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Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N 2 AMD (Neural-Network Non-Adiabatic Molecular Dynamics), which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. Distinct from conventional machine learning methods that predict key quantities in NAMD, N 2 AMD computes these quantities directly with a deep neural Hamiltonian, ensuring excellent accuracy, efficiency, and consistency. N 2 AMD not only achieves impressive efficiency in performing NAMD simulations at the hybrid functional level within the framework of the classical path approximation (CPA), but also demonstrates great potential in predicting non-adiabatic coupling vectors and suggests a method to go beyond CPA. Furthermore, N 2 AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework offers a reliable and efficient approach for conducting accurate NAMD simulations across various condensed materials. Accurate nonadiabatic molecular dynamics (NAMD) is crucial for studying excited-state dynamics in solids but is computationally expensive. Here, authors use machine learning to enhance the efficiency and accuracy of NAMD simulations in solids.

Calreticulin is a highly conserved and multifunctional molecular chaperone ubiquitously expressed in humans and animals. Beyond its well-established roles in calcium homeostasis, protein folding, and immune regulation, recent studies in aquatic species have suggested a previously unrecognized antimicrobial function of calreticulin. These findings raise the question of whether calreticulin also exerts antibacterial activity in terrestrial mammals, which has not been systematically investigated to date. To address this knowledge gap, we successfully constructed and expressed recombinant goat calreticulin using the Pichia pastoris expression system, yielding a protein of over 99% purity that predominantly exists in dimeric form. Functional assays demonstrated that both recombinant goat and human calreticulin exhibited preliminary inhibitory activity against Escherichia coli, Salmonella typhimurium, and Pasteurella multocida. Calreticulin was capable of binding to these three bacterial species as well as bacterial lipopolysaccharides (LPS). Notably, in the presence of Ca2+, calreticulin induced bacterial aggregation, indicating a potential mechanism for limiting bacterial dissemination and proliferation. Given the high anatomical, genetic, and physiological similarity between goats and humans—particularly in respiratory tract structure and mucosal immune function—neonatal goats were selected as a relevant model for evaluating the in vivo antimicrobial efficacy of calreticulin. Accordingly, we established an intranasal infection model using Pasteurella multocida to assess the protective role of calreticulin against respiratory bacterial challenge. Following infection, calreticulin expression was markedly upregulated in the nasal mucosa, trachea, and lung tissues. Moreover, intranasal administration of exogenous calreticulin significantly alleviated infection-induced pathological injury to the respiratory system and effectively decreased bacterial loads in infected tissues. Collectively, this study systematically elucidates the antimicrobial activity of calreticulin in a mammalian model and highlights its potential as a natural immune effector, providing novel insights for the development of host-targeted antimicrobial strategies.

Respiratory infections caused by pathogenic bacteria pose a rapidly growing public health threat. The nasal mucus layer serves as the first line of defense against pathogen invasion; however, in nasal mucus, the antimicrobial components and their underlying mechanisms remain unclear. Here, we collected nasal mucus from goat nasal mucosal explant models and identified significant antimicrobial activity in the total protein fraction. Subsequent fractionation indicated that proteins < 30 kDa exhibited the most potent bactericidal activity. Nano LC–ESI–MS/MS analysis identified galectin-3 as a key protein with potent activity against Gram-positive bacteria, particularly Streptococcus suis ( S. suis ). Galectin-3 targeted teichoic acids on the bacterial surface, disrupting membrane integrity. Additionally, it inhibited the synthesis of three critical bacterial proteins: enoyl-ACP reductase (FabK), carbamate kinase (CK), and small ribosomal subunit protein uS2 (rpsB), thereby destroying bacterial growth and metabolism. In the murine nasal infection model, galectin-3 accelerated the clearance of S. suis and alleviated pathological damage caused by the infection. Taken together, our findings provide the first evidence of the direct antimicrobial action of galectin-3 in nasal mucus and elucidate its mechanisms involving bacterial membrane disruption and inhibition of key metabolic proteins. These results highlight galectin-3 as a promising therapeutic target for S. suis infections.

The nasal cavity harbors diverse microbiota, and the nasal mucosal innate defense against microbial infiltration is crucial for respiratory infections. Both the nasal mucus covering the surface of the nasal cavity and the neutrophils beneath the nasal epithelia are the first line of innate defense against pathogens. Studying nasal mucus is challenging because of difficulties in collecting stable, high-quality samples from humans. Here, we investigated how nasal mucus cooperates with neutrophils to exert antimicrobial effects. Nasal mucus proteins, derived from nasal mucosal explants of goats, can promote neutrophil migration and increase their bactericidal activity. Calreticulin, identified from total mucus proteins, triggered ICAM-1-dependent transendothelial migration of neutrophils. Moreover, calreticulin activated the Rho GTPases of neutrophils via Toll-like receptor (TLR) 2 to promote their migration and further triggered the release of reactive oxygen species (ROS) and neutrophil extracellular traps (NETs) in a manner dependent on TLR2 and TLR4, accelerating the elimination of pathogens. In vivo studies also demonstrated that nasal inoculation with calreticulin induced neutrophil recruitment to the nasal mucosa and accelerated the clearance of Pasteurella multocida . Together, these findings highlight the synergistic interaction between nasal mucus and neutrophils as an important protective feature in the nasal mucosa. Highlights Nasal mucus proteins induce neutrophil migration and coordinate with neutrophils to enhance the bactericidal effect. Calreticulin was identified from the nasal mucus, which could activate ICAM-1-mediated transendothelial migration of neutrophils. Calreticulin stimulates neutrophil migration and enhances neutrophil-mediated bactericidal activity via TLR2 and TLR4 signaling pathways.

