The accurate and rapid prediction of materials’ physical properties, such as thermal transport and mechanical properties, are of particular importance for potential applications of featuring novel materials. We demonstrate, using graphene as an example, how machine learning potential, combined with the Boltzmann transport equation and molecular dynamics simulations, can simultaneously provide an accurate prediction of multiple-target physical properties, with an accuracy comparable to that of density functional theory calculation and/or experimental measurements. Benchmarked quantities include the Grneisen parameter, the thermal expansion coefficient, Young’s modulus, Poisson’s ratio, and thermal conductivity. Moreover, the transferability of commonly used empirical potential in predicting multiple-target physical properties is also examined. Our study suggests that atomic simulation, in conjunction with machine learning potential, represents a promising method of exploring the various physical properties of novel materials.

准确快速地预测材料的物理性能，例如热传输和机械性能，对于新材料的潜在应用具有特别重要的意义。我们以石墨烯为例，展示了机器学习潜力如何结合玻尔兹曼输运方程和分子动力学模拟，可以同时提供对多目标物理性质的准确预测，其准确度可与密度泛函理论计算相媲美， /或实验测量。基准量包括 Grneisen 参数、热膨胀系数、杨氏模量、泊松比和热导率。此外，还检查了预测多目标物理特性中常用经验势的可转移性。