Recent advances in computational materials design: methods, applications, algorithms, and informatics
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OpenAlex topics: Machine Learning in Materials Science · X-ray Diffraction in Crystallography · Catalysis and Oxidation Reactions
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References (26)
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1. Curtarolo S, Hart GL, Nardelli MB, Mingo N, Sanvito S, Levy O (2013) The high-throughput highway to computational materials design. Nat Mater 12(3):191–20110.1038/nmat3568 ↗
2. Martin RM (2020) Electronic structure: basic theory and practical methods, Cambridge university press10.1017/9781108555586 ↗
3. Marx D, Hutter J (2009) Ab initio molecular dynamics: basic theory and advanced methods, Cambridge University Press10.1017/CBO9780511609633 ↗
4. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A (2018) Machine learning for molecular and materials science. Nature 559(7715):547–55510.1038/s41586-018-0337-2 ↗
5. Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C (2017) Machine learning in materials informatics: recent applications and prospects. .pj Comp Mater 3(1):1–13
6. Bartel CJ (2022) Review of computational approaches to predict the thermodynamic stability of inorganic solids. J Mater Sci, 1–210.1007/s10853-022-06915-4 ↗
7. Uberuaga BP, Simonnin P, Rosso KM, Schreiber DK and Asta M (2022) The effect of Cr alloying on defect migration at Ni grain boundaries. J Mater Sci, 1–1810.1007/s10853-021-06590-x ↗
8. Sose AT, Mohammadi E, Wang F, and Deshmukh SA (2022) Investigation of structure and dynamics of water confined between hybrid layered materials of graphene, boron nitride, and molybdenum disulphide. J Mater Sci, 1–1810.1007/s10853-022-07073-3 ↗
9. Nikolov S, Tranchida J, Ramakrishna K, Lokamani M, Cangi A, and Wood M (2022) Dissociating the phononic, magnetic and electronic contributions to thermal conductivity: a computational study in alpha-iron. J Mater Sci, 1–1410.1007/s10853-021-06865-3 ↗
10. Mishra A, Echeverria MJ, Ma K, Parida S, Chen C, Galitskiy S and Dongare AM (2022) Virtual texture analysis to investigate the deformation mechanisms in metal microstructures at the atomic scale. J Mater Sci, 1–2010.1007/s10853-022-07108-9 ↗
11. Gupta AK, Gohil S (2022) Insights into structural difference between sodium polyacrylate PAA and sodium polymethacrylate PMA in salt solutions investigated by molecular simulations. J Mater Sci, 1–1610.1007/s10853-021-06836-8 ↗
12. Fey LT, Hunter A and Beyerlein IJ (2022) Phase-field dislocation modeling of cross-slip. J Mater Sci, 1–1510.1007/s10853-021-06716-1 ↗
13. Coutinho YA, Kunwar A and Moelans N (2022) Phase-field approach to simulate BCC-B2 phase separation in the AlnCrFe2Ni2 medium-entropy alloy. J Mater Sci, 1–1310.1007/s10853-022-07058-2 ↗
14. Siddique AB and Khraishi TA (2022) Multi-scale modeling of solute atom strengthening using 3D discrete dislocation dynamics. J Mater Sci, 1–1410.1007/s10853-022-07077-z ↗
15. Izvekov S, Larentzos JP, Brennan JK and Rice BM (2022) Bottom-up coarse-grain modeling of nanoscale shear bands in shocked α-RDX. J Mater Sci, 1–2210.1007/s10853-022-07069-z ↗
16. Li B, Gao T, Xue H and Sun Z (2022) Estimation of fatigue crack initiation in the very high cycle fatigue regime for AA7075-T6 alloy using crystal plasticity finite element method. J Mater Sci, 1–1510.1007/s10853-021-06790-5 ↗
17. Hartman ST and Pilania G (2022) Strong Zeeman splitting in orbital-hybridized valleytronic interfaces. J Mater Sci, 1–1310.1007/s10853-021-06723-2 ↗
18. Karabin M, Mondal W, Ostlin A, Ho W-GD, Dobrosavljevic V, Tam K-M, Terletska H, Chioncel L, Wang Y and Eisenbach M (2022) Ab initio approaches to high entropy alloys: a comparison of CPA, SQS, and Supercell Methods. J Mater Sci.10.1007/s10853-022-07186-9 ↗
19. Kumar R, Singh M et al (2022) Realizing high thermoelectric performance in p-type RbZn4P3 Zintl compound: a first-principles investigation. J Mater Sci, 1–11
20. Mehta V, Saini HS, Srivastava S, Kashyap MK and Tankeshwar K (2022) Ultralow diffusion barrier of double transition metal MoWC monolayer as Li-ion battery anode. J Mater Sci, 1–1210.1007/s10853-022-07237-1 ↗
21. Vemuri SK, Chaliyawala H, Ray A and Mukhopadhyay I (2022). A powerful approach to develop nitrogen-doped graphene sheets: theoretical and experimental framework. J Mater Sci, 1–1010.1007/s10853-022-07239-z ↗
22. Panneerselvam M, Kathiravan A, Johnee Britto N, Krishnan SM, Velmurugan G and Jaccob M (2022) Delineating the effect of substituent and π-bridge flip on the photophysical properties of pyrene derivatives: answers from DFT/TD-DFT calculations. J Mater Sci, 1–1210.1007/s10853-022-07095-x ↗
23. Mannodi-Kanakkithodi A and Chan MK (2022) Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning. J Mater Sci, 1–1910.1007/s10853-022-06998-z ↗
24. Geng X, Cheng Z, Wang S, Peng C, Ullah A, Wang H and Wu G (2022) A data-driven machine learning approach to predict the hardenability curve of boron steels and assist alloy design. J Mater Sci, 1–1410.1007/s10853-022-07132-9 ↗
25. Lin F, Liu Y, Song X, Liu X and Li X (2022) The numerical research on the effect of ultrasonic field on metallic powders produced by the ultrasonic-assisted electrical discharge process. J Mater Sci, 1–1110.1007/s10853-022-07015-z ↗
26. Kashyap J and Datta D (2022) Drug repurposing for SARS-CoV-2: a high-throughput molecular docking, molecular dynamics, machine learning, and DFT study. J Mater Sci, 1–2310.1007/s10853-022-07195-8 ↗