Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

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..., ,

合成预测是推动先进材料快速设计的关键支撑技术。然而，针对无机材料确定合成变量（如前驱体材料的选择）仍颇具挑战——这是由于人们对加热过程中的反应序列尚未形成充分认知。本研究依托从科学文献中经文本挖掘得到的29900条固态合成工艺知识库，自动学习为新型目标材料的合成推荐适配的前驱体。该数据驱动方法通过学习材料间的化学相似性，将新型目标材料的合成任务关联至相似材料的先例合成工艺，以此模拟人类的合成设计思路。在为2654个未在训练中出现的测试目标材料各推荐5组前驱体的场景下，该推荐策略的成功率至少可达82%。本方法以数学形式整合了数十年的启发式合成数据，使其可被应用于推荐引擎与自主……