Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Toward a Universal Model.

Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems with ab initio accuracy. Still, the community awaits universal interatomic models that can be applied to a wide range of materials without tuning neural network parameters. We develop a unified deep-learning interatomic potential (the DPA-Semi model) for 19 semiconductors ranging from group IIB to VIA, including Si, Ge, SiC, BAs, BN, AlN, AlP, AlAs, InP, InAs, InSb, GaN, GaP, GaAs, CdTe, InTe, CdSe, ZnS, and CdS. In addition, independent deep potential models for each semiconductor are prepared for detailed comparison. The training data are obtained by performing density functional theory calculations with numerical atomic orbitals basis sets to reduce the computational costs. We systematically compare various properties of the solid and liquid phases of semiconductors between different machine-learning models. We conclude that the DPA-Semi model achieves GGA exchange-correlation functional quality accuracy and can be regarded as a pretrained model toward a universal model to study group IIB to VIA semiconductors.

Fork coupling directs DNA replication elongation and termination.

DNA replication is initiated at multiple loci to ensure timely duplication of eukaryotic genomes. Sister replication forks progress bidirectionally, and replication terminates when two convergent forks encounter one another. To investigate the coordination of replication forks, we developed a replication-associated in situ HiC method to capture chromatin interactions involving nascent DNA. We identify more than 2000 fountain-like structures of chromatin contacts in human and mouse genomes, indicative of coupling of DNA replication forks. Replication fork interaction not only occurs between sister forks but also involves forks from two distinct origins to predetermine replication termination. Termination-associated chromatin fountains are sensitive to replication stress and lead to coupled forks-associated genomic deletions in cancers. These findings reveal the spatial organization of DNA replication forks within the chromatin context.

Cooperatively designed aptamer-PROTACs for spatioselective degradation of nucleocytoplasmic shuttling protein for enhanced combinational therapy.

Nucleocytoplasmic shuttling proteins (NSPs) have emerged as a promising class of therapeutic targets for many diseases. However, most NSPs-based therapies largely rely on small-molecule inhibitors with limited efficacy and off-target effects. Inspired by proteolysis targeting chimera (PROTAC) technology, we report a new archetype of PROTAC (PS-ApTCs) by introducing a phosphorothioate-modified aptamer to a CRBN ligand, realizing tumor-targeting and spatioselective degradation of NSPs with improved efficacy. Using nucleolin as a model, we demonstrate that PS-ApTCs is capable of effectively degrading nucleolin in the target cell membrane and cytoplasm but not in the nucleus, through the disruption of nucleocytoplasmic shuttling. Moreover, PS-ApTCs exhibits superior antiproliferation, pro-apoptotic, and cell cycle arrest potencies. Importantly, we demonstrate that a combination of PS-ApTCs-mediated nucleolin degradation with aptamer-drug conjugate-based chemotherapy enables a synergistic effect on tumor inhibition. Collectively, PS-ApTCs could expand the PROTAC toolbox to more targets in subcellular localization and accelerate the discovery of new combinational therapeutic approaches.

Speciation of La3+-Cl- Complexes in Hydrothermal Fluids from Deep Potential Molecular Dynamics.

The stability of rare earth element (REE) complexes plays a crucial role in quantitatively assessing their hydrothermal migration and transformation. However, reliable data are lacking under high-temperature hydrothermal conditions, which hampers our understanding of the association behavior of REE. Here a deep learning potential model for the LaCl3-H2O system in hydrothermal fluids is developed based on the first-principles density functional theory calculations. The model accurately predicts the radial distribution functions compared to ab initio molecular dynamics (AIMD) simulations. Furthermore, species of La-Cl complexes, the dissociation pathway of the La-Cl complexes dissociation process, and the potential of mean forces and corresponding association constants (logK) for LaCln3-n (n = 1-4) are extensively investigated under a wide range of temperatures and pressures. Empirical density models for logK calculation are fitted with these data and can accurately predict logK data from both experimental results and AIMD simulations. The distribution of La-Cl species is also evaluated across a wide range of temperatures, pressures, and initial chloride concentration conditions. The results show that La-Cl complexes are prone to forming in a low-density solution, and the number of bonded Cl- ions increases with rising temperature. In contrast, in a high-density solution, La3+ dominates and becomes the more prevalent species.

A cocktail therapeutic strategy based on clofarabine-containing aptamer-PROTAC for enhanced cancer therapy.

