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Hydrogen is poised to play a major role in decarbonizing the economy. Theneed to discover, develop, and understand low-cost, high-performance, durablematerials that can help maximize the cost of electrolysis as well as the needfor an intelligent tool to make evidence-based Hydrogen research fundingdecisions relatively easier warranted this study.In this work, we developed H2Golden Retriever (H2GR) system for Hydrogen knowledge discovery andrepresentation using Natural Language Processing (NLP), Knowledge Graph andDecision Intelligence. This system represents a novel methodology encapsulatingstate-of-the-art technique for evidence-based research grantmanship. RelevantHydrogen papers were scraped and indexed from the web and preprocessing wasdone using noise and stop-words removal, language and spell check, stemming andlemmatization. The NLP tasks included Named Entity Recognition using Stanfordand Spacy NER, topic modeling using Latent Dirichlet Allocation and TF-IDF. TheKnowledge Graph module was used for the generation of meaningful entities andtheir relationships, trends and patterns in relevant H2 papers, thanks to anontology of the hydrogen production domain. The Decision Intelligence componentprovides stakeholders with a simulation environment for cost and quantitydependencies. PageRank algorithm was used to rank papers of interest. Randomsearches were made on the proposed H2GR and the results included a list ofpapers ranked by relevancy score, entities, graphs of relationships between theentities, ontology of H2 production and Causal Decision Diagrams showingcomponent interactivity. Qualitative assessment was done by the experts andH2GR is deemed to function to a satisfactory level.

H2-Golden-Retriever: Methodology and Tool for an Evidence-Based Hydrogen Research Grantsmanship