A truncated multipole expansion can be re-expressed exactly using an appropriate arrangement of point charges. This means that groups of point charges that are shifted away from nuclear coordinates can be used to achieve accurate electrostatics for molecular systems. We introduce a multipolar electrostatic model formulated in this way for use in computationally efficient multipolar molecular dynamics simulations with well-defined forces and energy conservation in NVE (constant number-volume-energy) simulations. A framework is introduced to distribute torques arising from multipole moments throughout a molecule, and a refined fitting approach is suggested to obtain atomic multipole moments that are optimized for accuracy and numerical stability in a force field context. The formulation of the charge model is outlined as it has been implemented into CHARMM, with application to test systems involving H2O and chlorobenzene. As well as ease of implementation and computational efficiency, the approach can be used to provide snapshots for multipolar QM/MM calculations in QM/MM-MD studies and easily combined with a standard point-charge force field to allow mixed multipolar/point charge simulations of large systems.

We compare the performance of explicitly time-dependent density functional theory (DFT) with time-dependent configuration interaction (TDCI) to achieve the control task of a population inversion in LiCN. We assume that if a given pulse achieves the control task when used in TDCI, then there should be a pulse with similar frequency and intensity that achieves the task in time-dependent DFT (TDDFT). The present investigation indicates that this is not the case, if standard functionals are used in the adiabatic approximation.

We will show that adiabatic real-time TDDFT predicts a time- and energy-dependent electronic structure of molecules, which makes it hard to combine TDDFT with coherent control schemes that depend on resonance conditions. In this study, we use sequences of ultrashort pulses, separated by long intervals of field free evolution, to illustrate this phenomenon for two molecules and two functionals. In coherent control scenarios, long laser pulses, with many oscillation periods, excite the system continuously and, in this way produce, a time-dependent electronic structure.

Insulin dimerization and aggregation play important roles in the endogenous delivery of the hormone. One of the important residues at the insulin dimer interface is Phe, which is an invariant aromatic anchor that packs toward its own monomer inside a hydrophobic cavity formed by Val, Leu, Tyr, Cys, and Tyr. Using molecular dynamics and free-energy simulations within explicit solvent, the structural and dynamical consequences of mutations of Phe at position B24 to glycine (Gly), alanine (Ala), and d-Ala and the des-PheB25 variant are quantified. Consistent with experiments, it is found that the Gly and Ala modifications lead to insulin dimers with reduced stability by 4 and 5 kcal/mol from thermodynamic integration and 4 and 8 kcal/mol from results using molecular mechanics-generalized Born surface area, respectively. Given the experimental difficulties to quantify the thermodynamic stability of modified insulin dimers, such computations provide a valuable complement. Interestingly, the Gly mutant exists as a strongly and a weakly interacting dimer. Analysis of the molecular dynamics simulations shows that this can be explained by water molecules that replace direct monomer-monomer H-bonding contacts at the dimerization interface involving residues B24 to B26. It is concluded that such solvent molecules play an essential role and must be included in future insulin dimerization studies.

The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a DNetFF machine learning model where, the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method. File list (2) download file view on ChemRxiv argon_submitted_may2.pdf (7.97 MiB) download file view on ChemRxiv argon_si.pdf (847.95 KiB)