arXiv subjects

Dong H. Zhang

Publications and source records attributed to Dong H. Zhang.

10 recordsLinked to original sources

arXiv2026

Higher-Order Geometric Updates for Levenberg-Marquardt Method via Riemann Normal Coordinates

Nonlinear least-squares optimization is central to regression, physics-informed neural networks, and other machine-learning tasks. Such problems have a natural geometric interpretation, model predictions form a manifold in data space, while the chosen parameterization can introduce parameter-effects curvature that becomes a dominant source of nonlinearity. This exposes a limitation of the Levenberg-Marquardt (LM) method, its tangent-space step is applied as a straight update in parameter coordinates. Geodesic acceleration gives a second-order correction, but its removal of parameter-effect curvature is exact only in the infinitesimal-step limit. We propose a Riemann-normal-coordinate Levenberg-Marquardt method (RNC-LM) to improve this consistency for finite optimization steps. By reformulating the geodesic equation, RNC-LM extends geodesic acceleration to arbitrary-order corrections and constructs finite-step updates with progressively higher reparameterization consistency. A line search along the resulting RNC curve controls the traveled distance while keeping the cost close to standard LM. The method eliminates the tangential component of residual acceleration order by order in a moving tangent frame, making the actual objective reduction more consistent with the linear model prediction of LM. On classical nonlinear least-squares benchmarks, RNC-LM improves convergence and robustness in curved valleys and rank-deficient problems. On a reaction-diffusion PINN failure-mode benchmark, it reduces the relative L2 error to the order of 1e-3 and recovers a physically meaningful solution. On a large-scale machine-learning potential-energy-surface fitting task, it achieves a 34-fold speedup over standard LM.

cs.LG↗

arXiv2026

Quantum Mechanical Studies of Photodissociation Dynamics on Quantum Computers

Theoretical quantum dynamics calculations scale deeply with system size, rendering classical calculations intractable for complex systems. While quantum computing offers a natural solution, its application to nuclear quantum dynamics remains scarce. Here, we present a quantum algorithm to study photodissociation dynamics on quantum computers, benchmarked on the NOCl molecule. The wavefunction is propagated via a split-operator method, utilizing the Quantum Fourier Transform and unitary transformation matrix to switch representations. To impose outgoing boundary conditions on a truncated grid, we use a non-unitary absorbing potential propagator, implemented through a dilation scheme. The photodissociation cross section is calculated from the auto-correlation function, which is extracted using the Hadamard test. Our quantum computing results agree well with benchmarks under ideal conditions, and we further demonstrate that the algorithm is robust to noise and statistical sampling errors, indicating the promising application of noisy devices to quantum dynamics studies.

quant-ph↗

arXiv2026

Rigorous Quantum Thermodynamics from Entropic Path Integral Coarse-Graining

Nuclear quantum effects (NQEs) remain a major challenge for molecular simulations, as rigorous treatment requires imaginary-time path-integral methods with heavy computational overhead. Neglecting NQEs leads to systematic errors in thermodynamic properties and failures in predicting isotope effects, quantum tunnelling, and anharmonic zero-point motion. Here, we introduce entropic path-integral coarse-graining (EPIGS), which enables rigorous quantum thermodynamics at the cost of classical simulations by training size- and temperature-transferable effective potentials utilising absolute centroid free energy and entropy. Central to EPIGS is an instanton-based free-energy perturbation scheme that enables efficient and accurate evaluation of the centroid free energy and entropy for large systems, making construction of the EPIGS training dataset practical. Benchmarks against full path-integral simulations on representative hydrogen-bonded systems, including liquid water, show that EPIGS reproduces quantum free energies and enthalpies within 0.2 meV/atom at near-classical computational cost. EPIGS provides a highly accurate, scalable and low-cost framework for quantum thermodynamic simulations of complex systems across temperatures.

physics.chem-ph↗

arXiv2026

Monomeric machine learning potential for general covalent molecules: linear alkanes as an example

Machine-learning potentials (MLPs) have become important tools for modern molecular simulations. However, developing models that simultaneously achieve high accuracy and high computational efficiency remains a significant challenge. In this work, we extend the recently proposed MB-PIPNet framework to general covalently bonded molecular systems by combining monomer-based energy decomposition, permutationally invariant polynomial (PIP) descriptors, and neural networks within a fragmentation-based strategy. Within this framework, the total potential energy is represented as a sum of effective monomeric contributions, where PIPs provide compact and chemically motivated descriptions of both monomer internal structures and their local chemical environments. As a proof-of-concept application, we apply the MB-PIPNet framework to linear alkanes, using n-Tetradecane as a representative system, and benchmark its performance against established atomistic machine-learning models. The resulting MB-PIPNet potential accurately reproduces reference ab initio electronic energies and reliably captures key molecular properties, including torsional potential energy profiles, harmonic vibrational frequencies, and vibrational power spectra obtained from molecular dynamics simulations. Importantly, MB-PIPNet demonstrates a substantial advantage in computational efficiency over other MLP models for combined energy and force evaluations. These results establish MB-PIPNet as a scalable and efficient framework for constructing MLPs, providing an additional route for large-scale quantum and classical simulations of complex molecular systems.

