Performant automatic differentiation of local coupled cluster theories: Response properties and ab initio molecular dynamics.

In this work, we introduce a differentiable implementation of the local natural orbital coupled cluster (LNO-CC) method within the automatic differentiation framework of the PySCFAD package. The implementation is comprehensively tuned for enhanced performance, which enables the calculation of first-order static response properties on medium-sized molecular systems using coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)]. We evaluate the accuracy of our method by benchmarking it against the canonical CCSD(T) reference for nuclear gradients, dipole moments, and geometry optimizations. In addition, we demonstrate the possibility of property calculations for chemically interesting systems through the computation of bond orders and Mössbauer spectroscopy parameters for a [NiFe]-hydrogenase active site model, along with the simulation of infrared spectra via ab initio LNO-CC molecular dynamics for a protonated water hexamer.

Base-Promoted Annulated Aromatization of Aryl Acetylenes with Dimethyl Sulfoxide to Access Symmetrical/Unsymmetric m-Terphenyl.

A protocol for selective and efficient synthesis of symmetrical and unsymmetrical m-terphenyls is presented among aryl acetylene and DMSO in the presence of KOH and methanol. In this reaction, two molecules of aryl acetylene contribute four carbons, and DMSO, as a dual carbon donor, provides two carbons to a new aromatic ring. This protocol can be tolerated for the electron-donating or disubstituted phenylacetylenes as well as the heterocyclic acetylene derivatives.

Structural, Ionic, and Electronic Properties of Solid-State Phthalimide-Containing Polymers for All-Organic Batteries.

Redox-active polymers serving as the active materials in solid-state electrodes offer a promising path toward realizing all-organic batteries. While both cathodic and anodic redox-active polymers are needed, the diversity of the available anodic materials is limited. Here, we predict solid-state structural, ionic, and electronic properties of anodic, phthalimide-containing polymers using a multiscale approach that combines atomistic molecular dynamics, electronic structure calculations, and machine learning surrogate models. Importantly, by combining information from each of these scales, we are able to bridge the gap between bottom-up molecular characteristics and macroscopic properties such as apparent diffusion coefficients of electron transport (D app). We investigate the impact of different polymer backbones and of two critical factors during battery operation: state of charge and polymer swelling. Our findings reveal that the state of charge significantly influences solid-state packing and the thermophysical properties of the polymers, which, in turn, affect ionic and electronic transport. A combination of molecular-level properties (such as the reorganization energy) and condensed-phase properties (such as effective electron hopping distances) determine the predicted ranking of electron transport capabilities of the polymers. We predict D app for the phthalimide-based polymers and for a reference nitroxide radical-based polymer, finding a 3 orders of magnitude increase in D app (≈10-6 cm2 s-1) with respect to the reference. This study underscores the promise of phthalimide-containing polymers as highly capable redox-active polymers for anodic materials in all-organic batteries, due to their exceptional predicted electron transport capabilities.

Zero Voltage-Degradation of Li2MnO3 with Ultrathin Amorphous Li─Mn─O Coating.

Manganese-based lithium-rich layered oxides (Mn-LLOs) are promising candidate cathode materials for lithium-ion batteries, however, the severe voltage decay during cycling is the most concern for their practical applications. Herein, an Mn-based composite nanostructure constructed Li2MnO3 (LMO@Li2MnO3) is developed via an ultrathin amorphous functional oxide LixMnOy coating at the grain surface. Due to the thin and universal LMO amorphous surface layer etched from the lithiation process by the high-concentration alkaline solution, the structural and interfacial stability of Li2MnO3 are enhanced apparently, showing the significantly improved voltage maintenance, cycle stability, and energy density. In particular, the LMO@Li2MnO3 cathode exhibits zero voltage decay over 200 cycles. Combining with ex situ spectroscopic and microscopic techniques, the Mn2+/4+ coexisted behavior of the amorphous LMO is revealed, which enables the stable electrochemistry of Li2MnO3. This work provides new possible routes for suppressing the voltage decay of Mn-LLOs by modifying with the composite functional unit construction.

