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We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are…

Multireference methods such as multiconfiguration pair-density functional theory (MC-PDFT) offer an effective means of capturing electronic correlation in systems with significant multiconfigurational character. However, their application…

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic interactions. Here, fused multi-modal electron microscopy offers high signal-to-noise ratio (SNR)…

State-of-the-art deep neural networks (DNNs) are highly effective in solving many complex real-world problems. However, these models are vulnerable to adversarial perturbation attacks, and despite the plethora of research in this domain, to…

Machine Learning · Computer Science 2019-09-10 Gil Fidel , Ron Bitton , Asaf Shabtai

Meta-analysis of multiple genome-wide association studies (GWAS) is effective for detecting single or multi marker associations with complex traits. We develop a flexible procedure ("STAMP") based on mixture models to perform region based…

Methodology · Statistics 2018-01-01 Andriy Derkach , Ruth M. Pfeiffer

We present a spectral finite-element formulation of the optimized effective potential (OEP) method for atomic structure calculations in the random phase approximation (RPA). In particular, we develop a finite-element framework that employs…

Computational Physics · Physics 2026-01-28 Shubhang Krishnakant Trivedi , Phanish Suryanarayana

The practically unlimited high-dimensional composition space of high-entropy materials (HEMs) has emerged as an exciting platform for functional materials design and discovery. However, the identification of stable and synthesizable HEMs…

Materials Science · Physics 2024-03-01 Dibyendu Dey , Liangbo Liang , Liping Yu

Scanning near-field optical imaging (SNOM) using local active probes provides in general images of the electric part of the photonic local density of states. However, certain atomic clusters can supply more information by simultaneously…

Mesoscale and Nanoscale Physics · Physics 2020-04-28 Clément Majorel , Christian Girard , Aurélien Cuche , Arnaud Arbouet , Peter R. Wiecha

Small-angle X-ray scattering (SAXS) experiments are increasingly used to probe RNA structure. A number of \emph{forward models} that relate measured SAXS intensities and structural features, and that are suitable to model either…

Biomolecules · Quantitative Biology 2022-07-26 Mattia Bernetti , Kathleen B. Hall , Giovanni Bussi

We propose a local regional chemical potential (RCP) analysis method based on an energy window scheme to quantitatively estimate the selectivity of atomic and molecular adsorption on surfaces, as well as the strength of chemical bonding…

Materials Science · Physics 2025-05-30 Masahiro Fukuda , Masato Senami , Yoshiaki Sugimoto , Taisuke Ozaki

Localized-surface plasmon resonance is of importance in both fundamental and applied physics for the subwavelength confinement of optical field, but realization of quantum coherent processes is confronted with challenges due to strong…

Quantum Physics · Physics 2017-12-13 Pai Peng , Yong-Chun Liu , Da Xu , Qi-Tao Cao , Qihuang Gong , Yun-Feng Xiao

Concepts from information geometry are used to analyse parameter sensitivity for a nuclear energy density functional, representative of a class of semi-empirical functionals that start from a microscopically motivated ansatz for the density…

Nuclear Theory · Physics 2016-08-29 Tamara Niksic , Dario Vretenar

Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical…

While machine learning (ML) interatomic potentials (IPs) are able to achieve accuracies nearing the level of noise inherent in the first-principles data to which they are trained, it remains to be shown if their increased complexities are…

Materials Science · Physics 2023-10-05 Joshua A. Vita , Dallas R. Trinkle

We propose a new type of entropic descriptor that is able to quantify the statistical complexity (a measure of complex behaviour) by taking simultaneously into account the average departures of a system's entropy S from both its maximum…

Statistical Mechanics · Physics 2015-05-13 R. Piasecki , A. Plastino

We study sparse principal component analysis in the high-dimensional, sample-limited regime, aiming to recover a leading component supported on a few coordinates. Despite extensive progress, most methods and analyses are tailored to the…

Information Theory · Computer Science 2025-12-18 Mengchu Xu , Jian Wang , Yonina C. Eldar

The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and…

Computational Physics · Physics 2019-06-26 Mitchell A. Wood , Mary Alice Cusentino , Brian D. Wirth , Aidan P. Thompson

To further develop accurate and large-scale simulations of electrochemical interfaces, we propose a unified explicit electric potential framework to simultaneously predict atomic forces and electron density distributions. The framework…

Chemical Physics · Physics 2026-04-14 Jingwen Zhou , Yawen Yu , Xuwei Liu , Chungen Liu

Accurate atomistic simulations of gas-surface scattering require potential energy surfaces that remain reliable over broad configurational and energetic ranges while retaining the efficiency needed for extensive trajectory sampling. Here,…

Large-scale computer simulations of chemical atoms are used in a wide range of applications, including batteries, drugs, and more. However, there is a problem with efficiency as it takes a long time due to the large amount of calculation.…

Materials Science · Physics 2024-02-28 Hyun Gyu Park , Soohaeng Yoo Willow , D. ChangMo Yang , Chang Woo Myung