Institution · Citrine Informatics (United States)
Most cited papers from Citrine Informatics (United States)
What are the most cited papers from Citrine Informatics (United States)?
The most cited paper from Citrine Informatics (United States) is "Matminer: An open source toolkit for materials data mining" (Logan T. Ward, Alexander Dunn, Alireza Faghaninia et al., 2018), with 1,100 citations. Next come "High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds" (409) and "The 2019 materials by design roadmap" (343).
Showing 50 of 59 papers. Every paper with more than 15 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Matminer: An open source toolkit for materials data mining Logan T. Ward, Alexander Dunn, Alireza Faghaninia et al. · Computational Materials Science | 25 May 2018 | 1,100 |
| 2 | High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds Anton O. Oliynyk, Erin Antono, Taylor D. Sparks et al. · Chemistry of Materials | 18 Sep 2016 | 409 |
| 3 | The 2019 materials by design roadmap Kirstin Alberi et al. · Journal of Physics D Applied Physics | 9 Aug 2018 | 343 |
| 4 | Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery Bryce Meredig, Erin Antono, Carena Church et al. · Molecular Systems Design & Engineering | 2018 | 276 |
| 5 | Understanding thermoelectric properties from high-throughput calculations: trends, insights, and comparisons with experiment Wei Chen, Jan-Hendrik Pöhls, Geoffroy Hautier et al. · Journal of Materials Chemistry C | 2016 | 268 |
| 6 | Materials science with large-scale data and informatics: Unlocking new opportunities Joanne Hill, Gregory J. Mulholland, Kristin Aslaug Persson et al. · MRS Bulletin | May 2016 | 239 |
| 7 | Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties Michael W. Gaultois, Anton O. Oliynyk, Arthur Mar et al. · APL Materials | May 2016 | 221 |
| 8 | High-Dimensional Materials and Process Optimization Using Data-Driven Experimental Design with Well-Calibrated Uncertainty Estimates Julia Ling, Maxwell Hutchinson, Erin Antono et al. · Integrating materials and manufacturing innovation | 5 Jul 2017 | 180 |
| 9 | Machine-learned multi-system surrogate models for materials prediction Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker et al. · npj Computational Materials | 18 Apr 2019 | 179 |
| 10 | Identifying domains of applicability of machine learning models for materials science Christopher A. Sutton, Mario Boley, Luca M. Ghiringhelli et al. · Nature Communications | 4 Sep 2020 | 171 |
| 11 | Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches James E. Saal, Anton O. Oliynyk, Bryce Meredig · Annual Review of Materials Research | 18 May 2020 | 170 |
| 12 | Student self-reported communication skills, knowledge and confidence across standardised patient, virtual and traditional clinical learning environments Michelle Quail, Shelley B. Brundage, Josh S. Spitalnick et al. · BMC Medical Education | 26 Feb 2016 | 169 |
| 13 | Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning Marcel F. Langer, Alex Goeßmann, Matthias Rupp · npj Computational Materials | 16 Mar 2022 | 149 |
| 14 | Data mining our way to the next generation of thermoelectrics Taylor D. Sparks, Michael W. Gaultois, Anton O. Oliynyk et al. · Scripta Materialia | 15 May 2015 | 131 |
| 15 | Controlling the corrosion resistance of multi-principal element alloys John R. Scully, Samuel B. Inman, Angela Yu Gerard et al. · Scripta Materialia | 22 Jul 2020 | 118 |
| 16 | Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access Jordan O’Mara, Bryce Meredig, Kyle Jay Michel · JOM | 17 Jun 2016 | 115 |
| 17 | Aqueous passivation of multi-principal element alloy Ni38Fe20Cr22Mn10Co10: Unexpected high Cr enrichment within the passive film Angela Yu Gerard, Junsoo Han, Stephen John McDonnell et al. · Acta Materialia | 16 Jul 2020 | 107 |
| 18 | Building data-driven models with microstructural images: Generalization and interpretability Julia Ling, Maxwell Hutchinson, Erin Antono et al. · Materials Discovery | Dec 2017 | 98 |
| 19 | Interpretable models for extrapolation in scientific machine learning Eric S. Muckley, James E. Saal, Bryce Meredig et al. · Digital Discovery | 2023 | 94 |
| 20 | Five High-Impact Research Areas in Machine Learning for Materials Science Bryce Meredig · Chemistry of Materials | 10 Dec 2019 | 74 |
| 21 | Robust FCC solute diffusion predictions from ab-initio machine learning methods Henry Wu, Aren Lorenson, Ben Anderson et al. · Computational Materials Science | 7 Apr 2017 | 71 |
| 22 | Turbulent scalar flux in inclined jets in crossflow: counter gradient transport and deep learning modelling Pedro M. Milani, Julia Ling, John K. Eaton · Journal of Fluid Mechanics | 16 Nov 2020 | 67 |
| 23 | Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications Mengfei Yuan, Sean P. Paradiso, Bryce Meredig et al. · Integrating materials and manufacturing innovation | Dec 2018 | 67 |
| 24 | Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization Zachary del Rosario, Matthias Rupp, Yoolhee Kim et al. · The Journal of Chemical Physics | 9 Jul 2020 | 62 |
