{"id":1120032,"date":"2025-01-16T02:14:07","date_gmt":"2025-01-16T10:14:07","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-story&#038;p=1120032"},"modified":"2025-01-17T06:33:51","modified_gmt":"2025-01-17T14:33:51","slug":"ai-meets-materials-discovery","status":"publish","type":"msr-story","link":"https:\/\/www.microsoft.com\/en-us\/research\/story\/ai-meets-materials-discovery\/","title":{"rendered":"AI meets materials discovery"},"content":{"rendered":"\n<div class=\"wp-block-cover is-light is-style-default\" style=\"min-height:360px;aspect-ratio:unset;\"><span aria-hidden=\"true\" class=\"wp-block-cover__background has-black-background-color has-background-dim\"><\/span><video class=\"wp-block-cover__video-background intrinsic-ignore\" autoplay muted loop playsinline src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/2025-grid_loopable_HD_v5.mp4\" data-object-fit=\"cover\"><\/video><div class=\"wp-block-cover__inner-container is-layout-constrained wp-container-core-cover-is-layout-1 wp-block-cover-is-layout-constrained\">\n<div class=\"wp-block-group is-content-justification-left is-layout-constrained wp-container-core-group-is-layout-1 wp-block-group-is-layout-constrained\">\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer d-none d-sm-block\"><\/div>\n\n\n\n<h1 class=\"wp-block-heading is-style-display has-white-color has-text-color has-link-color wp-elements-82be00ca03c11c784f31a42c36ad084d\" id=\"ai-meets-materials-discovery-the-vision-behind-mattergen-and-mattersim\">AI meets materials discovery: The vision behind MatterGen and MatterSim<\/h1>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer d-none d-sm-block\"><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<article class=\"wp-block-group alignfull mt-0 is-layout-constrained wp-block-group-is-layout-constrained\">\n<div style=\"padding-bottom:0; padding-top:0\" class=\"wp-block-msr-immersive-section alignfull row has-background-gradient has-background-gradient-spectrum-3 wp-block-msr-immersive-section\">\n\t\n\t<div class=\"container\">\n\t\t<div class=\"wp-block-msr-immersive-section__wrapper\">\n\t\t\t<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n\n\n\n<div class=\"wp-block-columns is-style-dark-mode p-4 z-20 container theme-dark is-layout-flex wp-container-core-columns-is-layout-1 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:22%\"><\/div>\n\n\n\n<div class=\"wp-block-column headings-large is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:56%\">\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer is-style-default d-none d-md-block\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading is-style-default h3\" id=\"mattergen-and-mattersim-are-cutting-edge-tools-reshaping-how-we-design-and-innovate-advanced-materials-explore-the-journey-from-concept-to-creation-behind-these-ai-powered-technologies\">MatterGen and MatterSim are cutting edge tools reshaping how we design and innovate advanced materials. Explore the journey from concept to creation behind these AI-powered technologies.<\/h2>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"margin-callout\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 annotations__list--right\">\n\t\t<li class=\"annotations__list-item\">\n\t\t\t\t\t\t<span class=\"annotations__type d-block text-uppercase font-weight-semibold text-neutral-300 small\">Publication<\/span>\n\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mattergen-a-generative-model-for-inorganic-materials-design\/\" target=\"_blank\" class=\"annotations__link font-weight-semibold text-decoration-none\" data-bi-type=\"annotated-link\" aria-label=\"MatterGen: a generative model for inorganic materials design\" data-bi-aN=\"margin-callout\" data-bi-cN=\"MatterGen: a generative model for inorganic materials design\">\n\t\t\t\tMatterGen: a generative model for inorganic materials design&nbsp;<span class=\"glyph-append glyph-append-chevron-right glyph-append-xsmall\"><\/span>\n\t\t\t<\/a>\n\t\t\t\t\t<\/li>\n\t<\/ul>\n<\/div>\n\n\n\n<p>In 2018, Tian Xie was immersed in his doctoral studies at MIT, exploring the complex world of materials science and engineering. Midway through his PhD, a question began to take root\u2014both in his mind and in conversations with peers: <em>Can you build a model that takes constraints and criteria as input and generates a viable material as output?<\/em><\/p>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"margin-callout\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 annotations__list--right\">\n\t\t<li class=\"annotations__list-item\">\n\t\t\t\t\t\t<span class=\"annotations__type d-block text-uppercase font-weight-semibold text-neutral-300 small\">Publication<\/span>\n\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mattersim-a-deep-learning-atomistic-model-across-elements-temperatures-and-pressures\/\" target=\"_blank\" class=\"annotations__link font-weight-semibold text-decoration-none\" data-bi-type=\"annotated-link\" aria-label=\"MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures\" data-bi-aN=\"margin-callout\" data-bi-cN=\"MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures\">\n\t\t\t\tMatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures&nbsp;<span class=\"glyph-append glyph-append-chevron-right glyph-append-xsmall\"><\/span>\n\t\t\t<\/a>\n\t\t\t\t\t<\/li>\n\t<\/ul>\n<\/div>\n\n\n\n<p>Xie\u2019s answer was a confident yes. But he couldn\u2019t predict how swiftly this vision would materialize\u2014or just how transformative its applications would prove to be.