| 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
|
Journal description