physics.chem-ph↗

arXiv2025

On the role of nuclear quantum effects on the stability of peptides

Nuclear quantum effects (NQEs) arising from the light mass of hydrogen can influence the structure and stability of hydrogen-bonded biomolecules, yet their role in determining peptide and protein folding remains unclear. Experiments show that substituting H$_2$O with D$_2$O often stabilizes folded states, but the microscopic mechanism associated with this phenomena remains unresolved. Through ab initio-level path-integral molecular dynamics simulations enabled by machine-learning interatomic potentials, we address the fundamental question of the role of NQEs in peptides by investigating both their overall impact and isotope substitution effects. Overall, NQEs systematically destabilize compact three-dimensional structures across peptide systems, independent of secondary structure type or side-chain interactions. Contrary to the conventional picture that places central importance on hydrogen bonds, we find that the dominant destabilization instead arises from the quantum C-H vibrations. In addition, we reveal microscopic insights into the stabilization of folded peptides upon H$_2$O to D$_2$O substitution, showing that the H/D isotope substitution of active peptide hydrogens, previously considered unimportant, produces free-energy changes within the range of experimentally observed shifts. These findings provide a new interpretation of isotope effects in biological systems, indicating that seemingly small H$\to$D substitutions within peptides can be as important as, or even outweigh, solvent contributions.

physics.chem-ph↗

arXiv2025

Instanton Theory for Nonadiabatic Tunneling through Near-Barrier Crossings

Many reactions in chemistry and biology involve multiple electronic states, rendering them nonadiabatic in nature. These reactions can be formally described using Fermi's golden rule (FGR) in the weak-coupling limit. Nonadiabatic instanton theory presents a semiclassical approximation to FGR, which is directly applicable to molecular systems. However, there are cases where the theory has not yet been formulated. For instance, in many real-world reactions including spin-crossover or proton-coupled electron transfer, the crossing occurs near a barrier on a diabatic state. This scenario gives rise to competing nonadiabatic reaction pathways, some of which involve tunneling through a diabatic barrier while simultaneously switching electronic states. To date, no rate theory is available for describing tunneling via these unconventional pathways. Here we extend instanton theory to model this class of processes, which we term the ``non-convex'' regime. Benchmark tests on model systems show that the rates predicted by instanton theory are in excellent agreement with quantum-mechanical FGR calculations. Furthermore, the method offers new insights into multi-step tunneling reactions and the competition between sequential and concerted nonadiabatic tunneling pathways.

physics.chem-ph↗

arXiv2025

Interaction-Region Decoupling through Structured Absorbing Potentials: A Framework for Scalable Time-Dependent Quantum Dynamics Calculations

Accurate quantum mechanical treatment of molecular reactions remains a longstanding challenge, especially for reactions involving deep potential wells and long-lived intermediate complexes. Here, we introduce an interaction region decoupling (IRD) strategy that incorporates structured absorbing potentials to dynamically partition the interaction region into reactant and product subspaces. The IRD framework integrates naturally with standard TDWP propagation schemes and enables the construction of region-specific basis sets, dramatically enhancing computational efficiency. Benchmark applications to the F + HD and O + OH reactions demonstrate that this approach achieves state-resolved accuracy while reducing computational cost by over two orders of magnitude. This strategy paves the way for routine quantum mechanical treatment of complex-forming four-atom reactions previously considered intractable.

physics.chem-ph↗

arXiv2024

Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials

Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical interpretability in atomistic energy decomposition and the computational efficiency of traditional force fields has not been fully achieved. Here, we present a novel method that combines aspects of both approaches, and achieves state-of-the-art balance of accuracy and force field-level speed. This method utilizes a monomer-centered representation, where the potential energy is decomposed into the sum of chemically meaningful monomeric energies. Without sophisticated neural network design, the structural descriptors of monomers are described by 1-body and 2-body effective interactions, enforced by appropriate sets of PIPs as inputs to the feed forward NN. We demonstrate the performance of this method through systematic assessments of models for gas-phase water trimer, liquid water, and also liquid CO2. The high accuracy, fast speed, and flexibility of this method provide a new route for constructing accurate ML potentials and enabling large-scale quantum and classical simulations for complex molecular systems.

physics.chem-ph↗

arXiv2024

Quantum mechanical deconstruction of vibrational energy transfer rate and pathways modified by collective vibrational strong coupling

Recent experiments have demonstrated that vibrational strong coupling (VSC) between molecular vibrations and the optical cavity field can modify vibrational energy transfer (VET) processes in molecular systems. However, the underlying mechanisms and the behavior of individual molecules under collective VSC remain largely incomplete. In this work, we combine state-of-the-art quantum vibrational spectral calculation, quantum wavepacket dynamics simulations, and ab initio machine-learning potential to elucidate how the vibrational dynamics of water OH stretches can be altered by VSC. Taking the (H$_2$O)$_{21}$-cavity system as an example, we show that the collective VSC breaks the localization picture, promotes the delocalization of OH stretches, and opens new intermolecular vibrational energy pathways involving both neighboring and remote water molecules. The manipulation of the VET process relies on the alignment of the transition dipole moment orientations of the corresponding vibrational states. The emergence of new energy transfer pathways is found to be attributed to cavity-induced vibrational resonance involving OH stretches across different water molecules, along with alterations in mode coupling patterns. Our fully quantum theoretical calculations not only confirm and extend previous findings on cavity-modified energy transfer processes but also provide new insights in energy transfer processes under collective VSC.

physics.chem-ph↗

arXiv2024

Highly Accurate Description of Long-Range Interactions through the Combination of Neural Networks and Physical Models

We present a simple and general way to accurately describe long-range interactions between atoms and molecules through combining neural networks with physical models. Demonstrations on the H$_3$, Li$_3$ and 2KRb systems illustrate the exceptional extrapolation capabilities of the trained model, supported by underlying physical models. More importantly, the model exhibits high accuracy at energy scales below a few hundred millikelvin, where the reliability of $ab~initio$ methods diminishes.

physics.atom-ph↗