Overall, what conclusions can we draw from such analysis? First, it is worth pointing out that despite the seeming dominance of deep learning techniques in achieving good performance in tasks like language modeling or image generation, in the materials domain such techniques are still overall less popular than conventional techniques. As found in the matbench study , deep learning becomes much more attractive for larger data sets (a threshold of approximately 10,000 data points was found in that study). Many materials ML problems simply do not have the data to make effective use of deep learning techniques. A second conclusion is that common databases, software libraries, and techniques are now readily available for simulation-based machine learning. However, there still remains a need for similar large and concerted efforts in other domains of materials science such as synthesis, characterization, and materials processing data. Although such databases have been developed , they do not factor heavily in our literature review of materials ML and are still in the process of realizing a large, coordinated research community. This may potentially be due to complexity and heterogeneity of data in experimental domains, making data preprocessing (e.g., cleaning and standardization) cumbersome for subsequent ML. It is possible that the push for automation in experiments may help in this endeavor, however the complexity of describing materials samples in experiments makes this far from straightforward.