摘要：Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-ef ciency energy, personalized consumer prod- ucts, secure food supplies, and professional healthcare. New functional materials that are made and tai- lored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily avail- able, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic mate- rials. Finally, concluding remarks and an outlook are provided.

摘要：All living organisms share similar reactions within their central metabolism to provide precursors for all essential building blocksand reducing power. To identify whether alternative metabolic routes of glycolysis can operate in E. coli, we complementarilyemployed in silico design, rational engineering, and adaptive laboratory evolution. First, we used a genome-scale model andidentified two potential pathways within the metabolic network of this organism replacing canonical Embden-Meyerhof-Parnas(EMP) glycolysis to convert phosphosugars into organic acids. One of these glycolytic routes proceeds via methylglyoxal andthe other via serine biosynthesis and degradation. Then, we implemented both pathways in E. coli strains harboring defectiveEMP glycolysis. Surprisingly, the pathway via methylglyoxal seemed to immediately operate in a triosephosphate isomerasedeletion strain cultivated on glycerol. By contrast, in a phosphoglycerate kinase deletion strain, the overexpression ofmethylglyoxal synthase was necessary to restore growth of the strain. Furthermore, we engineered the “serine shunt” whichconverts 3-phosphoglycerate via serine biosynthesis and degradation to pyruvate, bypassing an enolase deletion. Finally, toexplore which of these alternatives would emerge by natural selection, we performed an adaptive laboratory evolution studyusing an enolase deletion strain. Our experiments suggest that the evolved mutants use the serine shunt. Our study reveals theflexible repurposing of metabolic pathways to create new metabolite links and rewire central metabolism.