{"id":1171172,"date":"2026-05-12T06:00:00","date_gmt":"2026-05-12T13:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=1171172"},"modified":"2026-05-12T06:29:42","modified_gmt":"2026-05-12T13:29:42","slug":"advancing-ai-for-materials-with-mattersim-experimental-synthesis-faster-simulation-and-multi-task-models","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/advancing-ai-for-materials-with-mattersim-experimental-synthesis-faster-simulation-and-multi-task-models\/","title":{"rendered":"Advancing AI for materials with MatterSim: experimental synthesis, faster simulation, and multi-task models"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"2560\" height=\"1441\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-scaled.jpg\" alt=\"Three minimalist white line icons on a blue-to-purple-to-pink gradient background: honeycomb, flowchart icon, scientific beaker with circled checkmark\" class=\"wp-image-1171177\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-scaled.jpg 2560w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-1536x865.jpg 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-2048x1153.jpg 2048w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSimv1_5-BlogHeroFeature-1400x788-1-1920x1080.jpg 1920w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\" \/><\/figure>\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__inner wp-block-msr-immersive-section__inner--narrow\">\n\t\t\t<div class=\"wp-block-columns mb-10 pb-1 pr-1 is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\" style=\"box-shadow:var(--wp--preset--shadow--outlined)\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<h2 class=\"wp-block-heading h3\" id=\"at-a-glance\">At a glance<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Experimental validation<\/strong>: Using high-throughput screening with MatterSim-v1, we <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/podcast\/abstracts-heat-transfer-and-deep-learning-with-hongxia-hao-and-bing-lv\/\" type=\"link\" id=\"https:\/\/www.microsoft.com\/en-us\/research\/podcast\/abstracts-heat-transfer-and-deep-learning-with-hongxia-hao-and-bing-lv\/\">previously<\/a> identified tetragonal tantalum phosphorus (TaP) as a potential high-performance thermal conductor. Now we have experimentally synthesized it and measured its thermal conductivity (152 W\/m\/K) to be close to the thermal conductivity of silicon.<\/li>\n\n\n\n<li><strong>Faster simulation<\/strong>: We have accelerated MatterSim-v1 model inference by 3-5x and integrated it with the LAMMPS software package, enabling large-scale simulations across multiple GPUs.<\/li>\n\n\n\n<li><strong>New model release<\/strong>: We are introducing MatterSim-MT, a multi-task foundation model for in silico materials characterization that enables the simulation of complex, multi-property phenomena beyond what potential energy surfaces alone can capture.<\/li>\n<\/ul>\n<\/div>\n<\/div>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n\n\n\n<p>Materials design underpins a wide range of technological advances, from nanoelectronics to semiconductor design and energy storage. Yet development cycles for novel materials remain slow and costly. Universal machine learning interatomic potentials aim to accelerate the materials design process by providing accurate stability and property predictions for a wide range of materials. These models are orders of magnitude faster than traditional first-principles simulations, turning previously impractical problems into routine computations that can be completed in a few hours. Since we <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mattersim-a-deep-learning-atomistic-model-across-elements-temperatures-and-pressures\/\" type=\"msr-research-item\" id=\"1033005\">launched<\/a> our MatterSim-v1 model, it has gained popularity in the materials science community for its ability to accurately simulate materials under realistic conditions, including finite temperature and pressure.<\/p>\n\n\n\n<p>Today, we have several exciting MatterSim updates to share. These include experimental validation of MatterSim predictions for thermal conductors, performance improvements for faster simulation, and the introduction of a new multi-task foundation model for materials characterization.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"experimental-validation\">Experimental validation<\/h2>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"264\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSim_fig1_v2.png\" alt=\"Right: Scatter plot of MatterSim's thermal conductivity predictions compared to ground-truth simulation and experiment. The plot shows a good agreement. | Left: Different views of the experimentally synthesized tetragonal tantalum phosphorus sample.