Mao Yuanqing, Yang Hongliang, Sheng Ye, Wang Jiping, Ouyang Runhai, Ye Caichao, Yang Jiong, Zhang Wenqing
Department of Physics & Guangdong Provincial Key Laboratory of Computational Science and Material Design, Southern University of Science and Technology, Shenzhen 518055, China.
Materials Genome Institute, Shanghai University, Shanghai 200444, China.
ACS Omega. 2021 May 26;6(22):14533-14541. doi: 10.1021/acsomega.1c01517. eCollection 2021 Jun 8.
It is well believed that machine learning models could help to predict the formation energies of materials if all elemental and crystal structural details are known. In this paper, it is shown that even without detailed crystal structure information, the formation energies of binary compounds in various prototypes at the ground states can be reasonably evaluated using machine-learning feature abstraction to screen out the important features. By combining with the "white-box" sure independence screening and sparsifying operator (SISSO) approach, an interpretable and accurate formation energy model is constructed. The predicted formation energies of 183 experimental and 439 calculated stable binary compounds ( = 0) are predicted using this model, and both show reasonable agreements with experimental and Materials Project's calculated values. The descriptor set is capable of reflecting the formation energies of binary compounds and is also consistent with the common understanding that the formation energy is mainly determined by electronegativity, electron affinity, bond energy, and other atomic properties. As crystal structure parameters are not necessary prerequisites, it can be widely applied to the formation energy prediction and classification of binary compounds in large quantities.
人们普遍认为，如果所有元素和晶体结构细节都已知，机器学习模型可以帮助预测材料的形成能。本文表明，即使没有详细的晶体结构信息，通过机器学习特征抽象筛选出重要特征，也可以合理评估各种原型中二元化合物基态的形成能。通过结合“白盒”确定独立性筛选和稀疏化算子（SISSO）方法，构建了一个可解释且准确的形成能模型。使用该模型预测了183种实验稳定二元化合物和439种计算稳定二元化合物（ = 0）的形成能，两者与实验值和材料项目的计算值均显示出合理的一致性。描述符集能够反映二元化合物的形成能，也与形成能主要由电负性、电子亲和能、键能和其他原子性质决定的普遍认识一致。由于晶体结构参数不是必要前提条件，它可以广泛应用于大量二元化合物的形成能预测和分类。
