arXiv subjects

Kipton Barros

Publications and source records attributed to Kipton Barros.

At least 37 records · Page 2Linked to original sources

arXiv2025

Sunny.jl: A Julia Package for Spin Dynamics

Sunny is a Julia package designed to serve the needs of the quantum magnetism community. It supports the specification of a very broad class of spin models and a diverse suite of numerical solvers. These include powerful methods for simulating spin dynamics both in and out of equilibrium. Uniquely, it features a broad generalization of classical and semiclassical approaches to SU(N) coherent states, which is useful for studying systems exhibiting strong spin-orbit coupling or local entanglement effects. Sunny also offers a well-developed framework for calculating the dynamical spin structure factor, enabling direct comparison with scattering experiments. Ease of use is a priority, with tools for symmetry-guided modeling and interactive visualization.

quant-ph↗

arXiv2025

Classical signatures of quenched and thermal disorder in the dynamics of correlated spin systems

Neutron scattering is frequently used to look for evidence of features indicative of quantum-entangled phases of matter such as continua from fractionalisation or quantised excitations. However, the non-specificity of these features and difficulty of both fully quantum treatments and semiclassical models of disorder, make the diagnosis of such states problematic. Here, we demonstrate the feasibility of semiclassical treatments of disordered systems for supercells of $\sim 10,000$ spins. By examining a number of classically disordered models we show the presence of quantised excitations, broad continua and anomalous damping originating from quenched disorder or large classical degeneracies.

cond-mat.str-el↗

arXiv2024

Electric field driven spin textures in heavy fermion van der Waals magnets

The recently discovered van der Waals material CeSiI exhibits both heavy fermion behavior and spiral order with strong magnetic anisotropy which makes it a potential host for topological spin textures such as skyrmions through electrical gating. A monolayer of CeSiI consists of two layers of Ce atoms on triangular lattices that sandwich a silicene layer. Motivated by the experiments, we explore magnetic phase diagram in van der Waals heavy fermion materials as a function of anisotropy and applied magnetic field using an effective spin model. We demonstrate that application of an external electric field can tune the Kondo coupling on each Ce layer differently, in turn allowing for controlling the intra- and interlayer magnetic couplings. Our analysis indicates that this fine-tuning leads to the coexistence of different magnetic orders in a single monolayer. In particular, we show that a novel vortex phase can be stabilized only in the presence of an external electric field. Our results highlight the unique advantages and the tunability of van der Waals heavy fermion materials for manipulation of chiral magnetic phases.

cond-mat.mes-hall↗

arXiv2024

Classical dynamics of the antiferromagnetic Heisenberg $S=1/2$ spin ladder

We employ a classical limit grounded in SU(4) coherent states to investigate the temperature-dependent dynamical spin structure factor of the $S = 1/2$ ladder consisting of weakly coupled dimers. By comparing the outcomes of this classical approximation with density matrix renormalization group and exact diagonalization calculations in finite size ladders, we demonstrate that the classical dynamics offers an accurate approximation across the entire temperature range when the interdimer coupling is weak and a good approximation in the high temperature regime even when the interdimer coupling is strong. This agreement is achieved after appropriately rescaling the temperature axis and renormalizing expectation values to satisfy a quantum sum rule, following D. Dahlbom et al. [Phys. Rev. B 109, 014427 (2024)]. We anticipate the method will be particularly effective when applied to 2D and 3D lattices composed of weakly-coupled dimers, situations that remain challenging for alternative numerical methods.

cond-mat.str-el↗

arXiv2024

Kernel Polynomial Method for Linear Spin Wave Theory

Calculating dynamical spin correlations is essential for matching model magnetic exchange Hamiltonians to momentum-resolved spectroscopic measurements. A major numerical bottleneck is the diagonalization of the dynamical matrix, especially in systems with large magnetic unit cells, such as those with incommensurate magnetic structures or quenched disorder. In this paper, we demonstrate an efficient scheme based on the kernel polynomial method for calculating dynamical correlations of relevance to inelastic neutron scattering experiments. This method reduces the scaling of numerical cost from cubic to linear in the magnetic unit cell size.

