Unlocking phonon properties of a large and diverse set of cubic crystals by indirect bottom-up machine learning approach
Unlocking phonon properties of a large and diverse set of cubic crystals by indirect bottom-up machine learning approach
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DOI：
10.1038/s43246-023-00390-3
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发表时间：
2023-08
影响因子：
7.8
通讯作者：
Alejandro Rodriguez;Changpeng Lin;Chen Shen;Kunpeng Yuan;Mohammed Al-Fahdi;Xiaoliang Zhang;Hongbin Zhang;Ming Hu
中科院分区：
文献类型：
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作者：
Alejandro Rodriguez;Changpeng Lin;Chen Shen;Kunpeng Yuan;Mohammed Al-Fahdi;Xiaoliang Zhang;Hongbin Zhang;Ming Hu
虽然基于第一性原理的非简谐晶格动力学是获得声子性质的最常用的方法之一，但是这种方法对于目标热材料的高通量搜索是不切实际的。我们开发了一个元素空间密度神经网络力场，作为一种自下而上的方法来准确预测跨越63种元素的~ 80，000个立方晶体的原子力。我们的间接机器学习模型的主要优势是声子输运物理学在同一水平上的第一原理的可访问性，允许从单个模型同时预测全面的声子属性。通过对3182个第一性原理数据的训练和对77,091个未探索结构的筛选，我们识别出13,461个动态稳定的立方结构，其晶格热导率低于1 Wm− 1 K −1，其中36个结构通过第一性原理计算得到验证。我们提出均方位移和键-反键作为两个低成本的描述符，以减轻快速筛选超低热导率的昂贵的第一原理计算的需求。我们的模型还定量地揭示了非对角相干性和对角种群之间的相关性，并确定了从粒子状到波动状热传导的独特交叉。我们的算法是有前途的加速发现新的声子晶体的新兴应用，如热电，超导和拓扑声子的量子信息技术。
Although first principles based anharmonic lattice dynamics is one of the most common methods to obtain phonon properties, such method is impractical for high-throughput search of target thermal materials. We develop an elemental spatial density neural network force field as a bottom-up approach to accurately predict atomic forces of ~80,000 cubic crystals spanning 63 elements. The primary advantage of our indirect machine learning model is the accessibility of phonon transport physics at the same level as first principles, allowing simultaneous prediction of comprehensive phonon properties from a single model. Training on 3182 first principles data and screening 77,091 unexplored structures, we identify 13,461 dynamically stable cubic structures with ultralow lattice thermal conductivity below 1 Wm−1K−1, among which 36 structures are validated by first principles calculations. We propose mean square displacement and bonding-antibonding as two low-cost descriptors to ease the demand of expensive first principles calculations for fast screening ultralow thermal conductivity. Our model also quantitatively reveals the correlation between off-diagonal coherence and diagonal populations and identifies the distinct crossover from particle-like to wave-like heat conduction. Our algorithm is promising for accelerating discovery of novel phononic crystals for emerging applications, such as thermoelectrics, superconductivity, and topological phonons for quantum information technology.