Molecular dynamics simulation driven by quantum chemistry theory for atomic scale interfacial polymerization evolution of polyamide membrane

The heterogeneous reactions of nitrophenols and their impact on HONO generation: The influence of mineral dusts.

The isomerization of dications of CS2 and OCS is investigated using combined experimental and theoretical methods. The dications were generated by a high-energy electron pulse and the fragment ions were detected by a momentum imaging time-of-flight spectrometer. Bond rearrangement reactions leading to C+ + S2+ and C+ + OS+ were identified through coincident measurements. Theoretically, potential energy surfaces along the reaction paths were calculated using high-level quantum chemistry methods. For CS2+, isomerization is initiated by ionization excitation followed by a decay process. In contrast, the isomerization channel for OCS2+ can open on the ground dicationic state through vibrational excitation. The predicted kinetic energy releases agree with the experimental data. This study demonstrates that, as a common process in dissociative ionization, isomerization mechanism may be influenced by molecular symmetry.

Low-frequency contributions in the radiative efficiencies of HFC-236fa, HFC-245fa and HFC-43-10mee over the 225–298 K temperature range

The density matrix renormalization group (DMRG) algorithm is a cornerstone computational method for studying quantum many-body systems, renowned for its accuracy and adaptability. Because DMRG provides a general framework applicable across various fields such as materials science, quantum chemistry, and quantum computing, one might expect a shared, flexible library to serve most users. Nevertheless, numerous independent implementations continue to appear, resulting in significant duplication of effort. To identify collaboration opportunities that can promote a more unified approach, we map the rapidly expanding DMRG software landscape and provide a comprehensive comparison of features across 37 existing packages. When comparing key features, such as parallelism strategies for high-performance computing and symmetry-adapted formulations that enhance efficiency, we found significant overlap among the packages. This overlap suggests opportunities for collaboration to modularize common functionality—e.g., tensor operations, symmetry representations, and eigensolvers—as the packages are mostly independent and share few third-party library dependencies. More collaboration on modularization could reduce duplication of effort, improve interoperability, and enable prioritization and quicker spread of new advances. We believe the current lack of modularity is more socially driven than a technical issue; hence, we see raising awareness about the existing implementations as a first step in the right direction. Ultimately, this work emphasizes the value of greater cohesion through modularity, which would benefit DMRG software and related tensor-network-centered software, enabling the solution of more complex and ambitious problems.

Novel mechanism of enhancing Arthrospira chlorophyll photostability by natural deep eutectic solvent: Axial coordination between betaine-xylitol and the porphyrin ring.

Regulation of charge transfer and photophysical properties of porphyrin-based hole transport materials by functional group substitution: DFT and TD-DFT investigations.

Electrical insulating materials with high breakdown strength and low space charge accumulation are very important for the development of high-voltage direct current (HVDC) transmission. In this work, low-density polyethylene (LDPE)-based composites were prepared by adding 0, 0.1, 0.3, and 0.7 wt % fluorinated graphene. The morphology, breakdown strength, space charge distribution, and surface potential decay were characterized. The experimental results illustrate that when the mass fraction of fluorinated graphene increases from 0 to 0.7 wt %, the breakdown strength increases at first and then decreases, whereas the space charge density reduces initially and grows later. The 0.3 wt % composite has the highest breakdown strength, increasing by 54.2%, and the lowest space charge density, reducing by 26.4%, compared with pure LDPE. According to the quantum chemistry calculations and the trap distribution, the effects of fluorinated graphene on charge transport and breakdown strength are revealed. This work is very helpful for improving the electrical properties of polymeric materials.

The molecular and electronic structures of NdF2- and NdF2 were elucidated through a combined anion photoelectron spectroscopy and quantum chemistry investigation. The anion photoelectron spectrum of NdF2- yielded an experimental vertical detachment energy (VDE) of 1.11 eV, in excellent agreement with the theoretical estimate of 1.106 eV. The calculated adiabatic electron affinity (AEA) of 1.055 eV lies close to the VDE, consistent with the similar optimized structures of the anion and neutral. Geometry optimizations confirm that both NdF2- and NdF2 adopt bent C2v structures with the anion possessing a 4A2 (4f36s2) ground state and the neutral possessing a 5A2 (4f36s1) ground state. Extensive multireference calculations reveal a dense manifold of low-lying neutral excited states. Moreover, we juxtapose NdF2-/0 with its periodic analogue UF2-/0, showing similar metal-centered s-electron photodetachment, bent anion/neutral structures, and largely nonbonding 4f/5f electrons. Together, these results demonstrate the collaborative utilization of aPES with relativistic multireference calculations for the electronic structure of f-element molecules.

