4.8 Editorial Material

Machine learning made easy for optimizing chemical reactions

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Multidisciplinary Sciences

Bayesian reaction optimization as a tool for chemical synthesis

Benjamin J. Shields et al.

Summary: Reaction optimization and parameter optimization are crucial in synthetic chemistry and artificial intelligence, respectively. Bayesian optimization algorithm has shown exceptional performance in tuning machine learning models and has been applied in chemistry. However, its application in reaction optimization in synthetic chemistry has not been explored. Adopting Bayesian optimization methods into everyday laboratory practices could lead to more efficient synthesis of functional chemicals.

NATURE (2021)

Editorial Material Chemistry, Multidisciplinary

Treating a Global Health Crisis with a Dose of Synthetic Chemistry

Melissa A. Hardy et al.

ACS CENTRAL SCIENCE (2020)

Article Chemistry, Multidisciplinary

Adaptive Optimization of Chemical Reactions with Minimal Experimental Information

Daniel Reker et al.

CELL REPORTS PHYSICAL SCIENCE (2020)

Review Chemistry, Multidisciplinary

Machine learning the ropes: principles, applications and directions in synthetic chemistry

Felix Strieth-Kalthoff et al.

CHEMICAL SOCIETY REVIEWS (2020)

Article Multidisciplinary Sciences

Holistic prediction of enantioselectivity in asymmetric catalysis

Jolene P. Reid et al.

NATURE (2019)

Review Chemistry, Multidisciplinary

Synthetic organic chemistry driven by artificial intelligence

A. Filipa de Almeida et al.

NATURE REVIEWS CHEMISTRY (2019)

Article Multidisciplinary Sciences

Prediction of higher-selectivity catalysts by computer-driven worlflow and machine learning

Andrew F. Zahrt et al.

SCIENCE (2019)

Editorial Material Chemistry, Multidisciplinary

The Engineering of Chemical Synthesis: Humans and Machines Working in Harmony

Steven V. Ley

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2018)

Review Nanoscience & Nanotechnology

Accelerating the discovery of materials for clean energy in the era of smart automation

Daniel P. Tabor et al.

NATURE REVIEWS MATERIALS (2018)