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-video aligncenter\"><video autoplay controls loop preload=\"auto\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/2025-0-bandgap_v5.mp4\" playsinline><\/video><figcaption class=\"wp-element-caption\">Diffusion generation process for a stable crystalline material having a target band gap value (3 eV) using the MatterGen model.<\/figcaption><\/figure>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>After a two-year postdoctoral stint at MIT\u2019s Computer Science and Artificial Intelligence Laboratory (CSAIL), Xie joined Microsoft Research\u2019s newly minted <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/lab\/microsoft-research-ai-for-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI for Science initiative<\/a> in 2022. What began as a big bet under renowned scientist Chris Bishop\u2014a bold experiment to harness AI for tackling humanity\u2019s most pressing challenges, from sustainability to drug discovery\u2014has since expanded into a global endeavor. In just two-and-a-half years, the team has grown into a far-reaching group spanning five time zones, united by a mission to redefine the boundaries of innovation.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"mattergen-and-mattersim\">MatterGen and MatterSim<\/h2>\n\n\n\n<p>Two of the transformative tools that play a central role in Microsoft\u2019s work on AI for science are MatterGen and MatterSim. In the world of materials discovery, each plays a distinct yet complementary role in reshaping how researchers design and validate new materials.&nbsp;<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"369\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/MatterGenSim_1600-1024x369.png\" alt=\"MatterGen proposes new materials while MatterSim simulates new materials\" class=\"wp-image-1120314\" style=\"width:618px;height:auto\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/MatterGenSim_1600-1024x369.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/MatterGenSim_1600-300x108.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/MatterGenSim_1600-768x277.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/MatterGenSim_1600-1536x554.png 1536w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/MatterGenSim_1600-240x87.png 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/MatterGenSim_1600.png 1600w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>MatterGen is the idea generator, the visionary in the partnership. It crafts detailed concepts of molecular structures by using advanced algorithms to predict potential materials with unique properties, grounded in scientific principles and computational precision.&nbsp;&#8220;MatterGen generates thousands of candidates with user-defined constraints to propose new materials that meet specific needs,\u201d Xie said. &#8220;This represents a paradigm shift in how materials are designed.\u201d&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-video aligncenter\"><video autoplay controls loop preload=\"auto\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/2025-1-bulk_v5.mp4\" playsinline><\/video><figcaption class=\"wp-element-caption\">Diffusion generation process for a stable crystalline material possessing high bulk modulus value using the MatterGen model.<\/figcaption><\/figure>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>This generative approach is a radical departure from traditional methods of screening existing materials. It replaces the meticulous observation and precise assembly required when fitting puzzle pieces from a box with a tool that designs entirely new puzzles customized to defined parameters. Once MatterGen has proposed its possibilities, MatterSim steps in as the gatekeeper, the realist to MatterGen&#8217;s visionary. MatterSim applies rigorous computational analysis to predict which of those imagined materials are stable and viable, like a sieve filtering out what\u2019s physically possible from what\u2019s merely theoretical. Together, these tools accelerate a process that once relied on years of trial and error in the lab. This tandem functionality enables researchers not only to explore the vast, uncharted territories of material possibilities but also allows them to do so with a newfound efficiency and confidence.&nbsp;<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-style-spectrum is-layout-flow wp-block-quote-is-layout-flow\">\n<p>\u201cFrom an industrial perspective, the potential here is enormous. Human civilization has always depended on material innovations. If we can use generative AI to make materials design more efficient, it could accelerate progress in industries like energy, healthcare, and beyond.