Journal of Chemical Theory and Computation最新文献

Relative free energy (RFE) calculations are now widely used in academia and the industry, but their accuracy is often limited by poor sampling of the complexes' conformational ensemble. To help address conformational sampling problems when simulating many relative binding free energies, we developed a novel method termed multiple topology replica exchange of expanded ensembles (MT-REXEE). This method enables parallel expanded ensemble calculations, facilitating iterative RFE computations while allowing conformational exchange between parallel transformations. These iterative transformations can be adaptable to any set of systems with a common backbone or central substructure. We demonstrate that the MT-REXEE method maintains thermodynamic cycle closure to the same extent as standard expanded ensemble calculations for both solvation free energy and relative binding free energy calculations. The transformations tested involve systems that incorporate diverse heavy atoms and multisite perturbations of a small molecule core resembling multisite λ dynamics, without necessitating modifications to the MD code. Our initial implementation is in GROMACS. We outline a systematic approach for the topology setup and provide instructions on how to perform inter-replica coordinate modifications. This work shows that MT-REXEE can be used to perform accurate and reproducible free energy estimates and prompts expansion to more complex test systems and other molecular dynamics simulation infrastructures.

Green's function theory has emerged as a powerful many-body approach not only in condensed matter physics but also in quantum chemistry in recent years. We have developed a new all-electron implementation of the BSE@GW formalism using numeric atom-centered orbital basis sets (Liu, C. J. Chem. Phys. 2020, 152, 044105). We present our recent developments in implementing this formalism for extended periodic systems. We discuss its numerical implementation and various convergence tests pertaining to numerical atom-centered orbitals, auxiliary basis sets for the resolution-of-identity formalism, and Brillouin zone sampling. Several proof-of-principle examples are presented to compare with other formalisms, illustrating the new all-electron BSE@GW method for extended periodic systems.

The long-held assumption that the optimization of parameters for NDDO-descendant semiempirical methods may be performed without precise geometry optimization is assessed in detail; the relevant equations for the analytical evaluation of the geometry-corrected derivatives of molecular properties that account for changes in the optimum geometry are then presented. The first and second derivatives calculated from our implementation of MNDO are used for a limited reparameterization of 1,113 CHNO molecules taken from the PM7 training set, demonstrating an improvement over the PARAM program used in the optimization of parameters for the PMx methods.

Enzyme-substrate interactions are essential to both biological processes and industrial applications. Advanced machine learning techniques have significantly accelerated biocatalysis research, revolutionizing the prediction of biocatalytic activities and facilitating the discovery of novel biocatalysts. However, the limited availability of data for specific enzyme functions, such as conversion efficiency and stereoselectivity, presents challenges for prediction accuracy. In this study, we developed BioStructNet, a structure-based deep learning network that integrates both protein and ligand structural data to capture the complexity of enzyme-substrate interactions. Benchmarking studies with different algorithms showed the enhanced predictive accuracy of BioStructNet. To further optimize the prediction accuracy for the small data set, we implemented transfer learning in the framework, training a source model on a large data set and fine-tuning it on a small, function-specific data set, using the CalB data set as a case study. The model performance was validated by comparing the attention heat maps generated by the BioStructNet interaction module with the enzyme-substrate interactions revealed from molecular dynamics simulations of enzyme-substrate complexes. BioStructNet would accelerate the discovery of functional enzymes for industrial use, particularly in cases where the training data sets for machine learning are small.

Electronic polarization and dispersion are decisive actors in determining interaction energies between molecules. These interactions have a particularly profound effect on excitation energies of molecules in complex environments, especially when the excitation involves a significant degree of charge reorganization. The direct reaction field (DRF) approach, which has seen a recent revival of interest, provides a powerful framework for describing these interactions in quantum mechanics/molecular mechanics (QM/MM) models of systems, where a small subsystem of interest is described using quantum chemical methods and the remainder is treated with a simple MM force field. In this paper we show how the DRF approach can be combined with the electrostatic potential fitted (ESPF) multipole operator description of the QM region charge density, which significantly improves the efficiency of the method, particularly for large MM systems, and for typical calculations effectively eliminates the dependence on MM system size. We also show how the DRF approach can be combined with fluctuating charge descriptions of the polarizable environment, as well as previously used atom-centered dipole-polarizability based models. We further show that the ESPF-DRF method provides an accurate description of molecular interactions in both ground and excited electronic states of the QM system and apply it to predict the gas to aqueous solution solvatochromic shifts in the UV/visible absorption spectrum of acrolein.