Combined effects of cadmium and sulfamethoxazole on Eisenia fetida: Insights into accumulation, subcellular partitioning, biomarkers and toxicological responses.

Cadmium (Cd) and sulfamethoxazole (SMX) frequently coexist in farmlands, yet their synergistic toxicological impacts on terrestrial invertebrates remain unexplored. In this study, earthworms were exposed to artificial soils percolated with Cd (5 mg/kg), SMX (5 mg/kg) or combination of them for 7 days, followed by a 12-day elimination phase in uncontaminated soil. The uptake of Cd and SMX by the earthworms, along with their subcellular distribution, was meticulously analyzed. Additionally, a suite of biomarkers-including superoxide dismutase (SOD), catalase (CAT), malondialdehyde (MDA), and weight loss-were evaluated to assess the health status of the earthworms and the toxicological effects of the Cd and SMX mixture. Notably, the cotreatment with Cd and SMX resulted in a significantly higher weight loss in Eisenia fetida (41.25 %) compared to exposure to Cd alone (26.84 %). Moreover, the cotreatment group exhibited substantially higher concentrations of Cd in the total internal body, fraction C (cytosol), and fraction E (tissue fragments and cell membranes) in Eisenia fetida compared to Cd alone counterparts. The combined exposure also significantly elevated the SMX levels in the total body and fraction C compared with the SMX-only treated earthworms. Additionally, Eisenia fetida subjected to the combined treatment showed markedly increased activities of SOD, CAT, and MDA compared to those treated with Cd alone. The effect addition indices (EAIs), ranging from 1.00 to 2.23, unequivocally demonstrated a synergistic effect of the combined treatments. Interestingly, relocating the earthworms to clean soil did not mitigate the observed adverse effects. These findings underscore the increased risk posed by the Cd-SMX complex to terrestrial invertebrates in agricultural areas.

The varicella-zoster virus ORF16 protein promotes both the nuclear transport and the protein abundance of the viral DNA polymerase subunit ORF28.

Although all herpesviruses utilize a highly conserved replication machinery to amplify their viral genomes, different members may have unique strategies to modulate the assembly of their replication components. Herein, we characterize the subcellular localization of seven essential replication proteins of varicella-zoster virus (VZV) and show that several viral replication enzymes such as the DNA polymerase subunit ORF28, when expressed alone, are localized in the cytoplasm. The nuclear import of ORF28 can be mediated by the viral DNA polymerase processivity factor ORF16. Besides, ORF16 could markedly enhance the protein abundance of ORF28. Noteworthily, an ORF16 mutant that is defective in nuclear transport still retained the ability to enhance ORF28 abundance. The low abundance of ORF28 in transfected cells was due to its rapid degradation mediated by the ubiquitin-proteasome system. We additionally reveal that radicicol, an inhibitor of the chaperone Hsp90, could disrupt the interaction between ORF16 and ORF28, thereby affecting the nuclear entry and protein abundance of ORF28. Collectively, our findings imply that the cytoplasmic retention and rapid degradation of ORF28 may be a key regulatory mechanism for VZV to prevent untimely viral DNA replication, and suggest that Hsp90 is required for the interaction between ORF16 and ORF28.

Kinetic network modeling with molecular simulation inputs: A proton-coupled phosphate symporter.

Phosphate, an essential metabolite involved in numerous cellular functions, is taken up by proton-coupled phosphate transporters of plants and fungi within the major facilitator family. Similar phosphate transporters have been identified across a diverse range of biological entities, including various protozoan parasites linked to human diseases, breast cancer cells with increased phosphate requirements, and osteoclast-like cells engaged in bone resorption. Prior studies have proposed an overview of the functional cycle of a proton-driven phosphate transporter (PiPT), yet a comprehensive understanding of the proposed reaction pathways necessitates a closer examination of each elementary reaction step within an overall kinetic framework. In this work, we leverage kinetic network modeling in conjunction with a "bottom-up" molecular dynamics approach to show how such an approach can characterize the proton-phosphate co-transport behavior of PiPT under different pH and phosphate concentration conditions. In turn, this allows us to reveal the prevailing reaction pathway within a high-affinity phosphate transporter under different experimental conditions and to uncover the molecular origin of the optimal pH condition of this transporter.

