Journal of Chemical Theory and Computation最新文献

The interplay of quantum and classical simulation and the delicate divide between them is in the focus of massively parallelized tensor network state (TNS) algorithms designed for high performance computing (HPC). In this contribution, we present novel algorithmic solutions together with implementation details to extend current limits of TNS algorithms on HPC infrastructure building on state-of-the-art hardware and software technologies. Benchmark results obtained via large-scale density matrix renormalization group (DMRG) simulations on single node multiGPU NVIDIA A100 system are presented for selected strongly correlated molecular systems addressing problems on Hilbert space dimensions up to 4.17 × 10³⁵.

While nowadays approaches for equilibrium free energy estimation are well established, nonequilibrium simulations represent both an appealing computational opportunity and a challenge. This kind of simulations allows for a trivially parallel scheme, but at the same time the significant amount of irreversible work often generated during the steering process (either alchemical or physical) can hinder the convergence of free energy estimators. Here, we discuss in depth this issue for the protein-ligand binding free energy estimation carried out via physical paths. We found that the water model and the parametrization of the path collective variables have a remarkable impact on the convergence rate of the estimators (e.g., Crooks). Finally, we provide practical recipes to enhance the convergence speed and minimize dissipation.

An approach to simulate nonadiabatic dynamics in solution is introduced, which relies on the propagation of the nuclear wavepacket with the Ab Initio Multiple Spawning (AIMS) method under the effect of potential energy calculated with a hybrid but fully quantum mechanical scheme (QM/QM'). The electronic energies of the excited states of the chromophore are calculated with multireference perturbation theory (CASPT2), and the embedding molecules are described with a tight binding Hamiltonian (GFN2-xTB). This implementation is fully open source and relies on the combination of PySpawn, OpenMolcas, and xTB. Additionally, ORCA is used to properly generate the initial conditions in solution, showing how the combination of cutting-edge implementations in several commonly used software can push the state of the art of nonadiabatic dynamics in solution toward a new high standard of accuracy. The dynamics of ethylene in vacuum, in acetone, and in chloroform is reported as a test case, with a detailed analysis of the AIMS runs that shows important geometrical and electronic effects of the solvents on the decay mechanism of the chromophore.

Organic diradical dications, due to reduced intermolecular interactions, exhibit a greater tendency to adopt high spin states in the solid phase compared to their neutral diradical counterparts. This characteristic makes them promising candidates for applications involving organic electronics. We present a theoretical study of a recently synthesized sulfur-based diradical dication, a unique system exhibiting a robust triplet ground state. Using a number of density functional theory (DFT)-based methods (e.g., standard broken-symmetry DFT, constrained DFT, spin-flip TDDFT) and wave function-based multireference CASSCF+NEVPT2 methods, we investigate its magnetic properties and explore the influence of chalcogen substitution on magnetic exchange coupling. An active space scanning method was adopted to overcome the difficulties in choosing the correct active space for multireference calculation. Our findings highlight the critical role of multireference methods in accurately capturing the magnetic behavior of highly π-conjugated systems. The study reveals a surprising variation in magnetic properties among sulfur, selenium, and tellurium-based diradical dications despite being elements of the same group. These results offer valuable insights into the design and tuning of magnetic properties in organic diradical dications.

In this work, we performed a theoretical calculation of the negative ion conversion by a neutral carbon atom beam grazing scattering from the KI(100) surface. The Madelung potential, image potential, and ML-polarization interaction contributions are included in the calculation of the electron capture energy defect of the valence band near surface anion sites along the projectile incidence direction. The loss of the formed negative ions does not originate from the electron loss to the unoccupied conduction band or neutral exciton states but results from the Coulomb barrier tunneling detachment of the loosely bound affinity electron to the vacuum level during the interaction with surface lattice anion sites. Here, the large fraction of negative-ion conversion (≥50%) within the projectile energy range of E_p ∈ [5.7,14.9] keV implies the present collision system could be directly used to design the next-generation negative carbon ion sources for the study of isotope shifts in electron affinity, electron correlation effects and for the promotion of nerve tissue repair and regeneration by negative carbon ion irradiation.

Model docking, which refers to ligand docking into the protein model structures, is becoming a promising avenue in drug discovery with advances in artificial intelligence (AI)-based protein structure prediction. However, a significant challenge remains; even when sampling was successful in model docking, typical docking score functions failed to identify correct solutions for two-thirds of them. This discrepancy between scoring and sampling majorly arises because these scoring functions poorly tolerate minor structural inaccuracies. In this work, we propose a deep neural network named DENOISer to address the scoring challenge in model-docking scenarios. In the network, ligand poses are ranked by the consensus score of two independent subnetworks: the Native-likeness prediction and the Binding energy prediction networks. Both networks incorporate physical knowledge as an inductive bias in order to enhance pose discrimination power while ensuring tolerance to small interfacial structural noises. Combined with Rosetta GALigandDock sampling, DENOISer outperformed existing docking tools on the PoseBusters model-docking benchmark set, as well as on a broad cross-docking benchmark set. Further analyses reveal that the physics-based components and the consensus ranking approach are the two most crucial factors contributing to its ranking success. We expect that DENOISer may assist future drug discovery endeavors by providing more accurate structural models for protein-ligand complexes.