By introducing a therapeutic nucleoside analogue tail to the parent Aptamer-PROTACs, a PROTAC-cocktail system (ApTCs-3X) was designed and evaluated. ApTCs-3X exhibited improved nuclease resistance and efficiently degraded target protein with subcellular localization preference. This cocktail therapy results in enhanced therapeutic outcomes, making it suitable for advancing PROTAC in combination therapy.

Implementation of the meta-GGA exchange-correlation functional in numerical atomic orbital basis: With systematic testing on SCAN, rSCAN, and r2SCAN functionals.

Kohn-Sham density functional theory (DFT) is nowadays widely used for electronic structure theory simulations, and the accuracy and efficiency of DFT rely on approximations of the exchange-correlation functional. By including the kinetic energy density τ, the meta-generalized-gradient approximation (meta-GGA) family of functionals achieves better accuracy and flexibility while retaining the efficiency of semi-local functionals. For example, the strongly constrained and appropriately normed (SCAN) meta-GGA functional has been proven to yield accurate results for solid and molecular systems. We implement meta-GGA functionals with both numerical atomic orbitals and plane wave bases in the ABACUS package. Apart from the exchange-correlation potential, we also discuss the evaluation of force and stress. To validate our implementation, we perform finite-difference tests and convergence tests with the SCAN, rSCAN, and r2SCAN meta-GGA functionals. We further test water hexamers, weakly interacting molecules from the S22 dataset, as well as 13 semiconductors using the three functionals. The results show satisfactory agreement with previous calculations and available experimental values.

Characterization of the Hydrogen-Bond Network in High-Pressure Water by Deep Potential Molecular Dynamics.

The hydrogen-bond (H-bond) network of high-pressure water is investigated by neural-network-based molecular dynamics (MD) simulations with first-principles accuracy. The static structure factors (SSFs) of water at three densities, i.e., 1, 1.115, and 1.24 g/cm3, are directly evaluated from 512 water MD trajectories, which are in quantitative agreement with the experiments. We propose a new method to decompose the computed SSF and identify the changes in the SSF with respect to the changes in H-bond structures. We find that a larger water density results in a higher probability for one or two non-H-bonded water molecules to be inserted into the inner shell, explaining the changes in the tetrahedrality of water under pressure. We predict that the structure of the accepting end of water molecules is more easily influenced by the pressure than by the donating end. Our work sheds new light on explaining the SSF and H-bond properties in related fields.

Cohesin maintains replication timing to suppress DNA damage on cancer genes.

Cohesin loss-of-function mutations are frequently observed in tumors, but the mechanism underlying its role in tumorigenesis is unclear. Here, we found that depletion of RAD21, a core subunit of cohesin, leads to massive genome-wide DNA breaks and 147 translocation hotspot genes, co-mutated with cohesin in multiple cancers. Increased DNA damages are independent of RAD21-loss-induced transcription alteration and loop anchor disruption. However, damage-induced chromosomal translocations coincide with the asymmetrically distributed Okazaki fragments of DNA replication, suggesting that RAD21 depletion causes replication stresses evidenced by the slower replication speed and increased stalled forks. Mechanistically, approximately 30% of the human genome exhibits an earlier replication timing after RAD21 depletion, caused by the early initiation of >900 extra dormant origins. Correspondingly, most translocation hotspot genes lie in timing-altered regions. Therefore, we conclude that cohesin dysfunction causes replication stresses induced by excessive DNA replication initiation, resulting in gross DNA damages that may promote tumorigenesis.

Rational Design of Trident Aptamer Scaffold for Rapid and Accurate Monitoring of 25-Hydroxyvitamin D3 Metabolism in Living Cells.

The level of 25-hydroxyvitamin D3 [25(OH)VD3] in human blood is considered as the best indicator of vitamin D status, and its deficiency or excess can lead to various health problems. Current methods for monitoring 25(OH)VD3 metabolism in living cells have limitations in terms of sensitivity and specificity and are often expensive and time-consuming. To address these issues, an innovative trident scaffold-assisted aptasensor (TSA) system has been developed for the online quantitative monitoring of 25(OH)VD3 in complex biological environments. Through the computer-aided design, the TSA system includes an aptamer molecule recognition layer that is uniformly oriented, maximizing binding site availability, and enhancing sensitivity. The TSA system achieved the direct, highly sensitive, and selective detection of 25(OH)VD3 over a wide concentration range (17.4-12,800 nM), with a limit of detection of 17.4 nM. Moreover, we evaluated the efficacy of the system in monitoring the biotransformation of 25(OH)VD3 in human liver cancer cells (HepG2) and normal liver cells (L-02), demonstrating its potential as a platform for drug-drug interaction studies and candidate drug screening.

