{"id":1184309,"date":"2026-08-20T06:33:38","date_gmt":"2026-08-20T13:33:38","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=1184309"},"modified":"2026-08-20T06:33:39","modified_gmt":"2026-08-20T13:33:39","slug":"molecular-implementation-of-the-machine-learned-skalaexchange-correlation-functional-in-cp2k-through-gauxc","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/molecular-implementation-of-the-machine-learned-skalaexchange-correlation-functional-in-cp2k-through-gauxc\/","title":{"rendered":"Molecular Implementation of the Machine-Learned SkalaExchange\u2013Correlation Functional in CP2K through GauXC"},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\">Machine-learned exchange\u2013correlation (XC) functionals offer a route to improve Kohn\u2013Sham density<br>functional theory without incurring the cost of explicitly correlated electronic-structure methods. Their use<br>in production simulation codes, however, requires a well-defined mapping between the learned model and the<br>host-code density representation. We formulate and implement a Skala-1.1 interface in CP2K through the<br>external GauXC library. CP2K supplies the geometry, Gaussian basis, spin-resolved atomic-orbital density<br>matrix, and communicator, while GauXC evaluates the XC energy, atomic-orbital potential matrix, and avail<br>able nuclear derivatives. The interface accepts both all-electron and valence-only density matrices. The latter<br>may arise from separable dual-space pseudopotentials or molecular effective-core potentials. Implementation<br>errors are isolated from functional differences by comparing the Perdew\u2013Burke\u2013Ernzerhof (PBE) functional<br>evaluated through GauXC with native CP2K PBE. The resulting interface gives consistent energies, forces<br>validated against finite-difference total-energy checks, and force-based molecular-virial diagnostics for repre<br>sentative molecular cases. The dietGMTKN55 benchmark suite is evaluated with an all-electron Gaussian<br>augmented plane-wave treatment for elements up to bromine and def2 effective-core potentials for the heavier<br>elements. The resulting aggregate mean absolute deviation of 1.255 kcalmol\u22121 is within 0.020 kcalmol\u22121 of<br>the corresponding Skala reference value of 1.235 kcalmol\u22121. This work establishes a validated molecular<br>implementation of Skala in CP2K through GauXC.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine-learned exchange\u2013correlation (XC) functionals offer a route to improve Kohn\u2013Sham densityfunctional theory without incurring the cost of explicitly correlated electronic-structure methods. Their usein production simulation codes, however, requires a well-defined mapping between the learned model and thehost-code density representation. We formulate and implement a Skala-1.1 interface in CP2K through theexternal GauXC library. CP2K supplies the [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Franz Poschel","user_id":0},{"type":"text","value":"Johann Pototschnig","user_id":0},{"type":"text","value":"Frederick Stein","user_id":0},{"type":"text","value":"Andreas Knupfer","user_id":0},{"type":"user_nicename","value":"Thijs Vogels","user_id":"43464"},{"type":"user_nicename","value":"Stefano Battaglia","user_id":"44283"},{"type":"user_nicename","value":"Sebastian Ehlert","user_id":"42804"},{"type":"text","value":" Jurg Hutter","user_id":0},{"type":"text","value":"Thomas D. 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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 computational efficiency of scalable semi-local DFT. At the heart of our efforts is Skala, a deep learning-based exchange-correlation (XC) functional&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/1149500"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1184309","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":4,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1184309\/revisions"}],"predecessor-version":[{"id":1184314,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1184309\/revisions\/1184314"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=1184309"}],"wp:term":[{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=1184309"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=1184309"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=1184309"},{"taxonomy":"msr-publisher","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publisher?post=1184309"},{"taxonomy":"msr-publication-cta","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-cta?post=1184309"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=1184309"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=1184309"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=1184309"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=1184309"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=1184309"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=1184309"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=1184309"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=1184309"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}