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Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.

 

Terpenoids constitute the largest class of plant primary and secondary metabolites with a broad range of biological and ecological functions. They are synthesized from isopentenyl diphosphate and dimethylallyl diphosphate, which in plastids are condensed by geranylgeranyl diphosphate synthases (GGPPSs) to produce GGPP (C20) for diterpene biosynthesis and by geranyl diphosphate synthases (GPPSs) to form GPP (C10) for monoterpene production. Depending on the plant species, unlike homomeric GGPPSs, GPPSs exist as homo- and heteromers, the latter of which contain catalytically inactive GGPPS-homologous small subunits (SSUs) that can interact with GGPPSs. By combining phylogenetic analysis with functional characterization of GGPPS homologs from a wide range of photosynthetic organisms, we investigated how different GPPS architectures have evolved within the GGPPS protein family. Our results reveal that GGPPS gene family expansion and functional divergence began early in nonvascular plants, and that independent parallel evolutionary processes gave rise to homomeric and heteromeric GPPSs. By site-directed mutagenesis and molecular dynamics simulations, we also discovered that Leu-Val/Val-Ala pairs of amino acid residues were pivotal in the functional divergence of homomeric GPPSs and GGPPSs. Overall, our study elucidated an evolutionary path for the formation of GPPSs with different architectures from GGPPSs and uncovered the molecular mechanisms involved in this differentiation.

 

Analysis of the updated reference tomato genome found 34 full‐length TPS genes and 18 TPS pseudogenes.

Biochemical analysis has now identified the catalytic activities of all enzymes encoded by the 34 TPS genes: one isoprene synthase, 10 exclusively or predominantly monoterpene synthases, 17 sesquiterpene synthases and six diterpene synthases. Among the monoterpene and sesquiterpene and diterpene synthases, some usetrans‐prenyl diphosphates, some usecis‐prenyl diphosphates and some use both. The isoprene synthase is cytosolic; six monoterpene synthases are plastidic, and four are cytosolic; the sesquiterpene synthases are almost all cytosolic, with the exception of one found in the mitochondria; and three diterpene synthases are found in the plastids, one in the cytosol and two in the mitochondria.

Newtrans‐prenyltransferases (TPTs) were characterised; together with previously characterised TPTs andcis‐prenyltransferases (CPTs), tomato plants can make allcisandtransC₁₀, C₁₅and C₂₀prenyl diphosphates. Every type of plant tissue examined expresses some TPS genes and some TPTs and CPTs.

Phylogenetic comparison of the TPS genes from tomato and Arabidopsis shows expansions in each clade of the TPS gene family in each lineage (and inferred losses), accompanied by changes in subcellular localisations and substrate specificities.

 

Engineering the thermal properties in solids is important for both fundamental physics ( e.g. electric and phonon transport) and device applications ( e.g. thermal insulating coating, thermoelectrics). In this paper, we report low thermal transport properties of four selenide compounds (BaAg 2 SnSe 4 , BaCu 2 GeSe 4 , BaCu 2 SnSe 4 and SrCu 2 GeSe 4 ) with experimentally-measured thermal conductivity as low as 0.31 ± 0.03 W m −1 K −1 at 673 K for BaAg 2 SnSe 4 . Density functional theory calculations predict κ < 0.3 W m −1 K −1 for BaAg 2 SnSe 4 due to scattering from weakly-bonded Ag–Ag dimers. Defect calculations suggest that achieving high hole doping levels in these materials could be challenging due to monovalent ( e.g. , Ag) interstitials acting as hole killers, resulting in overall low electrical conductivity in these compounds.