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Amorphous solids such as glass are ubiquitous in our daily life and have found broad applications ranging from window glass and solar cells to telecommunications and transformer cores. However, due to the lack of long-range order, the…

Uncertainty quantification appears today as a crucial point in numerous branches of science and engineering. In the past two decades, a growing interest has been devoted to stochastic finite element method (SFEM) for the propagation of…

Numerical Analysis · Mathematics 2020-08-11 Zhibao Zheng

A new approach is presented to obtain candidate structures from atomic pair distribution function (PDF) data in a highly automated way. It fetches, from web-based structural databases, all the structures meeting the experimenter's search…

Materials Science · Physics 2020-05-07 Long Yang , Pavol Juhás , Maxwell W. Terban , Matthew G. Tucker , Simon J. L. Billinge

In multicomponent lattice problems, e.g., in alloys, and at crystalline surfaces and interfaces, atomic arrangements exhibit spatial correlations that dictate the kinetic and thermodynamic phase behavior. These correlations emerge from…

Statistical Mechanics · Physics 2024-03-28 Abhijit Chatterjee

We present a novel formulation of structural design optimization problems specifically tailored to be solved by quantum annealing (QA). Structural design optimization aims to find the best, i.e., material-efficient yet high-performance,…

Computational Engineering, Finance, and Science · Computer Science 2024-04-17 Fabian Key , Lukas Freinberger

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train…

Computational Physics · Physics 2019-02-05 Michele Ceriotti , Michael J. Willatt , Gábor Csányi

This paper presents a new stochastic finite element method for computing structural stochastic responses. The method provides a new expansion of stochastic response and decouples the stochastic response into a combination of a series of…

Numerical Analysis · Mathematics 2021-04-28 Zhibao Zheng

The aim of this research is to introduce a novel structural design process that allows architects and engineers to extend their typical design space horizon and thereby promoting the idea of creativity in structural design. The theoretical…

Machine Learning · Computer Science 2018-09-25 Lukas Fuhrimann , Vahid Moosavi , Patrick Ole Ohlbrock , Pierluigi Dacunto

Site-occupancy disorder, defined as the non-periodic occupation of lattice sites in a crystal structure, is a ubiquitous phenomenon in solid-state physics and chemistry. Examples are mineral solid solutions, synthetic non-stoichiometric…

Materials Science · Physics 2016-02-10 Ricardo Grau-Crespo , Said Hamad

Ensembles of separate neural networks (NNs) have shown superior accuracy and confidence calibration over single NN across tasks. To improve the hardware efficiency of ensembles of separate NNs, recent methods create ensembles within a…

Machine Learning · Computer Science 2024-07-25 Martin Ferianc , Hongxiang Fan , Miguel Rodrigues

Predicting the ground state of alloy systems is challenging due to the large number of possible configurations. We identify an easily computed descriptor for the stability of binary surface alloys, the effective coordination number…

Materials Science · Physics 2017-02-17 Sananda Biswas , Shobhana Narasimhan

A quantitative first-principles description of complex substitutional materials like alloys is challenging due to the vast number of configurations and the high computational cost of solving the quantum-mechanical problem. Therefore,…

Materials Science · Physics 2025-06-24 Adrian Stroth , Claudia Draxl , Santiago Rigamonti

An attempt is made to bypass spectral analysis and fit internal coordinates of radicals directly to experimental liquid- and solid-state electron spin resonance (ESR) spectra. We take advantage of the recently introduced large-scale spin…

Chemical Physics · Physics 2014-07-16 G. T. P. Charnock , M. Krzystyniak , Ilya Kuprov

To better understand the spatial structure of large panels of economic and financial time series and provide a guideline for constructing semiparametric models, this paper first considers estimating a large spatial covariance matrix of the…

Machine Learning · Statistics 2015-03-19 Song Song

In this paper, we propose an efficient parallelization strategy for boundary element method (BEM) solvers that perform the electromagnetic analysis of structures with lossy conductors. The proposed solver is accelerated with the adaptive…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-11-30 Damian Marek , Shashwat Sharma , Piero Triverio

We propose a generalisation of molecular density functional theory to describe inhomogeneous solvent mixture, with the objective of modelling electrolytic solutions. Two electrolytic models are presented, both within the HNC approximation.…

Chemical Physics · Physics 2025-11-13 Guillaume Jeanmairet , Luc Belloni , Daniel Borgis

Factor-based Structural Equation Modeling (SEM) relies on likelihood-based estimation assuming a nonsingular sample covariance matrix, which breaks down in small-sample settings with $p>n$. To address this, we propose a novel estimation…

Machine Learning · Computer Science 2026-04-21 Hiroki Hasegawa , Aoba Tamura , Yukihiko Okada

We propose a simple route to evaluate the static structure, in terms of average coordination, of completely disordered solids with spherical constituents, from ca. 55% volume fraction up to random close packing, in the absence of structural…

Soft Condensed Matter · Physics 2009-05-08 Alessio Zaccone

A three-dimensional simulation model is proposed here to study the erosive wear of structure caused by solid particles, which accounts for the accumulation of surface deformation and degradation during the erosion process. Although there…

Fluid Dynamics · Physics 2025-03-04 Vinh D. X. Nguyen , A. Kiet Tieu , Damien Andre , Hongtao Zhu

Over the last decade, researchers have been working to improve a crucial aspect of quantum computing to predict Hamiltonian energy of solids. Quantum algorithms such as Variational Quantum Eigensolver (VQE) and Variational Quantum Deflation…

Materials Science · Physics 2025-09-03 Naman Khandelwal , Nidhi Verma , Pooja Jamdagni , Ashok Kumar