Year: 2025
Type: article
Abstract: probability (MAP). TOSS employs two looping structures over the large-sized dataset of crystal structures to obtain an emergent library of distance distributions as the foundation for chemically intuitive understanding and then determine the OSs by minimizing a loss function for each structure based on MAP and distance distributions in the whole dataset. We apply TOSS to a dataset of over one million crystal structures, achieving a superior success rate, and use the resulting OS dataset to train a graph convolutional network (GCN) model as an alternative. Both TOSS and the GCN model are benchmarked against a curated ICSD dataset of structures with human-assigned OSs, yielding high accuracies of 96.09% and 97.24%, respectively. We expect TOSS and the ML-model-based alternative to find a wide spectrum of applications, and this work also demonstrates an encouraging example for data-driven paradigms to explicitly compute the chemical intuition for tackling complex problems in chemistry.
Source: Chemical Science
DOI: 10.1039/d5sc05694b
Institution: Tsinghua University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: diamond
Awards: G3570824159, G5085971758, G8661098714
OpenAlex ID: W4414614636