cond-mat.str-el↗

arXiv2024

Physically interpretable approximations of many-body spectral functions

The rational function approximation provides a natural and interpretable representation of response functions such as the many-body spectral functions. We apply the Vector Fitting (VFIT) algorithm to fit a variety of spectral functions calculated from the Holstein model of electron-phonon interactions. We show that the resulting rational functions are highly efficient in their fitting of sharp features in the spectral functions, and could provide a means to infer physically relevant information from a spectral dataset. The position of the peaks in the approximated spectral function are determined by the location of poles in the complex plane. In addition, we developed an enhanced version of VFIT by introducing a regularization parameter that is slowly annealed to zero. With this new procedure, we demonstrate it is possible to achieve accurate spectral function fits that vary smoothly as a function of physical conditions.

cond-mat.stat-mech↗

arXiv2024

Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials

A light-matter hybrid quasiparticle, called a polariton, is formed when molecules are strongly coupled to an optical cavity. Recent experiments have shown that polariton chemistry can manipulate chemical reactions. Polariton chemistry is a collective phenomenon and its effects increase with the number of molecules in a cavity. However, simulating an ensemble of molecules in the excited state coupled to a cavity mode is theoretically and computationally challenging. Recent advances in machine learning techniques have shown promising capabilities in modeling ground state chemical systems. This work presents a general protocol to predict excited-state properties, such as energies, transition dipoles, and non-adiabatic coupling vectors with the hierarchically interacting particle neural network. Machine learning predictions are then applied to compute potential energy surfaces and electronic spectra of a prototype azomethane molecule in the collective coupling scenario. These computational tools provide a much-needed framework to model and understand many molecules' emerging excited-state polariton chemistry.

physics.chem-ph↗

arXiv2023

Active Learning approach to simulations of Strongly Correlated Matter with the Ghost Gutzwiller Approximation

Quantum embedding (QE) methods such as the Ghost Gutzwiller Approximation (gGA) offer a powerful approach to simulating strongly-correlated systems, but come with the computational bottleneck of computing the ground state of an auxiliary embedding Hamiltonian (EH) iteratively. In this work, we introduce an active learning (AL) framework integrated within the gGA to address this challenge. The methodology is applied to the single-band Hubbard model and results in a significant reduction in the number of instances where the EH must be solved. Through a principal component analysis (PCA), we find that the EH parameters form a low-dimensional structure that is largely independent of the geometric specifics of the systems, especially in the strongly-correlated regime. Our AL strategy enables us to discover this low-dimensionality structure on the fly, while leveraging it for reducing the computational cost of gGA, laying the groundwork for more efficient simulations of complex strongly-correlated materials.

cond-mat.str-el↗

arXiv2023

Renormalized Classical Theory of Quantum Magnets

We derive a renormalized classical spin (RCS) theory for $S > 1/2$ quantum magnets by constraining a generalized classical theory that includes all multipolar fluctuations to a reduced CP$^1$ phase space of dipolar SU($2$) coherent states. When the spin Hamiltonian $\hat{\cal{H}}^{S}$ is linear in the spin operators $\hat{\boldsymbol{S}}_j$ for each lattice site $j$, the RCS Hamiltonian $\tilde{\cal{H}}_{\rm cl}$ coincides with the usual classical model $\cal{H}_{\rm cl} = \lim_{S\rightarrow\infty} \hat{\cal{H}}^S$. In the presence of non-linear terms, however, the RCS theory is more accurate than $\cal{H}_{\rm cl}$. For the many materials modeled by spin Hamiltonians with (non-linear) single-ion anisotropy terms, the use of the RCS theory is essential to accurately model phase diagrams and to extract the correct Hamiltonian parameters from neutron scattering data

cond-mat.str-el↗

arXiv2023

Machine learning potentials with Iterative Boltzmann Inversion: training to experiment

Methodologies for training machine learning potentials (MLPs) to quantum-mechanical simulation data have recently seen tremendous progress. Experimental data has a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. We investigate a training procedure based on Iterative Boltzmann Inversion that produces a pair potential correction to an existing MLP, using equilibrium radial distribution function data. By applying these corrections to a MLP for pure aluminum based on Density Functional Theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require auto-differentiating through a molecular dynamics solver, and does not make assumptions about the MLP architecture. The results suggest a practical framework of incorporating experimental data into machine learning models to improve accuracy of molecular dynamics simulations.

