1
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Tom G, Schmid SP, Baird SG, Cao Y, Darvish K, Hao H, Lo S, Pablo-García S, Rajaonson EM, Skreta M, Yoshikawa N, Corapi S, Akkoc GD, Strieth-Kalthoff F, Seifrid M, Aspuru-Guzik A. Self-Driving Laboratories for Chemistry and Materials Science. Chem Rev 2024; 124:9633-9732. [PMID: 39137296 PMCID: PMC11363023 DOI: 10.1021/acs.chemrev.4c00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.
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Affiliation(s)
- Gary Tom
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Stefan P. Schmid
- Department
of Chemistry and Applied Biosciences, ETH
Zurich, Vladimir-Prelog-Weg 1, CH-8093 Zurich, Switzerland
| | - Sterling G. Baird
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Yang Cao
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Kourosh Darvish
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Han Hao
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
| | - Stanley Lo
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Sergio Pablo-García
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
| | - Ella M. Rajaonson
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Marta Skreta
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Naruki Yoshikawa
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Samantha Corapi
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Gun Deniz Akkoc
- Forschungszentrum
Jülich GmbH, Helmholtz Institute
for Renewable Energy Erlangen-Nürnberg, Cauerstr. 1, 91058 Erlangen, Germany
- Department
of Chemical and Biological Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Egerlandstr. 3, 91058 Erlangen, Germany
| | - Felix Strieth-Kalthoff
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- School of
Mathematics and Natural Sciences, University
of Wuppertal, Gaußstraße
20, 42119 Wuppertal, Germany
| | - Martin Seifrid
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Department
of Materials Science and Engineering, North
Carolina State University, Raleigh, North Carolina 27695, United States of America
| | - Alán Aspuru-Guzik
- Department
of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department
of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute
for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
- Acceleration
Consortium, 80 St. George
St, Toronto, Ontario M5S 3H6, Canada
- Department
of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
- Department
of Materials Science & Engineering, University of Toronto, Toronto, Ontario M5S 3E4, Canada
- Lebovic
Fellow, Canadian Institute for Advanced
Research (CIFAR), 661
University Ave, Toronto, Ontario M5G 1M1, Canada
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2
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Johnson NS, Mishra AA, Kirsch DJ, Mehta A. Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys. MATERIALS (BASEL, SWITZERLAND) 2024; 17:4038. [PMID: 39203216 PMCID: PMC11355945 DOI: 10.3390/ma17164038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Revised: 08/03/2024] [Accepted: 08/06/2024] [Indexed: 09/03/2024]
Abstract
The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.
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Affiliation(s)
- Nathan S. Johnson
- SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA; (A.A.M.); (A.M.)
| | | | - Dylan J. Kirsch
- Materials Science and Engineering Department, University of Maryland, College Park, MD 20742, USA;
| | - Apurva Mehta
- SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA; (A.A.M.); (A.M.)
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3
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Ozgulbas DY, Jensen D, Butler R, Vescovi R, Foster IT, Irvin M, Nakaye Y, Chu M, Dufresne EM, Seifert S, Babnigg G, Ramanathan A, Zhang Q. Robotic pendant drop: containerless liquid for μs-resolved, AI-executable XPCS. LIGHT, SCIENCE & APPLICATIONS 2023; 12:196. [PMID: 37596264 PMCID: PMC10439219 DOI: 10.1038/s41377-023-01233-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 06/30/2023] [Accepted: 07/15/2023] [Indexed: 08/20/2023]
Abstract
The dynamics and structure of mixed phases in a complex fluid can significantly impact its material properties, such as viscoelasticity. Small-angle X-ray Photon Correlation Spectroscopy (SA-XPCS) can probe the spontaneous spatial fluctuations of the mixed phases under various in situ environments over wide spatiotemporal ranges (10-6-103 s /10-10-10-6 m). Tailored material design, however, requires searching through a massive number of sample compositions and experimental parameters, which is beyond the bandwidth of the current coherent X-ray beamline. Using 3.7-μs-resolved XPCS synchronized with the clock frequency at the Advanced Photon Source, we demonstrated the consistency between the Brownian dynamics of ~100 nm diameter colloidal silica nanoparticles measured from an enclosed pendant drop and a sealed capillary. The electronic pipette can also be mounted on a robotic arm to access different stock solutions and create complex fluids with highly-repeatable and precisely controlled composition profiles. This closed-loop, AI-executable protocol is applicable to light scattering techniques regardless of the light wavelength and optical coherence, and is a first step towards high-throughput, autonomous material discovery.