\u201d<\/p>\n<cite>\u2014<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/tianxie\/\" target=\"_blank\" rel=\"noreferrer noopener\">Tian Xie<\/a>, Principal Research Manager, Microsoft Research AI for Science<\/cite><\/blockquote>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div style=\"padding-bottom:0; padding-top:0\" class=\"wp-block-msr-immersive-section alignfull row wp-block-msr-immersive-section\">\n\t\n\t<div class=\"container\">\n\t\t<div class=\"wp-block-msr-immersive-section__wrapper col-lg-11 col-xl-9 px-0 m-auto\">\n\t\t\t<div class=\"wp-block-media-text has-video  has-vertical-margin-none  has-vertical-padding-none  has-media-on-the-right is-stacked-on-mobile\" style=\"grid-template-columns:auto 60%\" data-bi-an=\"media-text\"><div class=\"wp-block-media-text__content\" data-bi-an=\"media-text\">\n<h3 class=\"wp-block-heading has-text-align-right\" id=\"microsoft-researchers-demo-the-shiksha-copilot\">MatterGen: A Generative Model for Materials Design<\/h3>\n<\/div><figure class=\"wp-block-media-text__media video-wrapper\"><iframe class=\"media-text__video\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/yWXPV3bsC2c?enablejsapi=1&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen><\/iframe><\/figure><\/div>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"building-bridges-between-research-and-real-world-innovation\">Building bridges between research and real-world innovation<\/h2>\n\n\n\n<p>Materials are, in many ways, the unsung heroes of human progress. From the steel girders forming the backbone of modern cities to silicon chips powering smartphones, advances in materials science have propelled technological innovation for centuries. Every leap in civilization\u2014from the Bronze Age to the Space Age\u2014has been defined by the human ability to discover, manipulate, and deploy materials. MRI machines, for example, rely on superconductors, which were only made possible through advances in materials science.&nbsp;<\/p>\n\n\n\n<p>Yet the process of developing new materials has traditionally been a slog. Despite their pivotal role, identifying and refining new materials has often required years of painstaking attempts, with researchers relying heavily on intuition, experience, and luck. This approach can cost millions, if not billions, of dollars, with no guarantee of success.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-video aligncenter\"><video autoplay controls loop preload=\"auto\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/2025-3-joint_v5.mp4\" playsinline><\/video><figcaption class=\"wp-element-caption\">Diffusion generation process for a stable crystalline material possessing high magnetic density and containing no critical elements using the MatterGen model.<\/figcaption><\/figure>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>In industries like energy and healthcare, where the right material can revolutionize markets, the stakes are higher than ever. Developing new efficient battery material could unlock the potential for more sustainable energy storage, while advances in superconductors could lead to groundbreaking improvements in medical imaging or quantum computing. But these outcomes hinge on solving an enduring challenge: identifying and testing viable materials at a speed and scale that match the urgency of global demands.&nbsp;By leveraging artificial intelligence, the AI for Science team has built tools that promise to reshape materials discovery entirely.&nbsp;<\/p>\n\n\n\n<p>&#8220;One of the fundamental ideas driving our approach is that the more computational power you invest in these tools, the more insights and discoveries you can generate,\u201d said AI for Science Principal Researcher Ziheng Lu. \u201cThis is a crucial mindset shift for the field.\u201d&nbsp;<\/p>\n\n\n\n<p>Lu began his work at Microsoft Research Asia working on sustainability initiatives. Like Xie, Lu did not realize how quickly AI for Science would evolve.&nbsp;<\/p>\n\n\n\n<p>\u201cWe thought it might be possible for machine-learning-based methods to replace 80\u201390% of quantum-mechanical calculations within one or two years,\u201d he says. \u201cBut progress happened so quickly that the tools began outperforming our initial expectations.\u201d&nbsp;<\/p>\n\n\n\n<p>Lu\u2019s work focuses on enabling <em>in silico<\/em> characterization of material properties, integrating factors like temperature and pressure to make predictions more realistic. This ambition has led to breakthroughs that could fundamentally alter subfields of materials science. One notable example is their work on the limits of heat transfer in matter, a question that has eluded scientists for 125 years.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading is-style-default\" id=\"a-glimpse-behind-the-curtain\">A glimpse behind the curtain<\/h2>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>For the researchers at AI for Science, the typical workday unfolds as a blend of intellectual exploration and strategic planning. Some moments are spent huddled over digital whiteboards or sketching ideas on scrap paper, brainstorming breakthroughs. Other times, the focus shifts to reviewing fresh results and debating whether the current approach needs recalibrating to meet the broader goals of the initiative.