arXiv subjects

Rongzhi Gao

Publications and source records attributed to Rongzhi Gao.

4 recordsLinked to original sources

arXiv2026

PEACE: Covariant learning of nonadiabatic manifolds with parity-resolved Hamiltonians

Nonadiabatic molecular dynamics provides mechanistic insight into light-driven processes and informs the design of molecules and materials for solar energy conversion, photocatalysis and photo switching. Accurately describing these processes requires a representation that respects electronic symmetry and consistently relates energies to interstate couplings. Here we introduce PEACE, which combines a parity-equivariant latent Hamiltonian with a learned electronic connection. Controlled ablations reveal the complementary roles of symmetry-allowed state mixing and electronic-frame variation in reproducing crossing structures and relaxation dynamics. PEACE closely reproduces excited-state population dynamics from first-principles simulations, while its extension to spin-orbit coupling enables simulations of intersystem crossing. These results demonstrate that a more complete incorporation of the underlying physics into learned electronic representations leads to more accurate predictions of nonadiabatic dynamics.

physics.chem-ph↗

arXiv2026

Voltage-embedded equivariant machine learning potential for open system simulations

Modeling electrochemical interfaces under operational non-equilibrium conditions is vital for energy technologies but remains bottlenecked by the expensive cost of ab initio methods. Current machine learning potentials, largely designed for closed systems under homogeneous electric fields, are limited in open quantum transport applications. To overcome this, we present an E(3)-equivariant graph neural network that embeds voltage bias for open-system simulations. Our approach decouples the system energy and forces into zero-bias and bias-dependent contributions, assigning distinct vector encodings to electrode and scattering-region atoms to capture non-equilibrium conditions. Trained on limited discrete bias data, the model achieves high predictive accuracy and robust extrapolation transferability. When applied to a lithium/water interface, our model successfully captures the field-induced dynamic reorientation of water molecules and reproduces asymmetric electrochemical behavior at different electrodes.

physics.chem-ph↗

arXiv2025

A foundation machine learning potential with polarizable long-range interactions for materials modelling

Long-range interactions are essential determinants of chemical system behaviour across diverse environments. We present a foundation framework that integrates explicit polarizable long-range physics with an equivariant graph neural network potential. It employs a physically motivated polarizable charge equilibration scheme that directly optimizes electrostatic interaction energies rather than partial charges. The foundation model, trained across the periodic table up to Pu, demonstrates strong performance across key materials modelling challenges. It effectively captures long-range interactions that are challenging for traditional message-passing mechanisms and accurately reproduces polarization effects under external electric fields. We have applied the model to mechanical properties, ionic diffusivity in solid-state electrolytes, ferroelectric phase transitions, and reactive dynamics at electrode-electrolyte interfaces, highlighting the model's capacity to balance accuracy and computational efficiency. Furthermore, we show that as a foundation model, it can be efficiently finetuned to achieve high-level accuracy for specific challenging systems.

physics.chem-ph↗

arXiv2025

Electron transport properties of heterogeneous interfaces in solid electrolyte interphase on lithium metal anodes

In rechargeable batteries, electron transport properties of inorganics in the solid-electrolyte interphase (SEI) critically determine the safety, lifespan and capacity loss of batteries. However, the electron transport properties of heterogeneous interfaces among different solid inorganics in SEI have not been studied experimentally or theoretically yet, although such heterogeneous interfaces exist inevitably. Here, by employing non-equilibrium Green's function (NEGF) method, we theoretically evaluated the atomic-scale electron transport properties under bias voltage for LiF/Li2O interfaces and single-component layers of them, since LiF and Li2O are common stable inorganics in the SEI. We reveal that heterogeneous interfaces orthogonal to the external electric-field direction greatly impede electron transport in SEI, whereas heterogeneous parallel-orientated interfaces enhance it. Structural disorders induced by densely distributed interfaces can severely interfere with electron transport. For each component, single-crystal LiF is highly effective to block electron transport, with a critical thickness of 2.9 nm, much smaller than that of Li2O (19.0 nm). This study sheds a new light into direct and quantitative understanding of the electron transport properties of heterogeneous interfaces in SEI, which holds promise for the advancement of a new generation of high-performance batteries.

cond-mat.mtrl-sci↗