Direct Prediction of Phonon Density of States With Euclidean Neural Networks. Issue 12 (16th March 2021)

• Record Type: Journal Article
• Authors: Chen, Zhantao; Andrejevic, Nina; Smidt, Tess; Ding, Zhiwei; Xu, Qian; Chi, Yen‐Ting; Nguyen, Quynh T.; Alatas, Ahmet; Kong, Jing; Li, Mingda
• Is Part Of: Advanced science. Volume 8:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-03-16
• Subjects: density of states -- machine learning -- phonons; Science -- Periodicals; 505
• DOI: 10.1002/advs.202004214
• Languages: English
• ISSNs: 2198-3844

Abstract

Abstract: Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density‐of‐states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high‐quality prediction using a small training set of ≈ 10 3 examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high‐performance thermal storage materials and phonon‐mediated superconductors. Abstract : A machine‐learning‐based predictive model allows a direct output of phonon density‐of‐states (DOS) by inputting atomic coordinates. The implemented Euclidean neural network preserves crystallographic symmetry and enables high‐quality prediction with small training data. The model allows for fast DOS calculation, reaching ab initio accuracy for 70–80% materials with much lowered computational cost, facilitating materials design especially in alloy systems.