\" class=\"wp-image-1171414\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSim_fig1_v2.png 800w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSim_fig1_v2-300x99.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSim_fig1_v2-768x253.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/MatterSim_fig1_v2-240x79.png 240w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\" \/><figcaption class=\"wp-element-caption\">Figure 1: Based on MatterSim\u2019s computational predictions, we have synthesized a potential high thermal conductor. Left: MatterSim predictions of thermal conductivity compared to ground-truth simulation and experiment (with \u00b150% error band shown for reference). Right: Different views of the experimentally synthesized tetragonal tantalum phosphorus (TaP) sample with measured thermal conductivity of <strong>152<\/strong> W\/m\/K.<\/figcaption><\/figure>\n\n\n\n<p>Materials with high thermal conductivity play a critical role in heat management, preventing overheating and improving energy efficiency. For example, established high thermal conductors like diamond, copper and silicon are widely used across a broad range of cooling applications. Designing next-generation thermal conductors may enable advances in computing, power electronics, and aerospace technologies. However, doing so requires accurate predictions of thermal conductivity values for candidate materials.<\/p>\n\n\n\n<p>In solids, heat is carried in two main ways: by vibrating atoms (phonons) and by moving electrons. The phonon contribution can be estimated using machine-learning interatomic potentials to enable screening of thousands of candidates, narrowing the search space to the most promising materials before expensive experimental validation.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>\u201cMatterSim has generated by far the largest database of computational thermal conductivities. This opens the door to exploring a far broader materials space than before [\u2026].\u201d<\/p>\n<cite>\u2013 Prof. Bing Lv, University of Texas Dallas<\/cite><\/blockquote>\n\n\n\n<p>In collaboration with the University of Texas Dallas (UT Dallas), University of Illinois Urbana-Champaign, and University of California Davis (UC Davis), we have used MatterSim-v1 to screen over 240,000 candidate materials for high thermal conductors. As shown in Fig. 1 (left), MatterSim\u2019s predictions have good agreement with first-principles simulations. Prof. Davide Donadio from UC Davis: &#8220;I was amazed by how the MatterSim model combined accuracy and computational efficiency to predict such a sensitive property as thermal conductivity. That was the key that unlocked screening at this scale, hundreds of thousands of crystals, that would have been completely out of reach with conventional methods.&#8221; Prof. Bing Lv from UT Dallas adds: \u201cMatterSim has generated by far the largest database of computational thermal conductivities. This opens the door to exploring a far broader materials space than before, enabling the community to uncover a broader set of viable materials even after imposing practical requirements.\u201d<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>\u201cFor the first time, we can test conventional understanding of what controls thermal conductivity at scale [\u2026]\u201d<\/p>\n<cite>\u2013 Prof. David Cahill, University of Illinois Urbana-Champaign<\/cite><\/blockquote>\n\n\n\n<p>Based on these predictions, we have identified tetragonal tantalum phosphorus (TaP) as a potential high thermal conductor. We have experimentally synthesized tetragonal tantalum phosphorus (TaP) at UT Dallas and measured its thermal conductivity at University of Illinois Urbana-Champaign (152 W\/m\/K for our best samples), close to the thermal conductivity of silicon. While we are not the first to synthesize tetragonal TaP, the material has not been considered as a thermal conductor before. These results demonstrate how MatterSim can enable the identification of functional materials: &#8220;For the first time, we can test conventional understanding of what controls thermal conductivity at scale, while enabling the discovery of new functional materials that balance it with other important constraints such as mass density, elemental abundance, and environmental stability&#8221;, says Prof. David Cahill from University of Illinois Urbana-Champaign.