Literature DB >> 32419277

An Autonomous Chemical Robot Discovers the Rules of Inorganic Coordination Chemistry without Prior Knowledge.

Luzian Porwol1, Daniel J Kowalski1, Alon Henson1, De-Liang Long1, Nicola L Bell1, Leroy Cronin1.   

Abstract

We present a chemical discovery robot for the efficient and reliable discovery of supramolecular architectures through the exploration of a huge reaction space exceeding ten billion combinations. The system was designed to search for areas of reactivity found through autonomous selection of the reagent types, amounts, and reaction conditions aiming for combinations that are reactive. The process consists of two parts where reagents are mixed together, choosing from one type of aldehyde, one amine and one azide (from a possible family of two amines, two aldehydes and four azides) with different volumes, ratios, reaction times, and temperatures, whereby the reagents are passed through a copper coil reactor. Next, either cobalt or iron is added, again from a large number of possible quantities. The reactivity was determined by evaluating differences in pH, UV-Vis, and mass spectra before and after the search was started. The algorithm was focused on the exploration of interesting regions, as defined by the outputs from the sensors, and this led to the discovery of a range of 1-benzyl-(1,2,3-triazol-4-yl)-N-alkyl-(2-pyridinemethanimine) ligands and new complexes: [Fe(L1 )2 ](ClO4 )2 (1); [Fe(L2 )2 ](ClO4 )2 (2); [Co2 (L3 )2 ](ClO4 )4 (3); [Fe2 (L3 )2 ](ClO4 )4 (4), which were crystallised and their structure confirmed by single-crystal X-ray diffraction determination, as well as a range of new supramolecular clusters discovered in solution using high-resolution mass spectrometry.
© 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Entities:  

Keywords:  algorithms; artificial intelligence; autonomous discovery robot; supramolecular chemistry

Year:  2020        PMID: 32419277     DOI: 10.1002/anie.202000329

Source DB:  PubMed          Journal:  Angew Chem Int Ed Engl        ISSN: 1433-7851            Impact factor:   15.336


  9 in total

Review 1.  Into the Unknown: How Computation Can Help Explore Uncharted Material Space.

Authors:  Austin M Mroz; Victor Posligua; Andrew Tarzia; Emma H Wolpert; Kim E Jelfs
Journal:  J Am Chem Soc       Date:  2022-10-07       Impact factor: 16.383

2.  The Commoditization of AI for Molecule Design.

Authors:  Fabio Urbina; Sean Ekins
Journal:  Artif Intell Life Sci       Date:  2022-01-24

Review 3.  Beyond Platonic: How to Build Metal-Organic Polyhedra Capable of Binding Low-Symmetry, Information-Rich Molecular Cargoes.

Authors:  Charlie T McTernan; Jack A Davies; Jonathan R Nitschke
Journal:  Chem Rev       Date:  2022-04-18       Impact factor: 72.087

Review 4.  Natural product-informed exploration of chemical space to enable bioactive molecular discovery.

Authors:  Adam Nelson; George Karageorgis
Journal:  RSC Med Chem       Date:  2020-12-16

5.  Using simulation to accelerate autonomous experimentation: A case study using mechanics.

Authors:  Aldair E Gongora; Kelsey L Snapp; Emily Whiting; Patrick Riley; Kristofer G Reyes; Elise F Morgan; Keith A Brown
Journal:  iScience       Date:  2021-03-02

6.  Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams.

Authors:  Sebastian Ament; Maximilian Amsler; Duncan R Sutherland; Ming-Chiang Chang; Dan Guevarra; Aine B Connolly; John M Gregoire; Michael O Thompson; Carla P Gomes; R Bruce van Dover
Journal:  Sci Adv       Date:  2021-12-17       Impact factor: 14.136

Review 7.  Intelligent host engineering for metabolic flux optimisation in biotechnology.

Authors:  Lachlan J Munro; Douglas B Kell
Journal:  Biochem J       Date:  2021-10-29       Impact factor: 3.857

8.  Accelerated AI development for autonomous materials synthesis in flow.

Authors:  Robert W Epps; Amanda A Volk; Kristofer G Reyes; Milad Abolhasani
Journal:  Chem Sci       Date:  2021-03-09       Impact factor: 9.825

9.  Simplifying inverse materials design problems for fixed lattices with alchemical chirality.

Authors:  Guido Falk von Rudorff; O Anatole von Lilienfeld
Journal:  Sci Adv       Date:  2021-05-19       Impact factor: 14.136

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.