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Affiliation(s)
- Doga Yamac Ozgulbas
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Don Jensen
- X-ray Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Rory Butler
- Departement of Computer Science, University of Chicago, 5801 S Ellis Ave, Chicago, IL, 60637, USA
| | - Rafael Vescovi
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Ian T Foster
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Michael Irvin
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Yasukazu Nakaye
- XRD Design and Engineering Department, Rigaku Corporation 3-9-12 Matsubara-cho, Akishima-shi, Tokyo, 196-8666, Japan
| | - Miaoqi Chu
- X-ray Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Eric M Dufresne
- X-ray Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Soenke Seifert
- X-ray Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Gyorgy Babnigg
- Bioscience Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Arvind Ramanathan
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, 60439, USA.
| | - Qingteng Zhang
- X-ray Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA.
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4
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Wang H, Fu T, Du Y, Gao W, Huang K, Liu Z, Chandak P, Liu S, Van Katwyk P, Deac A, Anandkumar A, Bergen K, Gomes CP, Ho S, Kohli P, Lasenby J, Leskovec J, Liu TY, Manrai A, Marks D, Ramsundar B, Song L, Sun J, Tang J, Veličković P, Welling M, Zhang L, Coley CW, Bengio Y, Zitnik M. Scientific discovery in the age of artificial intelligence. Nature 2023; 620:47-60. [PMID: 37532811 DOI: 10.1038/s41586-023-06221-2] [Citation(s) in RCA: 113] [Impact Index Per Article: 113.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/16/2023] [Indexed: 08/04/2023]
Abstract
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.
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Affiliation(s)
- Hanchen Wang
- Department of Engineering, University of Cambridge, Cambridge, UK
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- Department of Research and Early Development, Genentech Inc, South San Francisco, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Tianfan Fu
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Yuanqi Du
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Wenhao Gao
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Ziming Liu
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Payal Chandak
- Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA
| | - Shengchao Liu
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Peter Van Katwyk
- Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
- Data Science Institute, Brown University, Providence, RI, USA
| | - Andreea Deac
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Anima Anandkumar
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- NVIDIA, Santa Clara, CA, USA
| | - Karianne Bergen
- Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
- Data Science Institute, Brown University, Providence, RI, USA
| | - Carla P Gomes
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Shirley Ho
- Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA
- Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Physics and Center for Data Science, New York University, New York, NY, USA
| | | | - Joan Lasenby
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | - Arjun Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Le Song
- BioMap, Beijing, China
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Jimeng Sun
- University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Jian Tang
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- HEC Montréal, Montreal, Quebec, Canada
- CIFAR AI Chair, Toronto, Ontario, Canada
| | - Petar Veličković
- Google DeepMind, London, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Max Welling
- University of Amsterdam, Amsterdam, Netherlands
- Microsoft Research Amsterdam, Amsterdam, Netherlands
| | - Linfeng Zhang
- DP Technology, Beijing, China
- AI for Science Institute, Beijing, China
| | - Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yoshua Bengio
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Data Science Initiative, Cambridge, MA, USA.
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA.
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5
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Martin TB, Audus DJ. Emerging Trends in Machine Learning: A Polymer Perspective. ACS POLYMERS AU 2023; 3:239-258. [PMID: 37334191 PMCID: PMC10273415 DOI: 10.1021/acspolymersau.2c00053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 01/19/2023]
Abstract
In the last five years, there has been tremendous growth in machine learning and artificial intelligence as applied to polymer science. Here, we highlight the unique challenges presented by polymers and how the field is addressing them. We focus on emerging trends with an emphasis on topics that have received less attention in the review literature. Finally, we provide an outlook for the field, outline important growth areas in machine learning and artificial intelligence for polymer science and discuss important advances from the greater material science community.