| 25 | The role of chromium content in aqueous passivation of a non-equiatomic Ni38Fe20CrxMn21-0.5xCo21-0.5x multi-principal element alloy (x = 22, 14, 10, 6 at%) in acidic chloride solution Angela Yu Gerard, Elizabeth J. Kautz, Daniel K. Schreiber et al. · Acta Materialia | 13 Dec 2022 | 56 |
| 26 | Reflections on one million compounds in the open quantum materials database (OQMD) Jiahong Shen, Sean D. Griesemer, Abhijith Gopakumar et al. · Journal of Physics Materials | 23 Jun 2022 | 53 |
| 27 | Quantifying the performance of machine learning models in materials discovery Christopher K. H. Borg, Eric S. Muckley, Clara M. Nyby et al. · Digital Discovery | 2023 | 48 |
| 28 | Machine-learned metrics for predicting the likelihood of success in materials discovery Yoolhee Kim, Edward Kim, Erin Antono et al. · npj Computational Materials | 26 Aug 2020 | 47 |
| 29 | Beyond bulk single crystals: A data format for all materials structure–property–processing relationships Kyle Jay Michel, Bryce Meredig · MRS Bulletin | Aug 2016 | 43 |
| 30 | Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows Pedro M. Milani, Julia Ling, John K. Eaton · Journal of Turbomachinery | 30 Aug 2018 | 42 |
| 31 | Machine-Learning Guided Quantum Chemical and Molecular Dynamics Calculations to Design Novel Hole-Conducting Organic Materials Erin Antono, Nobuyuki Matsuzawa, Julia Ling et al. · The Journal of Physical Chemistry A | 17 Sep 2020 | 42 |
| 32 | Strategies for accelerating the adoption of materials informatics Logan T. Ward, Muratahan Aykol, Ben J. Blaiszik et al. · MRS Bulletin | Sep 2018 | 41 |
| 33 | Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior Ashley D. Spear, Surya R. Kalidindi, Bryce Meredig et al. · JOM | 14 May 2018 | 39 |
| 34 | Industrial materials informatics: Analyzing large-scale data to solve applied problems in R&D, manufacturing, and supply chain Bryce Meredig · Current Opinion in Solid State and Materials Science | 9 Mar 2017 | 39 |
| 35 | Revisiting the revised Ag-Pt phase diagram Gus L. W. Hart, Lance J. Nelson, Richard R. Vanfleet et al. · Acta Materialia | 14 Nov 2016 | 39 |
| 36 | Synthesis and properties of anhydrous rare-earth phosphates, monazite and xenotime: a review Saehwa Chong, Brian Joseph Riley, Xiaonan Lu et al. · RSC Advances | 2024 | 33 |
| 37 | Robust Bayesian Calibration of a k−ε Model for Compressible Jet-in-Crossflow Simulations Jaideep Ray, Lawrence Dechant, Sophia Lefantzi et al. · AIAA Journal | Oct 2018 | 33 |
| 38 | Utilization of machine learning to accelerate colloidal synthesis and discovery Anthony Y. Fong, Lenson A. Pellouchoud, Malcolm Davidson et al. · The Journal of Chemical Physics | 8 Jun 2021 | 32 |
| 39 | Quantifying uncertainty in high-throughput density functional theory: A comparison of AFLOW, Materials Project, and OQMD Vinay I. Hegde, Christopher K. H. Borg, Zachary del Rosario et al. · Physical Review Materials | 30 May 2023 | 31 |
| 40 | A Formation Energy Predictor for Crystalline Materials Using Ensemble Data Mining Ankit Kumar Agrawal, Bryce Meredig, Chris M. Wolverton et al. | Dec 2016 | 31 |
| 41 | Generalization of Machine-Learned Turbulent Heat Flux Models Applied to Film Cooling Flows Pedro M. Milani, Julia Ling, John K. Eaton · Journal of Turbomachinery | 5 Nov 2019 | 30 |
| 42 | Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities Eileen De Guire et al. · Journal of the American Ceramic Society | 12 Jul 2019 | 30 |
| 43 | Machine Learning for Alloy Composition and Process Optimization Julia Ling, Erin Antono, Saurabh Bajaj et al. · Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy | 11 Jun 2018 | 30 |
| 44 | Anomaly detection in scientific data using joint statistical moments Konduri Aditya, Hemanth Kolla, W. Philip Kegelmeyer et al. · Journal of Computational Physics | 13 Mar 2019 | 30 |
| 45 | Materials Data Infrastructure and Materials Informatics Joanne Hill, Arun Mannodi‐Kanakkithodi, Ramamurthy Ramprasad et al. | 10 Nov 2017 | 30 |
| 46 | Perspective: Materials informatics across the product lifecycle: Selection, manufacturing, and certification Gregory J. Mulholland, Sean P. Paradiso · APL Materials | 11 Apr 2016 | 30 |
| 47 | Machine learned synthesizability predictions aided by density functional theory Andrew S. Lee, Suchismita Sarker, James E. Saal et al. · Communications Materials | 12 Oct 2022 | 29 |
| 48 | Potential Dependent Mn Oxidation and Its Role in Passivation of Ni 38 Fe 20 Cr 22 Mn 10 Co 10 Multi-Principal Element Alloy Using Multi-Element Resolved Atomic Emission Spectroelectrochemistry Junsoo Han, Xuejie Li, Angela Yu Gerard et al. · Journal of The Electrochemical Society | May 2021 | 28 |
| 49 | MR Imaging of Syringobulbia John L. Sherman, Charles M. Citrin, Anthony J Barkovich · Journal of Computer Assisted Tomography | May 1987 | 27 |
| 50 | Roadmap on data-centric materials science Stefan Bauer et al. · Modelling and Simulation in Materials Science and Engineering | 17 May 2024 | 24 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for an institution when any of its authors gave it as an affiliation; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.