Materials Acceleration Platforms: On the way to autonomous experimentation

被引:82
|
作者
Flores-Leonar, Martha M. [1 ]
Mejia-Mendoza, Luis M. [1 ]
Aguilar-Granda, Andres [1 ]
Sanchez-Lengeling, Benjamin [2 ]
Tribukait, Hermann [3 ]
Amador-Bedolla, Carlos [4 ]
Aspuru-Guzik, Alan [1 ,5 ,6 ,7 ]
机构
[1] Univ Toronto, Dept Chem, Toronto, ON M5S 3H6, Canada
[2] Google Res, Brain Team, Cambridge, MA 02142 USA
[3] ChemOS Inc, Menlo Pk, CA 94025 USA
[4] Univ Nacl Autonoma Mexico, Fac Quim, Mexico City 04510, DF, Mexico
[5] Univ Toronto, Dept Comp Sci, Toronto, ON M5S 3H6, Canada
[6] Vector Inst Artificial Intelligence, Toronto, ON M5S 1M1, Canada
[7] Canadian Inst Adv Res CIFAR, Toronto, ON M5S 1M1, Canada
关键词
OPTIMIZATION; AUTOMATION; REACTIONWARE; TECHNOLOGY; MOLECULES;
D O I
10.1016/j.cogsc.2020.100370
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Materials Acceleration Platforms are an emerging paradigm to accelerate materials discovery as an effort to develop technology solutions that can help address or mitigate climate change concerns. These platforms combine artificial intelligence, robotic systems, and high-performance computing to achieve autonomous experimentation. Nevertheless, their development faces challenges to achieve full autonomy. In this work, we present state-of-the-art robotic platforms and machine learning approaches for autonomous experimentation, their integration, and applications, particularly in the field of materials for clean energy technologies. Later, we discuss the challenges and suggest improvements to be considered in the endeavor to accomplish autonomous experimentation.
引用
收藏
页数:9
相关论文
共 50 条
  • [41] Intelligent user support in autonomous remote experimentation environments
    Callaghan, Michael J.
    Harkin, Jim
    McGinnity, Thomas Martin
    Maguire, Liam P.
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2008, 55 (06) : 2355 - 2367
  • [42] A distributed architecture for autonomous unmanned aerial vehicle experimentation
    Doherty, P.
    Haslum, P.
    Heintz, F.
    Merz, T.
    Nyblom, P.
    Persson, T.
    Wingman, B.
    [J]. DISTRIBUTED AUTONOMOUS ROBOTIC SYSTEMS 6, 2007, : 233 - +
  • [43] Formation control of multiple autonomous robots: Theory and experimentation
    Kang, W
    Xi, N
    Tan, JD
    Wang, YC
    [J]. INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2004, 10 (04): : 277 - 293
  • [44] Experimentation and Simulation with Autonomous Coverage Path Planning for UAVs
    Biundini, Iago Z.
    Melo, Aurelio G.
    Coelho, Fabricio O.
    Honorio, Leonardo M.
    Marcato, Andre L. M.
    Pinto, Milena Faria
    [J]. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2022, 105 (02)
  • [45] Experimentation of an Adaptive and Autonomous RF Signalling Strategy for Detection
    Jones, Aaron M.
    Horne, Colin P.
    Griffiths, Hugh D.
    Smith, Graeme
    Mitchell, Adam
    John-Baptiste, Peter
    [J]. 2018 IEEE RADAR CONFERENCE (RADARCONF18), 2018, : 1213 - 1218
  • [46] Characterising Enzymes for Information Processing: Microfluidics for Autonomous Experimentation
    Jones, Gareth
    Lovell, Chris
    Morgan, Hywel
    Zauner, Klaus-Peter
    [J]. UNCONVENTIONAL COMPUTATION, PROCEEDINGS, 2010, 6079 : 191 - 191
  • [47] Improving Traversability Estimation Through Autonomous Robot Experimentation
    Sevastopoulos, Christos
    Oikonomou, Katerina Maria
    Konstantopoulos, Stasinos
    [J]. COMPUTER VISION SYSTEMS (ICVS 2019), 2019, 11754 : 175 - 184
  • [48] Constitutive Model Calibration via Autonomous Multiaxial Experimentation
    Phillips, P. L.
    Brockman, R. A.
    Buchanan, D. J.
    John, R.
    [J]. EXPERIMENTAL AND APPLIED MECHANICS, VOL 4, 2017, : 77 - 85
  • [49] ONE-WAY EXPERIMENTATION DOES NOT PROVE ONE-WAY CAUSATION
    SIMONTON, DK
    [J]. AMERICAN PSYCHOLOGIST, 1982, 37 (12) : 1404 - 1406
  • [50] Editorial: Modeling and experimentation of imperfections in materials
    Mechanical Engineering, San Diego State University, San Diego
    CA, United States
    不详
    不详
    不详
    不详
    NM, United States
    [J]. Front. Mater., 2024,