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Entanglement entropy (EE) plays a central role in the intersection of quantum information science and condensed matter physics. However, scanning the EE for two-dimensional and higher-dimensional systems still remains challenging. To address this challenge, we propose a quantum Monte Carlo scheme capable of extracting large-scale data of Rényi EE with high precision and low technical barrier. Its advantages lie in the following aspects: a single simulation can obtain the continuous variation curve of EE with respect to parameters, greatly reducing the computational cost; the algorithm implementation is simplified, and there is no need to alter the spacetime manifold during the simulation, making the code easily extendable to various many-body models. Additionally, we introduce a formula to calculate the derivative of EE without resorting to numerical differentiation from dense EE data. We then demonstrate the feasibility of using EE and its derivative to find phase transition points, critical exponents, and various phases.
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http://dx.doi.org/10.1038/s41467-025-61084-7 | DOI Listing |
J Am Chem Soc
September 2025
Kathleen Lonsdale Materials Chemistry, Department of Chemistry, University College London, London WC1H 0AJ, U.K.
The exceptional performance of ceria (CeO) in catalysis and energy conversion is fundamentally governed by its defect chemistry, particularly oxygen vacancies. The formation of each oxygen vacancy (V) is assumed to be compensated by two localized electrons on cations (Ce). Here, we show by combining theory with experiment that while this 1 V: 2Ce ratio accounts for the global charge compensation, it does not apply at the local scale, particularly in nanoparticles.
View Article and Find Full Text PDFJ Chem Phys
September 2025
Department of Earth Sciences, University College London, London WC1E 6BT, United Kingdom.
Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power make FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated.
View Article and Find Full Text PDFJ Chem Phys
September 2025
Theoretical Physics IV, University of Bayreuth, 95447 Bayreuth, Germany.
Density functional theory (DFT) is a cornerstone of modern electronic structure theory. In the Kohn-Sham scheme, the many-electron Schrödinger equation is replaced by a set of effective single-particle equations. Thus, the full complexity of the quantum mechanical many-particle effects is mapped to the exchange-correlation potential vxc(r).
View Article and Find Full Text PDFLuminescence
September 2025
Department of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora, Brazil.
Rare-earth ions (REIs), especially trivalent lanthanides (Ln ), are central to photonic technologies due to sharp intra-4f transitions, long lifetimes, and host-insensitive emission. However, modeling REIs remains challenging due to localized 4f orbitals, strong electron correlation, and multiplet structures. This review summarizes atomistic modeling strategies combining quantum chemistry and machine learning (ML).
View Article and Find Full Text PDFSmall Methods
September 2025
Institute of Applied Mechanics, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei, 106216, Taiwan.
2D lead-halide perovskites have garnered considerable attention owing to their superior environmental stability and tunable optoelectronic properties, which can be precisely controlled through varying quantum well (QW) width (denoted by the integer n). However, the commonly observed phenomenon of mixed QW width distributions poses a major obstacle to achieving optimal device performance, necessitating an in-depth understanding of how QW width distributions depend on chemical composition and thermodynamic stability. In this work, a robust machine learning (ML)-based energy model is developed, rigorously benchmarked against first-principles calculations, enabling extensive molecular-level simulations of 2D perovskites with butylammonium (BA) and phenethylammonium (PEA) spacer cations.
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