OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!
If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.
Requested Article:
Prediction and customized design of Curie temperature of Fe-based amorphous alloys based on interpretable machine learning
Chengcheng Liu, Yongchao Lu, Jianfa Feng, et al.
Materials Today Communications (2023) Vol. 38, pp. 107667-107667
Closed Access | Times Cited: 6
Chengcheng Liu, Yongchao Lu, Jianfa Feng, et al.
Materials Today Communications (2023) Vol. 38, pp. 107667-107667
Closed Access | Times Cited: 6
Showing 6 citing articles:
Prediction of martensite start temperature of steel combined with expert experience and machine learning
Chengcheng Liu, Hang Su
Science and Technology of Advanced Materials (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 6
Chengcheng Liu, Hang Su
Science and Technology of Advanced Materials (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 6
Application of machine learning in magnetocaloric materials: A review
Mo Wang, Jianfeng Wang, Guoliang Yuan, et al.
Materials Today Communications (2025), pp. 111933-111933
Closed Access
Mo Wang, Jianfeng Wang, Guoliang Yuan, et al.
Materials Today Communications (2025), pp. 111933-111933
Closed Access
Machine learning aided prediction of martensite transformation temperature of NiTi-based shape memory alloy
Chengcheng Liu, Hang Su
Materials Today Communications (2024), pp. 110720-110720
Closed Access | Times Cited: 3
Chengcheng Liu, Hang Su
Materials Today Communications (2024), pp. 110720-110720
Closed Access | Times Cited: 3
Prediction and Rational Design of Stacking Fault Energy of Austenitic Alloys Based on Interpretable Machine Learning and Chemical Composition
Chengcheng Liu, Hang Su
steel research international (2024) Vol. 95, Iss. 8
Closed Access | Times Cited: 2
Chengcheng Liu, Hang Su
steel research international (2024) Vol. 95, Iss. 8
Closed Access | Times Cited: 2
Prediction of glass transition temperature of oxide glasses based on interpretable machine learning and sparse data sets
Chengcheng Liu, Hang Su
Materials Today Communications (2024) Vol. 40, pp. 109691-109691
Closed Access | Times Cited: 2
Chengcheng Liu, Hang Su
Materials Today Communications (2024) Vol. 40, pp. 109691-109691
Closed Access | Times Cited: 2
Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures
Marian Arale Brännvall, Gabriel Persson, Luis CasillasâTrujillo, et al.
Physical Review Materials (2024) Vol. 8, Iss. 11
Open Access
Marian Arale Brännvall, Gabriel Persson, Luis CasillasâTrujillo, et al.
Physical Review Materials (2024) Vol. 8, Iss. 11
Open Access
