Retrospective on a decade of machine learning for chemical discovery.
TL;DR: This paper considers selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order to highlight specific achievements of machine learning models in the field of computational chemistry.
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Abstract: Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order.
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Graph neural networks for materials science and chemistry
Patrick Reiser,Marlen Neubert,André Eberhard,Luca Torresi,Chen Zhou,Chen Shao,Houssam Metni,Clint van Hoesel,Henrik Schopmans,Timo Sommer,Pascal Friederich +10 more
TL;DR: Graph Neural Networks (GNNs) as mentioned in this paper are one of the fastest growing classes of machine learning models and play an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials.
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
Enhancing smart farming through the applications of agriculture 4.0 technologies
TL;DR: Agriculture 4.0 represents the fourth agriculture revolution that uses digital technologies and moves toward a smarter, more efficient, environmentally responsible agriculture sector as discussed by the authors , which encompasses all digitalisation and automation processes in business and our daily lives, including Big Data, Artificial Intelligence (AI), robots, the Internet of Things (IoT), and virtual and augmented reality.
313
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
Quantum Machine Learning in Chemical Compound Space.
TL;DR: The case is made for quantum machine learning: An inductive molecular modeling approach which can be applied to quantum chemistry problems.
248
References
Self-Consistent Equations Including Exchange and Correlation Effects
Walter Kohn,L. J. Sham +1 more
TL;DR: In this paper, the Hartree and Hartree-Fock equations are applied to a uniform electron gas, where the exchange and correlation portions of the chemical potential of the gas are used as additional effective potentials.
Generalized neural-network representation of high-dimensional potential-energy surfaces.
Jörg Behler,Michele Parrinello +1 more
TL;DR: A new kind of neural-network representation of DFT potential-energy surfaces is introduced, which provides the energy and forces as a function of all atomic positions in systems of arbitrary size and is several orders of magnitude faster than DFT.
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Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.
TL;DR: A class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, as derived from quantum mechanical calculations, are introduced.
On representing chemical environments
TL;DR: It is demonstrated that certain widely used descriptors that initially look quite different are specific cases of a general approach, in which a finite set of basis functions with increasing angular wave numbers are used to expand the atomic neighborhood density function.
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp,Matthias Rupp,Alexandre Tkatchenko,Alexandre Tkatchenko,Klaus-Robert Müller,Klaus-Robert Müller,O. Anatole von Lilienfeld,O. Anatole von Lilienfeld +7 more
TL;DR: A machine learning model is introduced to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only, and applicability is demonstrated for the prediction of molecular atomization potential energy curves.