Identification of superior and rare haplotypes to optimize branch number in soybean.

KEY MESSAGE: Using the integrated approach in the present study, we identified eleven significant SNPs, seven stable QTLs and 20 candidate genes associated with branch number in soybean. Branch number is a key yield-related quantitative trait that directly affects the number of pods and seeds per soybean plant. In this study, an integrated approach with a genome-wide association study (GWAS) and haplotype and candidate gene analyses was used to determine the detailed genetic basis of branch number across a diverse set of soybean accessions. The GWAS revealed a total of eleven SNPs significantly associated with branch number across three environments using the five GWAS models. Based on the consistency of the SNP detection in multiple GWAS models and environments, seven genomic regions within the physical distance of ± 202.4 kb were delineated as stable QTLs. Of these QTLs, six QTLs were novel, viz., qBN7, qBN13, qBN16, qBN18, qBN19 and qBN20, whereas the remaining one, viz., qBN12, has been previously reported. Moreover, 11 haplotype blocks, viz., Hap4, Hap7, Hap12, Hap13A, Hap13B, Hap16, Hap17, Hap18, Hap19A, Hap19B and Hap20, were identified on nine different chromosomes. Haplotype allele number across the identified haplotype blocks varies from two to five, and different branch number phenotype is regulated by these alleles ranging from the lowest to highest through intermediate branching. Furthermore, 20 genes were identified underlying the genomic region of ± 202.4 kb of the identified SNPs as putative candidates; and six of them showed significant differential expression patterns among the soybean cultivars possessing contrasting branch number, which might be the potential candidates regulating branch number in soybean. The findings of this study can assist the soybean breeding programs for developing cultivars with desirable branch numbers.

AisNet: A Universal Interatomic Potential Neural Network with Encoded Local Environment Features.

This paper proposes a new interatomic potential energy neural network, AisNet, which can efficiently predict atomic energies and forces covering different molecular and crystalline materials by encoding universal local environment features, such as elements and atomic positions. Inspired by the framework of SchNet, AisNet consists of an encoding module combining autoencoder with embedding, the triplet loss function and an atomic central symmetry function (ACSF), an interaction module with a periodic boundary condition (PBC), and a prediction module. In molecules, the prediction accuracy of AisNet is comparabel with SchNet on the MD17 dataset, mainly attributed to the effective capture of chemical functional groups through the interaction module. In selected metal and ceramic material datasets, the introduction of ACSF improves the overall accuracy of AisNet by an average of 16.8% for energy and 28.6% for force. Furthermore, a close relationship is found between the feature ratio (i.e., ACSF and embedding) and the force prediction errors, exhibiting similar spoon-shaped curves in the datasets of Cu and HfO2. AisNet produces highly accurate predictions in single-commponent alloys with little data, suggesting the encoding process reduces dependence on the number and richness of datasets. Especially for force prediction, AisNet exceeds SchNet by 19.8% for Al and even 81.2% higher than DeepMD on a ternary FeCrAl alloy. Capable of processing multivariate features, our model is likely to be applied to a wider range of material systems by incorporating more atomic descriptions.

First-principles investigations on the anisotropic elasticity and thermodynamic properties of U3Si2-Al.

U3Si2 has been tested as a new type of nuclear fuel, and Al has been proven to improve its oxidation resistance. However, there is no research on its anisotropic mechanical and thermal properties. The mechanical and thermal properties of Al-alloyed U3Si2 nuclear fuel are calculated on the basis of first principles. Through the phonon dispersion curves, two kinetic stable structures sub-U3Si1.5Al0.5 and sub-U2.5Si2Al0.5(I) are screened out. It is found that the toughness of these two compounds after alloying are significantly improved compared to U3Si2. The three-dimensional Young's modulus shows that, the sub-U3Si1.5Al0.5 formed by Al alloying in U3Si2 maintains a higher mechanical isotropy, while sub-U2.5Si2Al0.5(I) shows higher mechanical anisotropy, which is consistent with the value of A u. The calculation result shows that the lattice thermal conductivity of sub-U3Si1.5Al0.5 and sub-U2.5Si2Al0.5(I) after alloying exhibits high isotropy as the temperature increases.