A Self-Evolutionary Methodology for Reverse Design of Novel MOFs.

A combination of high-throughput molecular simulation and machine learning (ML) algorithms has been widely adopted to seek promising metal-organic frameworks (MOFs) as energy gas carriers. However, the currently reported studies are mainly limited to extracting top performers from existing databases, not fully unleashing the ML capabilities for intelligently predicting novel structures with better performance. Herein, an efficient self-evolutionary methodology was proposed for searching high-performance MOFs that are unstructured in the origin database, in which a Tangent Adaptive Genetic Algorithm (TAGA) was newly put forward for structural evolution and the high-precision ML model of eXtreme Gradient Boosting (XGBoost) was employed as the fitness function. By taking CH4 storage in MOFs at room temperature as a showcase and using the database of 51,163 hMOFs, the TAGA-XGBoost coupling strategy rapidly suggested a certain number of possible combinations of the building blocks to form new structures with gravimetric storage capacity (35 bar) and volumetric working capacity (65-5.8 bar) higher than the best materials in the original database. The structures of some promising MOFs successfully used the finally optimized material genes for the two application conditions, and their performances were also confirmed by subsequent molecular simulations. The best materials can respectively reach a storage amount of 580 cm3(STP)/g at 35 bar and a working capacity of 218 cm3(STP)/cm3 between 65 and 5.8 bar. An analysis of the top 100 materials predicted from our method revealed that the choice of organic linkers has a systematic effect on the storage performance of MOFs. It might be believed that the proposed methodology offers an opportunity to expedite the discovery of unprecedented materials for other practical applications.

Genome-wide association and epistasis studies reveal the genetic basis of saline-alkali tolerance at the germination stage in rice.

Introduction: Saline-alkali stress is one of the main abiotic factors limiting rice production worldwide. With the widespread use of rice direct seeding technology, it has become increasingly important to improve rice saline-alkali tolerance at the germination stage. Methods: To understand the genetic basis of saline-alkali tolerance and facilitate breeding efforts for developing saline-alkali tolerant rice varieties, the genetic basis of rice saline-alkali tolerance was dissected by phenotyping seven germination-related traits of 736 diverse rice accessions under the saline-alkali stress and control conditions using genome-wide association and epistasis analysis (GWAES). Results: Totally, 165 main-effect quantitative trait nucleotides (QTNs) and 124 additional epistatic QTNs were identified as significantly associated with saline-alkali tolerance, which explained a significant portion of the total phenotypic variation of the saline-alkali tolerance traits in the 736 rice accessions. Most of these QTNs were located in genomic regions either harboring saline-alkali tolerance QTNs or known genes for saline-alkali tolerance reported previously. Epistasis as an important genetic basis of rice saline-alkali tolerance was validated by genomic best linear unbiased prediction in which inclusion of both main-effect and epistatic QTNs showed a consistently better prediction accuracy than either main-effect or epistatic QTNs alone. Candidate genes for two pairs of important epistatic QTNs were suggested based on combined evidence from the high-resolution mapping plus their reported molecular functions. The first pair included a glycosyltransferase gene LOC_Os02g51900 (UGT85E1) and an E3 ligase gene LOC_Os04g01490 (OsSIRP4), while the second pair comprised an ethylene-responsive transcriptional factor, AP59 (LOC_Os02g43790), and a Bcl-2-associated athanogene gene, OsBAG1 (LOC_Os09g35630) for salt tolerance. Detailed haplotype analyses at both gene promoter and CDS regions of these candidate genes for important QTNs identified favorable haplotype combinations with large effects on saline-alkali tolerance, which can be used to improve rice saline-alkali tolerance by selective introgression. Discussion: Our findings provided saline-alkali tolerant germplasm resources and valuable genetic information to be used in future functional genomic and breeding efforts of rice saline-alkali tolerance at the germination stage.