Autonomous Discovery in the Chemical Sciences Part II: Outlook.
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TL;DR: The majority of this article defines a large set of open research directions, including improving the ability to work with complex data, build empirical models, automate both physical and computational experiments for validation, select experiments, and evaluate whether to make progress toward the ultimate goal of autonomous discovery.
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Abstract: This two-part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to "discover" despite being incredibly useful as laboratory assistants. We must carefully consider how they have been and can be applied to future problems of chemical discovery in order to effectively design and interact with future autonomous platforms. The majority of this Review defines a large set of open research directions, including improving our ability to work with complex data, build empirical models, automate both physical and computational experiments for validation, select experiments, and evaluate whether we are making progress towards the ultimate goal of autonomous discovery. Addressing these practical and methodological challenges will greatly advance the extent to which autonomous systems can make meaningful discoveries.
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Citations
Scientific discovery in the age of artificial intelligence
Hanchen Wang,Tianfan Fu,Yuanqi Du,Wenhao Gao,Kexin Huang,Ziming Liu,Payal Chandak,Shengchao Liu,Peter Van Katwyk,A Deac,Animashree Anandkumar,Karianne J. Bergen,Carla Gomes,Shirley Ho,Pushmeet Kohli,L. Lasenby,Jure Leskovec,Tie-Yan Liu,Arjun K. Manrai,Debora Marks,Bharath Ramsundar,Le Song,Jimeng Sun,Jian Tang,Petar Veličković,Max Welling,Linfeng Zhang,Connor W. Coley,Yoshua Bengio,Marinka Zitnik +29 more
TL;DR: This work examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deeplearning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.
John A. Keith,Valentin Vassilev-Galindo,Bingqing Cheng,Stefan Chmiela,Michael Gastegger,Klaus-Robert Müller,Alexandre Tkatchenko +6 more
TL;DR: A critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design are reviewed.
334
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.
John A. Keith,Valentin Vassilev-Galindo,Bingqing Cheng,Stefan Chmiela,Michael Gastegger,Klaus-Robert Müller,Alexandre Tkatchenko +6 more
TL;DR: In this paper, the authors provide a review of the applications of computational chemistry and machine learning in molecular and materials modeling, retrosyntheses, catalysis, and drug design.
308
Autonomous discovery in the chemical sciences part I: Progress
TL;DR: This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences and describes many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science.
288
Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
Andrew S. Rosen,Shaelyn M. Iyer,Debmalya Ray,Zhenpeng Yao,Alán Aspuru-Guzik,Alán Aspuru-Guzik,Laura Gagliardi,Justin M. Notestein,Randall Q. Snurr +8 more
- 05 May 2021
TL;DR: This study introduces the Quantum MOF (QMOF) database, a publicly available database of computed quantum-chemical properties for more than 14,000 experimentally synthesized MOFs and demonstrates how machine learning models trained on the QMOF database can be used to rapidly discover MOFs with targeted electronic structure properties.
287
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Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery
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