physics.app-ph↗

arXiv2023

Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning

The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverage the plethora of data available as they require that each dataset be generated using the same QM method. Taking machine learning interatomic potentials (MLIPs) as an example, we show that meta-learning techniques, a recent advancement from the machine learning community, can be used to fit multiple levels of QM theory in the same training process. Meta-learning changes the training procedure to learn a representation that can be easily re-trained to new tasks with small amounts of data. We then demonstrate that meta-learning enables simultaneously training to multiple large organic molecule datasets. As a proof of concept, we examine the performance of a MLIP refit to a small drug-like molecule and show that pre-training potentials to multiple levels of theory with meta-learning improves performance. This difference in performance can be seen both in the reduced error and in the improved smoothness of the potential energy surface produced. We therefore show that meta-learning can utilize existing datasets with inconsistent QM levels of theory to produce models that are better at specializing to new datasets. This opens new routes for creating pre-trained, foundational models for interatomic potentials.

physics.chem-ph↗

arXiv2023

Charge order in the kagome lattice Holstein model: A hybrid Monte Carlo study

The Holstein model is a paradigmatic description of the electron-phonon interaction, in which electrons couple to local dispersionless phonon modes, independent of momentum. The model has been shown to host a variety of ordered ground states such as charge density wave (CDW) order and superconductivity on several geometries, including the square, honeycomb, and Lieb lattices. In this work, we study CDW formation in the Holstein model on the kagome lattice, using a recently developed hybrid Monte Carlo simulation method. We present evidence for $\sqrt{3} \times \sqrt{3}$ CDW order at an average electron filling of $\langle n \rangle =2/3$ per site, with an ordering wavevector at the $K$-points of the Brillouin zone. We estimate a phase transition occurring at $T_{c}\approx t/18$, where $t$ is the nearest-neighbor hopping parameter. Our simulations find no signature of CDW order at other electron fillings or ordering momenta for temperatures $T \geq t/20$.

cond-mat.str-el↗

arXiv2023

A hybrid Monte Carlo study of bond-stretching electron-phonon interactions and charge order in BaBiO$_3$

The relationship between electron-phonon ($e$-ph) interactions and charge-density-wave (CDW) order in the bismuthate family of high-temperature superconductors remains unresolved. We address this question using nonperturbative hybrid Monte Carlo calculations for the parent compound BaBiO$_3$. Our model includes the Bi $6s$ and O $2p_σ$ orbitals and coupling to the Bi-O bond-stretching branch of optical phonons via modulations of the Bi-O hopping integral. We simulate three-dimensional clusters of up to 4000 orbitals, with input model parameters taken from {\it ab initio} electronic structure calculations and a phonon energy $\hbarΩ_0 = 60$~meV. Our results demonstrate that the coupling to the bond-stretching modes is sufficient to reproduce the CDW transition in this system, despite a relatively small dimensionless coupling. We also find that the transition deviates from the weak-coupling Peierls' picture. This work demonstrates that off-diagonal $e$-ph interactions in orbital space are vital in establishing the bismuthate phase diagram.

cond-mat.str-el↗

arXiv2023

A flexible class of exact Hubbard-Stratonovich transformations

We consider a class of Hubbard-Stratonovich transformations suitable for treating Hubbard interactions in the context of quantum Monte Carlo simulations. A tunable parameter $p$ allows us to continuously vary from a discrete Ising auxiliary field ($p=\infty$) to a compact auxiliary field that couples to electrons sinusoidally ($p=0$). In tests on the single-band Hubbard model, we find that the severity of the sign problem decreases systematically with increasing $p$. Selecting $p$ finite, however, enables continuous sampling methods like the Langevin or Hamiltonian Monte Carlo methods. We explore the tradeoffs between various simulation methods through numerical benchmarks.

