{"id":1149500,"date":"2025-10-08T08:00:00","date_gmt":"2025-10-08T15:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&#038;p=1149500"},"modified":"2026-01-23T09:50:39","modified_gmt":"2026-01-23T17:50:39","slug":"dft","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/dft\/","title":{"rendered":"Density Functional Theory"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background  has-background- card-background--full-bleed\">\n\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1980\" height=\"720\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/09\/DFT_header.jpg\" class=\"attachment-full size-full\" alt=\"Background image\" style=\"\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/09\/DFT_header.jpg 1980w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/09\/DFT_header-300x109.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/09\/DFT_header-1024x372.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/09\/DFT_header-768x279.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/09\/DFT_header-1536x559.jpg 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/09\/DFT_header-240x87.jpg 240w\" sizes=\"auto, (max-width: 1980px) 100vw, 1980px\" \/>\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 \">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/lab\/microsoft-research-ai-for-science\/\" class=\"icon-link icon-link--reverse mb-2\" data-bi-cN=\"MSR AI for Science\">\n\t\t\t\t\t\t\t\t\t<span class=\"c-glyph glyph-chevron-left\" aria-hidden=\"true\"><\/span>\n\t\t\t\t\t\t\t\t\tMSR AI for Science\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 class=\"wp-block-heading\" id=\"density-functional-theory\">Density Functional Theory<\/h1>\n\n\n\n<p>Advancing the frontier of quantum chemistry by combining deep learning with Density Functional Theory (DFT) to unlock unprecedented accuracy and scalability in electronic structure simulations.<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\">\n<h2 class=\"wp-block-heading\" id=\"mission\">Mission<\/h2>\n\n\n\n<p>Our mission is to enable predictive modeling of laboratory experiments by achieving chemically accurate electronic structure predictions with deep learning powered <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/project\/dft\/learn-about-dft\/\"><strong>DFT<\/strong><\/a>, targeting errors below 1 kcal\/mol, while retaining the computational efficiency of scalable semi-local DFT.<\/p>\n\n\n\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=\"Deep learning for DFT\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/Zzt3h10KLp4?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<h2 class=\"wp-block-heading\" id=\"skala-functional\">Skala functional<\/h2>\n\n\n\n<p>At the heart of our efforts is <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/accurate-and-scalable-exchange-correlation-with-deep-learning\/\"><strong>Skala<\/strong><\/a>, a deep learning-based exchange-correlation (XC) functional that breaks the traditional trade-off between accuracy and efficiency. Unlike traditional XC functionals, Skala bypasses commonly used expensive hand-designed input features and instead learns complex non-local representations and uses these to make energy predictions in a data-driven manner. This is enabled by training the model using an unprecedented amount of high accuracy data which we generate in-house and in collaboration with world-leading experts of highly accurate but more expensive electronic structure methods.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"key-features\">Key Features:<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Learned non-local representations<\/strong>: Skala leverages a modern neural network to learn the nonlocal representations that are required to reach chemical accuracy. The model is trained using an unprecedented volume of high-accuracy reference data, generated using wavefunction-based methods.<\/li>\n\n\n\n<li><strong>Chemical Accuracy<\/strong>: Skala achieves chemical accuracy for atomization energies of small molecules.<\/li>\n\n\n\n<li><strong>Scalable generalization<\/strong>: With just a modest amount of additional training data, Skala achieves accuracy competitive with top-performing hybrid functionals across general main group chemistry, all at the cost of semi-local DFT.<\/li>\n\n\n\n<li><strong>Systematic improvement with data<\/strong>: Skala systematically improves with more training data, expanding its predictive power across diverse chemical domains.<\/li>\n\n\n\n<li><strong>Naturally supports GPU acceleration<\/strong>: Skala architecture is designed to take maximum advantage of GPU acceleration. The computational cost of Skala is the same as semilocal functionals.