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Polarization switching in ferroelectric BaTiO3 can proceed through fundamentally different mechanisms - yet the conditions that determine which pathway is realized remain poorly understood. Using machine-learning potential-based molecular…

Materials Science · Physics 2026-05-26 Po-Yen Chen , Teruyasu Mizoguchi

We discuss the topology of the parameter space of invertible phases with an onsite symmetry $G$, i.e., quantum many-body ground states that have neither fractionalization nor spontaneous breaking of the symmetry. The classification of…

Strongly Correlated Electrons · Physics 2024-09-17 Yuan Yao , Akira Furusaki

Tailoring the degree of structural disorder in Ge-Sb-Te alloys is important for the development of non-volatile phase-change memory and neuro-inspired computing. Upon crystallization from the amorphous phase, these alloys form a cubic…

This paper attacks time-domain identification for interaction parameters of a heterogeneous networked dynamic system (NDS), with each of its subsystems being described by a continuous-time descriptor quadratic-bilinear time-invariant (QBTI)…

Multiagent Systems · Computer Science 2025-11-11 Tong Zhou , Yubing Li

We work out two different analytical methods for calculating the boundary of the Mott-insulator-superfluid (MI-SF) quantum phase transition for scalar bosons in cubic optical lattices of arbitrary dimension at zero temperature which improve…

Statistical Mechanics · Physics 2009-11-13 F. E. A. dos Santos , A. Pelster

We study the bipartite entanglement per bond to determine characteristic features of the phase diagram of various quantum spin models in different spatial dimensions. The bipartite entanglement is obtained from a tensor network…

Quantum Physics · Physics 2016-07-07 B. Braiorr-Orrs , M. Weyrauch , M. V. Rakov

Stemming from the Heisenberg equations of motion, we study the time-evolution stability of the order parameters for the cold atoms on optical lattices. The requirement of this stability of the order parameters endows the phase diagram with…

Soft Condensed Matter · Physics 2007-05-23 Yue Yu

Machine learning has been establishing its potential in multiple areas of condensed matter physics and materials science. Here we develop and use an unsupervised machine learning workflow within a framework of first-principles-based…

Materials Science · Physics 2023-01-02 Adriana Ladera , Ravi Kashikar , S. Lisenkov , I. Ponomareva

We investigate lattices of instantons and the dimension-changing transitions between them. Our ultimate goal is the 3D->4D transition, which is holographically dual to the phase transition between the baryonic and the quarkyonic phases of…

High Energy Physics - Theory · Physics 2015-06-15 Vadim Kaplunovsky , Jacob Sonnenschein

In this paper, the finite volume method is developed to analyze coupled dynamic problems of nonlinear thermoelasticity. The major focus is given to the description of martensitic phase transformations essential in the modelling of shape…

Computational Engineering, Finance, and Science · Computer Science 2025-10-20 Linxiang X. Wang , Roderick V. N. Melnik

We report on a combined experimental and theoretical study of the low-entropy Mott transition for interacting bosons trapped in a three-dimensional (3D) cubic lattice -- namely, the interaction-induced superfluid-to-normal phase transition…

We present the general lattice model for a multi-component atomic Bose-Einstein system in the optical lattice. Using the model, we analytically study the quantum phase transition between Mott insulator and superfluid. A mean-field theory is…

Soft Condensed Matter · Physics 2009-11-07 Guang-Hong Chen , Yong-Shi Wu

Motivated by experimental observations on CuAlNi single crystals, we present a theoretical investigation of non-planar austenite-martensite interfaces. Our analysis is based on the nonlinear elasticity model for martensitic transformations…

Analysis of PDEs · Mathematics 2013-10-10 John M. Ball , Konstantinos Koumatos

The infinite time-evolving block decimation (iTEBD) algorithm [Phys. Rev. Lett. 98, 070201 (2007)] allows to simulate unitary evolution and to compute the ground state of one-dimensional quantum lattice systems in the thermodynamic limit.…

Statistical Mechanics · Physics 2009-11-13 Roman Orus , Guifre Vidal

In this work, we decompose the time-evolution of the Bose-Hubbard model into a sequence of logic gates that can be implemented on a continuous-variable photonic quantum computer. We examine the structure of the circuit that represents this…

Quantum Physics · Physics 2020-03-17 Timjan Kalajdzievski , Christian Weedbrook , Patrick Rebentrost

This paper presents primarily two Euclidean embeddings of the quotient space generated by matrices that are identified modulo arbitrary row permutations. The original application is in deep learning on graphs where the learning task is…

Functional Analysis · Mathematics 2022-03-16 Radu Balan , Naveed Haghani , Maneesh Singh

Employing the Ginzburg-Landau phase-field theory, a new coupled dynamic thermo-mechanical 3D model has been proposed for modeling the cubic-to-tetragonal martensitic transformations in shape memory alloy (SMA) nanostructures. The…

Materials Science · Physics 2015-05-20 R. Dhote , H. Gomez , R. Melnik , J. Zu

The ability to control the stress-induced phase transformation of the shape memory alloy, NiTi, is an important technological challenge that must be understood for their wide application in devices that can exploit their reversible strain…

Materials Science · Physics 2024-06-19 Himanshu Vashishtha , David M. Collins

A phase transition for bosonic atoms in a two-dimensional anisotropic optical lattice is considered. If the tunnelling rates in two directions are different, the system can undergo a transition between a two-dimensional superfluid and a…

Other Condensed Matter · Physics 2009-11-13 Magnus Rehn , Sara Bergkvist , Anders Rosengren , Robert Saers , Martin Zelán , Emil Lundh , Anders Kastberg

We address an inverse problem in modeling holographic superconductors. We focus our research on the critical temperature behavior depicted by experiments. We use a physics-informed neural network method to find a mass function $M(F^2)$,…

High Energy Physics - Theory · Physics 2025-05-08 Sejin Kim , Kyung Kiu Kim , Yunseok Seo