Automatic purpose-driven basis set truncation for time-dependent Hartree–Fock and density-functional theory

Real-time time-dependent density-functional theory (RT-TDDFT) and linear response time-dependent density-functional theory (LR-TDDFT) are two important approaches to simulate electronic spectra. However, the basis sets used in such calculations are usually the ones designed mainly for electronic ground state calculations. In this work, we propose a systematic and robust scheme to truncate the atomic orbital (AO) basis set employed in TDDFT and TD Hartree–Fock (TDHF) calculations. The truncated bases are tested for both LR- and RT-TDDFT as well as RT-TDHF approaches, and provide an acceleration up to an order of magnitude while the shifts of excitation energies of interest are generally within 0.2 eV. The procedure only requires one extra RT calculation with 1% of the total propagation time and a simple modification on basis set file, which allows an instant application in any quantum chemistry package supporting RT-/LR-TDDFT calculations. Aside from the reduced computational effort, this approach also offers valuable insight into the effect of different basis functions on computed electronic excitations and further ideas on the design of basis sets for special purposes

Magnetic Interactions in a [Co(II)3Er(III)(OR)4] Model Cubane through Forefront Multiconfigurational Methods

Strong electron correlation effects are one of the major challenges in modern quantum chemistry. Polynuclear transition metal clusters are peculiar examples of systems featuring such forms of electron correlation. Multireference strategies, often based on but not limited to the concept of complete active space, are adopted to accurately account for strong electron correlation and to resolve their complex electronic structures. However, transition metal clusters already containing four magnetic centers with multiple unpaired electrons make conventional active space based strategies prohibitively expensive, due to their unfavorable scaling with the size of the active space. In this work, forefront techniques, such as density matrix renormalization group (DMRG), full configuration interaction quantum Monte Carlo (FCIQMC), and multiconfiguration pair-density functional theory (MCPDFT), are employed to overcome the computational limitation of conventional multireference approaches and to accurately investigate the magnetic interactions taking place in a [Co(II)3Er(III)(OR)4] (chemical formula [Co(II)3Er(III)(hmp)4(μ2-OAc)2(OH)3(H2O)], hmp = 2-(hydroxymethyl)-pyridine) model cubane water oxidation catalyst. Complete active spaces with up to 56 electrons in 56 orbitals have been constructed for the seven energetically lowest different spin states. Relative energies, local spin, and spin–spin correlation values are reported and provide crucial insights on the spin interactions for this model system, pivotal in the rationalization of the catalytic activity of this system in the water-splitting reaction. A ferromagnetic ground state is found with a very small, ∼50 cm–1, highest-to-lowest spin gap. Moreover, for the energetically lowest states, S = 3–6, the three Co(II) sites exhibit parallel aligned spins, and for the lower states, S = 0–2, two Co(II) sites retain strong parallel spin alignment

A mixed black and whitelist approach for wildlife trade regulation in China: Biodiversity conservation is made of shades of gray

The Kunming‐Montreal Global Biodiversity Framework requires effective actions to bend the curve of biodiversity loss by 2030. Wildlife trade, a direct drive of biodiversity decline, calls for more effective regulations to both protect wildlife populations in the wild and facilitate sustainable use of wildlife resources to meet human needs. This call has become particularly urgent in light of the COVID‐19 pandemic. In 2021, China's List of State Key Protected Wild Animals, a list of fauna under the strictest protection by national legislation, has been updated in the year 2021, 32 years after its first release, increasing its coverage (from the original 13%) an 11% of species across taxa. Combined with the updated List of State Protected Terrestrial Wild Animals which covers species with lower protection priority, these two national lists already cover 77% terrestrial vertebrate species of China. Such a blacklist approach, placing threatened species under a list of legal protection, is a common practice globally in species conservation. We discussed pros and cons of this dominant strategy and further explored the potential integration with a whitelist approach, listing all wildlife and only permitting regulated uses of certain species. We propose a mixed approach combining black and whitelists at different administration levels which could perhaps be first adopted in China. This is mainly due to the fact that in addition to illegal harvesting from the wild, traded wildlife in China are mostly from captive breeding and related laundering of wild‐caught animals

Trajectory-based machine learning method and its application to molecular dynamics

Ab initio molecular dynamics (AIMD) has become a popular simulation technique but long simulation times are often hampered due to its high computational effort. Alternatively, classical molecular dynamics (MD) based on force fields may be used, which, however, has certain shortcomings compared to AIMD. In order to alleviate that situation, a trajectory-based machine learning (TrajML) approach is introduced for the construction of force fields by learning from AIMD trajectories. Only nuclear trajectories are required, which can be obtained by other methods beyond AIMD as well. We developed an easy-to-use MD machine learning package (TrajML MD) for instant modelling of the force field and system-focussed prediction of molecular configurations for MD trajectories. It consumes similar computational resources as classical MD but can simulate complex systems with a higher accuracy due to the targeted learning on the system of interest

