Journal of Chemical Theory and Computation最新文献

Nonadiabatic molecular dynamics (NAMD) simulations are crucial for revealing the underlying mechanisms of photochemical and photophysical processes. Typical NAMD simulation software packages rely on on-the-fly ab initio electronic structure and nonadiabatic coupling calculations, and thus become challenging when dealing with large complex systems. We here introduce a new Simulation Package for non-Adiabatic Dynamics in Extended systems (SPADE), which is designed to address the limitations of traditional surface hopping methods in dealing with these problems. By design, SPADE enables the users to define arbitrary quasi-diabatic Hamiltonians through parametrized functions and incorporates a variety of algorithms (e.g., global flux hopping probabilities, complex crossing and decoherence corrections), which can realize efficient and reliable NAMD simulations without using nonadiabatic couplings at all. All the employed methods and expressions for diabatic Hamiltonian matrix elements can be flexibly set through the input files. SPADE is mainly written in Fortran based on a modular design and has a great capacity for further implementation of new methods. SPADE can be used to simulate both model and atomistic systems as long as proper Hamiltonians are provided. As demonstrations, a series of representative models are studied to show the main features and capabilities.

Transcription factors (TFs) regulate gene expression by binding to specific DNA sequences, playing critical roles in cellular processes and disease pathways. Computational methods, particularly λ-Dynamics, offer a promising approach for predicting TF relative binding affinities. This study evaluates the effectiveness of different λ-Dynamics perturbation schemes in determining binding free energy changes (ΔΔG_b) of the WRKY transcription factor upon mutating its W-box binding site (GGTCAA) to a nonspecific sequence (GATAAA). Among the schemes tested, the single λ per base pair protocol demonstrated the fastest convergence and highest precision. Extending this protocol to additional mutants (GGTCCG and GGACAA) yielded ΔΔG_b values that successfully ranked binding affinities, showcasing its strong potential for high-throughput screening of DNA binding sites.

Creating analytic representations of multiple potential energy surfaces for modeling electronically nonadiabatic processes is a major challenge being addressed in various ways by the chemical dynamics community. In this work, we introduce a new method that can achieve convenient learning of multiple potential energy surfaces (PESs) and their gradients (negatives of the forces) for a polyatomic system. This new method, called compatibilization by deep neural network (CDNN), is demonstrated to be accurate and, even more importantly, to be automatic. The only required input is a database with geometries and potential energies. The method produces a matrix, called the compatible potential energy matrix (CPEM), that may be interpreted as the electronic Hamiltonian in an implicit nonadiabatic basis, and the analytic adiabatic potential energy surfaces and their gradients are obtained by diagonalization and automatic differentiation. We show that the CPEM, which is neither adiabatic nor necessarily diabatic, can be discovered automatically during the learning procedure by the special design of a CDNN architecture. We believe that the CDNN method will be very useful in practice for learning coupled PESs for polyatomic systems because it is accurate and fully automatic.

Accurate simulation of the many-electron nonadiabatic dynamics process at metal surfaces remains a significant challenge. In this work, we present an orbital surface hopping (OSH) algorithm rigorously derived from the orbital quantum-classical Liouville equation (o-QCLE) to address nonadiabatic dynamics in many-electron systems. This OSH algorithm is closely connected to the popular independent electron surface hopping (IESH) method, which has demonstrated remarkable success in addressing these nonadiabatic phenomena, except that electrons hop between orbitals. We compare the OSH approach with the IESH method and benchmark these two algorithms against the surface hopping method using a full configuration interaction (FCI) wave function. Our approach shows strong agreement with IESH and FCI-SH results for molecular orbital populations and kinetic energy relaxation, while also exhibiting high efficiency, thereby demonstrating the capability of the new OSH method to capture key aspects of many-electron nonadiabatic dynamics.

In this paper, we present the recent advances in the computation of the Dirac-Kohn-Sham (DKS) method of the BERTHA code. We show here that the simple underlined structure of the FORTRAN code also favors efficient porting of the code to GPUs, leading to a particularly efficient hybrid CPU/GPU implementation (OpenMP/OpenACC), where the most computationally intensive part for DKS matrix evaluation (three-center two-electron integrals evaluated via the McMurchie-Davidson scheme) is efficiently offloaded to the GPU via compiler directives based on the OpenACC programming model. This scheme in combination with the use of a linear algebra library optimized for GPUs (cuBLAS, cuSOLVER) significantly accelerates the DKS calculations. In addition, the low-level integral kernel developed here at FORTRAN level was used to port our real-time DKS (RT-TDDKS) implementation based on Python (PyBERTHART) for the utilization of the GPU. The results obtained on the new Tier-0 EuroHPC supercomputer (LEONARDO) of the CINECA Supercomputing Centre with a single NVIDIA A100 card are very satisfactory. We achieve a speedup up to 30 for Au₁₆ in a single-point DKS energy calculation and up to 10 for the Au₈ systems in an RT-TDDKS calculation, compared to our OpenMP (i.e., CPU only) parallel implementation (with 32 cores). The approach presented here is very general and, to our knowledge, represents the first port of a Python API to GPUs based on a FORTRAN kernel for the evaluation of two-electron integrals. The implementation is currently limited to the use of a single GPU accelerator, but future paths to an actual exascale implementation are discussed.

