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We present a comprehensive study of the short-ranged nuclear and magnetic order in the two-dimensional spin glass, Mn$_{0.5}$Fe$_{0.5}$PS$_3$. Nuclear neutron scattering data reveal a random distribution of Mn$^{2+}$ and Fe$^{2+}$ ions…

Materials Science · Physics 2020-08-12 J. N. Graham , M. J. Coak , S. Son , E. Suard , J. -G. Park , L. Clark , A. R. Wildes

In machine learning and molecular design, there exist two approaches: discriminative and generative. In the discriminative approach dubbed forward design, the goal is to map a set of features/molecules to their respective electronics…

Chemical Physics · Physics 2019-04-24 Alain Tchagang , Julio Valdés

Randomly diluted quantum boson and spin models in two dimensions combine the physics of classical percolation with the well-known dimensionality dependence of ordering in quantum lattice models. This combination is rather subtle for models…

Disordered Systems and Neural Networks · Physics 2007-05-23 N. Bray-Ali , J. E. Moore , T. Senthil , A. Vishwanath

The correlation between local chemical environment and atomic displacements in disordered CuAu alloy has been studied using Monte Carlo simulations based on the effective medium theory (EMT) of metallic cohesion. These simulations correctly…

Statistical Mechanics · Physics 2017-02-08 O. Malis , K. F. Ludwig, , D. L. Olmsted , B. Chakraborty

Fe-based metallic glasses are promising functional materials for advanced magnetism and sensor fields. Tailoring magnetic performance in amorphous materials requires a thorough knowledge of the correlation between structural disorder and…

A Monte Carlo study of the late time growth of $L1_2$ ordered domains on a fcc $A_3B$ binary alloy is presented. The energy of the alloy has been modeled by a nearest neighbor interaction Ising hamiltonian. The system exhibits a fourfold…

Condensed Matter · Physics 2009-10-28 Carlos Frontera , Eduard Vives , Teresa Castán , Antoni Planes

We introduce a new micro-macro Markov chain Monte Carlo method (mM-MCMC) to sample invariant distributions of molecular dynamics systems that exhibit a time-scale separation between the microscopic (fast) dynamics, and the macroscopic…

Numerical Analysis · Mathematics 2020-02-24 Hannes Vandecasteele , Giovanni Samaey

We present a preconditioned Monte Carlo method for computing high-dimensional multivariate normal and Student-$t$ probabilities arising in spatial statistics. The approach combines a tile-low-rank representation of covariance matrices with…

Computation · Statistics 2020-11-26 Jian Cao , Marc G. Genton , David E. Keyes , George M. Turkiyyah

Synchrotron X-ray diffraction measurements have been conducted on liquid phosphorus trichloride, tribromide and triiodide. Molecular Dynamics simulations for these molecular liquids were performed with a dual purpose: (1) to establish…

Disordered Systems and Neural Networks · Physics 2014-02-06 Szilvia Pothoczki , László Temleitner , László Pusztai

The mechanical loss angle of amorphous TiO$_2$-doped GeO$_2$ can be lower than 10$^{-4}$, making it a candidate for Laser Interferometer Gravitational-wave Observatory (LIGO) mirror coatings. Amorphous oxides have complex atomic structures…

Materials Science · Physics 2025-03-28 Jun Jiang , Rui Zhang , Kiran Prasai , Riccardo Bassiri , James N. Fry , Martin M. Fejer , Hai-Ping Cheng

Computing atomic-scale properties of chemically disordered materials requires an efficient exploration of their vast configuration space. Traditional approaches such as Monte Carlo or Special Quasirandom Structures either entail sampling an…

Materials Science · Physics 2026-03-17 Maciej J. Karcz , Luca Messina , Eiji Kawasaki , Emeric Bourasseau

The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a…

To learn intrinsic low-dimensional structures from high-dimensional data that most discriminate between classes, we propose the principle of Maximal Coding Rate Reduction ($\text{MCR}^2$), an information-theoretic measure that maximizes the…

Machine Learning · Computer Science 2020-06-16 Yaodong Yu , Kwan Ho Ryan Chan , Chong You , Chaobing Song , Yi Ma

We employ high-resolution total neutron scattering in conjunction with reverse Monte Carlo simulations to examine, in a detailed and unbiased manner, the crystal structure of the vacancy-ordered oxide pyrochlore Pb2Ru2O6.5 in light of its…

Materials Science · Physics 2011-07-25 Daniel P. Shoemaker , Anna Llobet , Makoto Tachibana , Ram Seshadri

Real-world networks have high-order relationships among objects and they evolve over time. To capture such dynamics, many works have been studied in a range of fields. Via an in-depth preliminary analysis, we observe two important…

Social and Information Networks · Computer Science 2025-08-26 Yunyong Ko , Da Eun Lee , Song Kyung Yu , Sang-Wook Kim

Amorphous materials are solids that lack long-range atomic order but possess complex short- and medium-range order. Unlike crystalline materials that can be described by unit cells containing few up to hundreds of atoms, amorphous materials…

Machine Learning · Computer Science 2026-04-01 Yan Lin , Jonas A. Finkler , Tao Du , Jilin Hu , Morten M. Smedskjaer

Lattice simulations are an important class of problems in crystalline solids, surface science, alloys, adsorption, absorption, separation, catalysis, to name a few. We describe a fast computational method for performing lattice…

Statistical Mechanics · Physics 2023-07-24 Suhail Haque , Abhijit Chatterjee

How atoms in covalent solids rearrange over a medium-range length-scale during amorphization is a long pursued question whose answer could profoundly shape our understanding on amorphous (a-) networks. Based on ab-intio calculations and…

Disordered Systems and Neural Networks · Physics 2017-03-14 Xianqiang Liu , Xianbin Li , Xinyang Wang , Yongqiang Cheng , Xuepeng Wang , Xiaodong Han , Ze Zhang , Shengbai Zhang

The nature of the ordering of the three-dimensional isotropic Heisenberg spin glass with nearest-neighbor random Gaussian coupling is studied by extensive Monte Carlo simulations. Several independent physical quantities are measured both…

Disordered Systems and Neural Networks · Physics 2013-05-29 Dao Xuan Viet , Hikaru Kawamura

We discuss an inverse approach for atomistic modeling of glassy materials. The focus is on structural modeling and electronic properties of hydrogenated amorphous silicon and glassy GeSe2 alloy. The work is based upon a new approach…

Disordered Systems and Neural Networks · Physics 2009-11-13 Parthapratim Biswas , D. A. Drabold