# School of Science Published articles for School of Science. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## PottsMPNN uses machine learning to improve computational protein design DevFeed: [PottsMPNN uses machine learning to improve computational protein design]() Original publisher: [Read original article]() Author: Lillian Eden | Department of Biology Published: 2026-08-27T19:20:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Machine learning](), [Artificial Intelligence]() Tags: [ai](), [ai-in-biology](), [amino-acids](), [amy-keating](), [artificial-intelligence](), [biological-engineering](), [biology](), [computational-protein-design](), [computer-science-and-technology](), [foster-birnbaum](), [machine-learning](), [mit-department-of-biology](), [native-sequence-recovery](), [novel-protein-design](), [pottsmpnn](), [proteins](), [research](), [school-of-engineering](), [school-of-science]() ### AI overview Researchers developed PottsMPNN, a machine-learning framework for computational protein design that incorporates physical principles governing protein structure and stability. The framework evaluates generated sequences by their likelihood of folding into desired structures and by its ability to predict mutation effects, rather than by how closely they match naturally occurring sequences. ### Source excerpt A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature. ## 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. ## Alexander Rakhlin named director of the MIT Statistics and Data Science Center DevFeed: [Alexander Rakhlin named director of the MIT Statistics and Data Science Center]() Original publisher: [Read original article]() Author: Institute for Data, Systems, and Society Published: 2026-08-03T19:50:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Statistics](), [Data Science](), [Machine learning]() Tags: [ai-in-statistics](), [alexander-sasha-rakhlin](), [alumni-ae](), [ankur-moitra](), [artificial-intelligence](), [brain-and-cognitive-sciences](), [computer-science-and-technology](), [data-science](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [fotini-christia](), [idss](), [laboratory-for-information-and-decision-systems-lids](), [leadership](), [lids](), [machine-learning](), [mit-bcs](), [mit-faculty-appointments](), [mit-idss](), [mit-leadership](), [mit-schwarzman-college-of-computing](), [mit-statistics-and-data-science-center-sdsc](), [philippe-rigollet](), [richard-dick-larson](), [school-of-engineering](), [school-of-science](), [statistics]() ### AI overview Alexander Rakhlin has been named the next director of MIT's Statistics and Data Science Center, succeeding Ankur Moitra. Rakhlin has been affiliated with the center since 2016 and has led its interdisciplinary doctoral program, overseeing more than 75 successful PhD defenses. ### Source excerpt An expert in machine learning, statistics, and computation, Rakhlin succeeds Professor Ankur Moitra. ## 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. ## Jesse Thaler named director of the Laboratory for Nuclear Science DevFeed: [Jesse Thaler named director of the Laboratory for Nuclear Science]() Original publisher: [Read original article]() Author: Julia C. Keller | School of Science Published: 2026-07-07T14:45:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [AI, ML & Data Engineering]() Tags: [ai](), [ai-in-physics](), [artificial-intelligence](), [bolek-wyslouch](), [data](), [department-of-energy-doe](), [faculty](), [idss](), [jesse-thaler](), [laboratory-for-nuclear-science](), [leadership](), [mit-center-for-theoretical-physics-ctp](), [mit-ctp-li](), [mit-faculty-appointments](), [mit-iaifi](), [mit-idss](), [mit-leadership](), [mit-lns](), [national-science-foundation-nsf](), [nergis-mavalvala](), [particle-physics](), [physics](), [quantum](), [research](), [school-of-science](), [science](), [tracey-slatyer]() ### AI overview Professor Jesse Thaler has been named director of MIT's Laboratory for Nuclear Science, effective Aug. 1. He succeeds Bolek Wyslouch and will lead the laboratory while continuing research combining particle physics and machine learning. ### Source excerpt The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.