1.8k citations · 2.9k across the 7 of their papers we have counts for
7 papers
What drives performance in molecular MPNNs? An operator-level factorial benchmark
Panyu Jiao, Shuizhou Chen, Yiheng Shen +3
Message-passing neural networks (MPNNs) are widely used for molecular property prediction, but their deployment as monolithic architectures makes it difficult to identify how speci…
Simultaneous Learning of Several Materials Properties from Incomplete Databases with Multi-Task SISSO
Runhai Ouyang, Emre Ahmetcik, Christian Carbogno +2
The identification of descriptors of materials properties and functions that capture the underlying physical mechanisms is a critical goal in data-driven materials science. Only su…
Artificial Intelligence for High-Throughput Discovery of Topological Insulators: the Example of Alloyed Tetradymites
Guohua Cao, Runhai Ouyang, Luca M. Ghiringhelli +4
Significant advances have been made in predicting new topological materials using high-throughput empirical descriptors or symmetry-based indicators. To date, these approaches have…
Analysis of Topological Transitions in Two-dimensional Materials by Compressed Sensing
Carlos Mera Acosta, Runhai Ouyang, Adalberto Fazzio +3
Quantum spin-Hall insulators (QSHIs), i.e., two-dimensional topological insulators (TIs) with a symmetry-protected band inversion, have attracted considerable scientific interest i…
New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides
Christopher J. Bartel, Christopher Sutton, Bryan R. Goldsmith +4
Predicting the stability of the perovskite structure remains a longstanding challenge for the discovery of new functional materials for many applications including photovoltaics an…
SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
Runhai Ouyang, Stefano Curtarolo, Emre Ahmetcik +2
The lack of reliable methods for identifying descriptors - the sets of parameters capturing the underlying mechanisms of a materials property - is one of the key factors hindering…
Learning physical descriptors for materials science by compressed sensing
Luca M. Ghiringhelli, Jan Vybiral, Emre Ahmetcik +4
The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in th…