Noncovalent interactions (NCI) play a central role in numerous physical, chemical, and biological phenomena. An accurate description of NCI is the key to success for any theoretical study in such areas. Although quantum mechanics (QM) methods such as dispersion-corrected density functional theory are sufficiently accurate, their applications are practical only for <300 atoms and <100 ps of simulation time. Thus, empirical force fields (FF) have generally been the only choice for systems with thousands to millions of atoms and for nanoseconds and longer. We want to develop a FF that can be applied to applications of thousands to millions of atoms with an accuracy comparable to QM methods. As the first step, we develop here a new general nonbonded potential (GNB) based on a novel functional form with four adjustable parameters for each element. We report here parameters for elements H-La, Hf-Rn (excluding lanthanides and actinides) by fitting the interaction energy of molecular complexes to QM calculations using the accurate Head-Gordon ωB97M-V density functional. We performed extensive testing of GNB for organic molecules, organometallic molecules, and metal organic-framework (MOF) systems. The mean absolute errors of GNB are 0.37 kcal/mol for the dispersion and mixed groups of the S66 × 8 benchmark set, 0.35 kcal/mol for CO₂ adsorption on MOF materials, and 4.53 kcal/mol for the XTMC43 benchmark. GNB outperforms existing FF and in many cases has accuracy comparable to that of QM methods such as PBE-D3. GNB can potentially replace the nonbonded part of existing FFs in a wide range of applications.

Kohn-Sham (KS) density functional theory (DFT) is an extremely popular, in-principle exact method, which can describe any many-electron system by introducing an auxiliary system of noninteracting electrons with the same density. When the number of electrons, N, changes continuously, taking on both integer and fractional values, the density has to be piecewise-linear, with respect to N. In this article, I explore how the piecewise-linearity property of the exact interacting density is reflected in the KS system. In particular, I suggest to express KS quantities using the two-point Taylor expansion in N and find how the expansion coefficients are restricted by the piecewise-linearity requirement. Focus is given to the total electron density, the KS subdensities, and the highest occupied (HOMO) orbital density. In addition to exact analytical results, common approximations for the HOMO, namely, the frozen and the linear regimes, are analyzed. A numerical investigation using various exchange-correlation approximations is performed to test the analytical findings. The outcomes of this work will help to remove density-driven errors in DFT calculations for open systems and ensembles.

We report a concurrent hybrid multiscale simulation method, in which a particle domain is coupled with a surrounding continuum domain. The particle domain consists of a coarse-grained model of poly(lactic acid) and the continuum domain is treated using the finite element method. The coarse-grained model is derived from an atomistic model, using the iterative Boltzmann inversion scheme. The particle- and the finite element-domains overlap in a bridging domain through anchor points. In this coupling, the information passes back and forth between the high- and the low-resolution domains, effectively bridging the gap between the nano and macro-scales. This scheme is employed to simulate the coupled particle-continuum domains under both stochastic and semistochastic boundary conditions. While the anchor points keep the volume of the particle domain fixed in the former case, there is no anchor point in the planes normal to the periodic direction, in the latter case. The stress-strain behavior of polymer under both stochastic and semistochastic boundary conditions is investigated and the results are compared with those calculated from pure finite element reference simulations. Furthermore, the stress-strain relationship for the coupled system under the semistochastic boundary conditions is examined under plane stress and plane strain conditions, and the results are compared with those of pure finite element reference simulations. The hybrid particle-continuum method reproduces the pure finite element simulation results well.

The standard definition of particle number fluctuations based on point-like particles neglects the excluded volume effect. This leads to a large and systematic finite-size scaling and an unphysical surface term in the isothermal compressibility. We correct these errors by introducing a modified pair distribution function that takes account of the finite size of the particles. For the hard sphere fluid in one-dimension, we show that the compressibility is strictly size-independent, and we reproduce this result from the number fluctuations calculated with the new theory. In general, the present method eliminates the leading finite-size effect, which makes it possible to compute density fluctuations accurately in very small sampling volumes, comparable to a single particle size. These findings open the way for obtaining the local compressibility from fluctuation theory at the nanometer scale.

This work introduces a dedicated thermostatization strategy for molecular dynamics simulations of gaseous systems. The proposed thermostat is based on the stochastic canonical velocity rescaling approach by Bussi and co-workers and is capable of ensuring an equal distribution of the kinetic energy among the translational, rotational, and vibrational degrees of freedom. The outlined framework ensures the correct treatment of the kinetic energy in gaseous systems, which is typically not the case in standard approaches due to the limited number of collisions between particles associated with a large free mean path. Additionally, an efficient strategy to effectively correct for intramolecular contributions to the virial in quantum mechanical simulations is presented. The equipartitioning thermostat was successfully tested by the determination of pV diagrams for carbon dioxide and methane at the density functional tight binding level of theory. The results unequivocally demonstrate that the equipartitioning thermostat can effectively achieve an equal distribution of the kinetic energy among the different degrees of freedom, thereby ensuring correct pressure in gaseous systems. Furthermore, RDF calculations show the capability of the proposed method to accurately depict the structure of gaseous systems, as well as enable an adequate treatment of gas molecules under confinement, as exemplified by an MD simulation of (CO₂)₅₀@MOF-5.