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Bottom-up coarse-grain modeling of nanoscale shear bands in shocked α-RDX
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Sergei Izvekov, James P. Larentzos, Betsy M. Rice
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57 |
23 |
10627–10648 |
2022 |
8.8 |
|
Average Rating: 0.0 / 5
(0 Reviews)
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Estimation of fatigue crack initiation in the very high cycle fatigue regime for AA7075-T6 alloy using crystal plasticity finite element method
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Bin Li, Tao Gao, Zhidan Sun
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57 |
23 |
10649–10663 |
2022 |
10.84 |
|
Average Rating: 0.0 / 5
(0 Reviews)
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Strong Zeeman splitting in orbital-hybridized valleytronic interfaces
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Steven T. Hartman, Ghanshyam Pilania
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57 |
23 |
10664–10676 |
2022 |
10.71 |
|
Average Rating: 0.0 / 5
(0 Reviews)
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Ab initio approaches to high-entropy alloys: a comparison of CPA, SQS, and supercell methods
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Mariia Karabin, Wasim Raja Mondal, Markus Eisenbach
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57 |
23 |
10677–10690 |
2022 |
5.88 |
|
Average Rating: 0.0 / 5
(0 Reviews)
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Realizing high thermoelectric performance in p-type RbZn4P3 Zintl compound: a first-principles investigation
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Sangeeta, Ramesh Kumar, Mukhtiyar Singh
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57 |
23 |
10691–10701 |
2022 |
8.77 |
|
Average Rating: 0.0 / 5
(0 Reviews)
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Ultralow diffusion barrier of double transition metal MoWC monolayer as Li-ion battery anode
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Veenu Mehta, Hardev S. Saini, K. Tankeshwar
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57 |
23 |
10702–10713 |
2022 |
6.81 |
|
Average Rating: 0.0 / 5
(0 Reviews)
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A powerful approach to develop nitrogen-doped graphene sheets: theoretical and experimental framework
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Suresh Kumar Vemuri, Harsh Chaliyawala, Indrajit Mukhopadhyay
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57 |
23 |
10714–10723 |
2022 |
7.82 |
|
Average Rating: 0.0 / 5
(0 Reviews)
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Delineating the effect of substituent and π-bridge flip on the photophysical properties of pyrene derivatives: answers from DFT/TD-DFT calculations
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Murugesan Panneerselvam, Arunkumar Kathiravan, Madhavan Jaccob
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57 |
23 |
10724–10735 |
2022 |
7.82 |
|
Average Rating: 0.0 / 5
(0 Reviews)
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Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning
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Arun Mannodi-Kanakkithodi, Maria K. Y. Chan
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57 |
23 |
10736–10754 |
2022 |
11.73 |
|
Average Rating: 0.0 / 5
(0 Reviews)
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A data-driven machine learning approach to predict the hardenability curve of boron steels and assist alloy design
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Xiaoxiao Geng, Zhuo Cheng, Guilin Wu
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57 |
23 |
10755–10768 |
2022 |
6.81 |
|
Average Rating: 0.0 / 5
(0 Reviews)
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