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Affiliation(s)
- Tyler B. Martin
- National Institute of Standards
and Technology, Gaithersburg, Maryland20899, United States
| | - Debra J. Audus
- National Institute of Standards
and Technology, Gaithersburg, Maryland20899, United States
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6
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Teixeira Parente M, Brandl G, Franz C, Stuhr U, Ganeva M, Schneidewind A. Active learning-assisted neutron spectroscopy with log-Gaussian processes. Nat Commun 2023; 14:2246. [PMID: 37076453 PMCID: PMC10115805 DOI: 10.1038/s41467-023-37418-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 03/16/2023] [Indexed: 04/21/2023] Open
Abstract
Neutron scattering experiments at three-axes spectrometers (TAS) investigate magnetic and lattice excitations by measuring intensity distributions to understand the origins of materials properties. The high demand and limited availability of beam time for TAS experiments however raise the natural question whether we can improve their efficiency and make better use of the experimenter's time. In fact, there are a number of scientific problems that require searching for signals, which may be time consuming and inefficient if done manually due to measurements in uninformative regions. Here, we describe a probabilistic active learning approach that not only runs autonomously, i.e., without human interference, but can also directly provide locations for informative measurements in a mathematically sound and methodologically robust way by exploiting log-Gaussian processes. Ultimately, the resulting benefits can be demonstrated on a real TAS experiment and a benchmark including numerous different excitations.
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Affiliation(s)
- Mario Teixeira Parente
- Jülich Centre for Neutron Science (JCNS) at Heinz Maier-Leibnitz Zentrum (MLZ), Forschungszentrum Jülich, Garching, Germany.
| | - Georg Brandl
- Jülich Centre for Neutron Science (JCNS) at Heinz Maier-Leibnitz Zentrum (MLZ), Forschungszentrum Jülich, Garching, Germany
| | - Christian Franz
- Jülich Centre for Neutron Science (JCNS) at Heinz Maier-Leibnitz Zentrum (MLZ), Forschungszentrum Jülich, Garching, Germany
| | - Uwe Stuhr
- Laboratory for Neutron Scattering and Imaging, Paul Scherrer Institute (PSI), Villigen, Switzerland
| | - Marina Ganeva
- Jülich Centre for Neutron Science (JCNS) at Heinz Maier-Leibnitz Zentrum (MLZ), Forschungszentrum Jülich, Garching, Germany.
| | - Astrid Schneidewind
- Jülich Centre for Neutron Science (JCNS) at Heinz Maier-Leibnitz Zentrum (MLZ), Forschungszentrum Jülich, Garching, Germany
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7
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Peng X, Wang X. Next-generation intelligent laboratories for materials design and manufacturing. MRS BULLETIN 2023; 48:179-185. [PMID: 36960275 PMCID: PMC9970134 DOI: 10.1557/s43577-023-00481-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
The contradiction between the importance of materials to modern society and their slow development process has led to the development of multiple methods to accelerate materials discovery. The recently emerged concept of intelligent laboratories integrates the developments in fields of high-throughput experimentation, automation, theoretical computing, and artificial intelligence to form a system that can autonomously carry out designed experiments and make scientific discoveries. We present the basic concepts and the foundations of this new research paradigm, demonstrate its typical application scenarios through case studies, and envision a collaborative human-machine meta laboratory in the future.