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-video aligncenter\"><video autoplay controls loop preload=\"auto\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/2025-2-chemsys_v5.mp4\" playsinline><\/video><figcaption class=\"wp-element-caption\">Diffusion generation process for a stable crystalline material containing a given set of elements using the MatterGen model.<\/figcaption><\/figure>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>\u201cWe hold a team meeting once a week for MatterGen and MatterSim,\u201d said Daniel Z\u00fcgner, a senior researcher with AI for Science. \u201cBelow that level, smaller groups from both teams collaborate on applying the models in new settings and transferring insights between them.\u201d&nbsp;<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-style-spectrum is-layout-flow wp-block-quote-is-layout-flow\">\n<p>\u201cAlthough MatterGen and MatterSim are often described as separate, they work together in an extraordinarily interconnected way, having complementary properties that create a whole which is far more than the sum of its parts.\u201d<\/p>\n<cite>\u2014Daniel Z\u00fcgner, Senior Researcher, Microsoft Research AI for Science<\/cite><\/blockquote>\n\n\n\n<p>Z\u00fcgner\u2019s journey to AI for Science began in 2022, fresh from completing his PhD at the Technical University of Munich, where he specialized in machine learning and graph neural networks. Z\u00fcgner has contributed to the development of MatterGen by helping to build its machine-learning architecture, optimizing it for property-guided materials design, and ensuring its scalability in collaboration with materials scientists. Reflecting on his early days at the organization, he recalled an emphatic message from Chris Bishop: \u201cThe first couple of years would be all about building a world-class organization for science. Only then could we hope to achieve the kind of transformative, large-scale impact we envisioned.\u201d&nbsp;<\/p>\n\n\n\n<p>Central to this vision is the connection of groundbreaking scientific research with real-world applications. A key collaborator is Microsoft\u2019s Azure Quantum team, with the Azure Quantum Elements platform, using high performance computing, artificial intelligence, and quantum computing to solve complex problems in chemistry and materials science. This partnership allows AI for Science researchers to stay closely aligned with industry needs, ensuring their work tackles challenges that matter most to our customers. It also means that the latest discoveries don\u2019t stay stuck in the lab. Instead, they\u2019re quickly integrated into the company\u2019s broader offerings, helping businesses across industries\u2014from healthcare to energy\u2014apply cutting-edge science to accelerate scientific discovery and solve big problems faster than ever before.&nbsp;<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div style=\"padding-bottom:0; padding-top:0\" class=\"wp-block-msr-immersive-section alignfull row wp-block-msr-immersive-section\">\n\t\n\t<div class=\"container\">\n\t\t<div class=\"wp-block-msr-immersive-section__wrapper col-lg-11 col-xl-9 px-0 m-auto\">\n\t\t\t<div class=\"wp-block-media-text has-vertical-margin-none  has-vertical-padding-none  has-media-on-the-right is-stacked-on-mobile\" style=\"grid-template-columns:auto 60%\" data-bi-an=\"media-text\"><div class=\"wp-block-media-text__content\" data-bi-an=\"media-text\">\n<h3 class=\"wp-block-heading has-text-align-right\" id=\"microsoft-researchers-demo-the-shiksha-copilot\">Listen or read along as&nbsp;<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/podcast\/ideas-ai-for-materials-discovery-with-tian-xie-and-ziheng-lu\/\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Research Podcast<\/a>&nbsp;guests Tian Xie and Ziheng Lu discuss their groundbreaking AI tools for materials discovery.<\/h3>\n<\/div><figure class=\"wp-block-media-text__media\"><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/podcast\/ideas-ai-for-materials-discovery-with-tian-xie-and-ziheng-lu\/\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/Tian-Ziheng_Abstracts_Hero_Feature_No_Text_1400x788-1024x576.jpg\" alt=\"Ideas podcast | illustration of Tian Xie and Ziheng Lu\" class=\"wp-image-1120947 size-full\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/Tian-Ziheng_Abstracts_Hero_Feature_No_Text_1400x788-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/Tian-Ziheng_Abstracts_Hero_Feature_No_Text_1400x788-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/Tian-Ziheng_Abstracts_Hero_Feature_No_Text_1400x788-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/Tian-Ziheng_Abstracts_Hero_Feature_No_Text_1400x788-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/Tian-Ziheng_Abstracts_Hero_Feature_No_Text_1400x788-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/Tian-Ziheng_Abstracts_Hero_Feature_No_Text_1400x788-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/Tian-Ziheng_Abstracts_Hero_Feature_No_Text_1400x788-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/Tian-Ziheng_Abstracts_Hero_Feature_No_Text_1400x788-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/Tian-Ziheng_Abstracts_Hero_Feature_No_Text_1400x788-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2025\/01\/Tian-Ziheng_Abstracts_Hero_Feature_No_Text_1400x788.jpg 