2020
DOI: 10.1038/s41467-020-19267-x
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Machine learning in chemical reaction space

Abstract: Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for ‘reactive’ ML, we establish a f… Show more

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Cited by 143 publications
(145 citation statements)
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“…recently published a very interesting study about the use of ML in chemical reaction networks, which shows that the prediction of new data points using ML methods is performed much faster than with DFT calculations, with equal accuracy. 76 Using COSMO-RS s-proles as data for ML methods, seems promising and has been implemented in various classical property regression models with very promising results, [77][78][79] but so far with only few implementations to ML algorithms. [80][81][82][83][84][85] classication and prediction.…”
Section: Ils As Input Datamentioning
confidence: 99%
“…recently published a very interesting study about the use of ML in chemical reaction networks, which shows that the prediction of new data points using ML methods is performed much faster than with DFT calculations, with equal accuracy. 76 Using COSMO-RS s-proles as data for ML methods, seems promising and has been implemented in various classical property regression models with very promising results, [77][78][79] but so far with only few implementations to ML algorithms. [80][81][82][83][84][85] classication and prediction.…”
Section: Ils As Input Datamentioning
confidence: 99%
“…Hence, nowadays, active learning strategies, as used in the ML community for quite some time [74], have become a standard approach for the construction of MLPs [75][76][77][78][79]. This search can be complemented by selecting non-redundant structural features from a large number of structures [80][81][82]. In both approaches the identification of relevant structures is even possible before carrying out expensive electronic structure calculations, allowing to focus the available computing time on the most important information.…”
Section: Discussion and Outlookmentioning
confidence: 99%
“…In doing so, the number of possible material structures is estimated to be 10 100 , which is larger than the number of particles in the universe [ 29 ]. In this vastness, the discovery of new materials must face resource and time constraints [ 30 , 31 ]. Expensive experiments must be well planned, ideally targeting lead structures with a high potential of generating new materials with useful properties.…”
Section: Materials Discoverymentioning
confidence: 99%