The ability to control electronic property of a material by externally applied voltage is greatly anticipated in modern electronics and graphene provide potential application foreground for this issue on account of its exotic ambipolar transport property. In this study, we proposed that inorganic-graphene intercalated nanosheet is an effective solution to optimize the transport property of graphene. As an example, lithium vanadate-graphene (LiVO-graphene) alternately intercalated nanosheets were designed and successfully synthesized. Theoretical calculation implied that its rocking chair configuration may provide a new pathway to switch the carrier in graphene layer between p-type and n-type while the position of embedded Li ions is controlled by an external field. Thus, a demo transistor was fabricated with layer-by-layer overlapping of LiVO-graphene nanosheets which proved that this inorganic-graphene structure could be used for electrical modulation in electronic devices.

High performance computing (HPC) plays an essential role in enabling first-principles calculations based on the Kohn–Sham density functional theory (KS-DFT) for investigating quantum structural and electronic properties of large-scale molecules and solids in condensed matter physics, quantum chemistry and materials science. This review focuses on recent advances for HPC software development in large-scale KS-DFT calculations containing tens of thousands of atoms on modern heterogeneous supercomputers, especially for the HPC software with independent intellectual property rights supported on the Chinese domestic exascale supercomputers. We first introduce three various types of DFT software developed on modern heterogeneous supercomputers, involving PWDFT (Plane-Wave Density Functional Theory), HONPAS (Hefei Order-N Packages for Ab initio Simulations) and DGDFT (Discontinuous Galerkin Density Functional Theory), respectively based on three different types of basis sets (plane waves, numerical atomic orbitals and adaptive local basis functions). Then, we describe the theoretical algorithms and parallel implementation of these three software on modern heterogeneous supercomputers in detail. Finally, we conclude this review and propose several promising research fields for future large-scale KS-DFT calculations towards exascale supercomputers.

The high cost associated with the evaluation of Hartree-Fock exchange (HFX) makes hybrid functionals computationally challenging for large systems. In this work, we present an efficient way to accelerate HFX calculations with numerical atomic basis sets. Our approach is based on the recently proposed interpolative separable density fitting (ISDF) decomposition to construct a low rank approximation of HFX matrix, which avoids explicit calculations of the electron repulsion integrals (ERIs) and significantly reduces the computational cost. We implement the ISDF method for hybrid functional (PBE0) calculations in the HONPAS package. We take benzene and polycyclic aromatic hydrocarbons molecules as examples and demonstrate that hybrid functionals with ISDF yields quite promising results at a significantly reduced computational cost. Especially, the ISDF approach reduces the total cost for evaluating HFX matrix by nearly 2 orders of magnitude compared to conventional approaches of direct evaluation of ERIs.

This work presents a dynamic parallel distribution scheme for the Hartree-Fock exchange~(HFX) calculations based on the real-space NAO2GTO framework. The most time-consuming electron repulsion integrals~(ERIs) calculation is perfectly load-balanced with 2-level master-worker dynamic parallel scheme, the density matrix and the HFX matrix are both stored in the sparse format, the network communication time is minimized via only communicating the index of the batched ERIs and the final sparse matrix form of the HFX matrix. The performance of this dynamic scalable distributed algorithm has been demonstrated by several examples of large scale hybrid density-functional calculations on Tianhe-2 supercomputers, including both molecular and solid states systems with multiple dimensions, and illustrates good scalability.

Hybrid density-functional calculation is one of the most commonly adopted electronic structure theory used in computational chemistry and materials science because of its balance between accuracy and computational cost. Recently, we have developed a novel scheme called NAO2GTO to achieve linear scaling (Order-N) calculations for hybrid density-functionals. In our scheme, the most time-consuming step is the calculation of the electron repulsion integrals (ERIs) part. So how to create an even distribution of these ERIs in parallel implementation is an issue of particular importance. Here, we present two static scalable distributed algorithms for the ERIs computation. Firstly, the ERIs are distributed over ERIs shell pairs. Secondly, the ERIs is distributed over ERIs shell quartets. In both algorithms, the calculation of ERIs is independent of each other, so the communication time is minimized. We show our speedup results to demonstrate the performance of these static parallel distributed algorithms in the Hefei Order-N packages for \\textit{ab initio} simulations (HONPAS).

First-principles density functional theory (DFT) with plane wave (PW) basis set is the most widely used method in quantum mechanical material simulations due to its advantages in accuracy and universality. However, a perceived drawback of PW-based DFT calculations is their substantial computational cost and memory usage, which currently limits their ability to simulate large-scale complex systems containing thousands of atoms. This situation is exacerbated in the new Sunway supercomputer, where each process is limited to a mere 16 GB of memory. Herein, we present a novel parallel implementation of plane wave density functional theory on the new Sunway supercomputer (PWDFT-SW). PWDFT-SW fully extracts the benefits of Sunway supercomputer by extensively refactoring and calibrating our algorithms to align with the system characteristics of the Sunway system. Through extensive numerical experiments, we demonstrate that our methods can substantially decrease both computational costs and memory usage. Our optimizations translate to a speedup of 64.8x for a physical system containing 4,096 silicon atoms, enabling us to push the limit of PW-based DFT calculations to large-scale systems containing 16,384 carbon atoms.