[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121591-en":3,"doc-seo-121591-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121591,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Discovery of materials for solar thermochemical hydrogen combining machine learning, computational chemistry, experiments and system simulations - Abstract","Solar thermochemical hydrogen production (STCH) offers a pathway to convert solar energy into heat and hydrogen without requiring electricity generation, yet commercialization is limited by the lack of durable, high-hydrogen-productivity redox materials and efficient reactor and plant designs. A methodology combining first-principles calculations, computational chemistry, system simulations, experiments, and machine learning identifies redox perovskite oxides by predicting stability and the oxygen-vacancy formation enthalpy (Δho). Random-forest regression and classification models reveal strong B-site compositional effects and lead to Ba0.875Ca0.125Zr0.875Mn0.125O3 (BCZM), reducing at up to 250 °C lower than CeO2, while using ML plus DFT refines Δho predictions and supports experimental interpretation.","npj | computational materials Article  \nPublished in partnership with the Shanghai Institute of Ceramics of the Chinese Academy of Sciences  \n[https://doi.org/10.1038/s41524-025-01726-y](https://doi.org/10.1038/s41524-025-01726-y)  \n\n| Discovery of materials for solar thermochemical hydrogen combining machine learning, computational chemistry, experiments and system simulations\u003Cbr> Check for updates |  |\n| --- | --- |\n| Jonathan Perry 1,2,6, Laura Molina 3,4,6, Alberto de la Calle 3 , Raul Peño 3,4, Timothy W. Jones 2\u003Cbr>,\u003Cbr>M. Verónica Ganduglia-Pirovano 3, Silvia Jiménez-Fernández 5, Scott W. Donne 1, Juan M. Coronado 3 & Alicia Bayon 3  |  |\n| This study integrates first-principles calculations, computational chemistry, system simulations, experiments, and machine learning to identify redox perovskite oxides for solar thermochemical hydrogen production. Using two random forest regressions and one classification model, the approach predicts materials’ stability and the enthalpy of oxygen vacancy formation (Δho), a critical property for selecting materials for thermochemical hydrogen production. B-site composition significantly influences Δho predictions. The methodology led to the discovery of\u003Cbr>Ba0.875Ca0.125Zr0.875 Mn0.125O3 (BCZM), which reduces at temperatures up to 250 °C lower than CeO2 and is expected to outperform other perovskites in water splitting. However, CeO2 remains the benchmark for solar thermochemical hydrogen production. The combined use of machine learning and DFT calculations refined 4ho predictions and provided insights into experimental results. This framework not only enhances database creation for material screening but also establishes a novel approach for perovskite discovery for hydrogen production applications. |  |\n| Green hydrogen, which involves water splitting using renewable energy sources, is gaining attention for its potential to replace liquid fossil fuels in the transport sector. Water splitting is an energy-intensive process that, in practice, requires more energy to drive the chemical reactions than the theoretical minimum: 237 kJ mol−1 or 13. 16 MJ per liter of water1. To enable large-scale hydrogen deployment, it is essential to produce it at an affordable cost. Nowadays, commercial green hydrogen is linked to renewable electricity, and it is still unclear how much ofit can be effectively generated by electrolyzers. Solar thermochemical hydrogen production (STCH) offers an alternative route that can convert solar energy into heat and lately into hydrogen without the need for electricity production. Even though it is a technology with great potential to achieve high solar-to-hydrogen efficiency, there are still uncertainties regarding its commercialization. Among the | main barriers to overcome are the development of suitable metal oxides with long durability and higher hydrogen productivity2–5, more efficient solar reactors6–8 and efficient plant configurations allowing heat recovery9, 10.\u003Cbr>Finding novel redox materials has gathered a great deal of attention since early 1970´s with non-volatile metal oxides being the most studied since 1990s, including iron oxides and ferrites11, 12, manganese oxides13–16, ceria and doped-ceria materials17–20, hercynite21 and perovskites2,22,23. In 2010, ceria was first tested in a solar reactor showing stability in multiple cycles24 and this work has led to the first demonstration of the production of solar kerosene in 202225. In 2013, perovskites were first proposed as redox material26 and more than 50 different compositions have been tested for hydrogen and carbon monoxide production (from CO2) splitting in the last decade27,28. However, only a few perovskites have been deeply studied, |\n\n1Discipline of Chemistry, University of Newcastle, Callaghan, NSW, 2308, Australia. 2CSIRO Energy, Mayfield West, NSW, 2304, Australia. 3Instituto de Catálisis y Petroleoquímica (ICP), CSIC, C/Marie Curie2,28049Madrid, Spain. 4Escuela de Doctorado, UniversidadAutónomade M","cbCaipponoqQBwKO","https://ap.wps.com/l/cbCaipponoqQBwKO","pdf",2775243,1,17,"English","en",105,"# Introduction and motivation\n## Barriers to STCH commercialization\n## Redox materials and prior work on perovskites\n# Integrated methodology\n## First-principles, computational chemistry, simulations, and experiments\n## Machine learning models and target properties\n# Key results\n## Predicting stability and oxygen-vacancy formation enthalpy (Δho)\n## Discovery and performance of BCZM vs CeO2\n# Impact and framework\n## Improving database creation and guiding perovskite discovery for hydrogen applications","[{\"question\":\"What material property is central to selecting redox perovskite oxides for STCH in this study?\",\"answer\":\"The study focuses on stability and the enthalpy of oxygen-vacancy formation (Δho), which is used to guide the selection of materials for thermochemical hydrogen production.\"},{\"question\":\"Which machine learning approaches are used to make the material predictions?\",\"answer\":\"Two random forest regression models and one classification model are employed to predict stability and Δho.\"},{\"question\":\"What is the main outcome of the methodology in terms of discovered materials?\",\"answer\":\"The approach leads to Ba0.875Ca0.125Zr0.875Mn0.125O3 (BCZM), which reduces at temperatures up to 250 °C lower than CeO2, while CeO2 remains the benchmark for solar thermochemical hydrogen production.\"}]","Discovery of materials for solar thermochemical hydrogen combining machine learning, computational chemistry, experiments and system simulations - Abstract | PDF",1785736388,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"discovery-of-materials-for-solar-thermochemical-hydrogen-combining-machine-learning-computational-chemistry-experiments-and-system-simulations-abstract","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/discovery-of-materials-for-solar-thermochemical-hydrogen-combining-machine-learning-computational-chemistry-experiments-and-system-simulations-abstract/121591/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What material property is central to selecting redox perovskite oxides for STCH in this study?","Question",{"text":75,"@type":76},"The study focuses on stability and the enthalpy of oxygen-vacancy formation (Δho), which is used to guide the selection of materials for thermochemical hydrogen production.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning approaches are used to make the material predictions?",{"text":80,"@type":76},"Two random forest regression models and one classification model are employed to predict stability and Δho.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main outcome of the methodology in terms of discovered materials?",{"text":84,"@type":76},"The approach leads to Ba0.875Ca0.125Zr0.875Mn0.125O3 (BCZM), which reduces at temperatures up to 250 °C lower than CeO2, while CeO2 remains the benchmark for solar thermochemical hydrogen production.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]