化学物理

We recently proposed a scheme to generalize collinear functionals to the noncollinear regime, termed the multicollinear approach. The resulting noncollinear functionals preserve spin symmetry while providing numerically stable higher-order…

In the present work, we examine how the recent quantum-computing algorithm known as the state-average orbital-optimized variational quantum eigensolver (SA-OO-VQE), viewed within the context of quantum chemistry as a type of…

Extracting from trajectory data meaningful information to understand complex molecular systems might be non-trivial. High-dimensional analyses are typically assumed to be desirable, if not required, to prevent losing important information.…

This study is due to various applications in physics, chemistry and especially in biology, where both bounded configuration domain and chemical anisotropy could play a great part. In fact we generalize the well-known Berg theory, which…

This paper reports the first gas sensor based on the plastic inorganic semiconductor GaPS4, pioneering the application of plastic inorganic semiconductors in the field of gas sensing. Unlike traditional rigid sensors, this device leverages…

Lithium diffusion in solid-state battery anodes occurs through thermally activated hops between metastable sites often separated by large energy barriers, making such events rare on ab initio molecular dynamics (AIMD) timescales. Here, we…

We present a quantum linear response (qLR) approach within an active-space framework for computing indirect nuclear spin-spin coupling constants, a key ingredient in NMR spectra predictions. The method employs the unitary coupled cluster…

Groundbreaking advances in materials and chemical research have been driven by the development of atomistic simulations. However, the broader applicability of atomistic simulations remains limited, as they inherently depend on energy models…

A quantum electrodynamical time-dependent density functional theory framework is applied to describe strongly coupled light--matter interactions in cavity environments. The formalism utilizes a tensor product approach, coupling real-space…

Ring polymer surface hopping (RPSH) is a mixed quantum-classical dynamics method for incorporating nuclear quantum effects (NQEs) into nonadiabatic dynamics simulations via the extended phase-space of a classical ring polymer. Here, we…

Efficient and reliable identification and optimization of transition state structures is a longstanding challenge in computational chemistry. Popular chain-of-states methods require hundreds if not thousands of ab initio calculations to…

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…

Machine-learned interatomic potentials (MLIPs) promise to significantly advance atomistic simulations by delivering quantum-level accuracy for large molecular systems at a fraction of the computational cost of traditional electronic…

The selective separation of same-charge ions is a longstanding challenge in resource recovery, battery recycling, and water treatment. Theoretical studies have shown that ratchet-based ion pumps (RBIPs) can separate ions with the same…

The mechanisms governing molecular photophysics under electronic strong coupling (ESC) remain elusive to date. Here, we use ultrafast pump-probe spectroscopy to study the nonradiative excited state relaxation dynamics of chlorin e6…

Calculating excited-state gradients and derivative couplings using time-dependent density functional theory (TDDFT) remains a computationally demanding task. An efficient variant, TDDFT with resolution of the identity and a minimal…

We present a simple and efficient method to incorporate anharmonic effects in the vibrational \textcolor{black}{analyses} of molecules within density functional theory (DFT) calculations. This approach is closely related to the traditional…

Active learning promises to provide an optimal training sample selection procedure in the construction of machine learning models. It often relies on minimizing the model's variance, which is assumed to decrease the prediction error. Still,…

A numerically solvable two-dimensional (2D) model, employed by the authors to study the dissociative recombination of H$_2^+$ in the ungerade symmetry [Phys. Rev. A $\mathbf{98}$, 062706 (2018)], is extended to describe the collision…

The crystal field theory as explained by Abragam and Bleaney in their landmark 1970 book on transition-ion electron paramagnetic resonance remains a cornerstone in the development of luminescence applications and molecular magnets based on…