arXiv subjects

D. ChangMo Yang

Publications and source records attributed to D. ChangMo Yang.

12 recordsLinked to original sources

arXiv2026

GW and Bethe-Salpeter Theory for Molecular Polaritons, Quasiparticles, and Excitons

The electron self-energy is central to quasiparticle theory, yet how an optical cavity enters it remains unclear. We address this question for a molecule in a single-mode cavity using the dipole-gauge Pauli-Fierz Hamiltonian and a coherent-state QED Hartree-Fock reference. The cavity enters through three channels: the static dipole self-energy (DSE) shift of reference orbital energies, direct DSE augmentation of the screened interaction, and the polariton pole carrying the bilinear electron-photon coupling. We benchmark QED-$GW$ ionization potentials (IPs) and electron affinities (EAs) against a cavity $\Delta$-method ladder from QED-HF to correlated wave-function methods, whose cavity-induced shifts agree within 1 meV where directly comparable. For closed-shell molecules with unbound anions, $GW$ systematically overestimates cavity-induced IP redshifts, whereas EA shifts are reproduced nearly quantitatively, although this does not imply comparable accuracy for absolute EAs. For ionic molecules with bound anions, this ordering reverses, consistent with published QED coupled-cluster results. Coupling and detuning scans show that the error is predominantly quadratic in $\lambda$ and DSE-driven rather than resonant. The spectral function develops a polariton-replica photoemission sideband with weight scaling as $\lambda^2$. In the static screened interaction used in the Bethe-Salpeter equation, bare-photon exchange cancels the matching DSE contribution to the direct interaction, while exchange and polariton-screening corrections remain. Their net effect on the lowest excitation is appreciable only for ammonia in the molecules studied. Exciton-binding energies involving unbound anions are strongly basis-dependent and should therefore be viewed as diagnostics of electron-hole interactions rather than basis-converged molecular quantities.

cond-mat.mtrl-sci↗

arXiv2026

Accelerated Discovery of Nitrogen-Coordinated Dual-Atom Hydrogen Evolution Reaction Electrocatalysts via Machine Learning Potentials

The hydrogen evolution reaction (HER) is central to sustainable hydrogen production, and nitrogen coordinated dual atom catalysts (DACs) offer a promising route to noble metal activity at low cost. Yet their vast compositional and coordination design space remains underexplored, as density functional theory (DFT) screening at scale is prohibitive. Here, we map the HER landscape of graphene supported TM2@Nx-Gr DACs, screening 23 transition metals across 20 nitrogen coordination motifs using a machine learning potential (MLP) benchmarked against DFT. Intermediate coordination (2N to 4N) consistently yields near-optimal {\Delta}GH*, with Ti2@2Na, Mn2@2Na, Fe2@2Na, Cu2@2Na, Rh2@2Na, Zr2@2Na, Zr2@2Nb, Zr2@2Nc, Nb2@2Nc, Zr2@2Nd, Mn2@2Ne, Mn2@2Nf, Ti2@3Na, Au2@3Na, Fe2@3Na, Pd2@3Nb, Rh2@3Nc, Rh2@3Nd, Au2@3Nd, V2@4Na, Ti2@4Nb, Pd2@4Nb, Ti2@4Nc, Cr2@4Nd, Ni2@4Nd, Cu2@4Nd emerging as standout, synthesizable candidates, most exhibiting metallic or narrow gap (<0.25 eV) character. The MLP reaches near-DFT accuracy, with a mean absolute error of 80 meV for Gibbs binding free energies at orders of magnitude lower computational cost, establishing MLP driven screening as a practical engine for next-generation catalyst discovery.

cond-mat.mtrl-sci↗

arXiv2026

Stochastic Loop Corrections to Belief Propagation for Tensor Network Contraction

Tensor network contraction is a fundamental computational challenge underlying quantum many-body physics, statistical mechanics, and machine learning. Belief propagation (BP) provides an efficient approximate solution, but introduces systematic errors on graphs with loops. Here, we introduce a hybrid method that achieves accurate results by stochastically sampling loop corrections to BP and showcase our method by applying it to the two-dimensional ferromagnetic Ising model. For any pairwise Markov random field with symmetric edge potentials, our approach exploits an exact factorization of the partition function into the BP contribution and a loop correction factor summing over all valid loop configurations, weighted by edge weights derived directly from the potentials. We sample this sum using Markov chain Monte Carlo with moves that preserve the loop constraint, combined with umbrella sampling to ensure efficient exploration across all correlation strengths. Our stochastic approach provides unbiased estimates with controllable statistical error in any parameter regime.

