Improving efficiency of autonomous material search via transfer learning from nontarget properties

Recently, autonomous material search methods combining machine learning and experiments/simulations have become indispensable for exploring the extremely vast material exploration space. However, conventional autonomous material search methods focus solely on target material properties and their descriptors, leaving room for improvement in search efficiency. More efficient autonomous material search can be realized by utilizing information on nontarget properties that are dormant in databases. Here, we propose a novel method for autonomous material search using transfer learning and an ensemble neural network. This method can perform an autonomous material search to optimize the target properties while transferring information on nontarget properties. To demonstrate the usefulness of this method, we applied it to search for ternary magnetic alloys with a high Curie temperature while transferring information on magnetic moment and spin polarization. Results indicate that the proposed method improves the efficiency of autonomous material search, particularly in the early stages of the search process.