Generative organic electronic molecular design informed by quantum chemistry.

Generative molecular design strategies have emerged as promising alternatives to trial-and-error approaches for exploring and optimizing within large chemical spaces. To date, generative models with reinforcement learning approaches have frequently used low-cost methods to evaluate the quality of the generated molecules, enabling many loops through the generative model. However, for functional molecular materials tasks, such low-cost methods are either not available or would require the generation of large amounts of training data to train surrogate machine learning models. In this work, we develop a framework that connects the REINVENT reinforcement learning framework with excited state quantum chemistry calculations to discover molecules with specified molecular excited state energy levels, specifically molecules with excited state landscapes that would serve as promising singlet fission or triplet-triplet annihilation materials. We employ a two-step curriculum strategy to first find a set of diverse promising molecules, then demonstrate the framework's ability to exploit a more focused chemical space with anthracene derivatives. Under this protocol, we show that the framework can find desired molecules and improve Pareto fronts for targeted properties versus synthesizability. Moreover, we are able to find several different design principles used by chemists for the design of singlet fission and triplet-triplet annihilation molecules.

Hydrothermal Synthesis of Functionalized Carbon Nanodots and Their Clusters as Ionic Probe for High Sensitivity and Selectivity for Sulfate Anions with Excellent Detection Level.

Nitrogen-doped carbon nanodots (CNDs) were synthesized and utilized as sensing probes to detect different anions and metallic ions within aqueous solutions. The pristine CNDs were developed through a one-pot hydrothermal synthesis. o-Phenylenediamine was used as the precursor. A similar hydrothermal synthesis technique in the presence of polyethylene glycol (PEG) was adopted to form the PEG-coated CND clusters (CND-100k). Through photoluminescence (PL) quenching, both CND and PEG-coated CND suspensions display ultra-high sensitivity and selectivity towards HSO4- anions (Stern-Volmer quenching constant (KSV) value: 0.021 ppm-1 for CND and 0.062 ppm-1 for CND-100k) with an ultra-low detection limit (LOD value: 0.57 ppm for the CND and 0.19 ppm for CND-100k) in the liquid phase. The quenching mechanism of N-doped CNDs towards HSO4- ions involves forming the bidentate as well as the monodentate hydrogen bonding with the sulfate anionic moieties. The detection mechanism of metallic ions analyzed through the Stern-Volmer formulation reveals that the CND suspension is well suited for the detection of Fe3+ (KSV value: 0.043 ppm-1) and Fe2+ (KSV value: 0.0191 ppm-1) ions, whereas Hg2+ (KSV value: 0.078 ppm-1) sensing can be precisely performed by the PEG-coated CND clusters. Accordingly, the CND suspensions developed in this work can be employed as high-performance PL probes for detecting various anions and metallic ions in the liquid phase.

Towards sustainable development: research on the green growth effect of digital agriculture in China.

With the rapid development of the Internet, modern agricultural digital technology will play a more critical role in the sustainable development of Chinese agriculture. In this paper, we used China's provincial data to examine the impact factors of digital transformation of agriculture and agricultural green total factor productivity indicators using the entropy value method and the SBM-GML index method from 2013 to 2019. We investigated the impact of digital agriculture on green agricultural growth using methods such as the fixed effects model and mediated effects model. Our findings suggest that green growth in agriculture is driven by the digital transformation of agriculture. It significantly enhances green technology innovation, agricultural scale operation, and agricultural cultivation structure optimization, thus contributing to green growth. Notably, the digital agricultural infrastructure and industrialization level catalyzed green agricultural development, while the role of digital agricultural subject quality could have been more significant. Therefore, strengthening the rural digital infrastructure and improving rural human capital promote sustainable agricultural development.

