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Large-scale atomistic simulations rely on interatomic potentials providing an efficient representation of atomic energies and forces. Modern machine-learning (ML) potentials provide the most precise representation compared to electronic…

Computational Physics · Physics 2025-04-23 David Immel , Ralf Drautz , Godehard Sutmann

Variational mutual information (MI) estimators are widely used in unsupervised representation learning methods such as contrastive predictive coding (CPC). A lower bound on MI can be obtained from a multi-class classification problem, where…

Machine Learning · Computer Science 2020-12-04 Jiaming Song , Stefano Ermon

We analyze two-particle binding factors of H2, LiH, and HeH+ molecules/ions with the help of our original exact diagonalization ab intio (EDABI) approach. The interelectronic correlations are taken into account rigorously within the second…

Strongly Correlated Electrons · Physics 2022-08-24 M. Hendzel , M. Fidrysiak , J. Spałek

A novel atomistic-continuum method (ACM) based on finite element method (FEM) is proposed to numerically simulate the nano-scaled Poisson's ratio and Young's modulus effect of Lithium (Li) body-centered cubic (BCC) structure. The potential…

Materials Science · Physics 2016-09-08 C. -Y. Chou , C. Yuan , Chung-Jung Wu , K. -N. Chiang

The zero range potential is constructed for a system of two particles interacting via the Coulomb potential. The singular part of the asymptote of the wave function at the origin which is caused by the common effect of the zero range…

Atomic Physics · Physics 2015-06-05 S. L. Yakovlev , V. A. Gradusov

Li$_6$PS$_5$Cl is a promising candidate for the solid electrolyte in all-solid-state Li-ion batteries. In applications, this material is in a polycrystalline state with grain boundaries (GBs) that can affect ionic conductivity. While…

Materials Science · Physics 2024-12-02 Yongliang Ou , Yuji Ikeda , Lena Scholz , Sergiy Divinski , Felix Fritzen , Blazej Grabowski

We propose a non-perturbative $ab$ $initio$ approach to calculate the electrical conductivity of a liquid metal. Our approach is based on the Kubo formula and the theory of electron-phonon coupling (EPC), and unlike the conventional…

Materials Science · Physics 2022-04-26 Xiao-Wei Zhang , Haoran Chen , En-Ge Wang , Junren Shi , Xin-Zheng Li

The electromagnetic(EM) interactions between charged protons on the correlations of nucleons are discussed by introducing the Anderson-Higgs mechanism of broken U(1) EM symmetry into the relativistic nuclear theory with a parametric photon…

Nuclear Theory · Physics 2016-09-08 Ji-sheng Chen , Jia-rong Li , Meng Jin

A generalized Anderson model for a magnetic impurity in an interacting one-dimensional electron gas is studied via a mapping onto a classical Coulomb gas. For weak potential scattering, the local-moment parameter regime expands as repulsive…

Condensed Matter · Physics 2009-10-28 Avraham Schiller , Kevin Ingersent

We investigate a model of a single resonant level coupled to the edge of a quantum wire in the Luttinger liquid phase or to the middle of a chiral Luttinger liquid via both tunneling and a contact interaction. Utilizing the Yuval-Anderson…

Mesoscale and Nanoscale Physics · Physics 2011-01-20 Moshe Goldstein , Yuval Weiss , Richard Berkovits

Understanding ion adsorption at electrified metal-electrolyte interfaces is essential for accurate modeling of electrochemical systems. Here, we systematically investigate the free energy profiles of Na$^+$, Cl$^-$, and F$^-$ ions at the…

Chemical Physics · Physics 2026-02-27 Fabrice Roncoroni , Abrar Faiyad , Yichen Li , Tao Ye , Ashlie Martini , David Prendergast

Efficient materials discovery requires reducing costly first-principles calculations for training machine-learned interatomic potentials (MLIPs). We develop an active learning (AL) framework that iteratively selects informative structures…

Machine Learning · Computer Science 2026-01-22 Mohammed Azeez Khan , Aaron D'Souza , Vijay Choyal

Electronic nearsightedness is one of the fundamental principles governing the behavior of condensed matter and supporting its description in terms of local entities such as chemical bonds. Locality also underlies the tremendous success of…

Computational Physics · Physics 2020-09-01 Andrea Grisafi , Jigyasa Nigam , Michele Ceriotti

Due consideration of proton optical--model potential (OMP) anomalies at sub-Coulomb energies for medium--weight nuclei is shown to be critical for the analysis of the unprecedented measurement of $^{59}$Cu$(p,\alpha)^{56}$Ni reaction cross…

Nuclear Theory · Physics 2022-08-29 Vlad Avrigeanu , Marilena Avrigeanu

Metal-semiconductor contacts are a pillar of modern semiconductor technology. Historically, their microscopic understanding has been hampered by the inability of traditional analytical and numerical methods to fully capture the complex…

Materials Science · Physics 2016-04-13 Daniele Stradi , Umberto Martinez , Anders Blom , Mads Brandbyge , Kurt Stokbro

Modeling lithium-ion battery (LIB) degradation offers significant cost savings and enhances the safety and reliability of electric vehicles (EVs) and battery energy storage systems (BESS). Whilst data-driven methods have received great…

Machine Learning · Computer Science 2025-01-22 James Sadler , Rizwaan Mohammed , Michael Castle , Kotub Uddin

Electrolyte solutions play critical role in a vast range of important applications, yet an accurate and scalable method of predicting their properties without fitting to experiment has remained out of reach, despite over a century of…

Chemical Physics · Physics 2024-11-18 Junji Zhang , Joshua Pagotto , Tim Gould , Timothy T. Duignan

Two- and three-dimensional electron gases with a uniform neutralizing background are studied at negative compressibility. Parametrized expressions for the dielectric function are used to access this strong-coupling regime, where the…

Condensed Matter · Physics 2009-11-07 Adriaan M. J. Schakel

We derive asymptotically exact expressions for the thermopower and figure of merit of a quantum impurity connecting two noninteracting leads in the linear response regime where the chemical potential and temperature differences between the…

Mesoscale and Nanoscale Physics · Physics 2015-09-23 Edward Taylor , Dvira Segal

Machine learning interatomic potentials (MLIPs) with broad chemical flexibility are important for atomistic simulations of compositionally complex materials such as high-entropy alloys. Here, we study two state-of-the-art MLIP frameworks,…

Materials Science · Physics 2026-04-06 Fei Shuang , Penghua Ying , Kai Liu , Zixiong Wei , Fengxian Liu , Zheyong Fan , Minqiang Jiang , Poulumi Dey