Find a detailed list of eXtremeMAT presentations and publications below.
Creep Behavior and Phase Equilibria in Model Precipitate Strengthened Alumina-Forming Austenitic Alloys
Yamamoto, Y., Brady, M.P., Ren, QQ. et al. Creep Behavior and Phase Equilibria in Model Precipitate Strengthened Alumina-Forming Austenitic Alloys. JOM 74, 1453–1468 (2022). https://doi.org/10.1007/s11837-022-05203-5
Assessment of Outliers in Alloy Datasets Using Unsupervised Techniques
Wenzlick, M., Mamun, O., Devanathan, R. et al. Assessment of Outliers in Alloy Datasets Using Unsupervised Techniques. JOM 74, 2846–2859 (2022). https://doi.org/10.1007/s11837-022-05204-4
Computational Design of Alloys for Energy Technologies
Devanathan, R., Capolungo, L. Computational Design of Alloys for Energy Technologies. JOM 74, 1376–1378 (2022). https://doi.org/10.1007/s11837-022-05208-0
Concurrent Precipitation of Nb(C,N) and Metastable M23C6 in Alloy 347H at 700°C and 750°C: Computer Simulations and Comparison to Experiment
Glazoff, M.V., Gao, M.C., Capolungo, L. et al. Concurrent Precipitation of Nb(C,N) and Metastable M23C6 in Alloy 347H at 700°C and 750°C: Computer Simulations and Comparison to Experiment. JOM 74, 1444–1452 (2022). https://doi.org/10.1007/s11837-021-05140-9
A Phase-Field Study on Internal to External Oxidation Transition in High-Temperature Structural Alloys
Wang, R., Ji, Y., Cheng, T. et al. A Phase-Field Study on Internal to External Oxidation Transition in High-Temperature Structural Alloys. JOM 74, 1435–1443 (2022). https://doi.org/10.1007/s11837-022-05174-7
Ab Initio Study of Energetics, Charge Transfer, and Atomic Structures of FCC Fe/NbC Interfaces with and Without N Doping: From Coherent to Semi-coherent Interfaces
Yu, J., Glazoff, M.V. & Gao, M.C. Ab Initio Study of Energetics, Charge Transfer, and Atomic Structures of FCC Fe/NbC Interfaces with and Without N Doping: From Coherent to Semi-coherent Interfaces. JOM 74, 1379–1386 (2022). https://doi.org/10.1007/s11837-022-05161-y
Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels
Mamun, O., Taufique, M.F.N., Wenzlick, M. et al. Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels. Sci Rep 12, 2083 (2022). https://doi.org/10.1038/s41598-022-06051-8
Machine-Learning Microstructure for Inverse Material Design
, , , , , , , , , Machine-Learning Microstructure for Inverse Material Design. Adv. Sci. 2021, 8, 2101207. https://doi.org/10.1002/advs.202101207
Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels
Mamun, O., Wenzlick, M., Sathanur, A. et al. Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels. npj Mater Degrad 5, 20 (2021). https://doi.org/10.1038/s41529-021-00166-5
A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys
Mamun, O., Wenzlick, M., Hawk, J. et al. A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys. Sci Rep 11, 5466 (2021). https://doi.org/10.1038/s41598-021-83694-z
Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction
Wenzlick, M., Mamun, O., Devanathan, R. et al. Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction. J. of Materi Eng and Perform 30, 823–838 (2021). https://doi.org/10.1007/s11665-020-05340-5
Coupling physics in machine learning to predict properties of high-temperatures alloys
Peng, J., Yamamoto, Y., Hawk, J.A. et al. Coupling physics in machine learning to predict properties of high-temperatures alloys. npj Comput Mater 6, 141 (2020). https://doi.org/10.1038/s41524-020-00407-2
Physics-Guided, Empirically-Constrained Machine Learning for Designing Fe-9Cr Alloys
V. Romanov (2020) “Physics-Guided, Empirically-Constrained Machine Learning for Designing Fe-9Cr Alloys” – Poster presentation at APS April Meeting 2020. Washington, DC, April 2020
Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers
J.L.W. Carter, A.K. Verma, N.M. Senanayake (2020) “Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers” MRS Advances. 5(7), 319-327. https://doi.org/doi:10.1557/adv.2020.132
Domain-Guided ML Tool for Designing New Fe-9Cr Steels
V. Romanov (2020) “Domain-Guided ML Tool for Designing New Fe-9Cr Steels” – Oral presentation at TMS Annual Meeting & Exhibition. San Diego, CA, February 2020
Materials Discovery and Design Using Heritage Data
A.K. Verma, J.A. Hawk, V. Romanov, J.L.W. Carter (2019) “Materials Discovery and Design Using Heritage Data” – Oral presentation at TMS Annual Meeting & Exhibition. San Diego, CA, February 2020
Data Assessment Method to Support the Development of Creep-Resistant Alloys
Wenzlick, M., Bauer, J.R., Rose, K. et al.
