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Machine-learned interatomic potentials enable large systems to be simulated for long time scales at near ab-initio accuracy. This accuracy is achieved by fitting extremely flexible model architectures to high quality reference data. In…

The lytic polysaccharide monooxygenase (LPMO) enzymes boost polysaccharide depolymerization through oxidative chemistry, which has fueled the hope for more energy-efficient production of biofuel. We have recently proposed a mechanism for…

Chemical Physics · Physics 2022-09-16 Ernst D. Larsson , Geng Dong , Valera Veryazov , Ulf Ryde , Erik D. Hedegård

The present work proposes an approach to obtain a basis-set correction based on density-functional theory (DFT) for the computation of molecular properties in wave-function theory (WFT). This approach allows one to accelerate the basis-set…

Chemical Physics · Physics 2022-05-18 Diata Traore , Julien Toulouse , Emmanuel Giner

The systematic underestimation of band gaps is one of the most fundamental challenges in semilocal density functional theory (DFT). In addition to hindering the application of DFT to predicting electronic properties, the band gap problem is…

Chemical Physics · Physics 2024-08-27 Kyle Bystrom , Stefano Falletta , Boris Kozinsky

We introduce multi-scale energy models to learn the prior distribution of images, which can be used in inverse problems to derive the Maximum A Posteriori (MAP) estimate and to sample from the posterior distribution. Compared to the…

Image and Video Processing · Electrical Eng. & Systems 2024-07-19 Jyothi Rikhab Chand , Mathews Jacob

While the success of density functional theory (DFT) has led to its use in a wide variety of fields such as physics, chemistry, materials science and biochemistry, it has long been recognised that conventional methods are very inefficient…

Materials Science · Physics 2007-05-23 D. R. Bowler , R. Choudhury , M. J. Gillan , T. Miyazaki

Density functional theory (DFT) remains the most widely used electronic structure method. Although exact in principle, in practice, it relies on approximations to the exchange-correlation (XC) functional, which is known to be a unique…

Computational Physics · Physics 2025-10-14 Vishal Subramanian , Bikash Kanungo , Vikram Gavini

Embedded density functional theory (e-DFT) is used to describe the electronic structure of strongly interacting molecular subsystems. We present a general implementation of the Exact Embedding (EE) method [J. Chem. Phys. 133, 084103 (2010)]…

Other Condensed Matter · Physics 2011-07-27 Jason D. Goodpaster , Taylor A. Barnes , Thomas F. Miller

Standard flavors of density-functional theory (DFT) calculations are known to fail in describing anions, due to large self-interaction errors. The problem may be circumvented by using localized basis sets of reduced size, leaving no…

Chemical Physics · Physics 2019-12-23 Francesco Nattino , Céline Dupont , Nicola Marzari , Oliviero Andreussi

We present results of systematic calculations of the isospin-symmetry-breaking corrections to the superallowed I=$0+,T=1 --> I=0+,T=1 beta-decays, based on the self-consistent isospin- and angular-momentum-projected nuclear density…

Nuclear Theory · Physics 2014-08-05 W. Satula , J. Dobaczewski , M. Konieczka , W. Nazarewicz

As emphasized in a recent review article [Chem. Rev. 122, 14180 (2022)], organic solar cell (OSC) photoconversion efficiency has been rapidly evolving with results increasingly comparable to those of traditional inorganic solar cells.…

Chemical Physics · Physics 2023-06-01 Taouali W , Alimi K , Nangraj A. S. , Casida M. E

We have been examining factors determining the accuracy of cluster expansion (CE), which is used in combination with many density functional theory (DFT) calculations. With the exception of multicomponent metallic or isovalent ionic…

Materials Science · Physics 2014-02-07 Atsuto Seko , Isao Tanaka

We present a detailed study of the energetics of water clusters (H$_2$O)$_n$ with $n \le 6$, comparing diffusion Monte Carlo (DMC) and approximate density functional theory (DFT) with well converged coupled-cluster benchmarks. We use the…

Materials Science · Physics 2015-06-04 M. J. Gillan , F. R. Manby , M. D. Towler , D. Alfè

Molecular dynamics (MD) simulation of dislocation migration requires semi-empirical potentials of the interatomic interaction. While there are many reliable semi-empirical potentials for the bcc Fe, the number of the available potentials…

Materials Science · Physics 2020-09-28 Mikhail I. Mendelev , Valery Borovikov

The development of new battery materials, particularly novel cathode chemistries, is essential for enabling next generation energy storage technologies. In this work, we employ a multi-fidelity screening protocol combining the Energy-GNoME…

Materials Science · Physics 2026-02-11 Nada Alghamdi , Paolo de Angelis , Pietro Asinari , Eliodoro Chiavazzo

This work builds a bridge between density functional theory (DFT) and model interpretations of Anderson's superexchange theory by constructing a $f$-$d$-$p$ model with DFT Wannier functions to enable a direct quantum many-body solution…

Materials Science · Physics 2020-11-03 Dian-Teng Chen , Jia Chen , Xiang-Guo Li , George Christou , Xiao-Guang Zhang , Hai-Ping Cheng

Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed. Some supervise the learning process by pairwise or tripletwise similarity constraints while others take advantage of structured similarity…

Machine Learning · Computer Science 2019-11-25 Xinshao Wang , Elyor Kodirov , Yang Hua , Neil Robertson

Quantum--Mechanical methods that are both computationally fast and accurate are not yet available for electronic excitations having charge transfer character. In this work, we present a significant step forward towards this goal for those…

Chemical Physics · Physics 2015-06-12 Michele Pavanello , Troy Van Voorhis , Lucas Visscher , Johannes Neugebauer

The alpha-particle energy deposition mechanism modifies the ignition conditions of the thermonuclear Deuterium-Tritium fusion reactions, and constitutes a key issue in achieving high gain in Inertial Confinement Fusion implosions.…

Plasma Physics · Physics 2017-08-02 Mauro Temporal , Benoit Canaud , Withold Cayzac , Rafael Ramis , Robert L. Singleton

Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on energy, force, and stress errors can exhibit error in…

Materials Science · Physics 2026-01-30 Teddy Koker , Abhijeet Gangan , Mit Kotak , Jaime Marian , Tess Smidt