<\/li>\n\n\n\n<li><strong>Emerging physical constraints<\/strong>: while we impose only a minimal set of exact constraints through Skala\u2019s model design, we find that adherence to additional exact physical constraints emerges as more data is added to the training set<\/li>\n<\/ul>\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-pill\"><a data-bi-type=\"button\" class=\"wp-block-button__link has-white-color has-blue-background-color has-text-color has-background has-link-color wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/project\/dft\/use-skala\/\">Use Skala<\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"accurate-chemistry-collection\">Accurate Chemistry Collection<\/h2>\n\n\n\n<p>Accurate electronic structure data with sub-chemical accuracy are essential for advancing computational chemistry methods with deep learning. However, existing datasets that reach this level of accuracy remain limited in size or scope. The Microsoft Research Accurate Chemistry Collection (MSR-ACC) aims to overcome this limitation. Its first release, MSR-ACC\/TAE25, comprises 76,879 total atomization energies at the CCSD(T)\/CBS level obtained with the W1-F12 thermochemical protocol. The dataset is constructed to exhaustively cover the chemical space of closed-shell, charge-neutral, covalently bound equilibrium molecular structures containing up to 5 non-hydrogen atoms drawn from elements up to argon and lacking significant multireference character. The dataset and its canonical train and validation splits are openly available on Zenodo in the QCSchema format under the CDLA Permissive 2.0 license. This first release of MSR-ACC enables data-driven approaches for developing predictive computational chemistry methods with unprecedented accuracy and scope.<\/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-pill\"><a data-bi-type=\"button\" class=\"wp-block-button__link has-white-color has-blue-background-color has-text-color has-background has-link-color wp-element-button\" href=\"https:\/\/zenodo.org\/records\/15387280\">Accurate Chemistry Collection<\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"early-access-program\">Early access program<\/h2>\n\n\n\n<p>We invite organizations of all sizes to join the <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/project\/dft\/early-access\/\"><strong>DFT Research Early Access Program<\/strong><\/a> <strong>(REAP)<\/strong> to explore the potential of our new Skala functional and accelerate innovation across industries through faster and more accurate density functional theory.<\/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-pill\"><a data-bi-type=\"button\" class=\"wp-block-button__link has-white-color has-blue-background-color has-text-color has-background has-link-color wp-element-button\" href=\"https:\/\/aka.ms\/DFT-REAP\">Microsoft Research DFT Early Access Program<\/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:33.33%\">\n<div class=\"annotations \" data-bi-aN=\"citation\">\n\t<article class=\"annotations__list card depth-16 bg-body p-4 \">\n\t\t<div 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\">Blog<\/span>\n\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/breaking-bonds-breaking-ground-advancing-the-accuracy-of-computational-chemistry-with-deep-learning\/\" data-bi-cN=\"Breaking bonds, breaking ground: Advancing the accuracy of computational chemistry with deep learning\" data-external-link=\"false\" data-bi-aN=\"citation\" data-bi-type=\"annotated-link\" class=\"annotations__link font-weight-semibold text-decoration-none\"><span>Breaking bonds, breaking ground: Advancing the accuracy of computational chemistry with deep learning<\/span>&nbsp;<span class=\"glyph-in-link glyph-append glyph-append-chevron-right\" aria-hidden=\"true\"><\/span><\/a>\t\t\t\t\t<\/div>\n\t<\/article>\n<\/div>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"citation\">\n\t<article class=\"annotations__list card depth-16 bg-body p-4 \">\n\t\t<div 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\/accurate-and-scalable-exchange-correlation-with-deep-learning\/\" data-bi-cN=\"Accurate and scalable exchange-correlation with deep learning\" data-external-link=\"false\" data-bi-aN=\"citation\" data-bi-type=\"annotated-link\" class=\"annotations__link font-weight-semibold text-decoration-none\"><span>Accurate and scalable exchange-correlation with deep learning<\/span>&nbsp;<span class=\"glyph-in-link glyph-append glyph-append-chevron-right\" aria-hidden=\"true\"><\/span><\/a>\t\t\t\t\t<\/div>\n\t<\/article>\n<\/div>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"citation\">\n\t<article class=\"annotations__list card depth-16 bg-body p-4 \">\n\t\t<div 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\/accurate-chemistry-collection-coupled-cluster-atomization-energies-for-broad-chemical-space\/\" data-bi-cN=\"Accurate