In the present work, we have employed machine learning (ML) techniques to evaluate ductile-brittle (DB) behaviors in intermetallic compounds (IMCs) which can form magnesium (Mg) alloys. This procedure was mainly conducted by a proxy-based method, where the ratio of shear (G)/bulk (B) moduli was used as a proxy to identify whether the compound is ductile or brittle. Starting from compounds information (composition and crystal structure) and their moduli, as found in open databases (AFLOW), ML-based models were built, and those models were used to predict the moduli in other compounds, and accordingly, to foresee the ductile-brittle behaviors of these new compounds. The results reached in the present work showed that the built models can effectively catch the elastic moduli of new compounds. This was confirmed through moduli calculations done by density functional theory (DFT) on some compounds, where the DFT calculations were consistent with the ML prediction. A further confirmation on the reliability of the built ML models was considered through relating between the DB behavior in MgBe₁₃ and MgPd₂, as evaluated by the ML-predicted moduli, and the nature of chemical bonding in these two compounds, which in turn, was investigated by the charge density distribution (CDD) and electron localization function (ELF) obtained by DFT methodology. The ML-evaluated DB behaviors of the two compounds was also consistent with the DFT calculations of CDD and ELF. These findings and confirmations gave legitimacy to the built model to be employed in further prediction processes. Indeed, as examples, the DB characteristics were investigated in IMCs that might from in three Mg alloy series, involving AZ, ZX and WE.

在目前的工作中，我们采用机器学习 (ML) 技术来评估可形成镁 (Mg) 合金的金属间化合物 (IMC) 中的韧性-脆性 (DB) 行为。此过程主要通过基于代理的方法进行，其中剪切 (G)/体积 (B) 模量的比率用作代理来确定化合物是延展性还是脆性。从开放数据库 (AFLOW) 中的化合物信息（成分和晶体结构）及其模量开始，建立了基于 ML 的模型，这些模型用于预测其他化合物的模量，并相应地预测延展性-这些新化合物的脆性行为。本工作取得的结果表明，所建立的模型可以有效地捕捉新化合物的弹性模量。这通过密度泛函理论 (DFT) 对某些化合物进行的模量计算得到证实，其中 DFT 计算与 ML 预测一致。通过 MgBe 中的 DB 行为之间的关联，进一步确认了构建的 ML 模型的可靠性₁₃和 MgPd ₂，如通过 ML 预测的模量评估的，以及这两种化合物中化学键合的性质，而这又通过 DFT 方法获得的电荷密度分布 (CDD) 和电子局域化函数 (ELF) 进行了研究. ML 评估的两种化合物的 DB 行为也与 CDD 和 ELF 的 DFT 计算一致。这些发现和确认为构建的模型提供了合法性，以便在进一步的预测过程中使用。实际上，作为示例，研究了可能来自三个镁合金系列（包括 AZ、ZX 和 WE）的 IMC 中的 DB 特性。