An orthogonally activatable CRISPR-Cas13d nanoprodrug to reverse chemoresistance for enhanced chemo-photodynamic therapy.

Orthogonal therapy that combines CRISPR-based gene editing and prodrug-based chemotherapy is a promising approach to combat multidrug-resistant cancer. However, its potency to precisely regulate different therapeutic modalities in vivo is limited due to the lack of an integrated platform with high spatiotemporal resolution. Taking advantage of CRISPR technology, a Pt(iv)-based prodrug and orthogonal emissive upconversion nanoparticles (UCNPs), we herein rationally designed the first logic-gated CRISPR-Cas13d-based nanoprodrug for orthogonal photomodulation of gene editing and prodrug release for enhanced cancer therapy. The nanoprodrug (URL) was constructed by encapsulating a green light-activatable Pt(iv) prodrug and UV light-activatable Cas13d gene editing tool into UCNPs. We demonstrated that URL maintained excellent orthogonal emission behaviors under 808 and 980 nm excitations, allowing wavelength-selective photoactivation of Cas13d and the prodrug for downregulation of the resistance-related gene and induction of chemo-photodynamic therapy, respectively. Moreover, the photomodulation superiority of URL for overcoming drug resistance was highlighted by integrating it with a Boolean logic gate for programmable modulation of multiple cell behaviors. Importantly, in vivo studies demonstrated that URL can promote Pt(iv) prodrug activation and ROS generation and massively induce on-target drug accumulation by Cas13d-mediated drug resistance attenuation, delivering an ultimate chemo-photodynamic therapeutic performance in efficiently eradicating primary tumors and preventing further liver metastasis. Collectively, our results suggest that URL expands the Cas13d-based genome editing toolbox into prodrug nanomedicine and accelerates the discovery of new orthogonal therapeutic approaches.

ZIF-67 Incorporated Sulfonated Poly (Aryl Ether Sulfone) Mixed Matrix Membranes for Pervaporation Separation of Methanol/Methyl Tert-Butyl Ether Mixture.

Mixed matrix membranes (MMMs) with nano-fillers dispersed in polymer matrix have been proposed as alternative pervaporation membrane materials. They possess both promising selectivity benefiting from the fillers and economical processing capabilities of polymers. ZIF-67 was synthesized and incorporated into the sulfonated poly (aryl ether sulfone) (SPES) matrix to prepare SPES/ZIF-67 mixed matrix membranes with different ZIF-67 mass fractions. The as-prepared membranes were used for pervaporation separation of methanol/methyl tert-butyl ether mixtures. X-ray diffraction (XRD), Scanning Electron Microscopy (SEM) and laser particle size analysis results show that ZIF-67 is successfully synthesized, and the particle size is mainly between 280 nm and 400 nm. The membranes were characterized by SEM, atomic force microscope (AFM), water contact angle, thermogravimetric analysis (TGA), mechanical property testing and positron annihilation technique (PAT), sorption and swelling experiments, and the pervaporation performance was also investigated. The results reveal that ZIF-67 particles disperse uniformly in the SPES matrix. The roughness and hydrophilicity are enhanced by ZIF-67 exposed on the membrane surface. The mixed matrix membrane has good thermal stability and mechanical properties, which can meet the requirements of pervaporation operation. The introduction of ZIF-67 effectively regulates the free volume parameters of the mixed matrix membrane. With increasing ZIF-67 mass fraction, the cavity radius and free volume fraction increase gradually. When the operating temperature is 40 °C, the flow rate is 50 L·h-1 and the mass fraction of methanol in feed is 15%, the mixed matrix membrane with ZIF-67 mass fraction of 20% shows the best comprehensive pervaporation performance. The total flux and separation factor reach 0.297 kg·m-2·h-1 and 2123, respectively.

Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System.

In the era of big data and artificial intelligence (AI), advanced data storage and processing technologies are in urgent demand. The innovative neuromorphic algorithm and hardware based on memristor devices hold a promise to break the von Neumann bottleneck. In recent years, carbon nanodots (CDs) have emerged as a new class of nano-carbon materials, which have attracted widespread attention in the applications of chemical sensors, bioimaging, and memristors. The focus of this review is to summarize the main advances of CDs-based memristors, and their state-of-the-art applications in artificial synapses, neuromorphic computing, and human sensory perception systems. The first step is to systematically introduce the synthetic methods of CDs and their derivatives, providing instructive guidance to prepare high-quality CDs with desired properties. Then, the structure-property relationship and resistive switching mechanism of CDs-based memristors are discussed in depth. The current challenges and prospects of memristor-based artificial synapses and neuromorphic computing are also presented. Moreover, this review outlines some promising application scenarios of CDs-based memristors, including neuromorphic sensors and vision, low-energy quantum computation, and human-machine collaboration.

