Journal ArticleDOI
Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties
TLDR
Quantitative Structure Property Relationship Modeling of Diverse Materials Properties Tu Le, V. Chandana Epa, Frank R. Burden, and David A. Winkler.Abstract:
Quantitative Structure Property Relationship Modeling of Diverse Materials Properties Tu Le, V. Chandana Epa, Frank R. Burden, and David A. Winkler* CSIRO Materials Science and Engineering, Bag 10, Clayton South MDC 3169, Australia CSIRO Materials Science and Engineering, 343 Royal Parade, Parkville 3052, Australia Monash Institute of Pharmaceutical Sciences, 381 Royal Parade, Parkville 3052, Australiaread more
Citations
More filters
Journal ArticleDOI
Deep learning for computational chemistry
TL;DR: Deep neural networks have been widely applied in the field of computational chemistry, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction as discussed by the authors.
Journal ArticleDOI
On a simple approach for determining applicability domain of QSAR models
TL;DR: The present study reports that the web application can be easily used for identification of the X-outliers for training set compounds and detection of the test compounds residing outside the AD using the descriptor pool of the training and test sets.
Journal ArticleDOI
Multiscale Studies on Ionic Liquids
TL;DR: The present review aims to summarize the recent advances in the fundamental and application understanding of ILs, and introduces the structures and properties of typical ILs.
Journal ArticleDOI
Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models
TL;DR: This paper clarifies some apparent confusion over the use of the coefficient of determination, R(2), as a measure of model fit and predictive power in QSAR and QSPR modeling and recommends a clearer and simpler alternative method to characterize model predictivity.
Journal ArticleDOI
Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
TL;DR: A target-driven method to predict undiscovered hybrid organic-inorganic perovskites (HOIPs) for photovoltaics based on bandgap, which can achieve high accuracy in a flash and be applicable to a broad class of functional material design.
References
More filters
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
Toxic Potential of Materials at the Nanolevel
TL;DR: The establishment of principles and test procedures to ensure safe manufacture and use of nanomaterials in the marketplace is urgently required and achievable.
Journal ArticleDOI
Ionic-liquid materials for the electrochemical challenges of the future.
TL;DR: The goal in this review is to survey the recent key developments and issues within ionic-liquid research in these areas, and to generate interest in the wider community and encourage others to make use of ionic liquids in tackling scientific challenges.
Journal ArticleDOI
Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins.
TL;DR: The main features of the CoMFA approach, exemplified by analyses of the affinities of 21 varied steroids to corticosteroid and testosterone-binding globulins, and a number of advances in the methodology of molecular graphics are described.
Journal ArticleDOI
Iterative partial equalization of orbital electronegativity – a rapid access to atomic charges
Johann Gasteiger,Mario Marsili +1 more
TL;DR: In this article, a method for the rapid calculation of atomic charges in σ-bonded and nonconjugated π-systems is presented, where only the connectivities of the atoms are considered.
Related Papers (5)
The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
QSAR Modeling: Where have you been? Where are you going to?
Artem Cherkasov,Eugene N. Muratov,Eugene N. Muratov,Denis Fourches,Alexandre Varnek,Igor I. Baskin,Mark T. D. Cronin,John C. Dearden,Paola Gramatica,Yvonne C. Martin,Roberto Todeschini,Viviana Consonni,Victor E. Kuz’min,Richard D. Cramer,Romualdo Benigni,Chihae Yang,James F. Rathman,Lothar Terfloth,Johann Gasteiger,Ann M. Richard,Alexander Tropsha +20 more