Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr.
暂无分享,去创建一个
Kexin Bi | W. Shi | Ting Song | Yiyang Dai | C. Liu | Xinyi Xiao | Li Zhou | Xu Ji | Min Cheng | Zhiyuan Zhang | Kong-Qiu Hu
[1] Süleyman İnan. Inorganic ion exchangers for strontium removal from radioactive waste : a review , 2022, Journal of Radioanalytical and Nuclear Chemistry.
[2] T. Truong,et al. Automatic strain sensor design via active learning and data augmentation for soft machines , 2022, Nature Machine Intelligence.
[3] M. Gaultois,et al. Machine‐Learning Prediction of Metal–Organic Framework Guest Accessibility from Linker and Metal Chemistry , 2021, Angewandte Chemie.
[4] R. Borrelli,et al. Assessment of alternative radionuclides for use in a radioisotope thermoelectric generator , 2021, Nuclear Engineering and Design.
[5] A. Henson,et al. Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network , 2021, ACS central science.
[6] Lei Zhang,et al. Crystallization Solvent Design Based on a New Quantitative Prediction Model of Crystal Morphology , 2021, AIChE Journal.
[7] Lei Zhang,et al. RetroSynX: A retrosynthetic analysis framework using hybrid reaction templates and group contribution-based thermodynamic models , 2021, Chemical Engineering Science.
[8] H. Yoshikawa,et al. Machine-Learning-Assisted Selective Synthesis of Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons. , 2021, Angewandte Chemie.
[9] L. Cronin,et al. Standardization and Control of Grignard Reactions in a Universal Chemical Synthesis Machine using online NMR , 2021, Angewandte Chemie.
[10] S. M. Moosavi,et al. Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks , 2021, Nature Chemistry.
[11] L. Cronin,et al. A robotic prebiotic chemist probes long term reactions of complexifying mixtures , 2021, Nature Communications.
[12] Piyush Karande,et al. Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning , 2021, J. Chem. Inf. Model..
[13] H. Uji‐i,et al. Failure-Experiment-Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks with Two-Dimensional Secondary Building Units. , 2021, Chemistry.
[14] Can Liu,et al. Selective recovery of strontium from oilfield water by ion-imprinted alginate microspheres modified with thioglycollic acid , 2021 .
[15] A. Cooper,et al. Digital navigation of energy–structure–function maps for hydrogen-bonded porous molecular crystals , 2021, Nature Communications.
[16] Qianxiao Li,et al. Two-step machine learning enables optimized nanoparticle synthesis , 2020, npj Computational Materials.
[17] Benjamin J. Bucior,et al. Inverse design of nanoporous crystalline reticular materials with deep generative models , 2020, Nature Machine Intelligence.
[18] Y. Ikeda,et al. Coordination Chemistry of Actinide Nitrates with Cyclic Amide Derivatives for the Development of the Nuclear Fuel Materials Selective Precipitation (NUMAP) Reprocessing Method , 2020 .
[19] Peng Ren,et al. Actinide separation inspired by self-assembled metal-polyphenolic nanocages. , 2020, Journal of the American Chemical Society.
[20] Beena Rai,et al. Applied machine learning for predicting the lanthanide-ligand binding affinities , 2020, Scientific Reports.
[21] Reiner Sebastian Sprick,et al. A mobile robotic chemist , 2020, Nature.
[22] Cassie Putman Micucci,et al. Representation of molecular structures with persistent homology for machine learning applications in chemistry , 2020, Nature Communications.
[23] L. Cronin,et al. An Autonomous Chemical Robot Discovers the Rules of Inorganic Coordination Chemistry without Prior Knowledge , 2020, Angewandte Chemie.
[24] Hansoo Lee,et al. A review of separation processes proposed for advanced fuel cycles based on technology readiness level assessments , 2019, Progress in Nuclear Energy.
[25] J. Lan,et al. Separation of actinides from lanthanides associated with spent nuclear fuel reprocessing in China: current status and future perspectives , 2019, Radiochimica Acta.
[26] Jianlong Wang,et al. Extraction and adsorption of U(VI) from aqueous solution using affinity ligand-based technologies: an overview , 2019, Reviews in Environmental Science and Bio/Technology.
[27] Reiner Sebastian Sprick,et al. Accelerated Discovery of Organic Polymer Photocatalysts for Hydrogen Evolution from Water through the Integration of Experiment and Theory , 2019, Journal of the American Chemical Society.
[28] Z. Chai,et al. Distinctive Two-Step Intercalation of Sr2+ into a Coordination Polymer with Record High 90Sr Uptake Capabilities , 2019, Chem.
[29] Wei-keng Liao,et al. ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition , 2018, Scientific Reports.
[30] Yanlong Wang,et al. Highly Selective and Rapid Uptake of Radionuclide Cesium Based on Robust Zeolitic Chalcogenide via Stepwise Ion-Exchange Strategy , 2016 .
[31] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[32] T. Garn,et al. Closed Fuel Cycle Waste Treatment Strategy , 2015 .
[33] Ch. Poinssot,et al. Assessment of the environmental footprint of nuclear energy systems. Comparison between closed and open fuel cycles , 2014 .
[34] Adam N. Swinburne,et al. Redox and environmentally relevant aspects of actinide(IV) coordination chemistry , 2014 .
[35] J. Reedijk,et al. Metal-ligand bond lengths and strengths: are they correlated? A detailed CSD analysis , 2013 .
[36] A. Majumdar,et al. Opportunities and challenges for a sustainable energy future , 2012, Nature.
[37] Chao Xu,et al. Solvent Extraction of Strontium and Cesium: A Review of Recent Progress , 2012 .
[38] David J. Hill,et al. Nuclear energy for the future. , 2008, Nature materials.
[39] Robin Taylor,et al. Research applications of the Cambridge Structural Database (CSD). , 2004, Chemical Society reviews.
[40] David Weininger,et al. SMILES, 3. DEPICT. Graphical depiction of chemical structures , 1990, J. Chem. Inf. Comput. Sci..
[41] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[42] W. Gordy. Dependence of Bond Order and of Bond Energy Upon Bond Length , 1947 .