Optofluidic Accumulation of Silica Beads on Gel-Based Three-Dimensional SERS Substrate To Enhance Sensitivity Using Multiple Photonic Nanojets.

This paper presents a gel-based three-dimensional (3D) substrate for surface-enhanced Raman spectroscopy (SERS) mediated by photonic nanojets (PNJs) to enhance the sensitivity of SERS detection. The porous structure of the gel-based substrate allowed small molecules to diffuse into the substrate, while the placement of silica beads on the substrate surface resulted in the generation of photonic nanojets during SERS measurements. Because the gel-based SERS substrate had electromagnetic (EM) hot spots along the Z-direction for several tens of microns, the focuses of the PNJs, which were located a few microns away from the substrate surface, could excite the EM hot spots located within the substrate. Our objective was to maximize SERS signal intensity by coating the substrate with a close-packed array of silica beads to enable the generation of multiple PNJs. The bead array was formed using an optical fiber decorated with gold nanorods (AuNRs) to create a temperature gradient in a mixture containing silica beads, thereby enabling their arrangement and deposition in arbitrary locations across the substrate. In experiments, the Raman enhancement provided by multiple PNJs significantly exceeded that provided by single PNJs. The proposed PNJ-mediated SERS method reduced the limit of detection for malachite green by 100 times, compared to SERS results obtained using the same substrate without beads. The proposed enhancement scheme using a gel-based 3D SERS substrate with a close-packed array of silica beads could be utilized to achieve high-sensitivity SERS detection for a variety of molecules in a diverse range of applications.

Reorganization Energy Predictions with Graph Neural Networks Informed by Low-Cost Conformers.

A critical bottleneck for the design of high-conductivity organic materials is finding molecules with low reorganization energy. To enable high-throughput virtual screening campaigns for many types of organic electronic materials, a fast reorganization energy prediction method compared to density functional theory is needed. However, the development of low-cost machine-learning-based models for calculating the reorganization energy has proven to be challenging. In this paper, we combine a 3D graph-based neural network (GNN) recently benchmarked for drug design applications, ChIRo, with low-cost conformational features for reorganization energy predictions. By comparing the performance of ChIRo to another 3D GNN, SchNet, we find evidence that the bond-invariant property of ChIRo enables the model to learn from low-cost conformational features more efficiently. Through an ablation study with a 2D GNN, we find that using low-cost conformational features on top of 2D features informs the model for making more accurate predictions. Our results demonstrate the feasibility of reorganization energy predictions on the benchmark QM9 data set without needing DFT-optimized geometries and demonstrate the types of features needed for robust models that work on diverse chemical spaces. Furthermore, we show that ChIRo informed with low-cost conformational features achieves comparable performance with the previously reported structure-based model on π-conjugated hydrocarbon molecules. We expect this class of methods can be applied to the high-throughput screening of high-conductivity organic electronics candidates.

Multi-site reaction dynamics through multi-fragment density matrix embedding.

The practical description of disordered chemical reactions, where the reactions involve multiple species at multiple sites, is presently a challenge using correlated electronic structure methods due to their high computational cost and steep scaling. Here, we describe the gradient theory of multi-fragment density matrix embedding theory, which potentially provides a minimal computational framework to model such processes at the correlated electron level. We present the derivation and implementation of the gradient theory, its validation on model systems and chemical reactions using density matrix embedding, and its application to a molecular dynamics simulation of proton transport in a small water cluster, a simple example of multi-site reaction dynamics.

The role of the electrolyte in non-conjugated radical polymers for metal-free aqueous energy storage electrodes.