Theoretical study on the free radical scavenging potency and mechanism of natural coumestans: Roles of substituent, noncovalent interaction and solvent.

The free radical scavenging potency and mechanisms of seven representative natural coumestans were systematically evaluated using density functional theory (DFT) approach. Thermodynamic feasibility of different mechanisms was assessed by various physio-chemical descriptors involved in the double (2H+/2e‒) radical-trapping processes. Energy diagram and related transition state structures of the reaction between wedelolactone (WEL) and hydroperoxyl radical were constructed to further uncover the radical-trapping details. Results showed that the studied coumestans prefer to scavenge radicals via formal hydrogen atom transfer (fHAT) mechanism in the gas phase and non-polar environment, whereas sequential proton loss electron transfer (SPLET) is favored in polar media. Moreover, the feasibility of second fHAT and SPLET processes was also revealed. Sequential double proton loss double electron transfer (SdPLdET) mechanism represents the preferred pathway in aqueous solution at physiological pH. Our findings highlight the essential role of ortho-dihydroxyl group, noncovalent interaction and solvents on radical-trapping potency. 4'-OH in D-ring was found to be the most favorable site to trap radical for most of the studied coumestans, whereas 3-OH in A-ring for lucernol (LUN).

Solvation structures of calcium and magnesium ions in water with the presence of hydroxide: a study by deep potential molecular dynamics.

The solvation structures of calcium (Ca2+) and magnesium (Mg2+) ions with the presence of hydroxide (OH-) ion in water are essential for understanding their roles in biological and chemical processes but have not been fully explored. Ab initio molecular dynamics (AIMD) is an important tool to address this issue, but two challenges exist. First, an accurate description of OH- from AIMD needs an appropriate exchange-correlation functional. Second, a long trajectory is needed to reach an equilibrium state for the Ca2+-OH- and Mg2+-OH- ion pairs in aqueous solutions. Herein, we adopt a deep potential molecular dynamics (DPMD) method to simulate 1 ns trajectories for the Ca2+-OH- and Mg2+-OH- ion pairs in water; the DPMD method provides efficient machine-learning-based models that have the accuracy of the SCAN exchange-correlation functional within the framework of density functional theory. The solvation structures of the cations and the OH- in terms of three different species have been systematically investigated. On the one hand, we find that OH- have more significant effects on the solvation structure of Ca2+ than that of Mg2+. We observe that the OH- substantially affects the orientation angles of water molecules surrounding the cation. Through the time correlation functions, we conclude that the water molecules in the first solvation shell of Ca2+ change their preferred orientation faster than those of Mg2+. On the other hand, with the presence of the cation in the first solvation shell of OH-, we find that the hydrogen bonds of OH- are severely altered, and the adjacent water molecules of OH- are squeezed. The two cations have substantially different effects on the solvation structure of OH-. Our work provides new insight into the solvation structures of Ca2+ and Mg2+ in water with the presence of OH-.

Low-Cost Antimony Selenosulfide with Tunable Bandgap for Highly Efficient Solar Cells.

About 10% efficient antimony selenosulfide (Sb2 (S,Se)3 ) solar cell is realized by using selenourea as a hydrothermal raw material to prepare absorber layers. However, tailoring the bandgap of hydrothermal-based Sb2 (S,Se)3 film to the ideal bandgap (1.3-1.4 eV) using the selenourea for optimal efficiency is still a challenge. Moreover, the expensive selenourea dramatically increases the fabricating cost. Here, a straightforward one-step hydrothermal method is developed to prepare high-quality Sb2 (S,Se)3 films using a novel precursor sodium selenosulfate as the selenium source. By tuning the Se/(Se+S) ratio in the hydrothermal precursor solution, a series of high-quality Sb2 (S,Se)3 films with reduced density of deep defect states and tunable bandgap from 1.31 to 1.71 eV is successfully prepared. Consequently, the best efficiency of 10.05% with a high current density of 26.01 mA cm-2 is achieved in 1.35 eV Sb2 (S,Se)3 solar cells. Compared with the traditional method using selenourea, the production cost for the Sb2 (S,Se)3  devices is reduced by over 80%. In addition, the device exhibits outstanding stability, maintaining more than 93% of the initial power conversion efficiency after 30 days of exposure in the atmosphere without encapsulation. The present work definitely paves a facile and effective way to develop low-cost and high-efficiency chalcogenide-based photovoltaic devices.

Dynamic Graph Message Passing Networks.

