Data, descriptors, and algorithms all play critical roles in machine learning and must be carefully optimized to maximize prediction accuracy.

Models cannot predict what they haven’t seen. Data diversity and representation, even in sparsely populated subspaces, enable more novel predictions.

Identify more promising leads and targets to reduce the need for unnecessary trials and experiments required to improve R&D efficiency.