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Affiliation(s)
- Xiting Peng
- Department of Chemical Engineering, Tsinghua University, Beijing, China
| | - Xiaonan Wang
- Department of Chemical Engineering, Tsinghua University, Beijing, China
- Key Laboratory of Industrial Biocatalysis (Tsinghua University), Ministry of Education, Beijing, China
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8
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Striolo A, Huang S. Upcoming Transformations in Integrated Energy/Chemicals Sectors: Some Challenges and Several Opportunities. THE JOURNAL OF PHYSICAL CHEMISTRY. C, NANOMATERIALS AND INTERFACES 2022; 126:21527-21541. [PMID: 36605781 PMCID: PMC9806836 DOI: 10.1021/acs.jpcc.2c05192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/30/2022] [Indexed: 06/17/2023]
Abstract
The sociopolitical events over the past few years led to transformative changes in both the energy and chemical sectors. One of the most evident consequences of these events is the significant focus on sustainability. In fact, rather than an engaging discussion within elite social circles, the search for sustainability is now one of the hard requirements investors impose on companies. The concept of sustainability itself has developed since its inception, and now it encompasses environmental as well as socioeconomic aspects. The major players in the energy and chemical sectors seem to embrace these changes and the related challenges; in most cases, tangible ambitious goals have been proposed. For example, bp aims "to become a net zero company by 2050 or sooner, and to help the world get to net zero". Although tragic events such as the war in Ukraine directly affect global supply chains, leading to some reconsiderations in medium-term industrial and political strategies, trends and public demands seem determined to pursue ambitious sustainable goals, as tangible as the European Union's "Fit for 55" climate package, approved on May 12, 2022, which effectively bans internal combustion engines for new passenger cars and light commercial vehicles from 2035. These trends will likely lead to profound changes in both the chemical and energy sectors. While some predictions may miss the target, speculating about upcoming challenges and opportunities could help us prepare for the future. This is the purpose of this brief Perspective.
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Affiliation(s)
- Alberto Striolo
- School
of Chemical, Biological and Materials Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States
- Department
of Chemical Engineering, University College
London, London, U.K. WC1E 7JE
| | - Shanshan Huang
- Applied
Sciences, Innovation and Engineering, BP
International Ltd., Sunbury-On-Thames, U.K. TW16 7LN
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9
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Guan Q, Zhou LL, Dong YB. Metalated covalent organic frameworks: from synthetic strategies to diverse applications. Chem Soc Rev 2022; 51:6307-6416. [PMID: 35766373 DOI: 10.1039/d1cs00983d] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Covalent organic frameworks (COFs) are a class of organic crystalline porous materials discovered in the early 21st century that have become an attractive class of emerging materials due to their high crystallinity, intrinsic porosity, structural regularity, diverse functionality, design flexibility, and outstanding stability. However, many chemical and physical properties strongly depend on the presence of metal ions in materials for advanced applications, but metal-free COFs do not have these properties and are therefore excluded from such applications. Metalated COFs formed by combining COFs with metal ions, while retaining the advantages of COFs, have additional intriguing properties and applications, and have attracted considerable attention over the past decade. This review presents all aspects of metalated COFs, from synthetic strategies to various applications, in the hope of promoting the continued development of this young field.
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Affiliation(s)
- Qun Guan
- College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Normal University, Jinan 250014, China.
| | - Le-Le Zhou
- College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Normal University, Jinan 250014, China.
| | - Yu-Bin Dong
- College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Normal University, Jinan 250014, China.
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10
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A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys. Sci Rep 2022; 12:11591. [PMID: 35804179 PMCID: PMC9270422 DOI: 10.1038/s41598-022-15618-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/27/2022] [Indexed: 11/09/2022] Open
Abstract
We demonstrate the capabilities of two model-agnostic local post-hoc model interpretability methods, namely breakDown (BD) and shapley (SHAP), to explain the predictions of a black-box classification learning model that establishes a quantitative relationship between chemical composition and multi-principal element alloys (MPEA) phase formation. We trained an ensemble of support vector machines using a dataset with 1,821 instances, 12 features with low pair-wise correlation, and seven phase labels. Feature contributions to the model prediction are computed by BD and SHAP for each composition. The resulting BD and SHAP transformed data are then used as inputs to identify similar composition groups using k-means clustering. Explanation-of-clusters by features reveal that the results from SHAP agree more closely with the literature. Visualization of compositions within a cluster using Ceteris-Paribus (CP) profile plots show the functional dependencies between the feature values and predicted response. Despite the differences between BD and SHAP in variable attribution, only minor changes were observed in the CP profile plots. Explanation-of-clusters by examples show that the clusters that share a common phase label contain similar compositions, which clarifies the similar-looking CP profile trends. Two plausible reasons are identified to describe this observation: (1) In the limits of a dataset with independent and non-interacting features, BD and SHAP show promise in recognizing MPEA composition clusters with similar phase labels. (2) There is more than one explanation for the MPEA phase formation rules with respect to the set of features considered in this work.