Chemistry Collection: Coupled cluster atomization energies for broad chemical space\" data-external-link=\"false\" data-bi-aN=\"citation\" data-bi-type=\"annotated-link\" class=\"annotations__link font-weight-semibold text-decoration-none\"><span>Accurate Chemistry Collection: Coupled cluster atomization energies for broad chemical space<\/span>&nbsp;<span class=\"glyph-in-link glyph-append glyph-append-chevron-right\" aria-hidden=\"true\"><\/span><\/a>\t\t\t\t\t<\/div>\n\t<\/article>\n<\/div>\n\n\n\n<div class=\"annotations \" data-bi-aN=\"citation\">\n\t<article class=\"annotations__list card depth-16 bg-body p-4 \">\n\t\t<div 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\">dataset<\/span>\n\t\t\t<a href=\"https:\/\/zenodo.org\/records\/15387280\" data-bi-cN=\"MSR-ACC\" target=\"_blank\" rel=\"noopener noreferrer\" data-external-link=\"true\" data-bi-aN=\"citation\" data-bi-type=\"annotated-link\" class=\"annotations__link font-weight-semibold text-decoration-none\"><span>MSR-ACC<\/span>&nbsp;<span class=\"glyph-in-link glyph-append glyph-append-open-in-new-tab\" aria-hidden=\"true\"><\/span><\/a>\t\t\t\t\t<\/div>\n\t<\/article>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<h2 class=\"wp-block-heading wp-embed-aspect-16-9 wp-has-aspect-ratio\" id=\"what-is-dft\">What is DFT?<\/h2>\n\n\n\n<p>Molecules and materials are made of atoms, which are held together by their electrons. These electrons act as a glue, determining the stability and properties of the chemical structure. Accurately computing the strength and properties of the electron glue is essential for predicting whether a chemical reaction will proceed, whether a candidate drug molecule will bind to its target protein, whether a material is suitable for carbon capture, or if a flow battery can be optimized for renewable energy storage. <\/p>\n\n\n\n<p>Unfortunately, a brute-force approach amounts to solving the many-electron Schr\u00f6dinger equation, which requires computation that scales exponentially with the number of electrons. Considering that an atom has dozens of electrons, and that molecules and materials have large numbers of atoms, we could easily end up waiting the age of the universe to complete our computation unless we restrict our attention to small systems with only a few atoms.<\/p>\n\n\n\n<p>DFT, introduced by Walter Kohn and collaborators in 1964-1965, was a true scientific breakthrough, earning Kohn the Nobel Prize in Chemistry in 1998. DFT provides an extraordinary reduction in the computational cost of calculating the electron glue in an exact manner, from exponential to cubic, making it possible to perform calculations of practical value within seconds to hours.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\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=\"What is Density Functional Theory (DFT)\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/wtB50-si1hI?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<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"what-is-the-grand-challenge-in-dft\">What is the grand challenge in DFT?&nbsp;<\/h2>\n\n\n\n<p>But there is a catch: the exact reformulation has a small but crucial term\u2014the exchange-correlation (XC) functional\u2014which Kohn proved is universal (i.e., the same for all molecules and materials), but for which no explicit expression is known. For 60 years, people have designed practical approximations for the XC functional. The magazine&nbsp;<em>Science<\/em>&nbsp;dubbed the gold rush to design better XC models the \u201c<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.science.org\/doi\/10.1126\/science.1077710\" target=\"_blank\" rel=\"noopener noreferrer\">pursuit of the Divine Functional<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>\u201d. With time, these approximations have grown into a zoo of hundreds of different XC functionals from which users must choose, often using experimental data as a guide. Owing to the uniquely favorable computational cost of DFT, existing functionals have enabled scientists to gain extremely useful insight into a huge variety of chemical problems. However, the limited accuracy and scope of current XC functionals mean that DFT is still mostly used to interpret experimental results rather than predict them.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\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=\"DFT for drug and material discovery\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/ckXVse-XZMQ?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<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<h2 class=\"wp-block-heading\" id=\"why-is-it-important-to-increase-the-accuracy-of-dft\">Why is it important to increase the accuracy of DFT?