Data Assessment Method to Support the Development of Creep-Resistant Alloys. Integr Mater Manuf Innov (2020).
https://doi.org/10.1007/s40192-020-00167-3.
Data Management with DOE FE’s Energy Data eXchange (EDX)
Madison Wenzlick, Chad Rowan, Kelly Rose, Ram Devanathan, Jen Bauer, Jeffrey Hawk. “Data Management with DOE FE’s Energy Data eXchange (EDX).” eXtremeMAT Stakeholder Advisory Board Meeting, Charlotte, NC, December 2019
Overview XMAT- National Laboratory Collaboration: Accelerating the Development of Extreme Environment Materials
Jeffrey Hawk, David Alman, “Overview XMAT- National Laboratory Collaboration: Accelerating the Development of Extreme Environment Materials.” eXtremeMAT Stakeholder Advisory Board Meeting, Charlotte, NC, December 2019
Discussion Group: Oxidation-Modeling Strategies
Rishi Pillai, “Discussion Group: Oxidation-Modeling Strategies.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Coupling Physics in Materials Data Analytics
Dongwon Shin, “Coupling Physics in Materials Data Analytics.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Database Assessment and Connection with CALPHAD
Michael Gao, “Database Assessment and Connection with CALPHAD.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Preliminary Engineering Scale Simulations with a Reduced Order Model Material in MOOSE
Stephanie Pitts, “Preliminary Engineering Scale Simulations with a Reduced Order Model Material in MOOSE.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Mechanical Evaluation and Microstructural Characterization of Tubular Test Specimens Under Internal Pressurization
Edgar Lara-Curzio, “Mechanical Evaluation and Microstructural Characterization of Tubular Test Specimens Under Internal Pressurization.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Subtask 2.3/4.2 Progress on Alloy Selection and Experimental Validation
Yukinori Yamamoto, “Subtask 2.3/4.2 Progress on Alloy Selection and Experimental Validation.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Creep Behavior and Phase Equilibria in Model Precipitate Strengthened Alumina-Forming Austenitic Alloys
Yamamoto, Y., Brady, M.P., Ren, QQ. et al. Creep Behavior and Phase Equilibria in Model Precipitate Strengthened Alumina-Forming Austenitic Alloys. JOM 74, 1453–1468 (2022). https://doi.org/10.1007/s11837-022-05203-5
Assessment of Outliers in Alloy Datasets Using Unsupervised Techniques
Wenzlick, M., Mamun, O., Devanathan, R. et al. Assessment of Outliers in Alloy Datasets Using Unsupervised Techniques. JOM 74, 2846–2859 (2022). https://doi.org/10.1007/s11837-022-05204-4
Computational Design of Alloys for Energy Technologies
Devanathan, R., Capolungo, L. Computational Design of Alloys for Energy Technologies. JOM 74, 1376–1378 (2022). https://doi.org/10.1007/s11837-022-05208-0
Concurrent Precipitation of Nb(C,N) and Metastable M23C6 in Alloy 347H at 700°C and 750°C: Computer Simulations and Comparison to Experiment
Glazoff, M.V., Gao, M.C., Capolungo, L. et al. Concurrent Precipitation of Nb(C,N) and Metastable M23C6 in Alloy 347H at 700°C and 750°C: Computer Simulations and Comparison to Experiment. JOM 74, 1444–1452 (2022). https://doi.org/10.1007/s11837-021-05140-9
A Phase-Field Study on Internal to External Oxidation Transition in High-Temperature Structural Alloys
