BetaCitation Atlas

RESEARCH PAPER · npj Computational Materials · 2020

Machine-learned metrics for predicting the likelihood of success in materials discovery

Yoolhee Kim, Edward Kim, Erin Antono, Bryce Meredig, Julia Ling

Read original paper ↗

See where this research leads.

38 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Machine-learned metrics for predicting the likelihood of success in materials discoveryOriginalDesign space visualization for guiding investments in biodegradable and sustainably sourced materials7Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks37Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the $σ-$phase as an example0Predicting the Outcomes of Material Syntheses with Deep Learning13Computational catalyst discovery: Active classification through myopic multiscale sampling10Open Catalyst 2020 (OC20) Dataset and Community Challenges765ATRAS: Adversarially Trained Robust Architecture Search1Sequential learning to accelerate discovery of alkali-activated binders15Discovery of complex oxides via automated experiments and data science37Supervised deep learning prediction of the formation enthalpy of complex phases using a DFT database: The σ− phase as an example19Efficient Surrogates Construction of Chemical Processes: Case studies on Pressure Swing Adsorption and Gas-to-Liquids1DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions0DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions4DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions3DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions28Epistemic Saturation in Materials Informatics: When More Data Stops Adding Meaning0Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas‐to‐liquids9Prediction of nature of band gap of perovskite oxides (ABO3) using a machine learning approach72Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization35Inorganic Crystal Structure Prototype Database Based on Unsupervised Learning of Local Atomic Environments11A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys33Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization1By how much can closed-loop frameworks accelerate computational materials discovery?21Improved performance of lead-free Perovskite solar cell incorporated with TiO${}_{2}$ ETL and CuI HTL using SCAPs46ET-AL: Entropy-targeted active learning for bias mitigation in materials data30Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)34Data driven design of alkali-activated concrete using sequential learning31Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Accelerating the prediction of stable materials with machine learning104Multi-principal element alloy discovery using directed energy deposition and machine learning35Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning70Batch active learning for microstructure–property relations in energetic materials4Deep learning methods for 2D material electronic properties9Small-data discovery of thermally robust Prussian blue analogues via objective-focused prioritisation0Catalyst Design for Sustainable Reactions: A Primer toward Knowledge-Driven Innovation0Ranking-based machine learning framework for CO2 uptake screening in MOF-801 variants under sparse data conditions0Transfer-learning-based refinement of volume size factors from first-principles calculations0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

38 direct citing papers saved.

  1. Design space visualization for guiding investments in biodegradable and sustainably sourced materials

    MRS Communications · 2020-01-14

    James S. Peerless, Emre Sevgen, Stephen D. Edkins, Jason Koeller, Edward Kim, Yoolhee Kim, Astha Gargt, Erin Antono, Julia Ling

    Read original paper ↗ ·

  2. Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks

    The Journal of Physical Chemistry C · 2020-06-12

    Yong Zhao, Kunpeng Yuan, Yinqiao Liu, Steph-Yves Louis, Ming Hu, Jianjun Hu

    Read original paper ↗ ·

  3. Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the $σ-$phase as an example

    arXiv (Cornell University) · 2020-11-21

    Jean‐Claude Crivello, Nataliya Sokolovska, Jean‐Marc Joubert

    Read original paper ↗ ·

  4. Predicting the Outcomes of Material Syntheses with Deep Learning

    Chemistry of Materials · 2021-01-05

    Shreshth A. Malik, Rhys E. A. Goodall, Alpha A. Lee

    Read original paper ↗ ·

  5. Computational catalyst discovery: Active classification through myopic multiscale sampling

    The Journal of Chemical Physics · 2021-03-25

    Kevin Tran, Willie Neiswanger, Kirby Broderick, Eric Xing, Jeff Schneider, Zachary W. Ulissi

    Read original paper ↗ ·

  6. Open Catalyst 2020 (OC20) Dataset and Community Challenges

    ACS Catalysis · 2021-05-04

    Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Rivière, Kevin Tran, Javier Heras‐Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon M. Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, Zachary W. Ulissi