&nbsp;<\/h2>\n\n\n\n<p>We can contrast the present state of computational chemistry with the state of aircraft engineering and design. Thanks to predictive simulations, aeronautical engineers no longer need to build and test thousands of prototypes to identify one viable design. However, this is exactly what we currently must do in molecular and materials sciences. We send thousands of potential candidates to the lab, because the accuracy of the computational methods is not sufficient to&nbsp;<em>predict<\/em>&nbsp;the experiments. To make a significant shift in the balance from laboratory to&nbsp;<em>in silico<\/em>&nbsp;experiments, we need to remove the fundamental bottleneck of the insufficient accuracy of present XC functionals. This amounts to bringing the error of DFT calculations with respect to experiments within&nbsp;<em>chemical accuracy<\/em>, which is around 1 kcal\/mol for most chemical processes. Present approximations typically have errors that are 3 to 30 times larger.<\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n<h2 class=\"wp-block-heading\" id=\"microsoft-dft-research-early-access-program-reap\">Microsoft DFT Research Early Access Program (REAP)<\/h2>\n\n\n\n<p>Accelerate Innovation in Computational Chemistry with Microsoft AI for Science<\/p>\n\n\n\n<p>The Microsoft DFT Research Early Access Program (REAP) is an exclusive initiative designed to empower select industry and academic partners to advance their computational chemistry research by giving them early access to Microsoft\u2019s cutting-edge DFT models\u2014such as the OneDFT Skala functional\u2014via Azure AI Foundry. By joining REAP, participants gain privileged early access to new AI-driven DFT technology, direct collaboration with Microsoft Research, and the opportunity to shape the future of digital chemistry.<\/p>\n\n\n\n<p>Participants benefit from white-glove onboarding, scoped pilots, and structured feedback loops, enabling them to integrate advanced DFT capabilities into their workflows while influencing future model development. For external collaborators, this means faster scientific validation and proof-of-concept wins; for Microsoft Research, it ensures market-fit validation, strategic feedback, and the cultivation of flagship use cases that can be showcased at events like Ignite and Davos.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"why-join-reap\">Why Join REAP?<\/h3>\n\n\n\n<p><strong>Early Access<\/strong>: Be among the first to explore and integrate the latest advancements in DFT modeling, with dedicated onboarding and support from Microsoft Research experts.<\/p>\n\n\n\n<p><strong>Collaborative Innovation<\/strong>: Work closely with Microsoft\u2019s AI for Science team to apply the Skala functional to real-world challenges in life sciences, materials science, and drug discovery.<\/p>\n\n\n\n<p><strong>Influence Product Development<\/strong>: Provide structured feedback that directly informs Microsoft\u2019s research roadmap, enabling us to develop Skala with the needs of industry and scientific community in mind.<\/p>\n\n\n\n<p><strong>Intellectual Property Protection<\/strong>: The program includes robust IP protection: Microsoft retains full ownership of the DFT models and Research API, while partners retain rights to their own model output data and improvements to their pre-existing work.<\/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-pill\"><a data-bi-type=\"button\" class=\"wp-block-button__link has-white-color has-blue-background-color has-text-color has-background has-link-color wp-element-button\" href=\"https:\/\/aka.ms\/DFT-REAP\">Microsoft Research DFT Early Access Program<\/a><\/div>\n<\/div>\n\n\n\n\n\n<h2 class=\"wp-block-heading\" id=\"foundry-labs\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/labs.ai.azure.com\/projects\/skala\/\">Foundry Labs<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/h2>\n\n\n\n<p>Skala, Microsoft\u2019s deep learning-based exchange-correlation functional for DFT, is available for experimentation through Foundry Labs. This allows users to experiment with the latest Skala models and workflows in a secure, cloud-based environment.<\/p>\n\n\n\n<p><strong>How to Access Skala in Foundry Labs<\/strong><\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li>Go to:&nbsp;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/labs.ai.azure.com\/projects\/skala\/\">https:\/\/labs.ai.azure.com\/projects\/skala\/<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li>Click on&nbsp;<strong>\u2018Try Skala in Foundry Catalog\u2019<\/strong>.<\/li>\n\n\n\n<li>Select&nbsp;<strong>\u2018Use this model\u2019<\/strong>.<\/li>\n\n\n\n<li>Create a new project to start using Skala.