logo
ResearchBunny Logo
Perovskite synthesizability using graph neural networks

Chemistry

Perovskite synthesizability using graph neural networks

G. H. Gu, J. Jang, et al.

Discover a groundbreaking graph neural network model developed by Geun Ho Gu and colleagues that accurately predicts the synthesizability of perovskites. This innovative approach sets a new standard with a remarkable true positive rate, surpassing traditional methods and providing a pathway to identifying new material candidates for diverse applications.

00:00
00:00
Playback language: English
Citation Metrics
Citations
0
Influential Citations
0
Reference Count
0

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

Related Publications

Explore these studies to deepen your understanding of the subject.

Accelerating network layouts using graph neural networks
Computer Science

Accelerating network layouts using graph neural networks

C. Both, N. Dehmamy, et al.

Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials
Engineering and Technology

Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials

M. Dai, M. F. Demirel, et al.

Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks
Engineering and Technology

Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks

M. Movahhedi, X. Liu, et al.

Continuous estimation of power system inertia using convolutional neural networks
Engineering and Technology

Continuous estimation of power system inertia using convolutional neural networks

D. Linaro, F. Bizzarri, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny