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 insights and understanding trends in surface reactivity. The accuracy of DFT for surface reaction energetics depends strongly on the exchange-correlation (XC) approximation. We show here that optimization of such XC functionals for surface binding energies can lead to a worse description of surface reaction barriers, unless important physical exact conditions are fulfilled.
Optimizing catalysis requires the efficient exploration of immense chemical spaces, particularly for high-entropy (multielement) systems where properties depend on complex variables like geometry, composition, and site-specific interactions. In this work, we demonstrate a global active learning framework to map these landscapes efficiently. By coupling genetic algorithms with deep neural networks trained on density functional theory data, our approach learns the potential energy surface while optimizing chemical structures simultaneously, bypassing costly density functional theory relaxations. We apply this framework to predict H2, N2, and NH3 adsorption energies on 10–50 atom clusters composed of Ag, Au, Cu, Ni, Pd, and Pt. The model achieves a mean absolute error of less than 0.10 eV against density functional theory validation. Results identify regions with moderate H2 physisorption (−0.1 to −0.5 eV) and other regions with strong chemisorption (−1.0 to −2.0 eV). Physisorption mainly consists of adsorption on Ag, Au, Ni, and Cu, while chemisorption involves Pd and Pt sites. High-entropy environments can provide a diverse distribution of local chemical conditions. This site variance is critical for multistep reactions where intermediate steps possess conflicting optimal binding energies that cannot be simultaneously satisfied by low-entropy surfaces, thereby offering entropy as a tunable parameter for catalyst design. This framework provides a scalable and resource-efficient strategy for high-throughput materials discovery in applications such as hydrogen storage and ammonia synthesis.
Johnathan 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, transition metal oxides and solid electrolytes.
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 exact exchange data. The proposed functional achieves improved accuracy in predicting barrier heights (BH) and band gaps compared with the PBE, SCAN, M06L, and revM06L functionals. In addition, tests on other quantities, including atomization energies (AE), ionization potentials (IP), and vibrational frequencies (VF), show that the developed functional also provides reliable performance for these properties.
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 functional theory, this review explores the critical role of exchange-correlation functionals in predicting key material properties such as structural, optoelectronic, magnetic, and thermal. We examine the challenges posed by quantum confinement, anisotropic screening, and van der Waals interactions, which conventional functionals often fail to describe. Advanced approaches, including meta-GGA, hybrid functionals, and many-body perturbation theory (e.g., GW and Bethe-Salpeter equation), are assessed for their improved accuracy in capturing electronic structure and excitonic effects. We further discuss the non-universality of functionals across different 2D material families and the emerging role of machine learning to enhance computational efficiency. Finally, the review outlines current limitations and emerging strategies, providing a roadmap for advancing exchange-correlation functionals and beyond, to enable the practical design and application of 2D materials.
Ahsan Javed, Mahvish Shaheen, Muhammad Shahbaz et al.· Nanotechnology· 1 citation
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 (MLIPs) to model large-scale ferroelectric systems of technological relevance, it is important to scrutinise the density functional theory exchange-correlation functionals which are used to compute the forces, energies and stresses the MLIP is trained on. Using the example of the prototypical ferroelectrics lead titanate, PbTiO3, and barium titanate, BaTiO3 we show that many widely used functionals tend to overestimate their lattice constants and spontaneous polarization. Conversely, non-local van der Waals functionals with C09 exchange accurately capture these properties compared to experiment, but with a larger computational overhead than, for example, GGA. We show that C09x-PBEc combines the accuracy provided by the C09 exchange with the computational affordability of GGA, making it an excellent candidate to be used in the training of MLIPs for ferroelectric perovskites. We also demonstrate that an MLIP trained using C09x-PBEc accurately reproduces the ferroelectric-to-paraelectric phase transition temperature of PbTiO3 with respect to experiment, showing a marked improvement on MLIPs trained using GGA.