cond-mat.str-el↗

arXiv2025

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing

Machine learning potentials (MLPs) have become essential for large-scale atomistic simulations, enabling ab initio-level accuracy with computational efficiency. However, current MLPs struggle with uncertainty quantification, limiting their reliability for active learning, calibration, and out-of-distribution (OOD) detection. We address these challenges by developing Bayesian E(3) equivariant MLPs with iterative restratification of many-body message passing. Our approach introduces the joint energy-force negative log-likelihood (NLL$_\text{JEF}$) loss function, which explicitly models uncertainty in both energies and interatomic forces, yielding substantially improved accuracy compared to conventional NLL losses. We systematically benchmark multiple Bayesian approaches, including deep ensembles with mean-variance estimation, stochastic weight averaging Gaussian, improved variational online Newton, and Laplace approximation by evaluating their performance on uncertainty prediction, OOD detection, calibration, and active learning tasks. We further demonstrate that NLL$_\text{JEF}$ facilitates efficient active learning by quantifying energy and force uncertainties. Using Bayesian active learning by disagreement (BALD), our framework outperforms random sampling and energy-uncertainty-based sampling. Our results demonstrate that Bayesian MLPs achieve competitive accuracy with state-of-the-art models while enabling uncertainty-guided active learning, OOD detection, and energy/forces calibration. This work establishes Bayesian equivariant neural networks as a powerful framework for developing uncertainty-aware MLPs for atomistic simulations at scale.

cs.LG↗

arXiv2025

Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuits

Quantum algorithms for simulating large and complex molecular systems are still in their infancy, and surpassing state-of-the-art classical techniques remains an ever-receding goal post. A promising avenue of inquiry in the meanwhile is to seek practical advantages through hybrid quantum-classical algorithms, which combine conventional neural networks with variational quantum circuits (VQCs) running on today's noisy intermediate-scale quantum (NISQ) hardware. Such hybrids are well suited to NISQ hardware. The classical processor performs the bulk of the computation, while the quantum processor executes targeted sub-tasks that supply additional non-linearity and expressivity. Here, we benchmark a purely classical E(3)-equivariant message-passing machine learning potential (MLP) against a hybrid quantum-classical MLP for predicting density functional theory (DFT) properties of liquid silicon. In our hybrid architecture, every readout in the message-passing layers is replaced by a VQC. Molecular dynamics simulations driven by the HQC-MLP reveal that an accurate reproduction of high-temperature structural and thermodynamic properties is achieved with VQCs. These findings demonstrate a concrete scenario in which NISQ-compatible HQC algorithm could deliver a measurable benefit over the best available classical alternative, suggesting a viable pathway toward near-term quantum advantage in materials modeling.

quant-ph↗

arXiv2024

A Bayesian Committee Machine Potential for Oxygen-containing Organic Compounds

Understanding the pivotal role of oxygen-containing organic compounds in serving as an energy source for living organisms and contributing to protein formation is crucial in the field of biochemistry. This study addresses the challenge of comprehending protein-protein interactions (PPI) and developing predicitive models for proteins and organic compounds, with a specific focus on quantifying their binding affinity. Here, we introduce the active Bayesian Committee Machine (BCM) potential, specifically designed to predict oxygen-containing organic compounds within eight groups of CHO. The BCM potential adopts a committee-based approach to tackle scalability issues associated with kernel regressors, particularly when dealing with large datasets. Its adaptable structure allows for efficient and cost-effective expansion, maintaing both transferability and scalability. Through systematic benchmarking, we position the sparse BCM potential as a promising contender in the pursuit of a universal machine learning potential.

cond-mat.mtrl-sci↗

arXiv2024

A Bayesian Committee Machine Potential for Organic Nitrogen Compounds

Large-scale computer simulations of chemical atoms are used in a wide range of applications, including batteries, drugs, and more. However, there is a problem with efficiency as it takes a long time due to the large amount of calculation. To solve these problems, machine learning interatomic potential (ML-IAP) technology is attracting attention as an alternative. ML-IAP not only has high accuracy by faithfully expressing the density functional theory (DFT), but also has the advantage of low computational cost. However, there is a problem that the potential energy changes significantly depending on the environment of each atom, and expansion to a wide range of compounds within a single model is still difficult to build in the case of a kernel-based model. To solve this problem, we would like to develop a universal ML-IAP using this active Bayesian Committee Machine (BCM) potential methodology for carbon-nitrogen-hydrogen (CNH) with various compositions. ML models are trained and generated through first-principles calculations and molecular dynamics simulations for molecules with only CNH. Using long amine structures to test an ML model trained only with short chains, the results show excellent consistency with DFT calculations. Consequently, machine learning-based models for organic molecules not only demonstrate the ability to accurately describe various physical properties but also hold promise for investigating a broad spectrum of diverse materials systems.

cond-mat.mtrl-sci↗

arXiv2020

Gapless and Massive 1D Singlet Dispersion Channel in Infinite Spin-1/2 Ladders ---Infinite Quasi-1D Entanglement Perturbation Theory for Excitation