Modelling long-range dependencies is critical for scene understanding tasks in computer vision. Although convolution neural networks (CNNs) have excelled in many vision tasks, they are still limited in capturing long-range structured relationships as they typically consist of layers of local kernels. A fully-connected graph, such as the self-attention operation in Transformers, is beneficial for such modelling, however, its computational overhead is prohibitive. In this paper, we propose a dynamic graph message passing network, that significantly reduces the computational complexity compared to related works modelling a fully-connected graph. This is achieved by adaptively sampling nodes in the graph, conditioned on the input, for message passing. Based on the sampled nodes, we dynamically predict node-dependent filter weights and the affinity matrix for propagating information between them. This formulation allows us to design a self-attention module, and more importantly a new Transformer-based backbone network, that we use for both image classification pretraining, and for addressing various downstream tasks (e.g. object detection, instance and semantic segmentation). Using this model, we show significant improvements with respect to strong, state-of-the-art baselines on four different tasks. Our approach also outperforms fully-connected graphs while using substantially fewer floating-point operations and parameters. Code and models will be made publicly available at https://github.com/fudan-zvg/DGMN2.

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials.

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training an ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), an ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model and then use the DeePKS model to label a much larger amount of configurations to train an ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open source and ready for use in various applications.

Neural Network-Based Autonomous Search Model with Undulatory Locomotion Inspired by Caenorhabditis Elegans.

Caenorhabditis elegans (C. elegans) exhibits sophisticated chemotaxis behavior with a unique locomotion pattern using a simple nervous system only and is, therefore, well suited to inspire simple, cost-effective robotic navigation schemes. Chemotaxis in C. elegans involves two complementary strategies: klinokinesis, which allows reorientation by sharp turns when moving away from targets; and klinotaxis, which gradually adjusts the direction of motion toward the preferred side throughout the movement. In this study, we developed an autonomous search model with undulatory locomotion that combines these two C. elegans chemotaxis strategies with its body undulatory locomotion. To search for peaks in environmental variables such as chemical concentrations and radiation in directions close to the steepest gradients, only one sensor is needed. To develop our model, we first evolved a central pattern generator and designed a minimal network unit with proprioceptive feedback to encode and propagate rhythmic signals; hence, we realized realistic undulatory locomotion. We then constructed adaptive sensory neuron models following real electrophysiological characteristics and incorporated a state-dependent gating mechanism, enabling the model to execute the two orientation strategies simultaneously according to information from a single sensor. Simulation results verified the effectiveness, superiority, and realness of the model. Our simply structured model exploits multiple biological mechanisms to search for the shortest-path concentration peak over a wide range of gradients and can serve as a theoretical prototype for worm-like navigation robots.

DP Compress: A Model Compression Scheme for Generating Efficient Deep Potential Models.

Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via deep neural networks to predict the energy and atomic forces, resulting in lower running efficiency as compared to the typical empirical force fields. Herein, we report a model compression scheme for boosting the performance of the Deep Potential (DP) model, a deep learning-based PES model. This scheme, we call DP Compress, is an efficient postprocessing step after the training of DP models (DP Train). DP Compress combines several DP-specific compression techniques, which typically speed up DP-based molecular dynamics simulations by an order of magnitude faster and consume an order of magnitude less memory. We demonstrate that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is publicly available online.

Structural and dynamic properties of solvated hydroxide and hydronium ions in water from ab initio modeling.

Predicting the asymmetric structure and dynamics of solvated hydroxide and hydronium in water from ab initio molecular dynamics (AIMD) has been a challenging task. The difficulty mainly comes from a lack of accurate and efficient exchange-correlation functional in elucidating the amphiphilic nature and the ubiquitous proton transfer behaviors of the two ions. By adopting the strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation functional in AIMD simulations, we systematically examine the amphiphilic properties, the solvation structures, the electronic structures, and the dynamic properties of the two water ions. In particular, we compare these results to those predicted by the PBE0-TS functional, which is an accurate yet computationally more expensive exchange-correlation functional. We demonstrate that the general-purpose SCAN functional provides a reliable choice for describing the two water ions. Specifically, in the SCAN picture of water ions, the appearance of the fourth and fifth hydrogen bonds near hydroxide stabilizes the pot-like shape solvation structure and suppresses the structural diffusion, while the hydronium stably donates three hydrogen bonds to its neighbors. We apply a detailed analysis of the proton transfer mechanism of the two ions and find the two ions exhibit substantially different proton transfer patterns. Our AIMD simulations indicate that hydroxide diffuses more slowly than hydronium in water, which is consistent with the experimental results.