Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Computation And Data Driven Discovery Of Topological Phononic Materials, Jiangxu Li, Jiaxi Liu, Stanley A. Baronett, Mingfeng Liu, Lei Wang, Ronghan Li, Yun Chen, Dianzhong Li, Qiang Zhu, Xing Qiu Chen
Computation And Data Driven Discovery Of Topological Phononic Materials, Jiangxu Li, Jiaxi Liu, Stanley A. Baronett, Mingfeng Liu, Lei Wang, Ronghan Li, Yun Chen, Dianzhong Li, Qiang Zhu, Xing Qiu Chen
Physics & Astronomy Faculty Research
© 2021, The Author(s). The discovery of topological quantum states marks a new chapter in both condensed matter physics and materials sciences. By analogy to spin electronic system, topological concepts have been extended into phonons, boosting the birth of topological phononics (TPs). Here, we present a high-throughput screening and data-driven approach to compute and evaluate TPs among over 10,000 real materials. We have discovered 5014 TP materials and grouped them into two main classes of Weyl and nodal-line (ring) TPs. We have clarified the physical mechanism for the occurrence of single Weyl, high degenerate Weyl, individual nodal-line (ring), nodal-link, nodal-chain, …
Pyxtal_Ff: A Python Library For Automated Force Field Generation, Howard Yanxon, David Zagaceta, Binh Tang, David S. Matteson, Qiang Zhu
Pyxtal_Ff: A Python Library For Automated Force Field Generation, Howard Yanxon, David Zagaceta, Binh Tang, David S. Matteson, Qiang Zhu
Physics & Astronomy Faculty Research
We present PyXtal_FF—a package based on Python programming language—for developing machine learning potentials (MLPs). The aim of PyXtal_FF is to promote the application of atomistic simulations through providing several choices of atom-centered descriptors and machine learning regressions in one platform. Based on the given choice of descriptors (including the atom-centered symmetry functions, embedded atom density, SO4 bispectrum, and smooth SO3 power spectrum), PyXtal_FF can train MLPs with either generalized linear regression or neural network models, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from ab-initio simulations. The trained MLP model from PyXtal_FF is interfaced with …