# Materials science and engineering Published articles for Materials 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. ## MIT spinout turns plastic waste into resilient building materials DevFeed: [MIT spinout turns plastic waste into resilient building materials]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-09-14T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [systems]() Tags: [3-d-printing](), [ai](), [aj-perez](), [alumni-ae](), [artificial-intelligence](), [atlas-composites](), [cleaner-industry](), [engineering](), [homes](), [housing](), [manufacturing](), [materials-science-and-engineering](), [matt-pouliot](), [mechanical-engineering](), [platform](), [pollution](), [production](), [recycled-plastic-building-materials](), [robotics](), [school-of-engineering](), [startups](), [sustainability](), [u-s-army](), [water]() ### AI overview MIT spinout Atlas Building Composites is commercializing an AI-powered robotic manufacturing platform that recycles single-use and low-grade plastic into durable building components. Its waterless process has been used for structures including a bridge supplied to the U.S. Army Corps of Engineers. ### Source excerpt Atlas Building Composites is commercializing MIT research to turn plastic waste into parts for buildings and other infrastructure. ## 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. ## 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.