Maschinelles Lernen für die organische Synthese: Ersetzen Roboter Chemiker?

Machine learning can predict the major regio-, site-, and diastereoselective outcomes of Diels-Alder reactions better than standardq uantum-mechanical methods and with accuracies exceeding 90 %provided that i) the diene/dienophile substrates are represented by "physical-organic" descriptors reflecting the electronic and steric characteristics of their substituents and ii)t he positions of such substituents relative to the reaction core are encoded ("vectorized") in an informative way.

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Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors

Beker

Gajewska

Badowski

et al. 2018

Angewandte Chemie

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Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond. Part B. The March of Simulation, for Better or Worse

Hoffmann

Malrieu

2020

Angewandte Chemie

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In the second part of this Essay, we leave philosophy, and begin by describing Roald's being trashed by simulation. This leads us to a general sketch of artificial intelligence (AI), Searle's Chinese room, and Strevens’ account of what a go‐playing program knows. Back to our terrain—we ask “Quantum Chemistry, † ca. 2020?” Then we move to examples of Big Data, machine learning and neural networks in action, first in chemistry and then affecting social matters, trivial to scary. We argue that moral decisions are hardly to be left to a computer. And that posited causes, even if recognized as provisional, represent a much deeper level of understanding than correlations. At this point, we try to pull the reader up, giving voice to the opposing view of an optimistic, limitless future. But we don't do justice to that view—how could we, older mammals on the way to extinction that we are? We try. But then we return to fuss, questioning the ascetic dimension of scientists, their romance with black boxes. And argue for a science of many tongues.

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Künstliche Intelligenz in der organischen Synthese – en route zu autonomer Synthese?

Empel

Koenigs

2019

Angewandte Chemie

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Maschinelles Lernen für die organische Synthese: Ersetzen Roboter Chemiker?

Abstract: Buchwald-Hartwig-Reaktion ·Hochdurchsatz-Syntheseroboter ·M aschinelles Lernen ·Reaktionen im Nanomolbereich Highlights Highlights 7097

Cited by 8 publications

References 12 publications

Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors

Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors

Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond. Part B. The March of Simulation, for Better or Worse

Künstliche Intelligenz in der organischen Synthese – en route zu autonomer Synthese?

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