<\/p>\n\n\n\n\t<div class=\"border-bottom border-top border-gray-300 mt-5 mb-5 msr-promo text-center text-md-left alignwide\" data-bi-aN=\"promo\" data-bi-id=\"670821\">\n\t\t\n\n\t\t<p class=\"msr-promo__label text-gray-800 text-center text-uppercase\">\n\t\t<span class=\"px-4 bg-white display-inline-block font-weight-semibold small\">Spotlight: Microsoft research newsletter<\/span>\n\t<\/p>\n\t\n\t<div class=\"row pt-3 pb-4 align-items-center\">\n\t\t\t\t\t\t<div class=\"msr-promo__media col-12 col-md-5\">\n\t\t\t\t<a class=\"bg-gray-300 display-block\" href=\"https:\/\/info.microsoft.com\/ww-landing-microsoft-research-newsletter.html\" aria-label=\"Microsoft Research Newsletter\" data-bi-cN=\"Microsoft Research Newsletter\" target=\"_blank\">\n\t\t\t\t\t<img decoding=\"async\" class=\"w-100 display-block\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/09\/Newsletter_Banner_08_2019_v1_1920x1080.png\" alt=\"\" \/>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"msr-promo__content p-3 px-5 col-12 col-md\">\n\n\t\t\t\t\t\t\t\t\t<h2 class=\"h4\">Microsoft Research Newsletter<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<p id=\"microsoft-research-newsletter\" class=\"large\">Stay connected to the research community at Microsoft.<\/p>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<div class=\"wp-block-buttons justify-content-center justify-content-md-start\">\n\t\t\t\t\t<div class=\"wp-block-button is-style-fill-chevron\">\n\t\t\t\t\t\t<a href=\"https:\/\/info.microsoft.com\/ww-landing-microsoft-research-newsletter.html\" aria-describedby=\"microsoft-research-newsletter\" class=\"btn btn-brand glyph-append glyph-append-chevron-right\" data-bi-cN=\"Microsoft Research Newsletter\" target=\"_blank\">\n\t\t\t\t\t\t\tSubscribe today\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div><!--\/.msr-promo__content-->\n\t<\/div><!--\/.msr-promo__inner-wrap-->\n\t<\/div><!--\/.msr-promo-->\n\t\n\n\n<h2 class=\"wp-block-heading\" id=\"performance-improvements\">Performance improvements<\/h2>\n\n\n\n<p>We are making MatterSim-v1 significantly faster by releasing several open-source performance and usability improvements. First, we speed up model inference through a combination of faster graph construction, ahead-of-time compilation and reduced conversion between atomic representations, resulting in a 3x speed-up of MatterSim-v1.0.0-5M and a 5x speed-up of MatterSim-v1.0.0-1M (see Fig. 2). To make MatterSim-v1 easier to use, we have integrated it into the widely used LAMMPS simulation software, allowing users to easily scale model inference across multiple GPUs in their existing workflows.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"2134\" height=\"1534\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_speedup.png\" alt=\"Bar charts comparing the inference time of the previous and updated MatterSim versions. The plot shows 3 times speed-up for the 5 million parameter model and 5 times speed-up for the 1 million parameter model.\" class=\"wp-image-1171180\" style=\"aspect-ratio:1.3911699352719544;object-fit:cover;width:600px\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_speedup.png 2134w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_speedup-300x216.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_speedup-1024x736.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_speedup-768x552.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_speedup-1536x1104.png 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_speedup-2048x1472.png 2048w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_speedup-240x173.png 240w\" sizes=\"auto, (max-width: 2134px) 100vw, 2134px\" \/><figcaption class=\"wp-element-caption\">Figure 2: 3x inference speed-up of MatterSim-v1.0.0-5M and 5x inference speedup of MatterSim-v1.0.0-1M (python). <\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"new-model-release\">New model release<\/h2>\n\n\n\n<p>Building on the success of MatterSim-v1, today we extend the MatterSim model family by announcing MatterSim-MT: a multi-task (MT) foundation model for in silico materials simulation and property characterization. The model natively predicts energies, forces, stress and several important materials properties.<\/p>\n\n\n\n<p>MatterSim-MT is pretrained on over 35 million first-principles-labelled structures covering 89 elements, temperatures up to 5000 K and pressures up to 1000 GPa. It is further fine-tuned on various properties including Bader charges, magnetic moments, Born effective charges, and dielectric matrices. Out of the box, MatterSim-MT serves as a foundation model for predicting material structure, dynamics and thermodynamics. Its multi-task architecture also enables a wide range of complex simulations that cannot be captured by potential energy surfaces alone. The ability to accurately simulate these phenomena is crucial for applications such as catalysis and energy storage.