A quantitative uncertainty metric controls error in neural network-driven chemical discovery

Machine learning (ML) models, such as artificial neural networks, have emerged as a complement to high-throughput screening, enabling characterization of new compounds in seconds instead of hours. The promise of ML models to enable large-scale chemical space exploration can only be realized if it is...

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Veröffentlicht in:	Chemical science (Cambridge) 2019-09, Vol.1 (34), p.7913-7922
Hauptverfasser:	Janet, Jon Paul, Duan, Chenru, Yang, Tzuhsiung, Nandy, Aditya, Kulik, Heather J
Format:	Artikel
Sprache:	eng
Schlagworte:	Active learning Artificial neural networks Calibration Chemistry Data points Errors Learning theory Machine learning Mathematical models Neural networks Organic chemistry Parameter uncertainty Predictive control Space exploration Training
Online-Zugang:	Volltext
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