Wang, R., Ji, Y., Cheng, T. et al. A Phase-Field Study on Internal to External Oxidation Transition in High-Temperature Structural Alloys. JOM 74, 1435–1443 (2022). https://doi.org/10.1007/s11837-022-05174-7
Ab Initio Study of Energetics, Charge Transfer, and Atomic Structures of FCC Fe/NbC Interfaces with and Without N Doping: From Coherent to Semi-coherent Interfaces
Yu, J., Glazoff, M.V. & Gao, M.C. Ab Initio Study of Energetics, Charge Transfer, and Atomic Structures of FCC Fe/NbC Interfaces with and Without N Doping: From Coherent to Semi-coherent Interfaces. JOM 74, 1379–1386 (2022). https://doi.org/10.1007/s11837-022-05161-y
Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels
Mamun, O., Taufique, M.F.N., Wenzlick, M. et al. Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels. Sci Rep 12, 2083 (2022). https://doi.org/10.1038/s41598-022-06051-8
Machine-Learning Microstructure for Inverse Material Design
, , , , , , , , , Machine-Learning Microstructure for Inverse Material Design. Adv. Sci. 2021, 8, 2101207. https://doi.org/10.1002/advs.202101207
Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels
Mamun, O., Wenzlick, M., Sathanur, A. et al. Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels. npj Mater Degrad 5, 20 (2021). https://doi.org/10.1038/s41529-021-00166-5
A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys
Mamun, O., Wenzlick, M., Hawk, J. et al. A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys. Sci Rep 11, 5466 (2021). https://doi.org/10.1038/s41598-021-83694-z
Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction
Wenzlick, M., Mamun, O., Devanathan, R. et al. Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction. J. of Materi Eng and Perform 30, 823–838 (2021). https://doi.org/10.1007/s11665-020-05340-5
Coupling physics in machine learning to predict properties of high-temperatures alloys
Peng, J., Yamamoto, Y., Hawk, J.A. et al. Coupling physics in machine learning to predict properties of high-temperatures alloys. npj Comput Mater 6, 141 (2020). https://doi.org/10.1038/s41524-020-00407-2
Physics-Guided, Empirically-Constrained Machine Learning for Designing Fe-9Cr Alloys
V. Romanov (2020) “Physics-Guided, Empirically-Constrained Machine Learning for Designing Fe-9Cr Alloys” – Poster presentation at APS April Meeting 2020. Washington, DC, April 2020
Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers
J.L.W. Carter, A.K. Verma, N.M. Senanayake (2020) “Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers” MRS Advances. 5(7), 319-327. https://doi.org/doi:10.1557/adv.2020.132
Domain-Guided ML Tool for Designing New Fe-9Cr Steels
V. Romanov (2020) “Domain-Guided ML Tool for Designing New Fe-9Cr Steels” – Oral presentation at TMS Annual Meeting & Exhibition. San Diego, CA, February 2020
Materials Discovery and Design Using Heritage Data
A.K. Verma, J.A. Hawk, V. Romanov, J.L.W. Carter (2019) “Materials Discovery and Design Using Heritage Data” – Oral presentation at TMS Annual Meeting & Exhibition. San Diego, CA, February 2020
Data Assessment Method to Support the Development of Creep-Resistant Alloys
Wenzlick, M., Bauer, J.R., Rose, K. et al.
Data Assessment Method to Support the Development of Creep-Resistant Alloys. Integr Mater Manuf Innov (2020).
https://doi.org/10.1007/s40192-020-00167-3.