We solve for the elementary excitation in infinite quasi-1D quantum lattices by extending the recently developed infinite quasi-1D entanglement perturbation theory. The wave function of an excited state is variationally determined by optimizing superposition of cluster operation, each of which is composed of simultaneous on-site operation inside a block of lattice sites, on the ground state in a form of plane wave. The excitation energy with respect to the wave number gives the spectra for an elementary excitation. Our method is artificial broadening free and is adaptive for various quasi-particle pictures. Using the triplet spectrum, the application to $\infty$-by-$N$ antiferromagnetic spin-$\frac{1}{2}$ ladders for $N=2, 4, 6, 8$, and $10$ confirms a previous report that there is a quantum dimensional transition, namely, the lattice transits from quasi-1D to 2D at a finite critical value $N_c=10$. The massless triplet dispersion at $\left( π, π\right)$ sees a vanishing gap. Our results detect the anomaly at $\left(π,0\right)$ in the triplet spectrum, agreeing well with the inelastic neutron scattering measurement of a macroscopic sample. Surprisingly, our results also reveal a gapless and massive 1D singlet dispersion channel that is much lower than the triplet excitation. We note, however, the dimensional transition is determined by the massless triplet dispersion.

cond-mat.str-el↗

arXiv2019

Supra-Binary Ferroelectricity in a Nanowire

We report the prediction and observation of supra-binary ferroelectricity in a ferroelectric nanowire (FNW) covered with a semi-cylindrical gate that provides an anisotropic electric field in the FNW. There are gate-voltage-driven transitions between four polarization phases in FNW's cross section, dubbed axial-up, axial-down, radial-in and radial-out. They are determined by the interplay between the topological depolarization energy and the free energy induced by an anisotropic external electric field, in clear distinction from the conventional film-based binary ferroelectricity. When the FNW is mounted on a biased graphene nanoribbon (GNR), these transitions induce exotic current-voltage hysteresis in the FNW-GNR transistor. Our discovery suggests new operating mechanisms of ferroelectric devices. In particular, it enables intrinsic multi-bit information manipulation in parallel to the binary manipulation employed in data storage devices.

cond-mat.mtrl-sci↗

arXiv2018

QMCPACK : An open source ab initio Quantum Monte Carlo package for the electronic structure of atoms, molecules, and solids

QMCPACK is an open source quantum Monte Carlo package for ab-initio electronic structure calculations. It supports calculations of metallic and insulating solids, molecules, atoms, and some model Hamiltonians. Implemented real space quantum Monte Carlo algorithms include variational, diffusion, and reptation Monte Carlo. QMCPACK uses Slater-Jastrow type trial wave functions in conjunction with a sophisticated optimizer capable of optimizing tens of thousands of parameters. The orbital space auxiliary field quantum Monte Carlo method is also implemented, enabling cross validation between different highly accurate methods. The code is specifically optimized for calculations with large numbers of electrons on the latest high performance computing architectures, including multicore central processing unit (CPU) and graphical processing unit (GPU) systems. We detail the program's capabilities, outline its structure, and give examples of its use in current research calculations. The package is available at http://www.qmcpack.org .

physics.comp-ph↗

arXiv2014

Quantum dissection of a covalent bond with the entanglement spectrum

We propose that spatial density matrices, which are singularly important in the study of quantum entanglement, encode the electronic fluctuations and correlations responsible for covalent bonding. From these density matrices, we develop tools that allow us to analyse how many body wave functions can be broken up into real space pieces. We apply these tools to the first row dimers, and in particular, we address the conflicting evidence in the literature about the presence of an inverted fourth bond and anti-ferromagnetic correlations in the $\text{C}_2$ molecule. Our results show that many body effects enhance anti-ferromagnetic fluctuations but are not related to the formation of an inverted fourth bond. We identify two inverted bonds in the $\text{C}_2$ molecule and establish their correspondence to the bonds in the $\text{Be}_2$ molecule. Additionally, we provide a new interpretation of the Mayer index, introduce partial bonds to fix deficiencies in molecular orbital theory, and prove the Hartree-Fock wave function for C$_{2}$ is not a triple bond. Our results suggest that entanglement-based methods can lead to a more realistic treatment of molecular and extended systems than possible before.

cond-mat.str-el↗

arXiv2014

Direct calculation of the Entanglement Spectrum in Quantum Monte Carlo with application to \textit{ab initio} Hamiltonians

Several algorithms have been proposed to calculate the spatial entanglement spectrum from high order Renyi entropies. In this work we present an alternative approach for computing the entanglement spectrum with quantum Monte Carlo for both continuum and lattice Hamiltonians. This method provides direct access to the matrix elements of the spatially reduced density matrix and we determine an estimator that can be used in variational Monte Carlo as well as other Monte Carlo methods. The algorithm is based on using a generalization of the Swap operator, which can be extended to calculate a general class of density matrices that can include combinations of spin, space, particle and even momentum coordinates. We demonstrate the method by applying it to the Hydrogen and Nitrogen molecules and describe for the first time how the spatial entanglement spectrum encodes a covalent bond that includes all the many body correlations.

cond-mat.str-el↗