Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature
This work addresses the challenge of synthesis design for inorganic materials, which is incremental by building on existing data-driven methods to automate precursor recommendation.
The paper tackles the problem of predicting precursor materials for inorganic synthesis by learning chemical similarity from 29,900 text-mined synthesis recipes, achieving a success rate of at least 82% when recommending five precursor sets for 2,654 unseen test materials.
Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of reactions during heating is not well understood. In this work, we use a knowledge base of 29,900 solid-state synthesis recipes, text-mined from the scientific literature, to automatically learn which precursors to recommend for the synthesis of a novel target material. The data-driven approach learns chemical similarity of materials and refers the synthesis of a new target to precedent synthesis procedures of similar materials, mimicking human synthesis design. When proposing five precursor sets for each of 2,654 unseen test target materials, the recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories.