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

Background of Study: Hydrogen is poised to play a major role in decarbonizing the economy. The need to discover, develop, and understand low-cost, high-performance, durable materials that can help maximize the cost of electrolysis as well as the need for an intelligent tool to make evidence-based Hydrogen research funding decisions relatively easier warranted this study. Aim: In this work, we developed H2 Golden Retriever (H2GR) system for Hydrogen knowledge discovery and representation using Natural Language Processing (NLP), Knowledge Graph and Decision Intelligence. This system represents a novel methodology encapsulating state-of-the-art technique for evidence-based research grantmanship. Methods: Relevant Hydrogen papers were scraped and indexed from the web and preprocessing was done using noise and stop-words removal, language and spell check, stemming and lemmatization. The NLP tasks included Named Entity Recognition using Stanford and Spacy NER, topic modeling using Latent Dirichlet Allocation and TF-IDF. The Knowledge Graph module was used for the generation of meaningful entities and their relationships, trends and patterns in relevant H2 papers, thanks to an ontology of the hydrogen production domain. The Decision Intelligence component provides stakeholders with a simulation environment for cost and quantity dependencies. the abstract. Technical articles of interest are then read to find relevant information and further insights to orient their keyword searches in a more promising direction. This makes keeping up with the torrential pace of publications very difficult, cumbersome, time-consuming and labor-intensive. Since Hydrogen production cost depends on multiple assumptions including materials, electrolyzer design, manufacturing practices, and soft costs, it is difficult to trace back the contribution of the price of a component to the overall system cost. This makes cost of a component to vary from one technical report to another; for instance, a study[12] reported that bipolar plates is the third contributor behind the Catalyst Coated Membrane (CCM) and Porous Transport Layer (PTL) to the stack cost while another article[13] reported it as the highest contributor. It is therefore hard to crystallize on which sub-component deserves the highest focus. Lastly, experts are on a race against time. Understanding how one component affects the overall cost is one thing but how much money must be invested in versus the time it will take to reduce the overall system cost must drive the decisions of the experts. In this work, we addressed the missing link between the slew of information available in literature and the decision to fund research and, by implication, propose an AI-augmented decision tool for knowledge acquisition, knowledge extraction and an evidence-based research funding decision support tool. techniques in include word tokenization, word stemming and lemmatization, topical modeling, named-entity recognition, summarization, word cloud and keyword extraction. These tasks use both linguistics and mathematics to connect the language of with the language of computers.