Trifluorodimethyl sulfide (CH3SCF3) has been proposed to be a potential alternative refrigerant based on various rigorous quantum chemistry calculations. It is revealed that the half F-substitution is capable of tuning the stability and reactivity of dimethyl sulfide (CH3SCH3) significantly. The strength of both S-C bonds is enhanced. The bond dissociation energies increase by 5-6 kcal/mol with respect to CH3SCH3, and decomposition temperature of CH3SCF3 is predicted to be 875 K. Meanwhile, the existence of CH3 group keeps the good reactivity of CH3SCF3 toward OH radicals in the troposphere. Complex-forming H-abstraction to produce H2O and CF3SCH2 radicals is the predominant mechanism accompanied by minor S-O association/elimination pathways. The atmospheric lifetime and radiative efficiency of CH3SCF3 is 0.2-1 years and 0.25 Wm2-ppb-1, respectively, leading to a global warming potential of 9-90 for a 100-year time horizon. The possible degradation products of CH3SCF3 in the atmosphere include both radicals, e.g., CF3SCH2OO, CF3SCH2OONO, CF3SCH2OONO2, CF3SCH2O, CF3S, and molecules, e.g., (CF3SCH2OO)2, CF3SCH2OOH, CH2O, and CH3S(O)CF3. The present computational work not only provides interesting insights into the dramatic impact of the partial fluorination on stability and reactivity of sulfides but also demonstrates that CH3SCF3 should be a viable refrigerant replacement for hydrofluorocarbons and even hydrofluoroolefins with excellent thermal stability and environmental sustainability.

DFT calculations are increasingly combined with molecular docking to rank drug candidates, yet most studies report the interaction energy (ΔEint), computed at the complex geometry, as a surrogate for binding affinity. This quantity omits the deformation energy (ΔEdef): the thermodynamic penalty of distorting both partners from their free-state geometries into their bound conformations. Because ΔEdef is always positive (typically 2-20 kcal mol-1) and molecule-dependent, its omission systematically overestimates binding strength and can reverse predicted rank-orderings. We present the energetic decomposition ΔEbind = ΔEint + ΔEdef, demonstrate using published crystallographic strain data from over 3,000 protein-ligand complexes that deformation energies do not cancel between structurally distinct ligands, and propose a minimal five-step correction protocol applicable to any DFT-based drug design study. The protocol requires only two additional geometry optimizations beyond the standard workflow, adding only modest additional computational cost. This work does not introduce new computational data; it highlights an energetic inconsistency in common computational practice and provides a straightforward correction to enable more consistent electronic binding energy evaluation and improved candidate comparison. METHODS: The analysis is based on the supramolecular energy decomposition framework and the activation strain model (ASM), in which binding energy is partitioned into interaction and deformation (strain) components using standard variational principles. No new DFT calculations are reported. The argument draws on published conformational strain datasets obtained at various DFT levels and molecular mechanics force fields from crystallographic analyses of the PDBBind database. The proposed correction protocol is general and can be applied with any DFT functional, basis set, and quantum chemistry software package (e.g., Gaussian, ORCA, or equivalent).

Photoactive yellow protein (PYP), a prototypical photoreceptor responsible for the photophobic response of the Halorhodospira halophila bacterium to harmful ultraviolet (UV) radiation, is known to undergo photooxidation in aqueous solution. However, the vertical detachment energy and electronic structure of the deprotonated chromophore that lies at the heart of PYP have not been measured in aqueous solution. Here, we use X-ray, extreme ultraviolet (EUV), and multiphoton UV liquid-microjet photoelectron spectroscopy, supported by high-level quantum chemistry calculations, to map out the electronic structure of the deprotonated PYP chromophore in aqueous solution. The vertical and adiabatic electron detachment energies are found to be 6.8 ± 0.1 eV and around 5.9 eV, respectively. Multiphoton UV photoelectron spectroscopy measurements confirm the existence of a high-lying two-photon resonance close to the detachment threshold that could be responsible for UV photooxidation, and they reveal the existence of a three-photon resonance in the detachment continuum. This work demonstrates the power of combining X-ray, EUV, and UV liquid-microjet photoelectron spectroscopy to unravel the electronic structure of weakly soluble organic chromophores, paving the way for deeper insights into their roles in photobiological processes.

