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Generative models trained on internet-scale data are capable of generating novel and realistic texts, images, and videos. A natural next question is whether these models can advance science, for example by generating novel stable materials.…

Machine Learning · Computer Science 2024-06-05 Sherry Yang , KwangHwan Cho , Amil Merchant , Pieter Abbeel , Dale Schuurmans , Igor Mordatch , Ekin Dogus Cubuk

Employing a recently developed method that is numerically accurate within a model space simulating the real-time dynamics of few-body systems interacting with macroscopic environmental quantum fields, we analyze the full dynamics of an…

Quantum Physics · Physics 2013-03-07 Javier Prior , Inés de Vega , Alex W. Chin , Susana F. Huelga , Martin B. Plenio

Crystal structure prediction (CSP) has proven to be a highly effective route for discovering new materials. Substantial advancements have been made in CSP of inorganic and molecular crystals, while hybrid materials, including metal-organic…

Materials Science · Physics 2024-12-17 Elizaveta Yakovenko , Iurii Nevolin , Anatoliy Chasovskikh , Artem Mitrofanov , Vadim Korolev

Incorporating known symmetries in data into machine learning models has consistently improved predictive accuracy, robustness, and generalization. However, achieving exact invariance to specific symmetries typically requires designing…

Machine Learning · Computer Science 2026-03-03 Cindy Y. Zhang , Elif Ertekin , Peter Orbanz , Ryan P. Adams

Symmetry-breaking bifurcations, where a flow state with a certain symmetry undergoes a transition to state with a different symmetry, are ubiquitous in fluid mechanics. Much can be understood about the nature of these transitions from…

Fluid Dynamics · Physics 2024-11-20 John F. Rudge , Dan McKenzie

Discrete time crystals are periodically driven systems that display spontaneous symmetry breaking of time translation invariance in the form of indefinite subharmonic oscillations. We introduce a thermodynamically consistent model for a…

Statistical Mechanics · Physics 2021-01-20 Lukas Oberreiter , Udo Seifert , Andre C. Barato

In microfluidic devices, inertia drives particles to focus on a finite number of inertial focusing streamlines. Particles on the same streamline interact to form one-dimensional microfluidic crystals (or "particle trains"). Here we develop…

Fluid Dynamics · Physics 2018-09-12 Kaitlyn Hood , Marcus Roper

Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. In this work, we introduce MOFFlow, the first deep generative model tailored for MOF…

Biomolecules · Quantitative Biology 2025-03-20 Nayoung Kim , Seongsu Kim , Minsu Kim , Jinkyoo Park , Sungsoo Ahn

This monograph introduces key concepts and problems in the new research area of Periodic Geometry and Topology for materials applications.Periodic structures such as solid crystalline materials or textiles were previously classified in…

Computational Geometry · Computer Science 2021-06-10 Olga Anosova , Vitaliy Kurlin

Periodic frameworks with crystallographic symmetry are investigated from the perspective of a general deformation theory of periodic bar-and-joint structures in $R^d$. It is shown that natural parametrizations provide affine section…

Metric Geometry · Mathematics 2011-10-24 Ciprian S. Borcea , Ileana Streinu

Understanding and controlling physical aging, i.e. the spontaneous temporal evolution of out-of-equilibrium systems, represents one of the greatest tasks in material science. Recent studies have revealed the existence of a complex atomic…

Materials Science · Physics 2016-02-17 V. M. Giordano , B. Ruta

The formation of a phase of matter can be associated with the spontaneous breaking of a symmetry. For crystallization, this broken symmetry is the spatial translation symmetry, as the atoms spontaneously localize in a periodic fashion. In…

The existence of a paradoxical supersolid phase of matter, possessing the apparently incompatible properties of crystalline order and superfluidity, was predicted 50 years ago. Solid helium was the natural candidate, but there supersolidity…

The discovery of new functional and stable materials is a big challenge due to its complexity. This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical…

Computational Physics · Physics 2023-10-18 Arsen Sultanov , Jean-Claude Crivello , Tabea Rebafka , Nataliya Sokolovska

Organic molecular crystals are appealing for next-generation optoelectronic applications, most notably due to their multiexciton generation process that can increase the efficiency of photovoltaic devices. However, a general understanding…

Materials Science · Physics 2021-11-01 Aaron R. Altman , Sivan Refaely-Abramson , Felipe H. da Jornada

We introduce a method that defines the species (representatives) of inorganic compounds, and studied the statistical distribution of the defined species among space groups (distribution of space groups), by using ICSD (Inorganic Crystal…

Applications · Statistics 2011-06-30 Miyako Fujiwara , Yoshiaki Itoh , Takeo Matsumoto , Hiroshi Takeda

Time crystals are many-body systems whose ground state spontaneously breaks time-translation symmetry and thus exhibits long-range spatiotemporal order and robust periodic motion. Using hydrodynamics, we have recently shown how an…

Statistical Mechanics · Physics 2025-12-15 R. Hurtado-Gutiérrez , C. Pérez-Espigares , P. I. Hurtado

A peculiar feature of quantum states is that they may embody so-called projective representations of symmetries rather than ordinary representations. Projective representations of space groups-the defining symmetry of crystals-remain…

Mesoscale and Nanoscale Physics · Physics 2023-02-14 Z. Y. Chen , Zheng Zhang , Shengyuan A. Yang , Y. X. Zhao

We derive a formulation of molecular dynamics that generates only symmetric configurations. We implement it for all 2D planar and 3D space groups. An atlas of 2D Lennard-Jones crystals under all planar groups is created with symmetric…

Statistical Mechanics · Physics 2022-06-22 Sam Cox , Andrew D. White

Flow Matching has emerged as a powerful framework for learning continuous transformations between distributions, enabling high-fidelity generative modeling. This work introduces Symmetrical Flow Matching (SymmFlow), a new formulation that…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Francisco Caetano , Christiaan Viviers , Peter H. N. De With , Fons van der Sommen
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