Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions  

作　　者：Evan R.Antoniuk Gowoon Cheon George Wang Daniel Bernstein William Cai Evan J.Reed 

机构地区：[1]Materials Science Division,Physical and Life Sciences Directorate,Lawrence Livermore National Laboratory,Livermore,CA,USA [2]Department of Chemistry,Stanford University,Stanford,CA,USA [3]Department of Applied Physics,Stanford University,Stanford,CA,USA [4]Google Research,Mountain View,CA,USA [5]Department of Physics,Stanford University,Stanford,CA,USA [6]Department of Mathematics,Stanford University,Stanford,CA,USA [7]Department of Computer Science,Stanford University,Stanford,CA,USA [8]Department of Materials Science and Engineering,Stanford University,Stanford,CA,USA

出　　处：《npj Computational Materials》2023年第1期733-743,共11页计算材料学（英文）

基　　金：This work was performed under the auspices of the U.S.Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.We would like to thank Prof.Tony Heinz for the original project inspiration and the human participants of the Synthesizability Quiz.

摘　　要：Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery.In this work,we develop a deep learning synthesizability model(SynthNN)that leverages the entire space of synthesized inorganic chemical compositions.By reformulating material discovery as a synthesizability classification task,SynthNN identifies synthesizable materials with 7×higher precision than with DFT-calculated formation energies.In a head-to-head material discovery comparison against 20 expert material scientists,SynthNN outperforms all experts,achieves 1.5×higher precision and completes the task five orders of magnitude faster than the best human expert.Remarkably,without any prior chemical knowledge,our experiments indicate that SynthNN learns the chemical principles of charge-balancing,chemical family relationships and ionicity,and utilizes these principles to generate synthesizability predictions.The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.

关 键 词：INORGANIC CRYSTALLINE utilize 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]