Aug 2026· 1 citation· ⚡ 1 influential· 184 references
Physics
Abstract
The application of density functional theory to heterogeneous catalysis is hindered by the shortcomings of conventional density functional approximations. We combine machine learning with explicitly non-local physically informed descriptors and introduce an exchange-correlation functional (CIDER26SS) framework regularized for wide transferability. CIDER26SS is size-extensive, highly efficient, provides a balanced and accurate description of both molecular and solid-state systems, and is specifically well-optimized for transition metal surface chemistry. Surpassing existing conventional functionals, CIDER26SS resolves the CO/Pt puzzle, identifying the correct binding site for CO adsorption on the Pt(111) surface, along with an accurate adsorption energy, Pt lattice constant, and surface energy. Predictions agree well with the experimental values, even when all bulk and surface data for Pt are excluded from the training set. Remarkably, CIDER26SS exceeds the accuracy of semilocal approximations even for systems far outside the training domain.
Density functional theory (DFT) often is the method of choice for simulating the electronic properties of extended solids and surfaces from first principles due to a favorable compromise between accuracy and computational cost. In the field of heterogeneous catalysis, DFT is indispensable for deriving mechanistic insig...
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J. von der Heyde, Walter Malone, A. Kara· Journal of Physical Chemistr...· 0 citations
A machine learning approach is presented that accelerates DFTB simulations by predicting optimal initial atomic charges and demonstrates that ML-predicted initial charges consistently and significantly improve SCC convergence across diverse chemical systems including organic molecules, biomolecules, water clusters, tra...
Maximilian L. Ach, Karsten Reuter, C. Panosetti· 0 citations
Self-interaction error (SIE) in density functional theory (DFT) leads to significant inaccuracies in the calculation of barrier heights of chemical reactions and band gaps of solid-state systems. In this work, we develop a neural-network-based exchange functional aimed at reducing SIE-related errors by training on exac...
A. Wijaya, D. Farhamsa· Physical Chemistry, Chemical...· 0 citations
The rapid development of two-dimensional (2D) materials has reshaped modern nanoscience, offering properties that differ fundamentally from their bulk counterparts. As experimental discovery accelerates, the need for reliable computational techniques has become increasingly important. Within the framework of density fu...
We present a novel exchange correlation functional, C09x-PBEc, which combines C09 exchange with PBE correlation, to accurately model the ferroelectric properties of perovskites while retaining the computational efficiency of GGA functionals. With a growing interest in developing machine learning interatomic potentials...
Owain T. Beynon, C. Gattinoni· 0 citations
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