Metal-free aqueous batteries can potentially address the projected shortages of strategic metals and safety issues found in lithium-ion batteries. More specifically, redox-active non-conjugated radical polymers are promising candidates for metal-free aqueous batteries because of the polymers' high discharge voltage and fast redox kinetics. However, little is known regarding the energy storage mechanism of these polymers in an aqueous environment. The reaction itself is complex and difficult to resolve because of the simultaneous transfer of electrons, ions and water molecules. Here we demonstrate the nature of the redox reaction for poly(2,2,6,6-tetramethylpiperidinyloxy-4-yl acrylamide) by examining aqueous electrolytes of varying chao-/kosmotropic character using electrochemical quartz crystal microbalance with dissipation monitoring at a range of timescales. Surprisingly, the capacity can vary by as much as 1,000% depending on the electrolyte, in which certain ions enable better kinetics, higher capacity and higher cycling stability.

The role of conformational change and key glutamic acid residues in the ClC-ec1 antiporter.

The triple glutamine (Q) mutant (QQQ) structure of a Cl-/H+ antiporter from Escherichia coli (ClC-ec1) displaying a novel backbone arrangement has been used to challenge the long-held notion that Cl-/H+ antiporters do not operate through large conformational motions. The QQQ mutant substitutes the glutamine residue for an external glutamate E148, an internal glutamate E203, and a third glutamate E113 that hydrogen-bonds with E203. However, it is unknown if QQQ represents a physiologically relevant state, as well as how the protonation of the wild-type glutamates relates to the global dynamics. We herein apply continuous constant-pH molecular dynamics to investigate the H+-coupled dynamics of ClC-ec1. Although any large-scale conformational rearrangement upon acidification would be due to the accumulation of excess charge within the protein, protonation of the glutamates significantly impacts mainly the local structure and dynamics. Despite the fact that the extracellular pore enlarges at acidic pHs, an occluded ClC-ec1 within the active pH range of 3.5-7.5 requires a protonated E148 to facilitate extracellular Cl- release. E203 is also involved in the intracellular H+ transfer as an H+ acceptor. The water wire connection of E148 with the intracellular solution is regulated by the charge states of the E113/E203 dyad with coupled proton titration. However, the dynamics extracted from our simulations are not QQQ-like, indicating that the QQQ mutant does not represent the behavior of the wild-type ClC-ec1. These findings reinforce the necessity of having a protonatable residue at the E203 position in ClC-ec1 and suggest that a higher level of complexity exists for the intracellular H+ transfer in Cl-/H+ antiporters.

Charge Transfer in Spatially Defined Organic Radical Polymers.

Charge transfer in nonconjugated redox-active polymers is influenced by redox site proximity and polymer flexibility, but it is challenging to observe these effects independently. In this work, spatially defined radical-containing polymers are synthesized by using acyclic diene metathesis (ADMET) polymerization of α,ω-dienes bearing a central activated ester. Postpolymerization functionalization with 4-amino-2,2,6,6-tetramethylpiperidine-1-oxyl (4-amino-TEMPO) introduces TEMPO radical groups onto the polymer backbone through amide linkages to yield spatially defined polymers with radical units every 9, 11, 15, and 21 carbons. Increased radical spacing leads to reduced spin-spin coupling and increased chain flexibility. The glass transition temperatures (Tg) range from 47.6 to -13.8 °C, depending on the radical spacing. The spatially defined TEMPO-substituted polymer with a spacing length of 15 carbons displays the lowest Tg and the shortest hopping distance, as shown through molecular dynamics simulations. Also, this polymer displays kinetics 1000 times faster than the commonly studied TEMPO-containing polymer poly(2,2,6,6-tetramethylpiperidinyloxy-4-ylacrylamide) (PTAm). Remarkably, comparison of the diffusion and kinetics attributed to the redox reaction reveals that both the apparent diffusion coefficient and the self-exchange reaction rate constant are correlated to the polymer's Tg as log[Dapp] and log[kex,app] ∼ Tg, respectively. Critically, these data demonstrate that controlling the spacing of redox-active groups along a polymer backbone strongly influences backbone flexibility and radical packing, which leads to synergetic improvements in the charge transfer kinetics of nonconjugated redox-active polymers.

Quantum harmonic free energies for biomolecules and nanomaterials.

