# DMSE Published articles for DMSE. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AI helps design new materials that work in the real world DevFeed: [AI helps design new materials that work in the real world]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-08-26T09:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Framework](), [Simulation and Design](), [Crystal]() Tags: [ai](), [artificial-intelligence](), [bowen-yu](), [chemical-engineering](), [chemistry](), [computer-chips](), [computer-science-and-technology](), [crystal](), [crysvcd](), [data-centers](), [department-of-energy-doe](), [diffusion-models](), [dmse](), [hao-tang](), [heather-kulik](), [ju-li](), [machine-learning](), [materials-design](), [materials-discovery](), [materials-science-and-engineering](), [mingda-li](), [mouyang-cheng](), [national-science-foundation-nsf](), [nuclear-science-and-engineering](), [paper](), [physics](), [research](), [school-of-engineering](), [school-of-science](), [semiconductors](), [weiliang-luo](), [weiwei-xie](), [yongqiang-cheng]() ### AI overview MIT researchers developed CrysVCD, a framework that applies chemistry-based valence constraints before material generation to improve the stability of generated designs. In tests, it achieved high lattice-dynamics stability in nearly 70 percent of computational material generations and supported targeting properties such as high thermal conductivity and high dielectric constant. ### Source excerpt The "CrysVCD" tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs. ## Paving the way for greener ammonia production DevFeed: [Paving the way for greener ammonia production]() Original publisher: [Read original article]() Author: David L. Chandler | Department of Materials Science and Engineering Published: 2026-08-20T18:45:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Materials science and engineering](), [acid]() Tags: [agriculture](), [ai-for-materials-science](), [artificial-intelligence](), [bilge-yildiz](), [catalysts](), [chemistry](), [cleaner-fertilizer](), [cleaner-industry](), [computational-materials-science](), [computer-modeling](), [dmse](), [electrochemical-ammonia-production](), [emissions](), [energy](), [fertilizer-production](), [food](), [fossil-fuel](), [green-ammonia](), [greener-fertilizer](), [haber-bosch-process](), [industry](), [machine-learning](), [materials-science-and-engineering](), [mit-dmse](), [nitrogen-dissociation](), [nuclear-science-and-engineering](), [pollution](), [research](), [school-of-engineering](), [school-of-science](), [science](), [sustainability](), [transition-metal-nitrides]() ### AI overview MIT researchers developed an approach to predict promising catalyst materials for electrochemical ammonia production. The method could speed the search for alloys that may help make this lower-emissions process more competitive with the fossil-fuel-dependent Haber-Bosch process. ### Source excerpt New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products. ## Q&A: Rethinking how innovation happens DevFeed: [Q&A: Rethinking how innovation happens]() Original publisher: [Read original article]() Author: Jason Sparapani | Department of Materials Science and Engineering Published: 2026-08-17T19:50:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Materials science and engineering](), [implementation]() Tags: [altruistic-science](), [artificial-intelligence](), [book](), [books-and-authors](), [business-and-management](), [dmse](), [economics](), [engineering](), [eugene-fitzgerald](), [faculty](), [fundamental-innovation](), [future](), [innovation](), [interview](), [invention](), [invisible-engine](), [misconceptions](), [mit-and-masdar-institute-cooperative-program](), [mit-books-and-authors](), [mit-dmse](), [mit-faculty-books](), [mit-faculty-interview](), [moore-s-law](), [q-a](), [research](), [research-commercialization](), [school-of-engineering](), [semiconductor-technologies](), [semiconductors](), [singapore-mit-alliance-for-research-and-technology-smart](), [strained-silicon](), [strategic-research](), [value-creation]() ### AI overview MIT professor Eugene Fitzgerald discusses his book The Invisible Engine: Why Innovation Evades Control, drawing on research programs and his experience co-inventing strained silicon. He describes innovation as an intersection of science, economics, market applications, technology, implementation, and society. ### Source excerpt In his latest book, Professor Eugene Fitzgerald examines the forces that turn breakthroughs into value -- and why innovation resists simple formulas. ## A better way to model the behavior of metal alloys DevFeed: [A better way to model the behavior of metal alloys]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-06-19T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Simulation](), [Machine learning](), [datasets]() Tags: [artificial-intelligence](), [chemical-engineering](), [chemistry](), [computer-modeling](), [computer-science-and-technology](), [daniel-xiao](), [datasets](), [dmse](), [killian-sheriff](), [lewis-r-owen](), [machine-learning](), [materials-science-and-engineering](), [mit-materials-science-and-engineering](), [models](), [phase-diagrams](), [predicting-new-materials](), [predictions](), [research](), [rodrigo-freitas](), [school-of-engineering](), [simulation](), [yifan-cao]() ### AI overview MIT researchers developed a machine-learning approach that uses diverse training datasets to model chemically complex metal alloys and predict their material properties more accurately across different conditions. ### Source excerpt MIT researchers' approach captures subtle atomic patterns, improving predictions of material properties.