8 citations · 10 across the 4 of their papers we have counts for
4 papers · 1 filter
Ab initio-based Deep-Learning Prediction of Carrier Mobility in Strongly Anharmonic Materials
Juan Zhang, Boheng Zhao, Yang Li +4
Predicting charge transport in strongly anharmonic materials, particularly ultralow thermal conductors, remains a major challenge for first-principles methods. In such systems, per…
Roadmap on Advancements of the FHI-aims Software Package
Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
Kisung Kang, Thomas A. R. Purcell, Christian Carbogno +1
Molecular dynamics (MD) employing machine-learned interatomic potentials (MLIPs) serve as an efficient, urgently needed complement to ab initio molecular dynamics (aiMD). By traini…
Spontaneous polarization in NaNbO film on NdGaO and DyScO substrates
Kisung Kang, Saud Bin Anooz, Jutta Schwarzkopf +1
Pure NaNbO is an antiferroelectric material at room temperature that irreversibly transforms to a ferroelectric polar state when subjected to an external electrical field or…