no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
1 |
Advisory Board and Contents
|
|
|
|
3 |
2 |
p. i |
article |
2 |
Chemist versus Machine: Traditional Knowledge versus Machine Learning Techniques
|
George, Janine |
|
|
3 |
2 |
p. 86-95 |
article |
3 |
Computational Data-Driven Materials Discovery
|
Mannodi-Kanakkithodi, Arun |
|
|
3 |
2 |
p. 79-82 |
article |
4 |
Defining and Exploring Chemical Spaces
|
Coley, Connor W. |
|
|
3 |
2 |
p. 133-145 |
article |
5 |
Euclidean Symmetry and Equivariance in Machine Learning
|
Smidt, Tess E. |
|
|
3 |
2 |
p. 82-85 |
article |
6 |
How the Shape of Chemical Data Can Enable Data-Driven Materials Discovery
|
Cole, Jacqueline M. |
|
|
3 |
2 |
p. 111-119 |
article |
7 |
Machine Learning in Chemistry: Looking Back, Looking Forward
|
|
|
|
3 |
2 |
p. 75-76 |
article |
8 |
Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and Best Practices for Machine Learning in Chemistry
|
Vishwakarma, Gaurav |
|
|
3 |
2 |
p. 146-156 |
article |
9 |
Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning
|
dos Passos Gomes, Gabriel |
|
|
3 |
2 |
p. 96-110 |
article |
10 |
Pursuing a Prospective Perspective
|
Kearnes, Steven |
|
|
3 |
2 |
p. 77-79 |
article |
11 |
Subscription and Copyright Information
|
|
|
|
3 |
2 |
p. e1 |
article |
12 |
Toward Machine Learning-Enhanced High-Throughput Experimentation
|
Eyke, Natalie S. |
|
|
3 |
2 |
p. 120-132 |
article |