Literature DB >> 29216413

Quantum Machine Learning in Chemical Compound Space.

O Anatole von Lilienfeld1.   

Abstract

Rather than numerically solving the computationally demanding equations of quantum or statistical mechanics, machine learning methods can infer approximate solutions, interpolating previously acquired property data sets of molecules and materials. The case is made for quantum machine learning: An inductive molecular modeling approach which can be applied to quantum chemistry problems.
© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  artificial intelligence; computational chemistry; machine learning; quantum mechanics

Year:  2018        PMID: 29216413     DOI: 10.1002/anie.201709686

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


  19 in total

1.  BIGDML-Towards accurate quantum machine learning force fields for materials.

Authors:  Huziel E Sauceda; Luis E Gálvez-González; Stefan Chmiela; Lauro Oliver Paz-Borbón; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  Nat Commun       Date:  2022-06-29       Impact factor: 17.694

Review 2.  Ab Initio Machine Learning in Chemical Compound Space.

Authors:  Bing Huang; O Anatole von Lilienfeld
Journal:  Chem Rev       Date:  2021-08-13       Impact factor: 60.622

3.  Machine learning meets volcano plots: computational discovery of cross-coupling catalysts.

Authors:  Benjamin Meyer; Boodsarin Sawatlon; Stefan Heinen; O Anatole von Lilienfeld; Clémence Corminboeuf
Journal:  Chem Sci       Date:  2018-07-13       Impact factor: 9.825

4.  Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies.

Authors:  Christoph A Bauer; Gisbert Schneider; Andreas H Göller
Journal:  J Cheminform       Date:  2019-09-11       Impact factor: 5.514

5.  Increasing the Potential of the Auristatin Cancer-Drug Family by Shifting the Conformational Equilibrium.

Authors:  Iris K Sokka; Filip S Ekholm; Mikael P Johansson
Journal:  Mol Pharm       Date:  2019-06-28       Impact factor: 4.939

Review 6.  The Roles of the NLRP3 Inflammasome in Neurodegenerative and Metabolic Diseases and in Relevant Advanced Therapeutic Interventions.

Authors:  Rameez Hassan Pirzada; Nasir Javaid; Sangdun Choi
Journal:  Genes (Basel)       Date:  2020-01-27       Impact factor: 4.096

7.  Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

Authors:  John A Keith; Valentin Vassilev-Galindo; Bingqing Cheng; Stefan Chmiela; Michael Gastegger; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  Chem Rev       Date:  2021-07-07       Impact factor: 60.622

8.  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.  Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids.

Authors:  Christoph Scherer; René Scheid; Denis Andrienko; Tristan Bereau
Journal:  J Chem Theory Comput       Date:  2020-04-24       Impact factor: 6.006

10.  Quantum machine learning for electronic structure calculations.

Authors:  Rongxin Xia; Sabre Kais
Journal:  Nat Commun       Date:  2018-10-10       Impact factor: 14.919

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