Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning

H Haowen Zhong Y Yilan Liu H Haibin Sun Y Yuru Liu R Rentao Zhang B Baochen Li Y Yi Yang Y Yuqing Huang (State Key Laboratory of Semiconductor Physics and Chip Technologies, Institute of Semiconductors) F Fei Yang F Frankie S. Mak K Klement Foo S Sen Lin (State Key Laboratory of Chemistry for NBC Hazards Protection, College of Chemistry) T Tianshu Yu P Peng Wang X Xiaoxue Wang

Article Details

Volume / Issue Vol. 16, Issue 1
Published May 15, 2025
ISSN 2041-1723
Publisher Nature Portfolio

Journal Info

Nature Communications

Nature Portfolio

ISSN: 2041-1723 Open Access Life Sciences

Authors (15)

H

Haowen Zhong

Y

Yilan Liu

H

Haibin Sun

Y

Yuru Liu

R

Rentao Zhang

B

Baochen Li

Y

Yi Yang

Y

Yuqing Huang

State Key Laboratory of Semiconductor Physics and Chip Technologies, Institute of Semiconductors

F

Fei Yang

F

Frankie S. Mak

K

Klement Foo

S

Sen Lin

State Key Laboratory of Chemistry for NBC Hazards Protection, College of Chemistry

T

Tianshu Yu

P

Peng Wang

X

Xiaoxue Wang