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 straightforward to identify when molecules and materials are outside the model's domain of applicability.
Established uncertainty metrics for neural network models are either costly to obtain e. We introduce the distance to available data in the latent space of a neural network ML model as a low-cost, quantitative uncertainty metric that works for both inorganic and organic chemistry.
Application of Cascade Correlation Networks for Structures to Chemistry | SpringerLink
The calibrated performance of this approach exceeds widely used uncertainty metrics and is readily applied to models of increasing complexity at no additional cost. Tightening latent distance cutoffs systematically drives down predicted model errors below training errors, thus enabling predictive error control in chemical discovery or identification of useful data points for active learning.
- Krofft Super Show Presents - Wonderbug, Down Mexico Way Coloring Book!
- Neural Networks in Chemistry and Drug Design: An Introduction, 2nd Edition.
- Neural Networks for the Prediction of Organic Chemistry Reactions.
- ISBN 13: 9781560817918.
- FIVE YEARS A CAVALRY MAN (Western Frontier Library)?
The article was received on 11 May , accepted on 11 Jul and first published on 11 Jul Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material and it is not used for commercial purposes. Information about reproducing material from RSC articles with different licences is available on our Permission Requests page. Fetching data from CrossRef. This may take some time to load. Jump to main content. Jump to site search. Journals Books Databases. Search Advanced.
Current Journals. Archive Journals. All Journals.
New Titles. Pick and Choose. Literature Updates. For Members. For Librarians. RSS Feeds.
Chemistry World. Education in Chemistry. Open Access. Historical Collection. Includes bibliographical references and index. Sprache: deutsch. Seller Inventory AB.
Neural Networks for Chemists : An Introduction
More information about this seller Contact this seller 6. This book has hardback covers. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,grams, ISBNX. More information about this seller Contact this seller 7. Condition: Used: Good. More information about this seller Contact this seller 8.
More information about this seller Contact this seller 9. Published by Vch Pub About this Item: Vch Pub, More information about this seller Contact this seller Seller Inventory X Soft Covers. Condition: Very Good. No Jacket. First Thus Edition.
Neural networks as tools to solve problems in physics and chemistry
Illustrated soft covers. Ships with Tracking Number! May not contain Access Codes or Supplements.
May be ex-library. Buy with confidence, excellent customer service!. Published by Wiley-VCH, Gebundene Ausgabe. Condition: Wie neu.
- Handbook Of Self-Defense in Pictures and Text?
- Neural Networks for Chemists: An Introduction.
- TORVS Research Team >> Neural Networks in Chemistry and Drug Design?
- Associated Data?
- Deep learning for molecules, introduction to chainer chemistry;
Item added to your basket View basket. Proceed to Basket. View basket. Continue shopping. Title: neural networks chemists introduction. Results 1 - 15 of United Kingdom. Search Within These Results:. Lincoln, United Kingdom Seller Rating:. Neural networks for chemists.
An Introduction. Zupan, Jure and Johann Gasteiger. Gasteiger; J.