Enhanced oil recovery (EOR) methods are essential for maximizing oil extraction from mature reservoirs. Given the ongoing reliance on crude oil, it is essential to advance enhanced oil recovery techniques to boost reservoir production and extend their lifespan. Among the chemical EOR methods, chemical flooding is a well-established technique that can theoretically be utilized across various reservoir conditions. In this paper three novel bis (ethanethioyl) oxalamide derivatives synthesized via an eco-friendly green chemistry route using ethanol solvent at ambient temperature as chemical flooding agents. Their molecular efficacy was rationalized by quantum chemical (DFT) calculations and FTIR spectroscopy, which linked optimal interfacial activity to specific structural features. They were tested as an agent in reducing the interfacial tension (IFT) between the injected water and crude oil and also, in altering the wettability of reservoir rock. The results indicated the efficiency of the new compound (bis N) in reducing the IFT from 27 to 5 mN/m also altering the rock's affinity for water than oil. Finally, this agent was used in chemical flooding experiments on real core plugs under reservoir conditions in terms of (temperature, pressure, and crude oil). From flooding experiments, these calculations indicate positive economics for enhanced oil recovery through this new compound where it can withstand severe reservoir conditions and achieve a recovery factor of 22.82%Sor, 29.75%Sor and 34.12%Sor in the case of 1g/l, 1.5g/l and 2g/l concentrations respectively, from the remaining oil.

With the advancement of artificial intelligence, molecular design based on generative models offers novel approaches to accelerate drug discovery. However, existing molecular generation methods suffer from inadequate representational capability in geometric structure and discrepancies between topological and geometric structure representations. These challenges result in generating chemically implausible and structurally unstable molecules. Furthermore, existing methods neglect the crucial properties of both quantum and drug-likeness in drug design. To address these challenges, we propose a novel Geometric Structure-Aware Diffusion Model for molecular generation and optimization tasks, named MolGD. First, we designed a Geometric Structure-Aware Network (GSAN) to directly predict structurally stable molecules from noisy inputs. Within GSAN, a Molecular Graph Attention Network (MGAT) is designed to incorporate geometric information during the topological message-passing process. Then, atomic spatial positions are updated by a Geometric Reconstruction Network (GRN) for enabling integrated modeling of molecular structures. Second, MolGD integrates quantum attributes as conditional constraints for precise quantum property regulation. These conditional constraints can guide MolGD to generate molecules with specific quantum properties. Finally, for drug-likeness property optimization, MolGD integrates self-optimization strategies (MolGD-RL) to guide the model toward generating high drug-likeness and easily synthesisable molecules. Experimental results on the quantum chemistry data set QM9 and the molecular conformation data set GEOM-Drugs demonstrate that the MolGD model outperforms existing molecular generation methods in terms of the effectiveness and stability of generated molecules, the generation of specific quantum properties, and high drug-likeness optimization. This validates its efficacy in molecular generation and optimization tasks, offering novel insights for intelligent molecular design.

We present Aitomia, an agentic framework for AI-driven atomistic and quantum chemical (QC) simulations that helps experts and nonexperts alike set up and run calculations, analyze results, and summarize them in textual and graphical forms through natural language interaction. Built on the MLatom software ecosystem, Aitomia supports AI-driven atomistic simulations as well as conventional quantum-chemical calculations, including density functional theory, semiempirical methods such as GFN2-xTB, and selected high-level wave function-based methods, through interfaces to widely used programs such as Gaussian, ORCA, PySCF, and xtb, covering tasks from ground- and excited-state calculations to geometry optimization, thermochemistry, and spectra simulations. By autonomously executing computational workflows, Aitomia can deliver infrared spectra in seconds and reaction thermochemistry in minutes, with results close to experiment or high-level theoretical references while greatly reducing the manual effort required from users. Aitomia lowers the barrier to performing atomistic simulations, thereby democratizing simulations and accelerating research and development in the relevant fields.

S‐adenosyl methionine (SAM) is a key biomolecule in cellular processes, acting as a primary methyl donor, having different roles in enzymatic reactions and being used as a scavenger of hydroxyl radicals. Due to the role played by SAM and its antioxidant properties, the one electron oxidation products of SAM produced by gamma radiolysis, which can mimic the oxidation effect produced by hydroxyl radical in the oxidative stress process, have been investigated in this study by collision‐induced dissociation tandem mass spectrometry (CID‐MS2) and infrared multiple photon dissociation spectroscopy (IRMPD). We have revealed the modified sites in the molecule and characterized the 3D structure of the oxo‐forms of SAM in the gas phase. Both CID‐MS2 and IRMPD experiments, when coupled with quantum chemistry calculations have shown that only the adenine moiety of the molecule is oxidized, protecting the methionine from oxidation.

