Chemical Physics
Accurate, global Potential Energy Surfaces (PES) expressed in sum-of-products (SOP) form are a prerequisite for efficient high-dimensional quantum dynamics simulations using the MCTDH method. This work introduces a methodology for…
In this work, we describe various improved implementations of the mapping approach to surface hopping (MASH) for simulating nonadiabatic dynamics. These include time-reversible and piecewise-continuous integrators, which is only formally…
We report on the control and characterization of the isomer population of ions inside superfluid helium nanodroplets, using two-color operation of a dual-oscillator infrared free-electron laser. The timing of both lasers is highly…
Predicting how protein mutations affect drug binding remains a major challenge, particularly when the mutations are distal from the binding site. In this study, we introduce a coupled simulation workflow that combines long-time-scale…
Liquid structure at solid-liquid interfaces is critical for many natural and engineered processes ranging from biological signal transduction to electrochemical energy conversion. Advanced experimental and computational methods have…
Machine learning models of chemical bioactivity are increasingly used for prioritizing a small number of compounds in virtual screening libraries for experimental follow-up. In these applications, assessing model accuracy by early hit…
The $\Delta$SCF DFT approach defines the system energy as a function of orbital occupancy. Inspired by Landau Fermi liquid theory, we develop an occupancy extrapolation (OE) method that captures excited-state energies via a Taylor expansion…
The third and fourth density and acoustic virial coefficients of neon were determined at temperatures between 10 and 5000 K from first principles employing the path-integral Monte Carlo (PIMC) approach. For these calculations, we used the…
Machine-learned interatomic potentials (MLIPs) are increasingly used to replace computationally demanding electronic-structure calculations to model matter at the atomic scale. The most commonly used model architectures are constrained to…
A time-domain representation of chromatographic peak shapes is presented as an analytic expression designed for high computational efficiency, which can be used for direct time-domain peak fitting with parameters that represent physical…
A faithful description of chemical processes requires exploring extended regions of the molecular potential energy surface (PES), which remains challenging for strongly correlated systems. Transferable deep-learning variational Monte Carlo…
Aqueous solutions of HF and HCl behave very differently at intermediate concentrations: HCl dissociates completely, whereas HF remains only partially dissociated and forms bifluoride (HF$_2^-$). This should lead to different excess-proton…
In complex molecular systems, the reaction coordinate (RC) that characterizes transition pathways is essential to understand underlying molecular mechanisms. This review surveys a framework for identifying the RC by applying deep learning…
Microalgae are an abundant bioorganic material source and play a significant role in life on Earth by conducting photosynthesis for carbon dioxide (CO2) capture and its conversion to oxygen (O2). In this study, a combination of microalgae…
We introduce a GPU-accelerated multigrid Gaussian-Plane-Wave density fitting (FFTDF) approach for efficient Fock builds and nuclear gradient evaluations within Kohn-Sham density functional theory, as implemented in the GPU4PySCF module of…
Previous research based on electronic structure calculations and molecular dynamics (MD) simulations have demonstrated that graphdiyne (GDY) is a very suitable two-dimensional membrane for the separation of small molecules in a gas mixture…
Machine-learned interatomic potentials (MLIPs), particularly graph neural network (GNN)-based models, offer a promising route to achieving near-density functional theory (DFT) accuracy at significantly reduced computational cost. However,…
By reducing resolution, coarse-grained models greatly accelerate molecular simulations, unlocking access to long-timescale phenomena, though at the expense of microscopic information. Recovering this fine-grained detail is essential for…
We present a theoretical investigation on the electronic structure and properties of radium monochalcogenides, with chalcogens O, S, and Se, as well as the ionic species RaO +/-. Our approach combines fully relativistic and partially…
We solve the orientation recovery of a tumbling protein in the gas phase from single-event measurements of the spatial positions of its ions after an X-ray laser induced explosion. We simulate diffracted X-ray signal and ion dynamics under…