<\/p>\n\n\n\n<p>Here, we illustrate these multi-task capabilities through three case studies: vibrational spectroscopy, ferroelectric switching, and electrochemical redox. Each example requires a distinct combination of property predictions. <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mattersim-mt-a-multi-task-foundation-model-for-in-silico-materials-characterization\/\">In the full manuscript<\/a>, we also show that MatterSim-MT scales well with more data and parameters, can be efficiently fine-tuned to higher levels of theory, and can be systematically extended to new systems via active learning.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"927\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_mt_multitask.png\" alt=\"Top left: Atomic representation of a material along with an overview of the multi-task capabilities of the model. Top right: Pressure-dependent phonon spectrum of Silicon Carbide (SiC) up to 100 GPa. Bottom left: Predicted hysteresis curve of the polarization density as a function of the electrical field along the z direction in the ferroelectric tetragonal Barium titanate material. Bottom right: Evolution of oxygen Bader charge distributions in Lithium Manganese dioxide during delithiation, with arrows indicating the formation of an oxygen molecule.\" class=\"wp-image-1171183\" style=\"aspect-ratio:1.1046545077958692;object-fit:cover;width:600px\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_mt_multitask.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_mt_multitask-300x272.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_mt_multitask-768x695.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/05\/mattersim_mt_multitask-199x180.png 199w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Figure\u202f3: MatterSim\u2011MT\u2019s multi-task prediction ability enables simulating complex material phenomena. (a) Illustration of the multi-task inference capabilities of MatterSim-MT, including predictions of energy (E), forces (F), stress (S), magnetic moments (\u03bc), Born effective charges (Z\u2217), and dielectric matrices (\u03b5\u221e) from atomic structures. (b) Pressure-dependent phonon spectrum of silicon carbide (SiC) up to 100 GPa, with inset comparing MatterSim\u2019s predicted longitudinal optical (LO) and transverse optical (TO) splitting against experimental measurements. (c) Predicted hysteresis curve of polarization density as a function of the electrical field along the z direction in the ferroelectric tetragonal BaTiO3 material. (d) Evolution of oxygen Bader charge distributions in Li<sub>1.2 \u2013 x<\/sub>Mn<sub>0.8<\/sub>O<sub>2<\/sub> during delithiation, with arrows indicating the formation of an O<sub>2<\/sub> molecule. <\/figcaption><\/figure>\n\n\n\n<p>First, we focus on vibrational spectroscopy, a technique that identifies substances by measuring how their atomic bonds naturally vibrate. We demonstrate how predictions of Born effective charges and dielectric properties enable the computation of phonon spectra in polar crystals. In these materials, oppositely charged ions vibrate against each other. Depending on the direction of vibration, this can lead to a buildup of charge that creates a macroscopic electric field, splitting the optical phonon modes into higher-frequency longitudinal (LO) and lower-frequency transverse (TO) branches. As a case study, we simulated this behavior in 3c-silicon carbide (3c-SiC), a material used in high-power electronics, under extreme pressures. As shown in Fig. 3(b), MatterSim-MT predicts a Born effective charge in close agreement with both theoretical and experimental values. The resulting LO-TO splitting of 5.26 THz deviates by only 0.06 THz from ab initio calculations and 0.03 THz from experimental measurements.<\/p>\n\n\n\n<p>The predicted Born effective charges also allow us to simulate how systems respond to an external electric field. In ferroelectric materials, ions adopt an asymmetric arrangement that gives the crystal a net electric polarization that can be flipped by an applied field. In Fig.