Direct Solving the Many-Electron Schr\"odinger Equation with a Language Model

The many-electron Schr\"odinger equation is solved straightforwardly with a Transformer-based neural-network architecture (QiankunNet), which requires no external training data and significantly improves the accuracy and efficiency of first-principles calculations compared to previous Fermionic ansatz. The intricate quantum correlations are effectively captured by incorporating the attention mechanism into our methodology. Additionally, the batched sampling strategy is used to significantly improve the sampling accuracy and efficiency. Furthermore, a pre-training stage which incorporates the truncated configuration interaction solution into the variational ansatz, ensuring high expressiveness and further improving computational efficiency. QiankunNet demonstrates the power of the Transformer-based language model in achieving unprecedented efficiency in quantum chemistry calculations, which opens new avenues to chemical discovery and has the potential to solve the large-scale Schr\"odinger equation with modest computational cost

NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry

Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampling and local energy calculation. We develop a high-performance NNQS method for \textit{ab initio} electronic structure calculations. The major innovations include: (1) A transformer based architecture as the quantum wave function ansatz; (2) A data-centric parallelization scheme for the variational Monte Carlo (VMC) algorithm which preserves data locality and well adapts for different computing architectures; (3) A parallel batch sampling strategy which reduces the sampling cost and achieves good load balance; (4) A parallel local energy evaluation scheme which is both memory and computationally efficient; (5) Study of real chemical systems demonstrates both the superior accuracy of our method compared to state-of-the-art and the strong and weak scalability for large molecular systems with up to $120$ spin orbitals.Comment: Accepted by SC'2

Linking stoichiometric homeostasis with ecosystem structure, functioning, and stability

Ecosystem structure, functioning, and stability have been a focus of ecological and environmental sciences during the past two decades. The mechanisms underlying their relationship, however, are not well understood. Based on comprehensive studies in Inner Mongolia grassland, here we show that species-level stoichiometric homeostasis was consistently positively correlated with dominance and stability on both 2-year and 27-year temporal scales and across a 1200-km spatial transect. At the community level, stoichiometric homeostasis was also positively correlated with ecosystem function and stability in most cases. Thus, homeostatic species tend to have high and stable biomass; and ecosystems dominated by more homeostatic species have higher productivity and greater stability. By modulating organism responses to key environmental drivers, stoichiometric homeostasis appears to be a major mechanism responsible for the structure, functioning, and stability of grassland ecosystems

Herbivores Alleviate the Negative Effects of Extreme Drought on Plant Community by Enhancing Dominant Species

Aims Both extreme drought and insect herbivores can suppress plant growth in grassland communities. However, most studies have examined extreme drought and insects in isolation, and there is reason to believe that insects might alter the ability of grasslands to withstand drought. Unfortunately, few studies have tested the interactive effects of extreme drought and insect herbivores in grassland communities. Methods Here, we tested the drought–herbivore interactions using a manipulative experiment that factorially crossed extreme drought with the exclusion of insect herbivores in a temperate semiarid grassland in Inner Mongolia. Important Findings Our results demonstrated that both extreme drought and insect herbivores separately decreased total plant cover. When combined, insect herbivores reduced the impact of drought on total cover by increasing the relative abundance of drought-resistant dominant species. Our results highlight that the negative effect of extreme drought on total plant cover could be alleviated by maintaining robust insect herbivore communities

Electrocaloric effect in ferroelectric ceramics with point defects

The electrocaloric effect has drawn much attention due to its potential application in cooling devices. A negative electrocaloric effect is predicted to be induced in defect-doped ferroelectrics by computational results [A. Grunebohm and T. Nishimatsu, Phys. Rev. B 93, 134101 (2016) and Ma et al., Phys. Rev. B 94, 094113 (2016)], but it need to be confirmed by experimental results. In this work, we prepared a 1mol. % Mn-doped Pb(Zr0.2,Ti0.8)O3 ceramics (Pb((Zr0.2,Ti0.8)0.99,Mn0.01)O3), and the electrocaloric effect of the defect-containing ferroelectric ceramics has been investigated by both direct and indirect methods. The indirect method shows a similar negative electrocaloric effect signal as the computational results predicted, while the direct method gives a positive electrocaloric effect. The absence of the negative electrocaloric effect obtained by the direct method may originate from: (a) the unavailability and the improper prediction of the Maxwell relation, (b) an improper assumption of fixed defects in the computational models, and (c) the offset of heat loss due to the application of a large electric field. In addition, we find a giant positive electrocaloric effect of 0.55K at room temperature in the aged ceramics where no phase transition takes place. We attribute this abnormal electrocaloric effect to the restoration force of the defect dipoles. Our results not only provide insights into the origin of the negative electrocaloric effect, but also offer opportunities for the design of electrocaloric materials

