We introduce a method for reducing the number of valence states entering the calculation of screened the Coulomb interaction W in GW calculations. In this way, denoting with N the generic size of a system, the computational cost is brought from the typical O(N4) to the more favorable O(N2 ln N). The method becomes effective for large model structures. For enhancing the potentialities of our scheme

Comprehending water dynamics is crucial in various fields, such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab initio molecular dynamics (AIMD) accurately portray water's structure, computing its dynamic properties over nanosecond time scales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamic

Atom-centered neural network (ANN) potentials have shown high accuracy and computational efficiency in modeling atomic systems. A crucial step in developing reliable ANN potentials is the proper selection of atom-centered symmetry functions (ACSFs), also known as atomic features, to describe atomic environments. Inappropriate selection of ACSFs can lead to poor-quality ANN potentials. Here, we propose

Empirical parametrization underpins many scientific methodologies including certain quantum-chemistry protocols [e.g., density functional theory (DFT), machine-learning (ML) models]. In some cases, the fitting requires a large amount of data, necessitating the use of data obtained using low-cost, and thus low-quality, means. Here we examine the effect of using low-quality data on the resulting method

Boron-doped graphene nanoribbons are promising platforms for developing organic materials with magnetic properties. Boron dopants can be used to create localized magnetic states in nanoribbons with tunable interactions. Controlling the coherence times of these magnetic states is the very first step in designing materials for quantum computation or information storage. In this work, we address the connection

This study presents two new relativistic Gaussian basis sets without variational prolapse of double- and triple-ζ quality, RPF-2Z and RPF-3Z, along with augmented versions including additional diffuse functions, aug-RPF-2Z and aug-RPF-3Z, which are available for all s and p block elements from Hydrogen to Oganesson. The exponents of the Correlation/Polarization (C/P) functions are obtained from a polynomial

The N-representability problem consists in determining whether, for a given p-body matrix, there exists at least one N-body density matrix from which the p-body matrix can be obtained by contraction, that is, if the given matrix is a p-body reduced density matrix (p-RDM). The knowledge of all necessary and sufficient conditions for a p-body matrix to be N-representable allows the constrained minimization

A plethora of key biological events occur at the cellular membrane where the large spatiotemporal scales necessitate dimensionality reduction or coarse-graining approaches over conventional all-atom molecular dynamics simulation. Constructing coarse-grained descriptions of membranes systematically from statistical mechanical principles has largely remained challenging due to the necessity of capturing

Due to their efficient molecular design, nonfullerene acceptors (NFAs) have significantly advanced organic photovoltaics (OPVs). However, the lack of models to screen and evaluate candidate NFAs based on the resulting device performance has impeded the rapid development of high-performance molecules. This work introduces a computational framework utilizing a kinetic Monte Carlo (kMC) model to derive

A multiscale force field (FF) is developed for an aqueous solution of trivalent actinide cations An3+ (An = U, Np, Pu, Am, Cm, Bk, and Cf) by using a 12-6-4 Lennard-Jones type potential considering ion-induced dipole interaction. Potential parameters are rigorously and automatically optimized by the meta-multilinear interpolation parametrization (meta-MIP) algorithm via matching the experimental properties

A significant challenge in applying machine learning to computational chemistry, particularly considering the growing complexity of contemporary machine learning models, is the scarcity of available experimental data. To address this issue, we introduce an approach that derives molecular features from an intricate neural network-based model and applies them to a simpler conventional machine learning

Biomolecular condensates are essential in various cellular processes, and their misregulation has been demonstrated to underlie disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated

Geometry optimization is an important tool used for computational simulations in the fields of chemistry, physics, and material science. Developing more efficient and reliable algorithms to reduce the number of force evaluations would lead to accelerated computational modeling and materials discovery. Here, we present a delta method-based neural network-density functional theory (DFT) hybrid optimizer

A gradual but steady tide in theoretical chemistry is favoring the exploration of atomic and molecular interactions through the dynamical forces perceived and exerted by the particles of a system. By integrating the quantum mechanical force operator over all the spin and all but one of the spatial coordinates of the electrons, the Ehrenfest force density field reveals these forces directly and is separable

Locating plausible transition paths and enhanced sampling of rare events are fundamental to understanding the functional dynamics of biomolecules. Here, a constraint-based constant advance replicas (CAR) formalism of reaction paths is reported for identifying the most probable transition path (MPTP) between two given states. We derive the temporal-integrated effective dynamics governing the projected

Vibrational non-Condon effects, which describe how molecular vibrational transitions are influenced by a system's rotational and translational degrees of freedom, are often overlooked in spectroscopy studies of biological macromolecules. In this work, we explore these effects in the modeling of infrared (IR) spectra for nucleic acids in the 1600-1800 cm-1 region. Through electronic structure calculations

There are various flows inside and outside cells in vivo. Nonequilibrium molecular dynamics (NEMD) simulation is a useful tool for understanding the effects of these flows on the dynamics of biomolecules. We propose an NEMD method to generate a Poiseuille-like flow between lipid bilayers. We extended the conventional equilibrium MD method to produce a flow by adding constant external force terms to