1400w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure><\/div>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"a-transformative-future\">A transformative future<\/h2>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"margin-callout\">\n\t<ul class=\"annotations__list card depth-16 bg-body p-4 annotations__list--right\">\n\t\t<li class=\"annotations__list-item\">\n\t\t\t\t\t\t<span class=\"annotations__type d-block text-uppercase font-weight-semibold text-neutral-300 small\">Nature PUBLICATION<\/span>\n\t\t\t<a href=\"https:\/\/www.nature.com\/articles\/s41586-025-08628-5\" target=\"_blank\" class=\"annotations__link font-weight-semibold text-decoration-none\" data-bi-type=\"annotated-link\" aria-label=\"A generative model for inorganic materials design\" data-bi-aN=\"margin-callout\" data-bi-cN=\"A generative model for inorganic materials design\">\n\t\t\t\tA generative model for inorganic materials design&nbsp;<span class=\"glyph-append glyph-append-share glyph-append-xsmall\"><\/span>\n\t\t\t<\/a>\n\t\t\t\t\t<\/li>\n\t<\/ul>\n<\/div>\n\n\n\n<p>Recent milestones reached by MatterGen and MatterSim researchers are a testament to their potential to redefine how complex problems are approached across disciplines. The open release of MatterSim has gained remarkable traction with tens of thousands of downloads, signaling broad interest in the tools the initiative is creating. A <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.nature.com\/articles\/s41586-025-08628-5\" target=\"_blank\" rel=\"noreferrer noopener\">recent publication in\u202f<em>Nature<\/em><span class=\"sr-only\"> (opens in new tab)<\/span><\/a> showcases in detail MatterGen\u2019s innovative approach to materials discovery.&nbsp;&nbsp;<\/p>\n\n\n\n<p>The impact of AI for Science extends beyond materials science to climate prediction and biomedicine. The atmospheric foundation model, Aurora, has already drawn significant interest in the climate research community. Another <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.nature.com\/articles\/s41586-024-08127-z\" target=\"_blank\" rel=\"noreferrer noopener\">recent paper in <em>Nature<\/em> highlights AI<sub><sup>2<\/sup><\/sub>BMD\u2019s advances<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> in biomolecular dynamics. These breakthroughs demonstrate how artificial intelligence can accelerate scientific understanding in fields that have traditionally required time-consuming experimentation.&nbsp;<\/p>\n\n\n\n<p>Yet AI for Science is not about chasing flashy milestones or producing publications for the sake of metrics.&nbsp;<\/p>\n\n\n\n<p>\u201cOur focus is on driving science in a meaningful way,\u201d Z\u00fcgner said. \u201cThe team isn\u2019t preoccupied with publishing papers for the sake of it. We\u2019re deeply committed to research that can have a positive, real-world impact, and this is just the beginning.\u201d<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-1 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-pill\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/msrchat.azurewebsites.net\/?askmsr=What%20is%20MatterGen,%20and%20how%20did%20Tian%20Xie%20describe%20its%20role%20in%20materials%20design\" target=\"_blank\" rel=\"noreferrer noopener\">What is MatterGen? Microsoft Research Copilot Experience<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:22%\"><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-group theme-dark is-style-default container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-2 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:22%\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:56%\">\n<h2 class=\"wp-block-heading is-style-default h1\" id=\"explore-more\">Explore more<\/h2>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:22%\"><\/div>\n<\/div>\n\n\n\n<div style=\"padding-bottom:0; padding-top:0\" class=\"wp-block-msr-immersive-section alignfull row wp-block-msr-immersive-section\">\n\t\n\t<div class=\"container\">\n\t\t<div class=\"wp-block-msr-immersive-section__wrapper\">\n\t\t\t<div class=\"wp-block-group is-style-default alignwide is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns spectrum-border spectrum-border--blue-green spectrum-border--w-50 spectrum-border--position-right py-5 wp-block-columns--stack-tablet px-3 px-md-0 is-layout-flex wp-container-core-columns-is-layout-4 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:58.31%\">\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Unlocking Real world solutions with AI \u2013 Chris Bishop\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/02FfvVTMvqQ?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<div class=\"wp-block-columns mt-5 pl-md-5 wp-block-columns--stack-on-tablet is-layout-flex wp-container-core-columns-is-layout-3 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"heading-wrapper\">\n<h2 class=\"wp-block-heading is-style-spectrum-fill\" id=\"mattergen-property-guided-materials-design\">MatterGen: Property-guided materials design<\/h2>\n<\/div>\n\n\n<p class=\"mb-4\">The central problem in materials science is to discover materials with desired properties. MatterGen enables broad property-guided materials design.