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DOIs for this ISSN

Showing the top 5 of 173 DOIs, ordered by correction priority.

#	Title	Missing	Priority	Citations
1	<i>Materials Genome Engineering Advances</i>: A new journal… (10.1002/mgea.9)	Abstract References ORCID	52.42	4
2	Prospects of materials genome engineering frontiers (10.1002/mgea.17)	ORCID	42.03	47
3	Editorial: Shaping the future of materials science through m… (10.1002/mgea.80)	Abstract References	23.86	2
4	Revolutionizing materials design through advanced artificial… (10.1002/mgea.70017)	Abstract References	15.05	1
5	TopoMAS: Large Language Model Driven Topological Materials M… (10.1002/mgea.70045)	ORCID	7.53	1
6	Interpretable Machine Learning Predicting Coercivity of Sm‐C… (10.1002/mgea.70053)	ORCID	7.53	1
7	Issue Information (10.1002/mgea.1)	References ORCID	0.00	0
8	Data‐driven and artificial intelligence accelerated steel ma… (10.1002/mgea.10)		0.00	52
9	Advances in data‐assisted high‐throughput computations for m… (10.1002/mgea.11)		0.00	91
10	Statistical in situ scanning electron microscopy investigati… (10.1002/mgea.12)		0.00	3
11	Opinion: Chemical potential and materials genome (10.1002/mgea.13)	Abstract	0.00	0
12	Multi‐objective optimization and its application in material… (10.1002/mgea.14)		0.00	37
13	Building materials genome from ground‐state configuration to… (10.1002/mgea.15)		0.00	2
14	Ab initio artificial intelligence: Future research of Materi… (10.1002/mgea.16)		0.00	11
15	How to work together for engineering materials! (10.1002/mgea.18)		0.00	1
16	3D characterization of abnormal grain growth in nanocrystall… (10.1002/mgea.19)		0.00	3
17	Issue Information (10.1002/mgea.2)	References ORCID	0.00	0
18	High‐throughput experimental techniques for corrosion resear… (10.1002/mgea.20)		0.00	25
19	The MatHub‐3d first‐principles repository and the applicatio… (10.1002/mgea.21)		0.00	28
20	First‐principle screening of corrosion resistant solutes (Al… (10.1002/mgea.22)		0.00	13
21	Cover Image (10.1002/mgea.23)	Abstract References ORCID	0.00	0
22	Cover Image (10.1002/mgea.24)	Abstract References ORCID	0.00	0
23	Navigating energy landscapes for materials discovery: Integr… (10.1002/mgea.25)		0.00	13
24	Prediction of ultimate tensile strength of Al‐Si alloys base… (10.1002/mgea.26)		0.00	23
25	Effect of signal‐to‐noise ratio on the automatic clustering… (10.1002/mgea.27)		0.00	4
26	Materials genome engineering accelerates the research and de… (10.1002/mgea.28)		0.00	46
27	Employing deep learning in non‐parametric inverse visualizat… (10.1002/mgea.29)		0.00	9
28	High throughput construction for the deformation mechanism d… (10.1002/mgea.3)		0.00	4
29	Applications of generative adversarial networks in materials… (10.1002/mgea.30)		0.00	28
30	A comparative study of machine learning in predicting the me… (10.1002/mgea.31)		0.00	14
31	Issue Information (10.1002/mgea.32)	References ORCID	0.00	0
32	Unexpected nucleation mechanism of T<sub>1</sub> precipitate… (10.1002/mgea.33)		0.00	3
33	Cover Image (10.1002/mgea.34)	Abstract References	0.00	0
34	An ensemble learning strategy for multi‐source hydrogen embr… (10.1002/mgea.35)		0.00	4
35	Design of advanced steels by integrated computational materi… (10.1002/mgea.36)		0.00	23
36	Predicting the effect of cooling rates and initial hydrogen… (10.1002/mgea.37)		0.00	4
37	A machine learning‐based crystal graph network and its appli… (10.1002/mgea.38)		0.00	16
38	Development of thermodynamic database of the Mn‐RE (RE = rar… (10.1002/mgea.39)		0.00	7
39	Atomistic simulations of nucleation and growth of CaCO<sub>3… (10.1002/mgea.4)		0.00	12
40	Issue Information (10.1002/mgea.40)	References ORCID	0.00	0
41	Issue Information (10.1002/mgea.41)	References ORCID	0.00	0
42	Issue Information (10.1002/mgea.42)	References ORCID	0.00	0
43	Integrated unified phase‐field modeling (UPFM) (10.1002/mgea.44)		0.00	37
44	Optimal design of high‐performance rare‐earth‐free wrought m… (10.1002/mgea.45)		0.00	12
45	On the potential of using ensemble learning algorithm to app… (10.1002/mgea.46)		0.00	1
46	The anodic dissolution kinetics of Mg alloys in water based… (10.1002/mgea.47)		0.00	10
47	Bond sensitive graph neural networks for predicting high tem… (10.1002/mgea.48)		0.00	10
48	Data mining accelerated the design strategy of high‐entropy… (10.1002/mgea.49)		0.00	13
49	Development of high‐throughput wet‐chemical synthesis techni… (10.1002/mgea.5)		0.00	27
50	A review on the applications of graph neural networks in mat… (10.1002/mgea.50)		0.00	47