Complete active space analysis of a reaction pathway: Investigation of the oxygen–oxygen bond formation

Water nucleophilic attack is an important step in water oxidation reactions, which have been widely studied using density functional theory (DFT). Nevertheless, a single‐determinant DFT picture may be insufficient for a deeper insight into the process, in particular during the oxygen–oxygen bond formation. In this work, we use complete active space self‐consistent field calculations and describe an approach for a complete active space analysis along a reaction pathway. This is applied to the water nucleophilic attack at a Ru‐based catalyst, which has successfully been used for efficient water oxidation and in silico design of new water oxidation catalysts recently

Fast Estimation of Møller–Plesset Correlation Energies Based on Atomic Contributions

Dynamic correlation plays an important role in the accurate calculation of chemical compounds such as the description of equilibrium structures in chemical systems. A model for the fast estimation of dynamic correlation energy is introduced in this work. This model is based on the idea of decomposition of the contribution of dynamic correlation energy calculated by nth order Møller–Plesset perturbation (MPn) theory with respect to atomic regions. Multiple levels of theory, including MP2, MP2.5, and MP4, are used as the reference, and the corresponding correlation energy densities are calculated. The proposed model is concise, fast, and promising for practical use, such as the prediction of reaction energies. It can also work as a baseline model or pretrained model for follow-up studies of machine learning

A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds

A proper treatment of electron correlation effects is indispensable for accurate simulation of compounds. Various post-Hartree–Fock methods have been adopted to calculate correlation energies of chemical systems, but time complexity usually prevents their usage in a large scale. Here, we propose a density functional approximation, based on machine learning using neural networks, which can be readily employed to produce results comparable to second-order Møller–Plesset perturbation (MP2) ones for organic compounds with reduced computational cost. Various systems have been tested and the transferability across basis sets, structures, and nuclear configurations has been evaluated. Only a small number of molecules at the equilibrium structure has been needed for the training, and generally less than 5% relative error has been achieved for structures outside the training domain and systems containing about 140 atoms. In addition, this approach has been applied to make predictions on correlation energies of nuclear configurations extracted from density functional theory-based molecular dynamics trajectories with only one or two structures as training data

A Concise Review on Recent Developments of Machine Learning for the Prediction of Vibrational Spectra

Machine learning has become more and more popular in computational chemistry, as well as in the important field of spectroscopy. In this concise review, we walk the reader through a short summary of machine learning algorithms and a comprehensive discussion on the connection between machine learning methods and vibrational spectroscopy, particularly for the case of infrared and Raman spectroscopy. We also briefly discuss state-of-the-art molecular representations which serve as meaningful inputs for machine learning to predict vibrational spectra. In addition, this review provides an overview of the transferability and best practices of machine learning in the prediction of vibrational spectra as well as possible future research directions

Magnetic Interactions in a [Co(II)3Er(III)(OR)4] Model Cubane Through Forefront Multiconfigurational Methods

Strong electron correlation effects are one of the major challenges in modern quantum chemistry. Poly-nuclear transition metal clusters are peculiar examples of systems featuring such forms of electron correlation. Multi-reference strategies, often based on, but not limited to, the concept of complete active space, are adopted to accurately account for strong electron correlation, and to resolve their complex electronic structures. However, transition metal clusters already containing four magnetic centers with multiple unpaired electrons, make conventional active space based strategies prohibitively expensive, due to their unfavorable scaling with the size of the active space. In this work, forefront techniques, such as DMRG, FCIQMC and MCPDFT, are employed to overcome the computational limitation of conventional multi-reference approaches and to accurately investigate the magnetic interactions taking place in a [Co(II)3Er(III)(OR)4] model cubane water oxidation catalyst. Complete active spaces with up to 56 electrons in 56 orbitals have been constructed for the seven energetically lowest different spin states. Relative energies, local spin and spin-spin correlation values are reported, and provide crucial insights on the spin interactions for this model system, pivotal in the rationalization of the catalytic activity of this system in the water-splitting reaction. A ferromagnetic ground state is found with a very small, ∼ 50 cm−1, highest-to-lowest spin gap. Moreover, for the energetically lowest states, S=3–6, the three Co(II) sites exhibit parallel aligned spins, and for the lower states, S=0–2, two Co(II) sites retain strong parallel spin alignment