    Read original paper ↗ ·

  7. ATRAS: Adversarially Trained Robust Architecture Search

    arXiv (Cornell University) · 2021-06-13

    Yigit Can Alparslan, Edward Kim

    Read original paper ↗ ·

  8. Sequential learning to accelerate discovery of alkali-activated binders

    Journal of Materials Science · 2021-07-19

    Christoph Völker, Rafia Firdous, Dietmar A. Stephan, Sabine Kruschwitz

    Read original paper ↗ ·

  9. Discovery of complex oxides via automated experiments and data science

    Proceedings of the National Academy of Sciences · 2021-09-10

    Lusann Yang, Joel A. Haber, Zan Armstrong, Samuel Yang, Kevin Kan, Lan Zhou, Matthias Hermann Richter, C. Roat, Nicholas Wagner, Marc Coram, Marc Berndl, Patrick Riley, John M. Gregoire

    Read original paper ↗ ·

  10. Supervised deep learning prediction of the formation enthalpy of complex phases using a DFT database: The σ− phase as an example

    Computational Materials Science · 2021-09-21

    Jean‐Claude Crivello, Jean‐Marc Joubert, Nataliya Sokolovska

    Read original paper ↗ ·

  11. Efficient Surrogates Construction of Chemical Processes: Case studies on Pressure Swing Adsorption and Gas-to-Liquids

    Unknown journal · 2021-10-07

    Zhimian Hao, Chonghuan Zhang, Alexei A. Lapkin

    Read original paper ↗ ·

  12. DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions

    Unknown journal · 2021-10-13

    Sterling G. Baird, Tran Diep, Taylor D. Sparks

    Read original paper ↗ ·

  13. DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions

    Unknown journal · 2021-10-18

    Sterling G. Baird, Tran Diep, Taylor D. Sparks

    Read original paper ↗ ·

  14. DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions

    Unknown journal · 2021-10-27

    Sterling G. Baird, Tran Diep, Taylor D. Sparks

    Read original paper ↗ ·

  15. DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions

    Digital Discovery · 2022-01-01

    Sterling G. Baird, Tran Diep, Taylor D. Sparks

    Read original paper ↗ ·

  16. Epistemic Saturation in Materials Informatics: When More Data Stops Adding Meaning

    Journal of Artificial Intelligence for Materials Science · 2022-01-01

    Kenji Mori, Yuki Sato, Rina Okabe, Takashi Ito

    Read original paper ↗ ·

  17. Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas‐to‐liquids

    AIChE Journal · 2022-01-25

    Zhimian Hao, Chonghuan Zhang, Alexei A. Lapkin

    Read original paper ↗ ·

  18. Prediction of nature of band gap of perovskite oxides (ABO3) using a machine learning approach

    Journal of Materiomics · 2022-04-27

    G. Sudha Priyanga, Manoj N. Mattur, N. Nagappan, Smarak Rath, Tiju Thomas

    Read original paper ↗ ·

  19. Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization

    npj Computational Materials · 2022-06-03

    Giovanni Trezza, Luca Bergamasco, Matteo Fasano, Eliodoro Chiavazzo

    Read original paper ↗ ·

  20. Inorganic Crystal Structure Prototype Database Based on Unsupervised Learning of Local Atomic Environments

    The Journal of Physical Chemistry A · 2022-06-22

    Shulin Luo, Bangyu Xing, Muhammad Faizan, Jiahao Xie, Kun Zhou, Ruoting Zhao, Tianshu Li, Xinjiang Wang, Yuhao Fu, Xin He, Jian Lv, Lijun Zhang

    Read original paper ↗ ·

  21. A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys

    Scientific Reports · 2022-07-08

    Kyungtae Lee, Mukil V. Ayyasamy, Yangfeng Ji, Prasanna V. Balachandran

    Read original paper ↗ ·

  22. Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization

    Research Square · 2022-09-08

    Alexander E. Siemenn, Zekun Ren, Qianxiao Li, Tonio Buonassisi

    Read original paper ↗ ·

  23. By how much can closed-loop frameworks accelerate computational materials discovery?

    Digital Discovery · 2023-01-01

    Lance Kavalsky, Vinay I. Hegde, Eric S. Muckley, Matthew S. Johnson, Bryce Meredig, Venkatasubramanian Viswanathan