<\/li>\n<\/ol>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"github-community-edition\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/github.com\/microsoft\/skala\">GitHub Community Edition<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/h2>\n\n\n\n<p>The Skala Community Edition is the open, research-focused release of Microsoft\u2019s deep learning-based exchange-correlation functional for density functional theory (DFT). It is designed to make Skala widely accessible to the scientific community for experimentation, research, and feedback.<\/p>\n\n\n\n<p><strong>How to Access Skala on GitHub<\/strong><\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li>Go to:&nbsp;<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/github.com\/microsoft\/skala\">https:\/\/github.com\/microsoft\/skala<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/li>\n\n\n\n<li>Follow the instructions in the README to set up and start using Skala.<\/li>\n<\/ol>\n\n\n\n<p>You can find installation steps and documentation directly in the repository.<\/p>\n\n\n\n\n\n<div style=\"padding-bottom:32px; padding-top:32px\" 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<h2 class=\"wp-block-heading has-text-align-center has-white-color has-text-color has-link-color wp-elements-9b29f3b10f16e11bfb68c7a30a83f686\" id=\"work-with-us-1\">Work with us<\/h2>\n\n\n\n<p class=\"has-text-align-center has-white-color has-text-color has-link-color wp-elements-88989855b724b88c8331ca9e108dbbf0\">Check out our open roles<\/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-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a data-bi-type=\"button\" class=\"wp-block-button__link has-text-align-left wp-element-button\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/lab\/microsoft-research-ai-for-science\/opportunities\/\">See careers<\/a><\/div>\n<\/div>\t\t<\/div>\n\t<\/div>\n\n\t<img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"667\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/09\/careers-banner-2.jpg\" class=\"wp-block-msr-immersive-section__background-image\" alt=\"\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/09\/careers-banner-2.jpg 1000w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/09\/careers-banner-2-300x200.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/09\/careers-banner-2-768x512.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/09\/careers-banner-2-240x160.jpg 240w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Advancing the frontier of quantum chemistry by combining deep learning with Density Functional Theory (DFT) to unlock unprecedented accuracy and scalability in electronic structure simulations. Our mission is to enable predictive modeling of laboratory experiments by achieving chemically accurate electronic structure predictions with deep learning powered DFT, targeting errors below 1 kcal\/mol, while retaining the [&hellip;]<\/p>\n","protected":false},"featured_media":1149501,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-1149500","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[1142455,1142459],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[1141626],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Rosa De Rosa","user_id":44078,"people_section":"Section name 0","alias":"rosaderosa"},{"type":"user_nicename","display_name":"Sebastian Ehlert","user_id":42804,"people_section":"Section name 0","alias":"sehlert"},{"type":"user_nicename","display_name":"Klaas Giesbertz","user_id":43906,"people_section":"Section name 0","alias":"kgiesbertz"},{"type":"user_nicename","display_name":"Paola Gori Giorgi","user_id":41746,"people_section":"Section name 0","alias":"pgorigiorgi"},{"type":"user_nicename","display_name":"Deniz Gunceler","user_id":42798,"people_section":"Section name 0","alias":"dgunceler"},{"type":"user_nicename","display_name":"Jan Hermann","user_id":42468,"people_section":"Section name 0","alias":"janhermann"},{"type":"user_nicename","display_name":"Chin-Wei Huang","user_id":41533,"people_section":"Section name 0","alias":"chinweihuang"},{"type":"user_nicename","display_name":"Derk Kooi","user_id":42702,"people_section":"Section name 0","alias":"derkkooi"},{"type":"user_nicename","display_name":"Stephanie Marisa Lanius","user_id":43905,"people_section":"Section name 0","alias":"slanius"},{"type":"user_nicename","display_name":"Giulia Luise","user_id":43903,"people_section":"Section name 0","alias":"giulialuise"},{"type":"user_nicename","display_name":"Gregor Simm","user_id":41811,"people_section":"Section name 0","alias":"gregorsimm"},{"type":"user_nicename","display_name":"Roberto Sordillo","user_id":44080,"people_section":"Section name 0","alias":"rsordillo"},{"type":"user_nicename","display_name":"P. 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