Screening of linear B-cell epitopes and its proinflammatory activities of Haemophilus parasuis outer membrane protein P2

Haemophilus parasuis is a commensal organism of the upper respiratory tract of pigs, but virulent strains can cause Glässer’s disease, resulting in significant economic losses to the swine industry. OmpP2 is an outer membrane protein of this organism that shows considerable heterogeneity between virulent and non-virulent strains, with classification into genotypes I and II. It also acts as a dominant antigen and is involved in the inflammatory response. In this study, 32 monoclonal antibodies (mAbs) against recombinant OmpP2 (rOmpP2) of different genotypes were tested for reactivity to a panel of OmpP2 peptides. Nine linear B cell epitopes were screened, including five common genotype epitopes (Pt1a, Pt7/Pt7a, Pt9a, Pt17, and Pt19/Pt19a) and two groups of genotype-specific epitopes (Pt5 and Pt5-II, Pt11/Pt11a, and Pt11a-II). In addition, we used positive sera from mice and pigs to screen for five linear B-cell epitopes (Pt4, Pt14, Pt15, Pt21, and Pt22). After porcine alveolar macrophages (PAMs) were stimulated with overlapping OmpP2 peptides, we found that the epitope peptides Pt1 and Pt9, and the loop peptide Pt20 which was adjacent epitopes could all significantly upregulated the mRNA expression levels of IL-1α, IL-1β, IL-6, IL-8, and TNF-α. Additionally, we identified epitope peptides Pt7, Pt11/Pt11a, Pt17, Pt19, and Pt21 and loop peptides Pt13 and Pt18 which adjacent epitopes could also upregulate the mRNA expression levels of most proinflammatory cytokines. This suggested that these peptides may be the virulence-related sites of the OmpP2 protein, with proinflammatory activity. Further study revealed differences in the mRNA expression levels of proinflammatory cytokines, including IL-1β and IL-6, between genotype-specific epitopes, which may be responsible for pathogenic differences between different genotype strains. Here, we profiled a linear B-cell epitope map of the OmpP2 protein and preliminarily analyzed the proinflammatory activities and effects of these epitopes on bacterial virulence, providing a reliable theoretical basis for establishing a method to distinguish strain pathogenicity and to screen candidate peptides for subunit vaccines

PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

In contrast to numerous NLP and 2D computer vision foundational models, the learning of a robust and highly generalized 3D foundational model poses considerably greater challenges. This is primarily due to the inherent data variability and the diversity of downstream tasks. In this paper, we introduce a comprehensive 3D pre-training framework designed to facilitate the acquisition of efficient 3D representations, thereby establishing a pathway to 3D foundational models. Motivated by the fact that informative 3D features should be able to encode rich geometry and appearance cues that can be utilized to render realistic images, we propose a novel universal paradigm to learn point cloud representations by differentiable neural rendering, serving as a bridge between 3D and 2D worlds. We train a point cloud encoder within a devised volumetric neural renderer by comparing the rendered images with the real images. Notably, our approach demonstrates the seamless integration of the learned 3D encoder into diverse downstream tasks. These tasks encompass not only high-level challenges such as 3D detection and segmentation but also low-level objectives like 3D reconstruction and image synthesis, spanning both indoor and outdoor scenarios. Besides, we also illustrate the capability of pre-training a 2D backbone using the proposed universal methodology, surpassing conventional pre-training methods by a large margin. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks. The consistent improvements in various settings imply the effectiveness of the proposed method. Code and models will be made available at https://github.com/OpenGVLab/PonderV2.Comment: arXiv admin note: text overlap with arXiv:2301.0015

OpenCL-accelerated first-principles calculations of all-electron quantum perturbations on HPC resources

We have proposed, for the first time, an OpenCL implementation for the all-electron density-functional perturbation theory (DFPT) calculations in FHI-aims, which can effectively compute all its time-consuming simulation stages, i.e., the real-space integration of the response density, the Poisson solver for the calculation of the electrostatic potential, and the response Hamiltonian matrix, by utilizing various heterogeneous accelerators. Furthermore, to fully exploit the massively parallel computing capabilities, we have performed a series of general-purpose graphics processing unit (GPGPU)-targeted optimizations that significantly improved the execution efficiency by reducing register requirements, branch divergence, and memory transactions. Evaluations on the Sugon supercomputer have shown that notable speedups can be achieved across various materials