Causal Inference Machine Learning Leads Original Experimental Discovery in CdSe/CdS Core/Shell Nanoparticles.

Abstract

The synthesis of CdSe/CdS core/shell nanoparticles was revisited with the help of a causal inference machine learning framework. The tadpole morphology with 1-2 tails was experimentally discovered. The causal inference model revealed the causality between the oleic acid (OA), octadecylphosphonic acid (ODPA) ligands, and the detailed tail shape of the tadpole morphology. Further, with the identified causality, a neural network was provided to predict and directly lead to the original experimental discovery of new tadpole-shaped structures. An entropy-driven nucleation theory was developed to understand both the ligand and temperature dependent experimental data and the causal inference from the machine learning framework. This work provided a vivid example of how the artificial intelligence technology, including machine learning, could benefit the materials science research for the discovery.