新型含能材料的设计与合成对我国能源、国防、航空航天等领域起到重要作用。本项目拟选取MBNx(M=Li,Na,K;x=2-5)为研究目标，利用第一性原理方法系统地研究高压下材料的晶体结构、力学性质和电子性质等，进一步使用机器学习（Machine Learning）探索材料结构和性质同机械感度的关联性，获得设计新型高能量密度、低感度含能材料的普适规律。（1）在不同压力条件下，通过CALYPSO软件预测MBNx的晶体结构，获得材料最稳定结构存在范围。（2）研究材料的体弹模量、弹性常数等力学性质；电子转移、态密度等电子性质；分析成键、电声耦合行为以及分子解离机制。（3）通过机器学习方法，分析材料特征物理参量（离子势、结合能、体弹模量和带隙等）和感度的定量关系。本项目有望推动理论量化机械感度的发展，为实验合成高能量密度和低感度的含能材料提供理论基础。

The design and synthesis of new energetic material play an important role in energy, national defense and aerospace. Taking MBNx(M= Li,Na,K; x=2-5) as prototypes, this project focuses on the exploration of crystal structures, mechanical and electronic properties under high pressure by using first-principles method. We will next explore the relationship between the physical features of material and its mechanical sensitivity by machine learning. The goal is to find some universal laws of designing new high energy density and low sensitivity materials. (1) We aim at the exploration of new stable structures of MBNx by using CALYPSO method. (2) Investigations on the elastic constants, bulk modulus, charge transfer, density of state and other properties. Analysis the chemical bonding nature, the electron-phonon coupling behavior and the decomposition mechanism. (3) Finding the actuating mechanisms of sensitivity and describing it in terms of a set of physically meaningful parameters (ionic potential, binding energy, bulk modulus, energy gap, etc). The project is expected to promote the development of quantifying mechanical sensitivity by theory. Our calculations provide important theoretical basis for experimentally synthesizing potential high energy and low sensitive materials.