The de novo design of amino acid sequences to fold into desired structures is a way to reach a more thorough understanding of how amino acid sequences encode protein structures and to supply methods for protein engineering. Notwithstanding significant breakthroughs, there are noteworthy limitations in current computational protein design. To overcome them needs computational models to complement current ones and experimental tools to provide extensive feedbacks to theory. Here we develop a comprehensive statistical energy function for protein design with a new general strategy and verify that it can complement and rival current well-established models. We establish that an experimental approach can be used to efficiently assess or improve the foldability of designed proteins. We report four de novo proteins for different targets, all experimentally verified to be well-folded, solved solution structures for two being in excellent agreement with respective design targets.

Properly handling missing data is a fundamental challenge in recommendation. Most present works perform negative sampling from unobserved data to supply the training of recommender models with negative signals. Nevertheless, existing negative sampling strategies, either static or adaptive ones, are insufficient to yield high-quality negative samples -both informative to model training and reflective of user real needs.In this work, we hypothesize that item knowledge graph (KG), which provides rich relations among items and KG entities, could be useful to infer informative and factual negative samples. Towards this end, we develop a new negative sampling model, Knowledge Graph Policy Network (KGPolicy), which works as a reinforcement learning agent to explore high-quality negatives. Specifically, by conducting our designed exploration operations, it navigates from the target positive interaction, adaptively receives knowledgeaware negative signals, and ultimately yields a potential negative item to train the recommender. We tested on a matrix factorization (MF) model equipped with KGPolicy, and it achieves significant improvements over both state-of-the-art sampling methods like DNS [39] and IRGAN [30], and KG-enhanced recommender models like KGAT [32]. Further analyses from different angles provide insights of knowledge-aware sampling. We release the codes and datasets at https://github.com/xiangwang1223/kgpolicy. CCS CONCEPTS• Information systems → Recommender systems.

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Environmentally friendly lead-free dielectric ceramics have attracted wide attention because of their outstanding power density, rapid charge/dischargerate, and superior stability. Nevertheless, as a hot material in dielectric ceramic capacitors, the energy storage performance of Na0.5Bi0.5TiO3-based ceramics has been not satisfactory because of their higher remnant polarization value and low dielectric breakdown strength, which is a problem that must be urgently overcome. In this work, the (1 – x) (0.6Na0.5Bi0.5TiO3 – 0.4Sr0.7Bi0.2TiO3) – xBa­(Mg1/3Ta2/3)­O3 (BNST-xBMT) systems were designed based on a dual optimization strategy of domain and bandgap to solve the above problems. As a result, a record-breaking ultrahigh energy density and excellent efficiency (W rec = 8.58 J/cm3, η = 93.5%) were obtained simultaneously under 565 kV/cm for the BNST-0.08BMT ceramic. The introduction of Sr0.7Bi0.2TiO3 induces the formation of nanodomains in BNT-based ceramics, leading to slim P-E curves, and the further modification of Mg/Ta reduces the grain sizes and increases the bandgap width, resulting in significant enhancement of the dielectric breakdown strength. Moreover, excellent stability and superior discharge performance (W d = 4.7 J/cm3, E = 320 kV/cm) in the BNST-0.08BMT ceramic were also achieved. The results suggest that the BNST-0.08BMT ceramic shows potential applicability for dielectric energy storage ceramics. Simultaneously, the composition-design concept in the system provides a good reference for the further development of ceramic dielectric capacitors.

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