This work introduces the Strategic Escape (SE) algorithm, an approach that systematically ensures effective exploration of the potential energy surface during global minimum searches for atomic clusters. The SE algorithm prioritizes the escape from local minima prior to geometry optimization, leveraging a combination of randomized direction vectors, distance-based uniqueness criteria, and covalent bonding heuristics. These principles enhance structural diversity and computational efficiency by reducing redundant geometry optimizations. Additionally, a symmetry-guided seed generation method based on an adaptive polygon is proposed to provide diverse and physically realistic initial configurations. Together, these methods achieve a 2.3-fold improvement in computational efficiency compared to conventional Basin-Hopping approaches. The effectiveness of the SE algorithm is demonstrated through its application to boron, metal clusters, and binary-composition clusters, achieving rapid convergence to global minimum structures with high reliability. These advancements establish the SE algorithm as a robust and scalable tool for exploring complex chemical systems.

Charge transfer (CT) processes that are electronically nonadiabatic are ubiquitous in chemistry, biology, and materials science, but their theoretical description requires diabatic states or adiabatic excited states. For complex systems, these latter states are more difficult to calculate than the adiabatic ground state. Here, we propose a simple method to obtain diabatic states, including energies and charges, by constraining the atomic charges within the charge equilibration framework. For two-state systems, the exact diabatic coupling can be determined, from which the adiabatic excited-state energy can also be calculated. The method can be viewed as an affordable alternative to constrained density functional theory (CDFT), and so we call it constrained charge equilibration (CQEq). We test the CQEq method on the anthracene-tetracyanoethylene CT complex and the reductive decomposition of ethylene carbonate on a lithium metal surface. We find that CQEq predicts diabatic energies, charges, and adiabatic excitation energies in good agreement with CDFT, and we propose that CQEq is promising for combination with machine learning force fields to study nonadiabatic CT in the condensed phase.

We present an efficient linear-scaling algorithm for evaluating the analytical force and stress contributions derived from the exact-exchange energy, a key component in hybrid functional calculations. The algorithm, working equally well for molecular and periodic systems, is formulated within the framework of numerical atomic orbital (NAO) basis sets and takes advantage of the localized resolution-of-identity (LRI) technique for treating the two-electron Coulomb repulsion integrals. The linear-scaling behavior is realized by fully exploiting the sparsity of the expansion coefficients resulting from the strict locality of the NAOs and the LRI ansatz. Our implementation is massively parallel, and enables efficient structural relaxation based on hybrid density functionals for bulk materials containing thousands of atoms. In this work, we will present a detailed description of our algorithm and benchmark the performance of our implementation using illustrating examples. By optimizing the structures of the pristine and doped halide perovskite material CsSnI₃ with different functionals, we find that in the presence of lattice strain, hybrid functionals provide a more accurate description of the stereochemical expression of the lone pair.

Machine learning potential (MLP), by learning global potential energy surfaces (PES), has demonstrated its great value in finding unknown structures and reactions via global PES exploration. Due to the diversity and complexity of the global PES data set, an outstanding challenge emerges in achieving PES high accuracy (e.g., error <1 meV/atom), which is essential to determine the thermodynamics and kinetics properties. Here, we develop a lightweight fine-tuning MLP architecture, namely, AtomFT, that can explore PES globally and simultaneously describe the PES of a target system accurately. The AtomFT potential takes the pretrained many-body function corrected global neural network (MBNN) potential as the basis potential, exploits and iteratively updates the atomic features from the pretrained MBNN model, and finally generates the fine-tuning energy contribution. By implementing the AtomFT architecture on the commonly available CPU platform, we show the high efficiency of AtomFT potential in both training and inference and demonstrate the high performance in challenging PES problems, including the oxides with low defect content, molecular reactions, and molecular crystals─in all systems, the AtomFT potentials enhance significantly the PES prediction accuracy to 1 meV/atom.

The critical importance of water in sustaining life highlights the need for accurate water models in computer simulations, aiming to mimic biochemical processes experimentally. The polarizable Gaussian multipole (pGM) model, recently introduced for biomolecular simulations, improves the handling of complex biomolecular interactions. As an integral part of our initial exploration, we examined a minimalist fixed geometry three-center pGM water model using ab initio quantum mechanical calculations of water oligomers. However, our final model development was based on liquid-phase water properties, leveraging automated machine learning (AutoML) techniques for optimization. This allows the development of a framework to refine both van der Waals and electrostatic parameters of the pGM model, aiming to accurately reproduce specific properties such as the oxygen-oxygen radial distribution function, density, and dipole moment, all at 298 K and 1.0 bar pressure. The efficacy of the optimized three-center pGM water model, pGM3P-25, was assessed through simulations of a water box of 512 water molecules, showcasing marked enhancements in both accuracy and practical utility. Notably, the model accurately reproduces thermodynamic properties not explicitly included in training while significantly reducing the time and human effort required for optimization. It was found that pGM3P-25 can reproduce temperature-dependent properties such as density, self-diffusion constants, heat capacity, second virial coefficient, and dielectric constant, which are important in biomolecular simulations. This study underscores the potential of AutoML-driven frameworks to streamline parameter refinement for molecular dynamics simulations, paving the way for broader applications in computational chemistry and beyond.