Chemical Physics
Ionic liquids (ILs) are an extremely exciting class of electrolytes for energy storage applications because of their unique combination of properties. Upon dissolving alkali metal salts, such as Li or Na based salts, with the same anion as…
Free-particle Green's function plays a central role in the theoretical description of electron scattering and autoionization processes in quantum physics and chemistry. Recently, Gaussian basis set approaches have become increasingly…
Understanding ion-transport under molecular confinement is essential for developing next-generation energy technologies, where ionic motion often occurs within nanoscale or angstrom-scale channels. In this study, we use the model system of…
In this work, we investigate the role of spacers in tuning singlet-fission (SF) activity in aza-BODIPY dimers using a diabatic electronic-structure framework. A series of nine spacer-linked dimers is analyzed employing high-level…
Computing long-timescale kinetics of biomolecular processes remains a major challenge for atomistic simulations. A way out is to exploit local kinetic information to construct the global stationary flux across the reaction space. The…
We introduce a stochastic framework based on complex absorbing potentials (CAPs) to investigate exciton transport in large molecular aggregates. Within this approach, CAPs act as non-Hermitian reservoirs and sinks that enable effective…
While providing a highly accurate framework for simulating laser-induced many-electron dynamics in atom and molecules, including linear and nonlinear steady-state and transient absorption spectra, time-dependent coupled-cluster theory does…
Marcus--Hush theory explains electron transfer in terms of reorganization energies, driving forces, electronic couplings, and reduced free-energy or energy-gap descriptions. These descriptions do not by themselves determine when the…
Reduced density-matrix functional theory (RDMFT) provides a variational route to electronic correlations beyond conventional density-functional approximations, but explicit evaluations of density-matrix functionals still scale exponentially…
Coarse-grained modeling in molecular simulations serves not only to extend accessible time and length scales beyond atomistic limits, but also to reduce high-dimensional chemical data to low-dimensional representations that expose the…
This work presents a decoupled framework for multi-step computational chemistry automation built on OpenClaw. OpenClaw serves as the general-purpose agent for task coordination and supervision. Planning skills externalize task descriptions…
Vibrational-encoded fluorescence spectro-microscopies are emerging as powerful tools for studying molecular vibrations with the unparalleled sensitivity of fluorescence spectroscopy. We recently described one such technique, termed…
Understanding the intricate interplay between soot dynamics and chemical reactions within catalytic diesel particulate filters (CDPF) is crucial for enhancing both filtration efficiency and regeneration performance. In this paper, we…
This work presents an innovative computational study of domain-based charge transfer that leverages the localized orbitals of pair coupled cluster doubles (pCCD). This method enables both directional monitoring and quantitative assessment…
We propose two new diagnostics for the degree to which static correlation impacts the quality of a coupled cluster calculation. The first is the change in the Matito static correlation diagnostic $\overline{I_{ND}}$ between CCSD and…
Non-equilibrium molecular-scale dynamics, where fast electron transport couples with slow chemical state evolution, underpins the complex behaviors of molecular memristors, yet a general model linking these dynamics to neuromorphic…
The constraint coordinate-momentum \textit{phase space} (CPS) has recently been developed to study nonadiabatic dynamics in gas-phase and condensed-phase molecular systems. Although the CPS formulation is exact for describing the discrete…
Most atomistic machine learning (ML) models rely on a locality ansatz, and decompose the energy into a sum of short-ranged, atom-centered contributions. This leads to clear limitations when trying to describe problems that are dominated by…
The accurate solution of dissipative quantum dynamics plays an important role on the simulation of open quantum systems. Here we propose a machine-learning-based universal solver for the hierarchical equations of motion, one of the most…
Machine learning model development in chemistry and materials science often grapples with the challenge of small scale, unbalanced labelled datasets, a common limitation in scientific experiments. These dataset imbalances can precipitate…