Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models
Abstract Accessing the synthesizability of crystal structures is crucial for transforming theoretical materials into real-world applications. Nevertheless, there is a significant gap between actual synthesizability and thermodynamic or kinetic stability commonly used to screen synthesizable structur...
Saved in:
| Main Authors: | Zhilong Song, Shuaihua Lu, Minggang Ju, Qionghua Zhou, Jinlan Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61778-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm
by: Zhilong Song, et al.
Published: (2025-01-01) -
Impact of data bias on machine learning for crystal compound synthesizability predictions
by: Ali Davariashtiyani, et al.
Published: (2024-01-01) -
RF SYNTHESIZER WITH DIRECT DIGITAL SYNTHESIZ
by: V. V. Murav’iov, et al.
Published: (2015-03-01) -
ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning
by: Mingyang Wang, et al.
Published: (2024-11-01) -
Large language models for accurate disease detection in electronic health records: the examples of crystal arthropathies
by: Kim Lauper, et al.
Published: (2024-12-01)