Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
Department of Materials Science and Engineering, University of California, Los Angeles, California 90095-1595, USA.
Phys Rev Lett. 2014 Oct 31;113(18):185501. doi: 10.1103/PhysRevLett.113.185501. Epub 2014 Oct 27.
First-principles prediction of lattice thermal conductivity κ(L) of strongly anharmonic crystals is a long-standing challenge in solid-state physics. Making use of recent advances in information science, we propose a systematic and rigorous approach to this problem, compressive sensing lattice dynamics. Compressive sensing is used to select the physically important terms in the lattice dynamics model and determine their values in one shot. Nonintuitively, high accuracy is achieved when the model is trained on first-principles forces in quasirandom atomic configurations. The method is demonstrated for Si, NaCl, and Cu(12)Sb(4)S(13), an earth-abundant thermoelectric with strong phonon-phonon interactions that limit the room-temperature κ(L) to values near the amorphous limit.
晶格热导率 κ(L)的第一性原理预测是固态物理中长期存在的挑战。我们利用信息科学的最新进展，提出了一种系统而严格的方法来解决这个问题，即压缩感知晶格动力学。压缩感知用于选择晶格动力学模型中物理上重要的项，并一次性确定它们的值。非直觉的是，当模型在准随机原子构型中的第一性原理力上进行训练时，可实现高精度。该方法已在 Si、NaCl 和 Cu(12)Sb(4)S(13)中得到验证，Cu(12)Sb(4)S(13)是一种丰富的室温下具有强声子-声子相互作用的热电材料，其限制了室温下 κ(L)的值接近非晶态极限。
