{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/DE260101596"},"data":{"type":"grant-details","id":"DE260101596","attributes":{"code":"DE260101596","administering-organisation":"Queensland University of Technology","announcement-administering-organisation":"Queensland University of Technology","scheme-name":"Discovery Early Career Researcher Award","grant-status":"Active","funding-commencement-year":2026,"years-funded":3,"project-start-date":"2026-01-07","anticipated-end-date":"2029-01-06","grant-summary":"Electrolyte engineering for CO2 reduction by machine learning force field. This project aims to bridge a critical knowledge gap in applying machine learning force field methods to CO2 reduction for high-value C2 products powered by renewable energy.  Leveraging the state-of-the-art machine learning force field for electrolyte prediction, this project proposes a novel approach to mitigating greenhouse gas emission, paving a new way for understanding the critical role of electrolyte composition, pH, cation/anion concentration, and electrode potentials at the solid-liquid interface. The outcome of this project is optimized electrolyte towards CO2 reduction to C2 products, significantly reducing greenhouse gas emissions and advancing green chemistry through machine learning-driven innovation in electrocatalysis.","funding-current":433360.00,"funding-at-announcement":430079,"investigators-current":[{"title":"Dr","firstName":"Xin","familyName":"Mao","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0009-0007-9706-3778 "}],"investigators-at-announcement":[{"title":"Dr","firstName":"Xin","familyName":"Mao","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0009-0007-9706-3778 "}],"organisations-current":[{"organisationName":"Queensland University of Technology","roleName":"Administering Organisation","state":"QLD"}],"organisations-at-announcement":[{"organisationName":"Queensland University of Technology","roleName":"Administering Organisation","state":"QLD"}],"field-of-research":[{"isPrimary":false,"code":"400404","name":"Electrochemical Energy Storage and Conversion","type":"FOR20"},{"isPrimary":true,"code":"4016","name":"Materials Engineering","type":"FOR20"},{"isPrimary":false,"code":"401605","name":"Functional Materials","type":"FOR20"},{"isPrimary":false,"code":"401807","name":"Nanomaterials","type":"FOR20"}],"socio-economic-objective":[{"code":"170599","name":"Environmentally Sustainable Energy Activities Not Elsewhere Classified","type":"SEO20"},{"code":"170899","name":"Renewable Energy Not Elsewhere Classified","type":"SEO20"},{"code":"280105","name":"Expanding Knowledge In the Chemical Sciences","type":"SEO20"}],"international-collaboration":["China (excludes SARs and Taiwan)","Japan","Singapore","United States of America"],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"This project addresses a critical global challenge by converting carbon dioxide (CO2), a major driver of climate change, into valuable chemicals and fuels. By leveraging cutting-edge machine learning force field methods, it aims to optimize electrolyte composition to enhance conversion efficiency and develop practical strategies for high-value product synthesis. Through machine learning-driven insights, this research bridges key knowledge gaps in CO2 electroreduction by refining electrolyte composition and unraveling the complexities of carbon-carbon coupling. A deeper understanding of reaction mechanisms, catalyst design, and interfacial dynamics could revolutionize CO2 utilization strategies. Beyond scientific advancements, this project offers substantial economic benefits for Australia, fostering sustainable industries, generating jobs, reducing reliance on imported chemicals, and leveraging the nation’s abundant renewable energy resources. Environmentally, it supports national efforts to achieve net-zero emissions while protecting and restoring ecosystems. To maximize societal impact, findings will be disseminated through scientific publications and public outreach, accelerating the adoption of breakthrough technologies."}}}