Existing machine learning models attempt to predict the energies of large molecules by training small molecules, but eventually fail to retain high accuracy as the errors increase with system size. Through an orbital pairwise decomposition of the correlation energy, a pretrained neural network model on hundred-scale data containing small molecules is demonstrated to be sufficiently transferable for accurately predicting large systems, including molecules and crystals. Our model introduces a residual connection to explicitly learn the pairwise energy corrections, and employs various low-rank retraining techniques to modestly adjust the learned network parameters. We demonstrate that with as few as only one larger molecule retraining the base model originally trained on only small molecules of (H₂O)₆, the MP2 correlation energy of the large liquid water (H₂O)₆₄ in a periodic supercell can be predicted at chemical accuracy. Similar performance is observed for large protonated clusters and periodic poly glycine chains. A demonstrative application is presented to predict the energy ordering of symmetrically inequivalent sublattices for distinct hydrogen orientations in the ice XV phase. Our work represents an important step forward in the quest for cost-effective, highly accurate and transferable neural network models in quantum chemistry, bridging the electronic structure patterns between small and large systems.

Validating the performance of exchange-correlation functionals is vital to ensure the reliability of density functional theory (DFT) calculations. Typically, these validations involve benchmarking data sets. Currently, such data sets are usually assembled in an unprincipled manner, suffering from uncontrolled chemical bias, and limiting the transferability of benchmarking results to a broader chemical space. In this work, a data-efficient solution based on active learning is explored to address this issue. Focusing─as a proof of principle─on pericyclic reactions, we start from the BH9 benchmarking data set and design a chemical reaction space around this initial data set by combinatorially combining reaction templates and substituents. Next, a surrogate model is trained to predict the standard deviation of the activation energies computed across a selection of 20 distinct DFT functionals. With this model, the designed chemical reaction space is explored, enabling the identification of challenging regions, i.e., regions with large DFT functional divergence, for which representative reactions are subsequently acquired as additional training points. Remarkably, it turns out that the function mapping the molecular structure to functional divergence is readily learnable; convergence is reached upon the acquisition of fewer than 100 reactions. With our final updated model, a more challenging─and arguably more representative─pericyclic benchmarking data set is curated, and we demonstrate that the functional performance has changed significantly compared to the original BH9 subset.

The dynamics of metal centers are challenging to describe due to the vast variety of ligands, metals, and coordination spheres, hampering the existence of general databases of transferable force field parameters for classical molecular dynamics simulations. Here, we present easyPARM, a Python-based tool that can calculate force field parameters for a wide range of metal complexes from routine frequency calculations with electronic structure methods. The approach is based on a unique labeling strategy, in which each ligand atom that coordinates the metal receives a unique atom type. This design prevents parameter shortage, labeling duplication, and the necessity to post-process output files, even for very complicated coordination spheres, whose parametrization process remain automatic. The program requires the Cartesian Hessian matrix, the geometry xyz file, and the atomic charges to provide reliable force-field parameters extensively benchmarked against density functional theory dynamics in both the gas and condensed phases. The procedure allows the classical description of metal complexes at a low computational cost with an accuracy as good as the quality of the Hessian matrix obtained by quantum chemistry methods. easyPARM v2.00 reads vibrational frequencies and charges in Gaussian (version 09 or 16) or ORCA (version 5 or 6) format and provides refined force-field parameters in Amber format. These can be directly used in Amber and NAMD molecular dynamics engines or converted to other formats. The tool is available free of charge in the GitHub platform (https://github.com/Abdelazim-Abdelgawwad/easyPARM.git).

Elucidating transition states (TSs) is crucial for understanding chemical reactions. The reliability of traditional TS search approaches depends on input conformations that require significant effort to prepare. Previous automated methods for generating input reaction conformations typically involve extensive exploration of a large conformational space. Such exhaustive search can be complicated by the rapid growth of the conformational space, especially for reactions involving many rotatable bonds, multiple reacting molecules, and numerous bond formations and dissociations. To address this problem, we propose a new approach that generates reaction conformations for TS searches with minimal reliance on sampling. This method constructs a pseudo-TS structure based on a reaction graph containing bond formation and dissociation information and modifies it to produce reactant and product conformations. Tested on three different benchmarks, our method consistently generated suitable conformations without necessitating extensive sampling, demonstrating its potential to significantly improve the applicability of automated TS searches. This approach offers a valuable tool for a broad range of applications such as reaction mechanism analysis and network exploration.