Chemical Physics
Recent advances in machine learning have facilitated numerically accurate solution of the electronic Schr\"{o}dinger equation (SE) by integrating various neural network (NN)-based wavefunction ansatzes with variational Monte Carlo methods.…
We investigate the core-level ionization energies of the bare uranyl ion (UO$_2^{2+}$) and its interaction with X-rays when it is hosted in the Cs$_2$UO$_2$Cl$_4$ crystalline environment using a recent implementation of the…
Solvation free energy is an important design parameter in reaction kinetics and separation processes, making it a critical property to predict during process development. In previous research, directed message passing neural networks…
The formation of excited dimer states, so called excimers, is an important phenomenon in many organic molecular semiconductors. In contrast to Frenkel exciton-polaron excited states, an excimer is long-lived and energetically low-lying due…
Accurate electronic structure calculations are essential in modern materials science, but strongly correlated systems pose a significant challenge due to their computational cost. Traditional methods, such as complete active space…
Simulating large proteins using traditional molecular dynamics (MD) is computationally demanding. To address this challenge, we propose a novel tree-structured coarse-grained model that efficiently captures protein dynamics. By leveraging a…
Ostwald ripening is a well-known physicochemical phenomenon in which smaller particles, characterized by high surface energy, dissolve and feed the bigger ones that are thermodynamically more stable. The effect is commonly observed in solid…
This study investigates the influence of quantum effects on Coulomb explosion dynamics using time-dependent density functional theory (TDDFT) simulations, comparing classical, semi-classical, and quantum approaches. The goal is to elucidate…
Neural network potentials (NNPs) offer a fast and accurate alternative to ab-initio methods for molecular dynamics (MD) simulations but are hindered by the high cost of training data from high-fidelity Quantum Mechanics (QM) methods. Our…
We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for charge-neutral iso-electronic diatomics at fixed interatomic distance $d_0$. To account for variations in distance, we combine AHA with this…
We show that following the standard mantra of quantum chemistry and diagonalizing the Born-Oppenheimer (BO) Hamiltonian $\hat H_{\rm BO}(\bm R)$ is not the optimal means to construct potential energy surfaces. A better approach is to…
The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic…
We recently introduced the Alchemical Integral Transform (AIT) enabling the prediction of energy differences, and guessed an Ansatz to parametrize space $\pmb{r}$ in some alchemical change $\lambda$. Here, we present a rigorous derivation…
We present an application of our new theoretical formulation of quantum dynamics, moment propagation theory (MPT) (Boyer et al., J. Chem. Phys. 160, 064113 (2024)), for employing machine-learning techniques to simulate the quantum dynamics…
The bottleneck in enhanced sampling lies in finding collective variables (CVs) that can effectively accelerate protein conformational changes. True reaction coordinates (tRCs) that can predict the committor are considered the optimal CVs,…
We present an approach for augmenting Gaussian atomic orbitals with correct nuclear cusps. Like the atomic orbital basis set itself, and unlike previous cusp corrections, this approach is independent of the many-body method used to prepare…
In molecular dynamics (MD) simulations, transitions between states are often rare events due to energy barriers that exceed the thermal temperature. Because of their infrequent occurrence and the huge number of degrees of freedom in…
Perovskite materials are highly promising for a range of optoelectronic applications including energy conversion technologies, owing to their high charge-carrier mobilities, adaptability of bandgap tuning, and exceptional light-harvesting…
Glyoxal CHOCHO is a trace gas in the atmosphere, often used as an indicator of biogenic emissions. It is frequently compared to formaldehyde concentrations, which serve as indicators of anthropogenic emissions, to gain insights into the…
We have studied the electronic and optical properties of three low-symmetry graphene quantum dots (GQDs), with the point-group symmetries $C_{2v}$, and $C_{2h}$. For the calculations of linear optical absorption spectra, we employed both…