&nbsp;3(c), we&nbsp;demonstrate&nbsp;this by&nbsp;simulating&nbsp;barium&nbsp;titanate (BaTiO<sub>3<\/sub>) under an applied&nbsp;electric&nbsp;field, reproducing&nbsp;the switching of&nbsp;its&nbsp;polarization. The&nbsp;resulting&nbsp;hysteresis curve&nbsp;correctly shows that&nbsp;finite-temperature effects&nbsp;at 300 K&nbsp;make it easier to flip&nbsp;the&nbsp;polarization, even though the predicted spontaneous polarization&nbsp;(38&nbsp;\u03bcC\/cm<sup>2<\/sup>)&nbsp;is slightly higher than&nbsp;the experimental value (26&nbsp;\u03bcC\/cm<sup>2<\/sup>).&nbsp;This discrepancy is likely due to the well-known&nbsp;underbinding&nbsp;of the underlying first-principles calculations.<\/p>\n\n\n\n<p>Finally, we predict atomic charges to study the electronic degrees of freedom in chemical bonding and redox processes. We examine the behavior of the cathode material Li<sub>1.2 \u2013 x<\/sub>Mn<sub>0.8<\/sub>O<sub>2<\/sub> during a simulated battery charging process. These lithium-rich transition-metal oxides are promising next-generation batteries due to their high energy density but suffer from irreversible capacity loss associated with the anionic oxygen redox mechanism. We reproduced this phenomenon by running molecular dynamics simulations at 1000 K and progressively extracting Lithium to mimic battery charging. We observe a clear shift over time: at first, the manganese (Mn) atoms supply the electrons needed for charging, but as more lithium is removed, oxygen atoms are forced to give up electrons instead (cationic to anionic redox), as shown by the shift to less negative Bader charges over time (Fig. 3(d)). This destabilises the structure with oxygen atoms pairing up to form O<sub>2<\/sub> dimers (Fig. 3(d), inset). Notably, this comprehensive picture of the cationic-to-anionic redox transition and lattice degradation naturally emerges from the multi-task predictions, without any task-specific training on battery materials.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"next-steps\">Next steps<\/h2>\n\n\n\n<p>With experimental validation, substantial performance improvements, and new multi-task capabilities, MatterSim is advancing toward more practical, decision-relevant use in materials design. Together, these developments are helping materials scientists move more quickly from large-scale computational screening to targeted experimental follow-up and decision-relevant scientific workflows. We are excited to see how the materials science community applies these advances in their own domains.<\/p>\n\n\n\n<p>We look forward to continued collaboration as MatterSim is tested, extended, and integrated into real-world materials discovery pipelines.<\/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 is-style-fill-github\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/github.com\/microsoft\/mattersim\" target=\"_blank\" rel=\"noreferrer noopener\">MatterSim updated<\/a><\/div>\n\n\n\n<div class=\"wp-block-button is-style-fill\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/arxiv.org\/abs\/2605.07927v1\">MatterSim-MT Pre-Print<\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"acknowledgements\">Acknowledgements<\/h2>\n\n\n\n<p>This work is the product of a highly collaborative and interdisciplinary effort led by Microsoft Research AI for Science in partnership with Microsoft Research Accelerator and collaborators at the University of Texas Dallas (supported by MSR Accelerator), University of Illinois Urbana-Champaign and University of California Davis. Contributors to this work include Han Yang, Xixian Liu, Chenxi Hu, Yichi Zhou, Yu Shi, Chang Liu, Junfu Tan, Jielan Li, Guanzhi Li, Qian Wang, Yu Zhu, Zekun Chen, Shuizhou Chen, Fabian Thiemann, Claudio Zeni, Matthew Horton, Robert Pinsler, Andrew Fowler, Daniel Z\u00fcgner, Tian Xie, Lixin Sun, Yicheng Chen, Lingyu Kong, Yeqi Bai, Deniz Gunceler, Frank No\u00e9, Hongxia Hao, Ziheng Lu, Zixin Zhai, Mengfan Wu, Haoke Qiu, Mingfa Tang, Tie-Yan Liu, Haiguang Liu, Tao Qin, David G. Cahill, Bing Lv, Davide Donadio, Shoko Ueda, and Kenji Takeda.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>MatterSim is expanding what AI can do for materials science\u2014from faster large-scale simulations to MatterSim-MT, a new multi-task model for simulating properties beyond potential energy surfaces alone.<\/p>\n","protected":false},"author":43868,"featured_media":1171177,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Andrew Fowler","user_id":"41850"},{"type":"user_nicename","value":"Claudio Zeni","user_id":"41817"},{"type":"user_nicename","value":"Daniel Z\u00fcgner","user_id":"42990"},{"type":"user_nicename","value":"Fabian Thiemann","user_id":"44168"},{"type":"user_nicename","value":"Han 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