# Nuclear science and engineering Published articles for Nuclear science and engineering. 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. ## MIT projects selected for funding under US Department of Energy's Genesis Mission DevFeed: [MIT projects selected for funding under US Department of Energy's Genesis Mission]() Original publisher: [Read original article]() Author: Office of the Vice President for Research Published: 2026-07-23T12:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Supercomputing](), [systems]() Tags: [administration](), [ai](), [artificial-intelligence](), [center-for-computational-science-and-engineering](), [chemical-engineering](), [department-of-energy](), [department-of-energy-doe](), [doe](), [eaps](), [engineering](), [faculty](), [funding](), [genesis](), [genesis-mission](), [industry](), [initiative](), [laboratory-for-nuclear-science](), [manufacturing](), [mathematics](), [mechanical-engineering](), [mission](), [national-security](), [nuclear-science-and-engineering](), [physics](), [plasma-science-and-fusion-center](), [projects](), [quantum](), [quantum-computing](), [quantum-systems](), [research](), [research-laboratory-of-electronics](), [school-of-engineering](), [school-of-science](), [science](), [supercomputing](), [us]() ### AI overview MIT researchers will contribute to 15 collaborative projects selected for funding under Phase I of the U.S. Department of Energy's Genesis Mission. The projects apply AI, supercomputing, quantum systems, and scientific instruments to research areas including energy, materials, fusion, and national security. Funding remains pending completion of award negotiations. ### Source excerpt Initial research projects advance national priorities across natural resources, manufacturing, nuclear physics, and more. ## Startup's nuclear-inspired cooling system could make data centers more sustainable DevFeed: [Startup's nuclear-inspired cooling system could make data centers more sustainable]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-06-10T04:00:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [data centers](), [Artificial Intelligence](), [cloud-infrastructure](), [Computer science]() Tags: [ai](), [ai-data-centers](), [artificial-intelligence](), [computer-science-and-technology](), [cooling](), [data-center-cooling](), [data-center-sustainability](), [data-centers](), [efficiency](), [energy](), [energy-efficiency](), [energy-storage](), [ferveret](), [matteo-bucci](), [nuclear-science-and-engineering](), [power](), [reza-azizian](), [school-of-engineering](), [startups](), [sustainability](), [sustainable-computing](), [technology-and-society](), [water]() ### AI overview Ferveret, a startup founded by two MIT researchers, is adapting nuclear-reactor heat-transfer methods to cool AI data-center servers with a specialized liquid. Its Adaptive Phase Cooling system uses small, frequently detaching bubbles to improve heat transfer without water and with less electricity. ### Source excerpt Founded by two researchers from MIT, Ferveret reduces the amount of energy and water required to cool the chips that power AI.