Chemical Physics
In order to establish the thermodynamic stability of a system, knowledge of its Gibbs free energy is essential. Most often, the Gibbs free energy is predicted within the CALPHAD framework using models employing thermodynamic properties,…
We conducted a first-principles study of the dielectric properties of liquid propylene glycol (PG) and polypropylene glycol (PPG) using a recently developed chemical bond-based machine learning (ML) model for dipole moments [T. Amano et al.…
Generative AI has the potential to revolutionize drug discovery. Yet, despite recent advances in deep learning, existing models cannot generate molecules that satisfy all desired physicochemical properties. Herein, we describe IDOLpro, a…
This work presents the automatic generation of analytic first derivatives of the energy for general coupled-cluster models using the \text{tenpi} toolchain. We report the first implementation of expectation values for CCSDT and CCSDTQ…
Chemical synthesis, as a foundational methodology in the creation of transformative molecules, exerts substantial influence across diverse sectors from life sciences to materials and energy. Current chemical synthesis practices emphasize…
The problem of calculating the electron-positively charged particle correlation energy poses a challenge in the field of quantum chemistry beyond the adiabatic approximation. In this study, a toy model called Exotic Harmonium is developed…
This work presents new Gaussian single- and double-zeta basis sets optimized for stochastic density functional theory (sDFT) using real-space auxiliary grids. Previous studies showed standard basis sets like STO-3G and 6-31G are sub-optimal…
Accurately modeling open quantum system dynamics is crucial for advancing quantum technologies, yet traditional methods struggle to balance accuracy and efficiency. Machine learning (ML) provides a promising alternative, particularly…
This note responds to Schwartz and Hutchison's Comment (arXiv:2403.06001) on our article (DOI:10.1002/adma.202309393). We think differences have arisen not in the experimental results themselves but in their interpretation: our more…
The electrocatalytic CO2 reduction reaction (CO2RR) is a complex multi-proton-electron transfer process that generates a vast network of reaction intermediates. Accurate prediction of free energy changes (G) of these intermediates and…
Understanding the solvation structure of electrolytes is critical for optimizing the electrochemical performance of rechargeable batteries, as it directly influences properties such as ionic conductivity, viscosity, and electrochemical…
The first experimental evidence of quantum Drude oscillator behavior in liquids is determined using probabilistic machine learning-augmented iterative Boltzmann inversion applied to noble gas radial distribution functions. Furthermore,…
We analytically derive transfer probabilities and efficiencies for an artificial light-harvesting photosynthetic system, which consists of a ring coupled to a central acceptor. For an incident photon pair, we find near-perfect single…
The integration of machine learning (ML) with density functional theory has emerged as a promising strategy to enhance the accuracy of density functional methods. While practical implementations of density functional approximations (DFAs)…
Electrochemical CO2 reduction (eCO2R) represents a pivotal strategy for mitigating global carbon emissions while simultaneously converting renewable energy into storable chemical fuels. Copper-based catalysts have been extensively explored…
We have developed a new theory relating partial molar volumes of binary mixtures to the specific (Voronoi) volumes. A simple relation gives new insight into the physical meaning of partial molar volumes in terms of the actual volumes…
We extend first-order multiconfigurational symmetry-adapted perturbation theory, SAPT(MC), [Hapka M. et al. JCTC, 2021, 17], to account for double-exchange effects, where up to two electron pairs are exchanged between interacting monomers.…
This study aims at examination of the lower-order spectral moments in collision-induced absorption (CIA), taking the translational He-Ar band as an example. General quantum corrections for the zeroth and first spectral moments are derived…
The accurate treatment of long-range energy terms such as van der Waals interactions is crucial for reliable free energy calculations in molecular simulations. Methods like force switching, potential switching, potential shifting, and Ewald…
We describe the development of machine-learned potentials of atmospheric gases with flexible monomers for molecular simulations. A recently suggested permutationally invariant polynomial neural network (PIP-NN) approach is utilized to…