Obtaining the free energy of large molecules from quantum mechanical energy functions is a long-standing challenge. We describe a method that allows us to estimate, at the quantum mechanical level, the harmonic contributions to the thermodynamics of molecular systems of large size, with modest cost. Using this approach, we compute the vibrational thermodynamics of a series of diamond nanocrystals, and show that the error per atom decreases with system size in the limit of large systems. We further show that we can obtain the vibrational contributions to the binding free energies of prototypical protein-ligand complexes where exact computation is too expensive to be practical. Our work raises the possibility of routine quantum mechanical estimates of thermodynamic quantities in complex systems.

Generalized Transition State Theory Treatment of Water-Assisted Proton Transport Processes in Proteins.

Transition state theory (TST) is widely employed for estimating the transition rate of a reaction when combined with free energy sampling techniques. A derivation of the transition theory rate expression for a general n-dimensional case is presented in this work which specifically focuses on water-assisted proton transfer/transport reactions, especially for protein systems. Our work evaluates the TST prefactor calculated at the transition state dividing surface compared to one sampled, as an approximation, in the reactant state in four case studies of water-assisted proton transport inside membrane proteins and highlights the significant impact of the prefactor position dependence in proton transport processes.

Accurate pKa Calculations in Proteins with Reactive Molecular Dynamics Provide Physical Insight Into the Electrostatic Origins of Their Values.

Classical molecular dynamics simulations are a versatile tool in the study of biomolecular systems, but they usually rely on a fixed bonding topology, precluding the explicit simulation of chemical reactivity. Certain modifications can permit the modeling of reactions. One such method, multiscale reactive molecular dynamics, makes use of a linear combination approach to describe condensed-phase free energy surfaces of reactive processes of biological interest. Before these simulations can be performed, models of the reactive moieties must first be parametrized using electronic structure data. A recent study demonstrated that gas-phase electronic structure data can be used to derive parameters for glutamate and lysine which reproduce experimental pKa values in both bulk water and the staphylococcal nuclease protein with remarkable accuracy and transferability between the water and protein environments. In this work, we first present a new model for aspartate derived in similar fashion and demonstrate that it too produces accurate pKa values in both bulk and protein contexts. We also describe a modification to the prior methodology, involving refitting some of the classical force field parameters to density functional theory calculations, which improves the transferability of the existing glutamate model. Finally and most importantly, this reactive molecular dynamics approach, based on rigorous statistical mechanics, allows one to specifically analyze the fundamental physical causes for the marked pKa shift of both aspartate and glutamate between bulk water and protein and also to demonstrate that local steric and electrostatic effects largely explain the observed differences.

Local structure elucidation of tungsten-substituted vanadium dioxide (V[Formula: see text]W[Formula: see text]O[Formula: see text]).

Initially, vanadium dioxide seems to be an ideal first-order phase transition case study due to its deceptively simple structure and composition, but upon closer inspection there are nuances to the driving mechanism of the metal-insulator transition (MIT) that are still unexplained. In this study, a local structure analysis across a bulk powder tungsten-substitution series is utilized to tease out the nuances of this first-order phase transition. A comparison of the average structure to the local structure using synchrotron x-ray diffraction and total scattering pair-distribution function methods, respectively, is discussed as well as comparison to bright field transmission electron microscopy imaging through a similar temperature-series as the local structure characterization. Extended x-ray absorption fine structure fitting of thin film data across the substitution-series is also presented and compared to bulk. Machine learning technique, non-negative matrix factorization, is applied to analyze the total scattering data. The bulk MIT is probed through magnetic susceptibility as well as differential scanning calorimetry. The findings indicate the local transition temperature ([Formula: see text]) is less than the average [Formula: see text] supporting the Peierls-Mott MIT mechanism, and demonstrate that in bulk powder and thin-films, increasing tungsten-substitution instigates local V-oxidation through the phase pathway VO[Formula: see text] V[Formula: see text]O[Formula: see text] V[Formula: see text]O[Formula: see text].