Chemical vapor deposition is an important method for the preparation of boron carbide. Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature, inlet gas composition, total pressure, reactor configuration, and total flow rate) has not been completely determined. In this work, a novel approach to identify the kinetic mechanisms for the deposit composition is presented. Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition. It has been shown that ML, combined with CFD, can reduce the prediction error from about 25% to 7%, compared with the ML approach alone. The sensitivity coefficient study shows that BHCl₂ and BCl₃ produce the most boron atoms, while C₂H₄ and CH₄ are the main sources of carbon atoms. The new approach can accurately predict the deposited boron–carbon ratio and provide a new design solution for other multi-element systems.

化学气相沉积是制备碳化硼的重要方法。尚未完全确定沉积物的相组成与沉积条件（温度，进气成分，总压力，反应器配置和总流速）之间的相关性。在这项工作中，提出了一种新颖的方法来确定沉积物成分的动力学机制。利用机器学习（ML）和计算流体力学（CFD）技术来确定影响沉积物成分的核心因素。已经证明，与单独的ML方法相比，ML与CFD结合可以将预测误差从大约25％降低到7％。灵敏度系数研究表明BHCl ₂和BCl ₃产生最多的硼原子，而C ₂ H ₄和CH ₄是碳原子的主要来源。新方法可以准确预测沉积的硼碳比，并为其他多元素系统提供新的设计解决方案。