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/mattergen-property-guided-materials-design\/\" target=\"_blank\" rel=\"noreferrer noopener\">Read the blog<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"padding-left:8.3%;flex-basis:41.69%\">\n<h2 class=\"wp-block-heading w-sm-75 mt-sm-5 mt-md-0\" id=\"mattergen-a-new-paradigm-of-materials-design-with-generative-ai\">MatterGen: A new paradigm of materials design with generative AI&nbsp;<\/h2>\n\n\n\n<p class=\"w-sm-75 mt-sm-5 mt-md-0\">MatterGen is a generative AI tool that tackles materials discovery from a different angle. Instead of screening the candidates, it directly generates novel materials given prompts of the design requirements for an application.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/mattergen-a-new-paradigm-of-materials-design-with-generative-ai\/\" target=\"_blank\" rel=\"noreferrer noopener\">Read the blog<\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading w-sm-75\" id=\"mattersim-a-deep-learning-model-for-materials-under-real-world-conditions\">MatterSim: A deep-learning model for materials under real-world conditions<\/h2>\n\n\n\n<p class=\"w-sm-75\">Property prediction for materials under realistic conditions has been a long-standing challenge within the digital transformation of materials design. MatterSim investigates atomic interactions from the very fundamental principles of quantum mechanics.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/mattersim-a-deep-learning-model-for-materials-under-real-world-conditions\/\" target=\"_blank\" rel=\"noreferrer noopener\">Read the blog<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n\n\n\n<div style=\"padding-bottom:64px; padding-top:64px\" class=\"wp-block-msr-immersive-section alignfull row wp-block-msr-immersive-section\">\n\t\n\t<div class=\"container\">\n\t\t<div class=\"wp-block-msr-immersive-section__wrapper col-lg-11 col-xl-9 px-0 m-auto\">\n\t\t\t<p><em><strong>Story contributors<\/strong>: Neeltje Berger, Kristina Dodge, David Celis Garcia, Alyssa Hughes, Lindsay Kalter, Ziheng Lu, Amanda Melfi, Brenda Potts, Kenji Takeda, Amber Tingle, Tian Xie<\/em>, <em>Daniel Z\u00fcgner.<\/em><\/p>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-6 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\">\n<h3 class=\"wp-block-heading is-style-default\" id=\"lightning-talks\">Other resources<\/h3>\n\n\n\n<p><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-5 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-top is-layout-flow wp-block-column-is-layout-flow\">\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/podcast\/\">Microsoft Research Podcast<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\">Microsoft Research Blog<\/a><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-vertically-aligned-top is-layout-flow wp-block-column-is-layout-flow\">\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/lab\/microsoft-research-ai-for-science\/\">Microsoft Research AI for Science<\/a><\/p>\n\n\n\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/register.researchforum.microsoft.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft Research Forum series registration<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div style=\"height:60px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<\/article>\n","protected":false},"excerpt":{"rendered":"<p>Researchers pull back the curtain on MatterGen and MatterSim, the cutting-edge tools reshaping how we design and innovate advanced materials. Explore the journey from concept to creation driving these AI-powered technologies.<\/p>\n","protected":false},"featured_media":1120326,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556,13553],"msr-locale":[268875],"msr-post-option":[],"class_list":["post-1120032","msr-story","type-msr-story","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-medical-health-genomics","msr-locale-en_us"],"related-researchers":[{"type":"user_nicename","display_name":"Tian Xie","user_id":41413,"people_section":"Section name 0","alias":"tianxie"}],"related-publications":[1101876,1121811,990378,1033005],"related-downloads":[],"related-videos":[1040124],"related-projects":[],"related-groups":[],"related-events":[],"related-posts":[1117392,1120956,990009,1031883],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-story\/1120032","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-story"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-story"}],"version-history":[{"count":128,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-story\/1120032\/revisions"}],"predecessor-version":[{"id":1122384,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-story\/1120032\/revisions\/1122384"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/1120326"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=1120032"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=1120032"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=1120032"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=1120032"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}