Predicción de catalizadores de mayor selectividad mediante flujo de trabajo controlado por computadora y aprendizaje

Andrew F Zahrt1, Jeremy J Henle1, Brennan T Rose1

  • 1Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.

Science (New York, N.Y.)
|January 19, 2019
PubMed
Resumen

Este estudio introduce un enfoque computacional para la selección de catalizadores quirales, acelerando las reacciones asimétricas. Los modelos de aprendizaje automático predicen con precisión la selectividad del catalizador, mejorando la eficiencia en la síntesis química.

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