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11
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Abstract
Machine learning (ML) is believed to have enabled a paradigm shift in materials research, and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of new materials and autonomizing materials laboratories. In this Perspective, current research progress in materials data which are the backbones of ML are reviewed, focusing on high-throughput data generation, standardized data storage, and data representation. More importantly, the challenging issues in materials data that should be overcome to unlock the full potential of ML in materials research and development, including classic 5V (volume, velocity, variety, veracity, and value) issues, 3M (multicomponent, multiscale, and multistage) challenges, co-mining of experimental and computational data, and materials data toward transferable/explainable ML or causal ML, are discussed.
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Affiliation(s)
- Linggang Zhu
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- Center for Integrated Computational Materials Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
| | - Jian Zhou
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- Center for Integrated Computational Materials Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
| | - Zhimei Sun
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- Center for Integrated Computational Materials Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
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12
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MacLeod BP, Parlane FGL, Rupnow CC, Dettelbach KE, Elliott MS, Morrissey TD, Haley TH, Proskurin O, Rooney MB, Taherimakhsousi N, Dvorak DJ, Chiu HN, Waizenegger CEB, Ocean K, Mokhtari M, Berlinguette CP. A self-driving laboratory advances the Pareto front for material properties. Nat Commun 2022; 13:995. [PMID: 35194074 PMCID: PMC8863835 DOI: 10.1038/s41467-022-28580-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/26/2022] [Indexed: 01/22/2023] Open
Abstract
Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front reports the optimal trade-offs between these conflicting objectives. Here we use a self-driving laboratory, Ada, to define the Pareto front of conductivities and processing temperatures for palladium films formed by combustion synthesis. Ada discovers new synthesis conditions that yield metallic films at lower processing temperatures (below 200 °C) relative to the prior art for this technique (250 °C). This temperature difference makes possible the coating of different commodity plastic materials (e.g., Nafion, polyethersulfone). These combustion synthesis conditions enable us to to spray coat uniform palladium films with moderate conductivity (1.1 × 105 S m-1) at 191 °C. Spray coating at 226 °C yields films with conductivities (2.0 × 106 S m-1) comparable to those of sputtered films (2.0 to 5.8 × 106 S m-1). This work shows how a self-driving laboratoy can discover materials that provide optimal trade-offs between conflicting objectives.
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Affiliation(s)
- Benjamin P MacLeod
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
- Stewart Blusson Quantum Matter Institute, The University of British Columbia, 2355 East Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Fraser G L Parlane
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
- Stewart Blusson Quantum Matter Institute, The University of British Columbia, 2355 East Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Connor C Rupnow
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
- Stewart Blusson Quantum Matter Institute, The University of British Columbia, 2355 East Mall, Vancouver, BC, V6T 1Z4, Canada
- Department of Chemical and Biological Engineering, The University of British Columbia, 2360 East Mall, Vancouver, BC, V6T 1Z3, Canada
| | - Kevan E Dettelbach
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
| | - Michael S Elliott
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
| | - Thomas D Morrissey
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
- Stewart Blusson Quantum Matter Institute, The University of British Columbia, 2355 East Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Ted H Haley
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
| | - Oleksii Proskurin
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
| | - Michael B Rooney
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
| | - Nina Taherimakhsousi
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
| | - David J Dvorak
- Stewart Blusson Quantum Matter Institute, The University of British Columbia, 2355 East Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Hsi N Chiu
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
| | | | - Karry Ocean
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
| | - Mehrdad Mokhtari
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
| | - Curtis P Berlinguette
- Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada.
- Stewart Blusson Quantum Matter Institute, The University of British Columbia, 2355 East Mall, Vancouver, BC, V6T 1Z4, Canada.
- Department of Chemical and Biological Engineering, The University of British Columbia, 2360 East Mall, Vancouver, BC, V6T 1Z3, Canada.
- Canadian Institute for Advanced Research (CIFAR), MaRS Centre, 661 University Avenue Suite 505, Toronto, ON, M5G 1M1, Canada.
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