Data Management with DOE FE’s Energy Data eXchange (EDX)
Madison Wenzlick, Chad Rowan, Kelly Rose, Ram Devanathan, Jen Bauer, Jeffrey Hawk. “Data Management with DOE FE’s Energy Data eXchange (EDX).” eXtremeMAT Stakeholder Advisory Board Meeting, Charlotte, NC, December 2019
Overview XMAT- National Laboratory Collaboration: Accelerating the Development of Extreme Environment Materials
Jeffrey Hawk, David Alman, “Overview XMAT- National Laboratory Collaboration: Accelerating the Development of Extreme Environment Materials.” eXtremeMAT Stakeholder Advisory Board Meeting, Charlotte, NC, December 2019
Discussion Group: Oxidation-Modeling Strategies
Rishi Pillai, “Discussion Group: Oxidation-Modeling Strategies.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Coupling Physics in Materials Data Analytics
Dongwon Shin, “Coupling Physics in Materials Data Analytics.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Database Assessment and Connection with CALPHAD
Michael Gao, “Database Assessment and Connection with CALPHAD.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Preliminary Engineering Scale Simulations with a Reduced Order Model Material in MOOSE
Stephanie Pitts, “Preliminary Engineering Scale Simulations with a Reduced Order Model Material in MOOSE.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Mechanical Evaluation and Microstructural Characterization of Tubular Test Specimens Under Internal Pressurization
Edgar Lara-Curzio, “Mechanical Evaluation and Microstructural Characterization of Tubular Test Specimens Under Internal Pressurization.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Subtask 2.3/4.2 Progress on Alloy Selection and Experimental Validation
Yukinori Yamamoto, “Subtask 2.3/4.2 Progress on Alloy Selection and Experimental Validation.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Development of Interatomic Potentials and MD Simulations to Model the Deformation Behaviors in Ni-Base Superalloys
Ridwan Sikidia, “Development of Interatomic Potentials and MD Simulations to Model the Deformation Behaviors in Ni-Base Superalloys.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Molecular Dynamics Simulation of Dislocation Migration in Austenitic Steels
Mikhail Mendelev, “Molecular Dynamics Simulation of Dislocation Migration in Austenitic Steels.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Tight Binding Models for Atomistic Simulations of Oxygen and Carbon in Fe/Ni/Cr Steels
Marc Cawkwell, Romain Perriot, “Tight Binding Models for Atomistic Simulations of Oxygen and Carbon in Fe/Ni/Cr Steels.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
First-Principles Modeling of Oxidation Phenomena
Brandon Wood, “First-Principles Modeling of Oxidation Phenomena.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Discussion Group: Modeling and Simulations Data Needs
Jeffrey Hawk, “Discussion Group: Modeling and Simulations Data Needs.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Modeling Cavity Nucleation
Aaron Kohnert, “Modeling Cavity Nucleation.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Phase-Field Modeling of Internal to External Oxidation Transition
Youhai Wen, “Phase-Field Modeling of Internal to External Oxidation Transition.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
A Guideline for the Assessment of Uniaxial Creep and Creep-Fatigue Data and Models
Calvin Stewart, “A Guideline for the Assessment of Uniaxial Creep and Creep-Fatigue Data and Models.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
Task 2.4: Constitutive Modeling and Homogenization
Ricardo Lebensohn, “Task 2.4: Constitutive Modeling and Homogenization.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
eXtremeMAT: Overall Address
Jeffrey Hawk, “eXtremeMAT: Overall Address.” eXtremeMAT Workshop, Los Alamos National Laboratory, Los Alamos, NM, December 2019.
eXtremeMAT: Modeling and Simulation
Laurent Capolungo, “eXtremeMAT: Modeling and Simulation.”; December 2019 Stakeholder Advisory Board Meeting, Charlotte, NC.
eXtremeMAT Task 3: Data Science and Analytics
Ram Devanathan, Gary Black, Arun Sathanur, Dongwon Shin, Kelly Rose, Madison Wenzlick, and Jeffrey Hawk “eXtremeMAT Task 3: Data Science and Analytics.”; December 2019 Stakeholder Advisory Board Meeting, Charlotte, NC.
Overview XMAT- National Laboratory Collaboration: Accelerating the Development of Extreme Environment Materials
Jeffrey Hawk, David Alman, “Overview XMAT- National Laboratory Collaboration: Accelerating the Development of Extreme Environment Materials.”; December 2019 Stakeholder Advisory Board Meeting, Charlotte, NC.
Data Analytics for Designing Fe-9Cr Steels (Tensile Strength)
V. Romanov and J.A. Hawk (2019) “Data Analytics for Designing Fe-9Cr Steels (Tensile Strength)” – Oral presentation at AIChE Annual Meeting. Orlando, FL, November 2019
Overview XMAT- National Laboratory Collaboration: Accelerating the Development of Extreme Environment Materials
Jeffrey Hawk, David Alman, “Overview XMAT- National Laboratory Collaboration: Accelerating the Development of Extreme Environment Materials.”; November 2019 Peer Review Meeting.
eXtremeMAT Task 3: Data Science and Analytics
Ram Devanathan, Gary Black, Arun Sathanur, Dongwon Shin, Kelly Rose, Madison Wenzlick, and Jeffrey Hawk “eXtremeMAT Task 3: Data Science and Analytics.”; November 2019 Peer Review Meeting.
