Journal of Chemical Theory and Computation最新文献

The recent development of organic visible-light active photosensitizers has enabled the development of many novel triplet transformations, the mechanistic studies of which often rely on computation due to the short lifetime of the excited state intermediates. However, in contrast to studies of ground state reactivity, there has been little discussion of the best practices when using density functional theory to model triplet state reactions. Here, we report the first benchmark of density functionals on triplet reaction mechanisms. Barrier heights and thermodynamic values were computed for a set of 50 organic reactions using 45 functionals, with reference values obtained using high-level DLPNO-CCSD(T) calculations extrapolated to the complete basis set limit. In the course of this study, we observed a common tendency for triplet SCF calculations to converge non-Aufbau solutions, resulting in catastrophic predictions in both thermochemistry and activation energy barriers and leading to errors as high as 26.4 kcal/mol. Modifications to the initial SCF guess are proposed as a solution to such errors, enabling accurate comparison of functional performance. Range-separated hybrid functionals were found to consistently outperform their non-range-separated versions, while rungs below hybrid meta-GGA produce high errors compared to reference values. We recommend the best-performing single hybrid functionals ωM06, ωB97M, M06-2X, and M05-2X for their balance of high accuracy and computational efficiency.

With the growing availability of machine-learned interatomic potential (MLIP) models for materials simulations, there is an increasing demand for robust, automated, and chemically informed benchmarking methodologies. In response, we here introduce LiPS-25, a curated benchmark data set for a canonical series of solid-state electrolyte materials from the Li₂S-P₂S₅ pseudobinary compositional line, including crystalline and amorphous configurations. Together with the data set, we present a suite of performance tests that range from conventional numerical error metrics to physically motivated evaluation tasks. With a focus on graph-based MLIP architectures, we then show examples of using this data set to conduct numerical experiments, systematically assessing (i) the effect of hyperparameters on task-level performance and (ii) the fine-tuning behavior of selected pretrained ("foundational") MLIP models. Beyond the Li-P-S solid-state electrolytes, we expect that such benchmarks and accompanying code can be readily adapted to other material systems.

A unitary coupled-cluster (UCC)-based self-consistent electron propagator theory (EPT) is proposed for the description of electron-detached and electron-attached states. Two practical schemes, termed IP/EA-UCC3 and IP/EA-qUCCSD, are developed and implemented within the UCC singles and doubles (UCCSD) framework using the perturbative and commutator-based truncation strategies for the similarity-transformed Hamiltonian H̅. The numerical performance of these UCC-based EPT methods is evaluated primarily using full configuration interaction (FCI) reference data and compared with established approaches, including IP/EA-ADC(3), IP/EA-ADC(4) and IP/EA-EOM-CCSD. Benchmark calculations demonstrate that IP-qUCCSD achieves the highest overall accuracy among Hermitian ionized-state methods for one-hole (1h)-dominated IPs of closed-shell systems, with a mean absolute deviation (MAD) of 0.19 eV and standard deviation (SD) of 0.13 eV. Remarkably, despite the absence of triple-excitation contributions, IP-qUCCSD outperforms the higher-order ADC(4) method. For one-particle (1p)-dominated EA calculations, all tested methods exhibit comparable accuracy.

Free energy perturbation (FEP) calculations using classical force fields remain the dominant approach for large-scale, computational drug discovery efforts, but the accuracy is fundamentally limited by simplified forms that cannot quantitatively reproduce ab initio methods without significant fine-tuning. Machine Learning force fields (MLFFs) offer a promising avenue to retain quantum mechanical accuracy with significantly reduced computational cost compared with ab initio molecular dynamics (AIMD) simulations. Thus far, direct applications of ML force fields to FEP calculations lack systematic protocols and extensive benchmarking. In this work, we take a step in this direction by presenting a general and robust workflow for solvation (hydration) free energy (HFE) calculations which is independent of the details of the particular MLFF architecture used. Combining a broadly trained ML force field, Organic_MPNICE, with sufficient statistical and conformational sampling empowered by the solute-tempering technique, affords sub-kcal/mol average errors in HFE predictions relative to experimental estimates. This approach outperforms state-of-the-art classical force fields and DFT-based implicit solvation models on a diverse set of 59 organic molecules and provides a route to ab initio-quality HFE predictions, advancing the use of ML force fields in thermodynamic property prediction.

We present a quantum linear response (qLR) approach within an active space framework for computing indirect nuclear spin-spin coupling constants, a key ingredient in NMR spectra predictions. The method employs the unitary coupled cluster (UCC) ansatz and its orbital-optimized variant (ooUCC), both suitable for quantum computing implementations, to evaluate spin-spin coupling constants via qLR. Test calculations on five small molecules are compared with CASCI, CASSCF, CCSD, and CC3 results. qLR with UCC/ooUCC yields spin-spin coupling constants comparable to those of classical methods. We further examine the role of orbital optimization and find that ooUCC markedly affects the computed couplings; the orbital-optimized results show better agreement with CCSD and CC3. These findings indicate that orbital response is important for accurate NMR coupling predictions within quantum-computing-friendly correlated methods.