cond-mat.str-el↗

arXiv2023

Lightweight and Effective Tensor Sensitivity for Atomistic Neural Networks

Atomistic machine learning focuses on the creation of models which obey fundamental symmetries of atomistic configurations, such as permutation, translation, and rotation invariances. In many of these schemes, translation and rotation invariance are achieved by building on scalar invariants, e.g., distances between atom pairs. There is growing interest in molecular representations that work internally with higher rank rotational tensors, e.g., vector displacements between atoms, and tensor products thereof. Here we present a framework for extending the Hierarchically Interacting Particle Neural Network (HIP-NN) with Tensor Sensitivity information (HIP-NN-TS) from each local atomic environment. Crucially, the method employs a weight tying strategy that allows direct incorporation of many-body information while adding very few model parameters. We show that HIP-NN-TS is more accurate than HIP-NN, with negligible increase in parameter count, for several datasets and network sizes. As the dataset becomes more complex, tensor sensitivities provide greater improvements to model accuracy. In particular, HIP-NN-TS achieves a record mean absolute error of 0.927 kcal/mol for conformational energy variation on the challenging COMP6 benchmark, which includes a broad set of organic molecules. We also compare the computational performance of HIP-NN-TS to HIP-NN and other models in the literature.

physics.chem-ph↗

arXiv2023

Field-induced spin level crossings within a quasi-XY antiferromagnetic state in Ba$_{2}$FeSi$_{2}$O$_{7}$

We present a high-field study of the strongly anisotropic easy-plane square lattice $S$ = 2 quantum magnet Ba$_{2}$FeSi$_{2}$O$_{7}$. This compound is a rare high-spin antiferromagnetic system with very strong easy-plane anisotropy, such that the interplay between spin level crossings and antiferromagnetic order can be studied. We observe a magnetic field-induced spin level crossing occurring within an ordered state. This spin level crossing appears to preserve the magnetic symmetry while producing a non-monotonic dependence the order parameter magnitude. The resulting temperature-magnetic field phase diagram exhibits two dome-shaped regions of magnetic order overlapping around 30 T. The ground state of the lower-field dome is predominantly a linear combination of $| S^{z} = 0 \rangle$ and $ |S^{z} = 1 \rangle$ states, while the ground state of the higher-field dome can be approximated by a linear combination of $| S^{z} = 1 \rangle $ and $ | S^{z} = 2\rangle$ states. At 30 T, where the spin levels cross, the magnetization exhibits a slanted plateau, {\color {black}the magnetocaloric effect shows a broad hump, and the electric polarization shows a weak slope change}. We determined the detailed magnetic phase boundaries and the spin level crossings using measurements of magnetization, electric polarization, and the magnetocaloric effect in pulsed magnetic fields to 60 T. We calculate these properties using a mean field theory based on direct products of SU(5) coherent states and find good agreement. Finally, we measure and calculate the magnetically-induced electric polarization that reflects magnetic ordering and spin level crossings. This multiferroic behavior provides another avenue for detecting phase boundaries and symmetry changes.

cond-mat.str-el↗

arXiv2023

Semi-Empirical Shadow Molecular Dynamics: A PyTorch implementation

Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented in the semiempirical PyTorch-based software PySeQM. The implementation includes finite electronic temperatures, canonical density matrix perturbation theory, and an adaptive Krylov Subspace Approximation for the integration of the electronic equations of motion within the XL-BOMB approach (KSA-XL-BOMD). The PyTorch implementation leverages the use of GPU and machine learning hardware accelerators for the simulations. The new XL-BOMD formulation allows studying more challenging chemical systems with charge instabilities and low electronic energy gaps. Current public release of PySeQM continues our development of modular architecture for large-scale simulations employing semiempirical quantum mechanical treatment. Applied to molecular dynamics simulation of 840 carbon atoms, one integration time step executes in 4 seconds on a single Nvidia RTX A6000 GPU.

physics.comp-ph↗

arXiv2023

Reduction of Autocorrelation Times in Lattice Path Integral Quantum Monte Carlo via Direct Sampling of the Truncated Exponential Distribution

In Monte Carlo simulations, proposed configurations are accepted or rejected according to an acceptance ratio, which depends on an underlying probability distribution and an a priori sampling probability. By carefully selecting the probability distribution from which random variates are sampled, simulations can be made more efficient, by virtue of an autocorrelation time reduction. In this paper, we illustrate how to directly sample random variates from a two dimensional truncated exponential distribution. We show that our direct sampling approach converges faster to the target distribution compared to rejection sampling. The direct sampling of one and two dimensional truncated exponential distributions is then applied to a recent Path Integral Monte Carlo (PIMC) algorithm for the simulation of Bose-Hubbard lattice models at zero temperature. The new sampling method results in improved acceptance ratios and reduced autocorrelation times of estimators, providing an effective speed up of the simulation.

physics.comp-ph↗