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Ankit Agrawal, Ph.D. - Publications

Affiliations: 
2009 Computer Science Iowa State University, Ames, IA, United States 
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25 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2020 Jha D, Choudhary K, Tavazza F, Liao WK, Choudhary A, Campbell C, Agrawal A. Author Correction: Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nature Communications. 11: 3643. PMID 32669549 DOI: 10.1038/S41467-020-17054-2  0.325
2020 Yang Z, Papanikolaou S, Reid ACE, Liao WK, Choudhary AN, Campbell C, Agrawal A. Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations. Scientific Reports. 10: 8262. PMID 32427971 DOI: 10.1038/S41598-020-65157-Z  0.303
2019 Jha D, Choudhary K, Tavazza F, Liao WK, Choudhary A, Campbell C, Agrawal A. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nature Communications. 10: 5316. PMID 31757948 DOI: 10.1038/S41467-019-13297-W  0.346
2019 Paul A, Furmanchuk A, Liao WK, Choudhary A, Agrawal A. Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees. Molecular Informatics. PMID 31503423 DOI: 10.1002/Minf.201900038  0.318
2019 Paul A, Acar P, Liao W, Choudhary A, Sundararaghavan V, Agrawal A. Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation Computational Materials Science. 160: 334-351. DOI: 10.1016/J.Commatsci.2019.01.015  0.308
2019 Yang Z, Yabansu YC, Jha D, Liao W, Choudhary AN, Kalidindi SR, Agrawal A. Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches Acta Materialia. 166: 335-345. DOI: 10.1016/J.Actamat.2018.12.045  0.337
2018 Jha D, Ward L, Paul A, Liao WK, Choudhary A, Wolverton C, Agrawal A. ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition. Scientific Reports. 8: 17593. PMID 30514926 DOI: 10.1038/S41598-018-35934-Y  0.344
2018 Yang Z, Li X, Catherine Brinson L, Choudhary AN, Chen W, Agrawal A. Microstructural Materials Design Via Deep Adversarial Learning Methodology Journal of Mechanical Design. 140. DOI: 10.1115/1.4041371  0.321
2018 Agrawal A, Choudhary A. An online tool for predicting fatigue strength of steel alloys based on ensemble data mining International Journal of Fatigue. 113: 389-400. DOI: 10.1016/J.Ijfatigue.2018.04.017  0.323
2018 Yang Z, Yabansu YC, Al-Bahrani R, Liao W, Choudhary AN, Kalidindi SR, Agrawal A. Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets Computational Materials Science. 151: 278-287. DOI: 10.1016/J.Commatsci.2018.05.014  0.349
2017 Furmanchuk A, Saal JE, Doak JW, Olson GB, Choudhary A, Agrawal A. Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach. Journal of Computational Chemistry. PMID 28960343 DOI: 10.1002/Jcc.25067  0.328
2016 Agrawal A, Choudhary A. Perspective: Materials informatics and big data: Realization of the "fourth paradigm" of science in materials science Apl Materials. 4. DOI: 10.1063/1.4946894  0.311
2016 Furmanchuk A, Agrawal A, Choudhary A. Predictive analytics for crystalline materials: bulk modulus Rsc Advances. 6: 95246-95251. DOI: 10.1039/C6Ra19284J  0.332
2016 Ward L, Agrawal A, Choudhary A, Wolverton C. A general-purpose machine learning framework for predicting properties of inorganic materials Npj Computational Materials. 2. DOI: 10.1038/Npjcompumats.2016.28  0.365
2016 Tripathy A, Agrawal A, Rath SK. Classification of sentiment reviews using n-gram machine learning approach Expert Systems With Applications. 57: 117-126. DOI: 10.1016/J.Eswa.2016.03.028  0.304
2016 Cheng Y, Agrawal A, Liu H, Choudhary A. Legislative prediction with dual uncertainty minimization from heterogeneous information Statistical Analysis and Data Mining: the Asa Data Science Journal. 10: 107-120. DOI: 10.1002/Sam.11309  0.301
2015 Liu R, Kumar A, Chen Z, Agrawal A, Sundararaghavan V, Choudhary A. A predictive machine learning approach for microstructure optimization and materials design. Scientific Reports. 5: 11551. PMID 26100717 DOI: 10.1038/Srep11551  0.34
2012 Zhang Y, Misra S, Agrawal A, Patwary MM, Liao WK, Qin Z, Choudhary A. Accelerating pairwise statistical significance estimation for local alignment by harvesting GPU's power. Bmc Bioinformatics. 13: S3. PMID 22537007 DOI: 10.1186/1471-2105-13-S5-S3  0.353
2012 Zhang Y, Patwary MA, Misra S, Agrawal A, Liao Wk, Qin Z, Choudhary A. Par-PSSE: Software for Pairwise statistical significance estimation in parallel for local sequence alignment International Journal of Digital Content Technology and Its Applications. 6: 200-208. DOI: 10.4156/Jdcta.Vol6.Issue5.24  0.332
2011 Agrawal A, Choudhary A, Huang X. Sequence-specific sequence comparison using pairwise statistical significance. Advances in Experimental Medicine and Biology. 696: 297-306. PMID 21431570 DOI: 10.1007/978-1-4419-7046-6_30  0.485
2011 Agrawal A, Huang X. Pairwise statistical significance of local sequence alignment using sequence-specific and position-specific substitution matrices. Ieee/Acm Transactions On Computational Biology and Bioinformatics / Ieee, Acm. 8: 194-205. PMID 21071807 DOI: 10.1109/Tcbb.2009.69  0.5
2011 Agrawal A, Misra S, Honbo D, Choudhary A. Parallel pairwise statistical significance estimation of local sequence alignment using Message Passing Interface library Concurrency Computation Practice and Experience. 23: 2269-2279. DOI: 10.1002/Cpe.1798  0.358
2009 Agrawal A, Huang X. Pairwise statistical significance of local sequence alignment using multiple parameter sets and empirical justification of parameter set change penalty. Bmc Bioinformatics. 10: S1. PMID 19344477 DOI: 10.1186/1471-2105-10-S3-S1  0.469
2009 Agrawal A, Huang X. PSIBLAST_PairwiseStatSig: reordering PSI-BLAST hits using pairwise statistical significance. Bioinformatics (Oxford, England). 25: 1082-3. PMID 19251771 DOI: 10.1093/Bioinformatics/Btp089  0.496
2008 Agrawal A, Brendel VP, Huang X. Pairwise statistical significance and empirical determination of effective gap opening penalties for protein local sequence alignment. International Journal of Computational Biology and Drug Design. 1: 347-67. PMID 20063463 DOI: 10.1504/Ijcbdd.2008.022207  0.5
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