Octahedral Fe(II) complexes with bidentate N-heterocyclic (NHC) ligands are solid candidates for photoactive materials based on first-row transition metals. Despite the remarkable advances in ligand design and excited-state control, the theoretical basis for describing the complexation mechanism from a molecular and electronic point of view is lacking. This work reveals the molecular motions that drive the formation of bidentate [Fe(C^N)3]2+ complexes and how they couple with the electronic structure. Quantum chemistry methods are used to describe the chemical reactions that lead to the [Fe(pyIm)3]2+ (pyIm = pyridine-imidazol-2-ylidene) complex as a model case. The molecular model employed is based on the canonical synthesis using FeCl2 in an organic solvent and a strong Brønsted base to generate the pyIm ligand in situ. The energy profiles indicate that almost all reactivity takes place in the quintet state, whereas the singlet ground state is only populated in the last coordination step. Both d-activated dissociative interchange (Id) and purely dissociative (D) mechanisms compete, although the former is expected to be slightly more favorable. A global description of the coordination mechanism, consistent with the available experimental data, is provided through an analysis of the kinetic competition between the pathways and the thermodynamic stability of the intermediates.

In recent years, there has been a surge in artificial intelligence (AI)-based drug-protein (or pesticide-protein) affinity (DPA) prediction tools. The field has been primarily driven by evolving deep-learning architectures and increasingly complex representations, leading to a growing demand for training data sourced from limited experimental data. In this work, we present DeepDOX1, a dual-drive DPA prediction tool featuring the tight integration of a concise AI architecture and an interpretable, quantum chemistry-based representation of protein-ligand interactions. To be more specific, the first-principles quantum chemistry-generated features incorporating the interactions between the drug and the protein binding pocket allow a relatively simple convolutional neural network (CNN) model trained on a relatively small training set (9,938 binders) to exhibit exceptional generalization capabilities across extensive testing involving 1,281 binders. Notably, DeepDOX1 outperforms popular AI-based DPA prediction methods in the tests simulating real-world hit-to-lead optimization (HLO) scenarios and a highly challenging test set featuring covalent ligands, halogenated ligands, and metalloproteins, even though its training set does not contain any covalent ligands. To further validate its practical utility, we designed a series of novel covalent inhibitors targeting the diabetes target molecule Hu-FBPase using DeepDOX1. Subsequent experimental validation, including enzyme-level bioactivity assays and crystal structure determination, revealed strengthened activity of the newly designed compound and confirmed DeepDOX1's effectiveness in real-world drug design applications. It is conceivable that the combination of deep-learning architecture and first-principles quantum chemistry might be one of the next breakthroughs in DPA prediction.

We apply the recently introduced aperiodic defect model (ADM) to a negatively charged monovacancy in a phosphorene monolayer. In contrast to conventional supercell approaches, the ADM treats a single defect embedded in the true non-defective crystalline mean field, thereby avoiding spurious defect-defect interactions and the need for charge corrections. At the same time, it effectively reduces the calculation to a fragment, enabling the use of high-level molecular electronic-structure methods. Converging the Hartree-Fock and correlation contributions to the thermodynamic limit yields a benchmark CCSD(T)/POB-TZVP-rev2 formation energy of 0.81eV for the negatively charged monovacancy in the (5|9) configuration. The excitation energy to the lowest singlet excited state of this defect at the EOM-CCSD/POB-TZVP-rev2 level is found to be 1.95eV. Overall, the ADM provides a highly promising route toward quantitatively accurate and systematically improvable descriptions of defects in solids and on surfaces, bridging the gap between solid-state physics and molecular quantum chemistry.

In this work, we present the TiDES (Time-Dependent Electronic Structure) code, an open-source real-time electronic structure theory package. The software is written in Python and interfaces with the Python-based Simulations of Chemistry Framework (PySCF), an open-source quantum chemistry library. The philosophy of the TiDES software package is to provide an incredibly modular real-time software package to allow for easy development and implementation of new methodology. The package allows for explicit time propagation of chemical systems within the spin-restricted, unrestricted, and generalized frameworks of both real-time time-dependent Hartree-Fock and real-time time-dependent density functional theory. Additional features include the abinitio Ehrenfest dynamics method for the simulation of coupled electronic and nuclear motion and the incorporation of a complex absorbing potential that enables the simulation of ionization events. To illustrate both the value of real-time dynamics and the intuitive nature of TiDES, we simulate spectroscopic properties and non-equilibrium electron and spin dynamics in several example systems. We also show how general external potentials can be defined and easily applied during time propagation. Lastly, the features within PySCF, such as spin-orbit coupling, interface readily with the real-time calculations, providing a powerful tool for simulating electron and spin dynamics with the capacity for extensive customization.