Adam M. Krajewski

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Zi-Kui Liu, Dorothy Pate Enright Profes...The Pennsylvania State UniversityVerifisert e-postadresse på psu.edu
Shun-Li Shang (商顺利)Research Professor, The Pennsylvania State UniversityVerifisert e-postadresse på psu.edu
Allison M. BeesePennsylvania State UniversityVerifisert e-postadresse på matse.psu.edu
Wesley F ReinhartPenn State UniversityVerifisert e-postadresse på psu.edu
Hui SunThe Pennsylvania State UniversityVerifisert e-postadresse på psu.edu
Arindam DebnathGraduate Student, Department of Materials Science and Engineering, Pennsylvania State UniversityVerifisert e-postadresse på psu.edu
Shuang LinThe Pennsylvania State UniversityVerifisert e-postadresse på psu.edu
Brandon J. BocklundLawrence Livermore National LabVerifisert e-postadresse på llnl.gov
Yi WangPenn StateVerifisert e-postadresse på psu.edu
Jonathan W. SiegelAssistant Professor, Texas A&M UniversityVerifisert e-postadresse på tamu.edu
Jinchao XuProfessor of Applied Mathematics and Computational Sciences, KAUSTVerifisert e-postadresse på kaust.edu.sa
Sanghyeok ImMaterials Science and Engineering, Penn StateVerifisert e-postadresse på psu.edu
Timothy LichtensteinArgonne National LaboratoryVerifisert e-postadresse på anl.gov
Hojong KimMaterials Science and Engineering, The Pennsylvania State UniversityVerifisert e-postadresse på psu.edu
Lavanya RamanUniversity of Wisconsin MadisonVerifisert e-postadresse på wisc.edu
Dongwon ShinSenior R&D Staff Member of Materials Science and Technology Division, Oak Ridge National LaboratoryVerifisert e-postadresse på ornl.gov

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Adam M. Krajewski

Research Associate at The Pennsylvania State University

Verifisert e-postadresse på psu.edu - Startside

materials science artificial intelligence machine learning materials informatics HEA


Tittel Sorter etter sitater Sorter etter år Sorter etter tittel	Sitert av Sitert av	År
Generative deep learning as a tool for inverse design of high-entropy refractory alloys A Debnath, AM Krajewski, H Sun, S Lin, M Ahn, W Li, S Priya, J Singh, ... Journal of Materials Informatics 1 (3), 2021	44	2021
Extensible Structure-Informed Prediction of Formation Energy with improved accuracy and usability employing neural networks AM Krajewski, JW Siegel, J Xu, ZK Liu Computational Materials Science 208, 111254, 2022	41	2022
Correlation analysis of materials properties by machine learning: illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys X Chong, SL Shang, AM Krajewski, JD Shimanek, W Du, Y Wang, J Feng, ... Journal of Physics: Condensed Matter 33 (29), 295702, 2021	31	2021
Thermodynamic properties of the Nd-Bi system via emf measurements, DFT calculations, machine learning, and CALPHAD modeling S Im, SL Shang, ND Smith, AM Krajewski, T Lichtenstein, H Sun, ... Acta Materialia 223, 117448, 2022	28*	2022
Forming mechanism of equilibrium and non-equilibrium metallurgical phases in dissimilar aluminum/steel (Al–Fe) joints SL Shang, H Sun, B Pan, Y Wang, AM Krajewski, M Banu, J Li, ZK Liu Scientific reports 11 (1), 24251, 2021	23	2021
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange ML Evans, J Bergsma, A Merkys, CW Andersen, OB Andersson, D Beltrán, ... Digital Discovery 3 (8), 1509-1533, 2024	12	2024
Comparing forward and inverse design paradigms: A case study on refractory high-entropy alloys A Debnath, L Raman, W Li, AM Krajewski, M Ahn, S Lin, S Shang, ... Journal of Materials Research 38 (17), 4107-4117, 2023	10	2023
Efficient generation of grids and traversal graphs in compositional spaces towards exploration and path planning AM Krajewski, AM Beese, WF Reinhart, ZK Liu npj Unconventional Computing 1 (1), 12, 2024	4*	2024
Efficient structure-informed featurization and property prediction of ordered, dilute, and random atomic structures AM Krajewski, JW Siegel, ZK Liu Computational Materials Science 247, 113495, 2024	3	2024
Design and validation of refractory alloys using machine learning, CALPHAD, and experiments W Li, L Raman, A Debnath, M Ahn, S Lin, AM Krajewski, S Shang, S Priya, ... International Journal of Refractory Metals and Hard Materials 121, 106673, 2024	3	2024
MaterialsMap: A CALPHAD-based tool to design composition pathways through feasibility map for desired dissimilar materials, demonstrated with resistance spot welding joining of … H Sun, B Pan, Z Yang, AM Krajewski, B Bocklund, SL Shang, J Li, ... Materialia 36, 102153, 2024	2	2024
nimCSO: A Nim package for Compositional Space Optimization K Adam M., A Debnath, WF Reinhart, AM Beese, ZK Liu Journal of Open Source Software 9 (103), 6731, 2024	1*	2024
Efficient Materials Informatics between Rockets and Electrons AM Krajewski The Pennsylvania State University, 2024	1	2024
Data-driven inverse design of MoNbTiVWZr refractory multicomponent alloys: Microstructure and mechanical properties L Raman, A Debnath, E Furton, S Lin, A Krajewski, S Ghosh, N Liu, M Ahn, ... Materials Science and Engineering: A 918, 147475, 2024		2024

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