    Read original paper ↗ ·

  24. Improved performance of lead-free Perovskite solar cell incorporated with TiO$${}_{2}$$ ETL and CuI HTL using SCAPs

    Applied Physics A · 2023-01-20

    Nur Syamimi Noorasid, Faiz Arith, Ahmad Nizamuddin Muhammad Mustafa, Puvaneswaran Chelvanathan, Mohammad Istiaque Hossain, Mohd Asyadi Azam, Nowshad Amin

    Read original paper ↗ ·

  25. ET-AL: Entropy-targeted active learning for bias mitigation in materials data

    Applied Physics Reviews · 2023-04-10

    James M. Rondinelli, Wei Chen

    Read original paper ↗ ·

  26. Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)

    npj Computational Materials · 2023-05-26

    Alexander E. Siemenn, Zekun Ren, Qianxiao Li, Tonio Buonassisi

    Read original paper ↗ ·

  27. Data driven design of alkali-activated concrete using sequential learning

    Journal of Cleaner Production · 2023-07-22

    Christoph Völker, Benjamí Moreno Torres, Tehseen Rug, Rafia Firdous, Ghezal Ahmad Jan Zia, Stefan Lüders, Horacio Lisdero Scaffino, Michael Höpler, Felix Böhmer, Matthias Pfaff, Dietmar A. Stephan, Sabine Kruschwitz

    Read original paper ↗ ·

  28. Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations

    Results in Materials · 2023-09-01

    Desmond Edem Primus Klenam, Tabiri Kwayie Asumadu, Mobin Vandadi, Nima Rahbar, Fred McBagonluri, Winston Oluwole Soboyejo

    Read original paper ↗ ·

  29. In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

    Journal of the American Chemical Society · 2023-09-27

    Joshua Schrier, Alexander J. Norquist, Tonio Buonassisi, Jakoah Brgoch

    Read original paper ↗ ·

  30. Accelerating the prediction of stable materials with machine learning

    Nature Computational Science · 2023-11-09

    Sean D. Griesemer, Yi Xia, Chris M. Wolverton

    Read original paper ↗ ·

  31. Multi-principal element alloy discovery using directed energy deposition and machine learning

    Materials Science and Engineering A · 2023-11-28

    Phalgun Nelaturu, Jason R. Hattrick-Simpers, Michael J. Moorehead, Vrishank Jambur, Izabela Szlufarska, Adrien Couet, Dan J. Thoma

    Read original paper ↗ ·

  32. Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning

    npj Computational Materials · 2024-01-10

    Hajime Shimakawa, Akiko Kumada, Masahiro Sato

    Read original paper ↗ ·

  33. Batch active learning for microstructure–property relations in energetic materials

    Mechanics of Materials · 2025-03-02

    Ozge Ozbayram, Daniel Olsen, Maruthi Annamaraju, Andreas E. Robertson, Aditya Venkatraman, Surya R. Kalidindi, Min Zhou, Lori L. Graham-Brady

    Read original paper ↗ ·

  34. Deep learning methods for 2D material electronic properties

    Digital Discovery · 2025-12-09

    Artem Mishchenko, Anupam Bhattacharya, Xiangwen Wang, Henry Kelbrick Pentz, Yihao Wei, Qian Yang

    Read original paper ↗ ·

  35. Small-data discovery of thermally robust Prussian blue analogues via objective-focused prioritisation

    Journal of Materials Chemistry A · 2026-01-01

    Yuta Shudo, Ryo Taguchi, Mai Akashi, Sota Shimizu, Tohru Kawamoto, Akira Takahashi

    Read original paper ↗ ·

  36. Catalyst Design for Sustainable Reactions: A Primer toward Knowledge-Driven Innovation

    Chemical Reviews · 2026-07-20

    Pedro S.F. Mendes, Daniel P. Costa, Jeroen Lauwaert, Joris W. Thybaut

    Read original paper ↗ ·

  37. Ranking-based machine learning framework for CO2 uptake screening in MOF-801 variants under sparse data conditions

    Separation and Purification Technology · 2026-08-28

    Peng Zhao, Guanchu Lu, Yu Hao, Takahiko Miyazaki, Yi Huang, Kyaw Thu

    Read original paper ↗ ·

  38. Transfer-learning-based refinement of volume size factors from first-principles calculations

    Materialia · 2026-09-04

    Tokuteru Uesugi, Shunto Matsuura

    Read original paper ↗ ·
Citation Atlas · A map of research, not a ranking.