Accurate machine learning models based on small dataset of energetic materials through spatial matrix featurization methods  

作　　者：Chao Chen Danyang Liu Siyan Deng Lixiang Zhong Serene Hay Yee Chan Shuzhou Li Huey Hoon Hng 

机构地区：[1]School of Materials Science and Engineering,Nanyang Technological University,50 Nanyang Avenue,Singapore 639798,Singapore

出　　处：《Journal of Energy Chemistry》2021年第12期364-375,I0009,共13页能源化学（英文版）

基　　金：support from the Ministry of Education(MOE) Singapore Tier 1 (RG8/20)。

摘　　要：A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the development of proper featurization method based on physicochemical nature of target proprieties can improve the predictive power of ML models with a smaller database.In this work,we show that two new featurization methods,volume occupation spatial matrix and heat contribution spatial matrix,can improve the accuracy in predicting energetic materials' crystal density(ρ_(crystal)) and solid phase enthalpy of formation(H_(f,solid)) using a database containing 451 energetic molecules.Their mean absolute errors are reduced from 0.048 g/cm~3 and 24.67 kcal/mol to 0.035 g/cm~3 and 9.66 kcal/mol,respectively.By leave-one-out-cross-validation,the newly developed ML models can be used to determine the performance of most kinds of energetic materials except cubanes.Our ML models are applied to predict ρ_(crystal) and H_(f,solid) of CHON-based molecules of the 150 million sized PubChem database,and screened out 56 candidates with competitive detonation performance and reasonable chemical structures.With further improvement in future,spatial matrices have the potential of becoming multifunctional ML simulation tools that could provide even better predictions in wider fields of materials science.

关 键 词：Small database machine learning Energetic materials screening Spatial matrix featurization method Crystal density Formation enthalpy n-Body interactions 

分 类 号：TQ560.1[化学工程—炸药化工] TP181[自动化与计算机技术—控制理论与控制工程]