus of knowledge through literature analysis. 2. Distilling knowledge into structured raw context (summary, reasoning, details). 3. Generating triples for graph construction (concepts and relationships). 4. Concatenating triples into a global graph. 5. Analyzing the global graph using node embeddings (deep learning text encoder model). 6. Utilizing the ontological knowledge graph for multimodal graph reasoning. The process further includes adding new graphs or sub-graphs, extracting sub-graphs via in-context queries (shortest path traversals, multi-hop analyses), and integrating new knowledge (generative processes, simulations, original data sources). Large language models (LLMs) used include open source models and proprietary models like GPT-4/V and Claude-4 Opus. The choice of models is based on their capabilities for specific tasks. X-LORA, inspired by biological principles, dynamically rearranges its structure before responding, enabling reasoning across diverse scientific domains. The global ontological knowledge graph was constructed from a corpus of 1,000 scientific papers (Figure 1b). Natural language processing steps involved converting articles into markup language, then text chunks, distilling content into summaries, and generating triples for graph construction (Figure 1b). The global graph's deep and wide connectivity is visualized (Figure 2). Graph statistics, including degree distribution, principal component analysis (PCA) of node embeddings, and community analysis were calculated (Figure 3, Table 1). The giant component of the global graph was analyzed as it represents the most interconnected and information-rich part. Graph statistics such as community size, average node degree, clustering coefficient, and betweenness centrality were analyzed (Figure 4). A power-law fit of the degree distribution was performed, indicating a scale-free nature of the graph (Figure 4f, Table 2). Multiple graph traversal paths were extracted using ranked combinatorial analysis of cosine similarities of node embeddings (Section 2.2). Graph traversal and path sampling were employed for question answering (Section 2.3). Isomorphism analysis was used to find structural parallels across different knowledge domains (Section 2.4). New data was generated through conversations with generative models and incorporated into the graph (Section 2.5). The graph was augmented with data from additional scientific literature on mycelium (Section 2.6). Finally, joint analysis of artistic images with graph reasoning and image synthesis was conducted for materials design (Section 2.7).

The global knowledge graph constructed from 1,000 scientific papers exhibited a scale-free topology with a power-law degree distribution (α = 2.8786), indicating a few highly connected nodes (hubs) and many with few connections. Analysis of the giant component (11,878 nodes, 15,396 edges) revealed similar connectivity patterns to the global graph. Community analysis identified 80 communities within the giant component, displaying a right-skewed distribution of sizes and variable average degrees, clustering coefficients, and betweenness centrality. A novel path sampling strategy based on ranked cosine similarities of node embeddings was developed to connect disparate concepts, including finding relationships between 'graphene' and 'silk', and 'inkjet printer for living tissues' and 'spider silk proteome'. Graph traversal-based question answering enabled sophisticated reasoning by overlaying extracted graph structures with LLM understanding. Multiple LLMs (X-LORA, BioinspiredLLM-Mixtral, GPT-4) were used to answer questions about relationships between seemingly unrelated concepts (e.g., 'flower' and 'nacre-inspired cement'), highlighting the benefits of using multiple models for comparative analysis. Follow-up queries for detailed material designs were made. Isomorphism analysis revealed detailed structural parallels between domains, such as biological materials and Beethoven's 9th Symphony, revealing shared patterns of complexity. The algorithm proposed a hierarchical mycelium-based composite by integrating path sampling with principles from Kandinsky's 'Composition VII', integrating concepts of chaos and order, adjustable porosity, mechanical strength, and complex chemical functionalization. New data was generated using conversations with LLMs (e.g., calculating protein unfolding energy) and adversarial multi-agent modeling (X-LORA agents), expanding the knowledge graph and revealing new connections. The integration of three additional papers on mycelium further enriched the graph. Bridging centrality analysis identified key nodes connecting disparate concepts, suggesting research opportunities (e.g., nanocellulose in mycelium materials). Joint analysis of artistic images (Kandinsky's 'Composition VII' and Delaunay's 'Le Premier Disque') with graph reasoning and image synthesis (DALL-E 3) was used to generate novel mycelium composite designs. The resulting material was described in detail and visualized using a text-to-image model, producing highly detailed and functional microstructures, demonstrating the potential for AI-driven materials design.

The findings demonstrate the potential of generative AI for accelerating scientific discovery. The scale-free nature of the knowledge graph highlights the importance of central hubs, while the path sampling strategy allows for connections between seemingly disparate concepts. The use of multiple LLMs provided comparative insights and demonstrated the power of multimodal AI. The isomorphism analysis highlighted the underlying structural similarities across diverse knowledge domains, suggesting universal principles governing complex systems. The generation and integration of new data via LLM conversations and multi-agent modeling significantly enriched the knowledge graph, highlighting the dynamic nature of the approach. The integration of artistic principles in the design process revealed the potential for creative inspiration in scientific innovation. The combination of graph reasoning, LLM analysis, and text-to-image generation resulted in a novel mycelium composite design, illustrating the potential for AI-driven materials discovery. This integration of diverse data modalities and advanced computational methods establishes a novel framework for accelerating scientific discovery and innovation across multiple domains.

This study demonstrates a powerful framework for accelerating scientific discovery by integrating generative AI, knowledge graph representation, and multimodal reasoning. The scale-free knowledge graph, path sampling strategy, and isomorphism analysis revealed previously unknown relationships between scientific concepts and across disciplines. The use of multimodal AI models enhanced novelty and explorative capacity. A novel mycelium composite design was proposed and visualized, highlighting the potential of this approach for materials design and other fields. Future research should focus on expanding the knowledge graph, incorporating more sophisticated LLMs, and exploring further the use of various graph theoretical metrics such as clustering coefficient. Investigating manufacturability and integrating experimental data into the design process will also be crucial for practical applications. The ethical implications of AI-driven knowledge generation and materials design need to be carefully considered.

The current study is limited by the scope of the initial dataset (1,000 papers focused on bioinspired materials) and the computational constraints of using large language models. Expanding the dataset and incorporating more sophisticated LLMs could further enhance the richness and depth of the knowledge graph. The manufacturability of the proposed materials has not been explicitly explored. Further research is needed to fully leverage graph analysis metrics such as clustering coefficients. The ethical and philosophical implications of using AI for scientific discovery warrant further consideration.