1 |
GRAY Jim. Talk given by Jim Gray to the NRC-CSTB in Mountain View[R/OL]. , 2007.
|
2 |
OLIVETTI Elsa A, COLE Jacqueline M, KIM Edward, et al. Data-driven materials research enabled by natural language processing and information extraction[J]. Applied Physics Reviews, 2020, 7(4): 041317.
|
3 |
FRIEDERICH Pascal, KRENN Mario, TAMBLYN Isaac, et al. Scientific intuition inspired by machine learning-generated hypotheses[J]. Machine Learning: Science and Technology, 2021, 2(2): 025027.
|
4 |
ZHANG Linfeng, LIN Deye, WANG Han, et al. Active learning of uniformly accurate interatomic potentials for materials simulation[J]. Physical Review Materials, 2019, 3(2): 023804.
|
5 |
MRDJENOVICH David, HORTON Matthew K, MONTOYA Joseph H, et al. Propnet: A knowledge graph for materials science[J]. Matter, 2020, 2(2): 464-480.
|
6 |
DE PABLO Juan J, JACKSON Nicholas E, WEBB Michael A, et al. New frontiers for the materials genome initiative[J]. NPJ Computational Materials, 2019, 5: 41.
|
7 |
Unlocking the “Chemome” with DNA-Encoded Chemistry and Machine Learning [EB/OL]//Google AI Blog. [2021-11-19]. .
|
8 |
Homepage[EB/OL]. [2021-11-19]. .
|
9 |
REIZMAN Brandon J, WANG Yiming, BUCHWALD Stephen L, et al. Suzuki-Miyaura cross-coupling optimization enabled by automated feedback[J]. Reaction Chemistry & Engineering, 2016, 1(6): 658-666.
|
10 |
KNOWLES J. ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(1): 50-66.
|
11 |
BAUMGARTNER Lorenz M, COLEY Connor W, REIZMAN Brandon J, et al. Optimum catalyst selection over continuous and discrete process variables with a single droplet microfluidic reaction platform[J]. Reaction Chemistry & Engineering, 2018, 3(3): 301-311.
|
12 |
EYKE Natalie S, GREEN William H, JENSEN Klavs F. Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening[J]. Reaction Chemistry & Engineering, 2020, 5(10): 1963-1972.
|
13 |
COLEY C W, THOMAS D A, LUMMISS J A M, et al. A robotic platform for flow synthesis of organic compounds informed by AI planning[J]. Science, 2019, 365(6453): eaax1566.
|
14 |
YAN Chong, LI Haoran, CHEN Xiang, et al. Regulating the inner Helmholtz plane for stable solid electrolyte interphase on lithium metal anodes[J]. Journal of the American Chemical Society, 2019, 141(23): 9422-9429.
|
15 |
WILLIAMSON M J, TROMP R M, VEREECKEN P M, et al. Dynamic microscopy of nanoscale cluster growth at the solid-liquid interface[J]. Nature Materials, 2003, 2(8): 532-536.
|
16 |
GROMSKI Piotr S, HENSON Alon B, GRANDA Jarosław M, et al. How to explore chemical space using algorithms and automation[J]. Nature Reviews Chemistry, 2019, 3(2): 119-128.
|
17 |
CAO Liwei, RUSSO Danilo, FELTON Kobi, et al. Optimization of formulations using robotic experiments driven by machine learning DoE[J]. Cell Reports Physical Science, 2021, 2(1): 100295.
|
18 |
LI Junqi, BALLMER Steven G, GILLIS Eric P, et al. Synthesis of many different types of organic small molecules using one automated process[J]. Science, 2015, 347(6227): 1221-1226.
|
19 |
LI Jun, EASTGATE Martin D. Making better decisions during synthetic route design: Leveraging prediction to achieve greenness-by-design[J]. Reaction Chemistry & Engineering, 2019, 4(9): 1595-1607.
|
20 |
WANG Zheng, ZHAO Wei, HAO Gefei, et al. Mapping the resources and approaches facilitating computer-aided synthesis planning[J]. Organic Chemistry Frontiers, 2021, 8(4): 812-824.
|
21 |
AYRES Lucas B, GOMEZ F, Linton Jeb, et al. Taking the leap between analytical chemistry and artificial intelligence: A tutorial review[J]. Analytica Chimica Acta, 2021, 1161: 338403.
|
22 |
This is the kiwi-biolab, a BMBF KI Future Lab![EB/OL]. [2021-11-18]. .
|
23 |
Home-Data Analytics and Process Modeling-Datahow Zürich[EB/OL]. [2021-11-18]. .
|
24 |
Inauguration of a research consortium for the automated and digitalized chemical synthesis with the University of Cambridge and AstraZeneca.[EB/OL]. [2021-11-19]. .
|
25 |
“机器化学家”带来科研新范式——中国科学技术大学科研人员深耕精准智能化学领域[N]. 人民日报, 2023-04-24(19). .
|
26 |
YUAN Xiangzhou, SUVARNA Manu, Sean LOW, et al. Applied machine learning for prediction of CO2 adsorption on biomass waste-derived porous carbons[J]. Environmental Science & Technology, 2021, 55(17): 11925-11936.
|
27 |
GUAN Jian, HUANG Tan, LIU Wei, et al. Design and prediction of metal organic framework-based mixed matrix membranes for CO2 capture via machine learning[J]. Cell Reports Physical Science, 2022, 3(5): 100864.
|
28 |
XU Shidang, LI Jiali, CAI Pengfei, et al. Self-improving photosensitizer discovery system via Bayesian search with first-principle simulations[J]. Journal of the American Chemical Society, 2021, 143(47): 19769-19777.
|
29 |
Flore MEKKI-BERRADA, REN Zekun, HUANG Tan, et al. Two-step machine learning enables optimized nanoparticle synthesis[J]. NPJ Computational Materials, 2021, 7: 55.
|
30 |
YANG Haitao, LI Jiali, Kai Zhuo LIM, et al. Automatic strain sensor design via active learning and data augmentation for soft machines[J]. Nature Machine Intelligence, 2022, 4(1): 84-94.
|
31 |
LI Jiali, TELYCHKO Mykola, YIN Jun, et al. Machine vision automated chiral molecule detection and classification in molecular imaging[J]. Journal of the American Chemical Society, 2021, 143(27): 10177-10188.
|
32 |
ZHANG Shuyuan, LIANG Xiao, HUANG Xinye, et al. Precise and fast microdroplet size distribution measurement using deep learning[J]. Chemical Engineering Science, 2022, 247: 116926.
|
33 |
ZHANG Shuyuan, QIN Kang, HUANG Xinye, et al. Insight into microdispersion flows with a novel video deep learning method[J]. Advanced Intelligent Systems, 2022, 4(11): 2200098.
|
34 |
BI Kexin, BEYKAL Burcu, AVRAAMIDOU Styliani, et al. Integrated modeling of transfer learning and intelligent heuristic optimization for a steam cracking process[J]. Industrial & Engineering Chemistry Research, 2020, 59(37): 16357-16367.
|
35 |
BI Kexin, QIU Tong. Novel naphtha molecular reconstruction process using a self-adaptive cloud model and hybrid genetic algorithm-particle swarm optimization algorithm[J]. Industrial & Engineering Chemistry Research, 2019, 58(36): 16753-16760.
|
36 |
HUANG Xinye, ZHANG Shuyuan, LI Haoran, et al. An integrated method of Bayesian optimization and D-optimal design for chemical experiment optimization[J]. Processes, 2022, 11(1): 87.
|