Predictions of Long-term Creep Life for the Family of 9-12 wt% Cr Martensitic Steels
K. Verma, J.A. Hawk, V.N. Romanov and J.L.W. Carter,
“Predictions of Long-term Creep Life for the Family of 9-12 wt% Cr Martensitic Steels,” J. Alloys Comp., (2019)
(Online first: October 1, 2019: https://doi.org/10.1016/j.allcom.2019.152417.)
Mapping Non-Linear Influence of Alloying Elements on Tensile Strength of Martensitic Steels
V. Romanov and J.A. Hawk “Mapping Non-Linear Influence of Alloying Elements on Tensile Strength of Martensitic Steels” – Poster presentation and paper in Proc. IEEE International Conference on Big Data (IEEE Big Data 2018). Seattle, WA, December 2018
Mapping Non-Linear Influence of Alloying Elements on Tensile Strength of Martensitic Steels
V. Romanov and J.A. Hawk “Mapping Non-Linear Influence of Alloying Elements on Tensile Strength of Martensitic Steels” – Poster abstract accepted for Defense TechConnect Fall Summit & Expo. Tampa, FL, October 23-25, 2018
XMAT Task 3 Overview – Data Science
Ram Devanathan, Jennifer R. Bauer, Gary D. Black, Michael Gao, Michael Glazoff, Jeffrey A. Hawk, Carina Lansing, Tom Lograsso, Lianshan Lin, Turab Lookman, Pratik Ray, Vyacheslav Romanov, Kelly Rose, Arun Sathanur, Dongwon Shin, and Yuki Yamamoto,
October 2018 Stakeholder Advisory Board Meeting – Columbus, OH.
eXtremeMAT: Task 5.0- Manufacturing
Jeffrey Hawk,
October 2018 Stakeholder Advisory Board Meeting – Columbus, OH
Data-Driven Mechanism Modeling of Creep Behavior of 9Cr-Steels
A.K. Verma, V.N. Romanov, J.A. Hawk, R.H. French, J.L.W. Carter “Data-Driven Mechanism Modeling of Creep Behavior of 9Cr-Steels” – 18th International Conference on the Strength of Materials (ICSMA 18), Columbus, OH, July 2018
Mapping Multivariate Influence of Alloying Elements on Creep Behavior for New Martensitic Steels
A.K. Verma, J.H. Hawk, L.S. Bruckman, V.N. Romanov, R.H. French, J.L.W. Carter “Mapping Multivariate Influence of Alloying Elements on Creep Behavior for New Martensitic Steels” – Machine Learning in Science and Engineering. Carnegie Mellon University and Georgia Institute of Technology, Pittsburgh, PA, June 2018
Mapping Multivariate Influence of Alloying Elements on Creep Behavior for Design of New Martensitic Steels
A.K. Verma, J.A. Hawk, L.S. Bruckman, V.N. Romanov, R.H. French, J.L.W. Carter “Mapping Multivariate Influence of Alloying Elements on Creep Behavior for New Martensitic Steels” – Poster presentation at Research ShowCASE 2018. Case Western Reserve University, Cleveland, OH, April 2018
Materials Data Analytics for Advanced Alloy Development: 9-12% Cr Steel
V. Romanov, N. Krishnamurthy, J.A. Hawk “Materials Data Analytics for Advanced Alloy Development: 9-12% Cr Steel” – Poster presentation at APS April Meeting 2018. Columbus, OH, April 2018
Computational Modeling and Simulation (Task 2)
Joel D. Kress, “Computational Modeling and Simulation (Task 2).” 2018 Review Meeting For Crosscutting Research, Pittsburgh, PA April 10, 2018. https://netl.doe.gov/sites/default/files/netl-file/20180410_1100A_Presentation_FE-850-17-FY17_LANL.pdf
Extreme Environment Materials – Data Analytics
Ram Devanathan, “Extreme Environment Materials – Data Analytics.” 2018 Review Meeting For Crosscutting Research, Pittsburgh, PA April 10, 2018. https://netl.doe.gov/sites/default/files/netl-file/20180410_1130A_Presentation_FWP-71133_PNNL.pdf
Data Analytics for Alloy Qualification
N. Krishnamurthy, S. Maddali, A.K. Verma, L.S. Bruckman, J.L.W. Carter, R.H. French, V.N. Romanov and J.A. Hawk,
“Data Analytics for Alloy Qualification,” Office of Fossil Energy, NETL-PUB-21550, 20 March 2018, 46 pages
(https://www.osti.gov/biblio/1456238-data-analytics-alloy-qualification)
