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.

Artikel-ID: 61 // A.R. acknowledges the financial support by the Department of Energy, Office of Nuclear Energy, Integrated University Program Graduate Fellowship (IUP) under award no. DE-NE-0000095 and NASA SC Space Grant Consortium REAP Program (Award No.: 521383-RP-SC004). Research reported in this work was supported in part by NSF under awards 2030128 and 2110033, SC EPSCoR Program under award number (23-GC01), and an ASPIRE grant from the Office of the Vice President for Research at the University of South Carolina (project 80005046)