Materials Data Analytics for 9-12% Cr Steel
V. Romanov, N. Krishnamurthy, A.K. Verma, L.S. Bruckman, R.H. French, J.L.W. Carter, J.A. Hawk “Materials Data Analytics for 9-12% Cr Steel” – Poster presentation at Conference on Data Analysis (CoDA 2018). LANL, Santa Fe, NM, March 2018
Application of Data Science to the Study and Design of 9-12% Cr Steel
A.K. Verma, J.A. Hawk, L. Bruckman, V. Romanov, R.H. French, J.L.W. Carter “Application of Data Science to the Study and Design of 9-12% Cr Steel” – Oral presentation at the Materials Science & Technology 2017 (MS&T17), Salt Lake City, October 2017, UT
Application of Data Science to the Study and Design of 9-12% Cr Steel
A.K. Verma, J.A. Hawk, L. Bruckman, V. Romanov, R.H. French, J.L.W. Carter “Application of Data Science to the Study and Design of 9-12% Cr Steel” – Poster presentation at 3rd Annual Data and Life Sciences Collaboration and Symposium. Case Western Reserve University, Cleveland, OH, August 2017
Application of Data Science to the Study and Design of 9-12% Cr Steel
A.K. Verma, J.A. Hawk, L. Bruckman, V. Romanov, R.H. French, J.L.W. Carter “Application of Data Science to the Study and Design of 9-12% Cr Steel” – Poster presentation at Gordon Research Conference – Physical Metallurgy. Biddeford, ME, July 2017
Alloy Modeling: Predictive Analysis of Composition, Processing and Environment on Properties of 9Cr Steel
N. Krishnamurthy, S. Maddali, V. Romanov, J. Hawk “Alloy Modeling: Predictive Analysis of Composition, Processing and Environment on Properties of 9Cr Steel” Presentation. March 2017
Segmentation of 9Cr Steel Samples Based on Composition and Mechanical Property
N. Krishnamurthy, S. Maddali, J. Hawk, V. Romanov “Segmentation of 9Cr Steel Samples Based on Composition and Mechanical Property” Poster. APS March Meeting 2017, New Orleans, LA, March 2017
Predictive Analysis of the Influence of the Chemical Composition and Pre-Processing Regimen on Structural Properties of Steel Alloys Using Machine Learning Techniques
S. Maddali, N. Krishnamurthy, J. Hawk, V. Romanov “Predictive Analysis of the Influence of the Chemical Composition and Pre-Processing Regimen on Structural Properties of Steel Alloys Using Machine Learning Techniques” – Oral presentation at APS March Meeting 2017, New Orleans, LA, March 2017
Use of Data Analytics in Advanced Alloy Development: Trends and Modeling
V. Romanov “Use of Data Analytics in Advanced Alloy Development: Trends and Modeling” – Oral presentation at the 2017 Project Review Meeting for Crosscutting Research and Analysis Portfolio, Pittsburgh, PA, March 2017.
Statistical Analysis of Heritage Data of 9Cr-Steels, Using a Robust, Open-Source, Data Analytics Design Approach
A.K. Verma, M. Elsaeiti, L.S. Bruckman, J.A. Hawk, V. Romanov, R.H. French, J.W. Carter “Statistical Analysis of Heritage Data of 9Cr-Steels, Using a Robust, Open-Source, Data Analytics Design Approach” Oral presentation at Materials Science & Technology, October 2016, Salt Lake City, UT
Development of Predictive Analytics and Visualization Methods for 9Cr Steel Alloys
D. Etim and V. Romanov “Development of Predictive Analytics and Visualization Methods for 9Cr Steel Alloys” – Oral presentation at the 3rd International Conference on Modern Engineering & Technological Advances, September 2016, Toronto, Canada.
Innovative Process Technologies (IPT): Data Science Initiative
V. Romanov “Innovative Process Technologies (IPT): Data Science Initiative” Poster 9, Annual Crosscutting Research & Rare Earth Elements Portfolios Review, April 2016
Data Science for Fossil Energy
V. Romanov “Data Science for Fossil Energy” Poster 62, Conf. on Data Analysis, Santa Fe, NM, March 2016