Data-Driven Studies of Li-Ion-Battery Materials

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works

LixCoO2 (0<x<-1): A new cathode material for batteries of high energy density

Mizushima, K., Jones, P. C., Wiseman, P. J., & Goodenough, J. B. (1980) · Materials Research Bulletin · doi:10.1016/0025-5408(80)90012-4 · Cited by 3,866 · see citing works

Detecting Novel Associations in Large Data Sets

Reshef, D. N., Reshef, Y., Finucane, H. K., Grossman, S. R., McVean, G., Turnbaugh, P. J., Lander, E. S., Mitzenmacher, M., & Sabeti, P. C. (2011) · Science · doi:10.1126/science.1205438 · Cited by 3,345 · see citing works

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312 · see citing works

Machine learning phases of matter

Carrasquilla, J., & Melko, R. G. (2017) · Nature Physics · doi:10.1038/nphys4035 · Cited by 1,448 · see citing works

Big Data of Materials Science: Critical Role of the Descriptor

Ghiringhelli, L. M., Vybiral, J., Levchenko, S. V., Draxl, C., & Scheffler, M. (2015) · Physical Review Letters · doi:10.1103/physrevlett.114.105503 · Cited by 920 · see citing works

Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., & Tkatchenko, A. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b00831 · Cited by 815 · see citing works

Combinatorial screening for new materials in unconstrained composition space with machine learning

Meredig, B., Agrawal, A., Kirklin, S. J., Saal, J. E., Doak, J. W., Thompson, A. E., Zhang, K., Choudhary, A., & Wolverton, C. M. (2014) · Physical Review B · doi:10.1103/physrevb.89.094104 · Cited by 736 · see citing works

Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudsPreprint

Thomas, N., Smidt, T., Kearnes, S. M., Yang, L., Li, L., Kohlhoff, K., & Riley, P. F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.08219 · Cited by 726 · see citing works

Predicting the Band Gaps of Inorganic Solids by Machine Learning

Zhuo, Y., Tehrani, A. M., & Brgoch, J. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00124 · Cited by 458 · see citing works

Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

Sendek, A. D., Yang, Q., Cubuk, E. D., Duerloo, K.-A. N., Cui, Y., & Reed, E. J. (2016) · Energy & Environmental Science · doi:10.1039/c6ee02697d · Cited by 445 · see citing works

High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds

Oliynyk, A. O., Antono, E., Sparks, T. D., Ghadbeigi, L., Gaultois, M. W., Meredig, B., & Mar, A. (2016) · Chemistry of Materials · doi:10.1021/acs.chemmater.6b02724 · Cited by 387 · see citing works

Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles

Hu, X., Li, S. E., & Yang, Y. (2016) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2015.2512237 · Cited by 375 · see citing works

Identifying an efficient, thermally robust inorganic phosphor host via machine learning

Zhuo, Y., Tehrani, A. M., Oliynyk, A. O., Duke, A. C., & Brgoch, J. (2018) · Nature Communications · doi:10.1038/s41467-018-06625-z · Cited by 363 · see citing works

Machine Learning Directed Search for Ultraincompressible, Superhard Materials

Tehrani, A. M., Oliynyk, A. O., Parry, M. E., Rizvi, Z., Couper, S., Lin, F., Miyagi, L., Sparks, T. D., & Brgoch, J. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.8b02717 · Cited by 318 · see citing works

How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics

Cao, B., Adutwum, L. A., Oliynyk, A. O., Luber, E. J., Olsen, B. C., Mar, A., & Buriak, J. M. (2018) · ACS Nano · doi:10.1021/acsnano.8b04726 · Cited by 316 · see citing works

Representation of compounds for machine-learning prediction of physical properties

Seko, A., Hayashi, H., Nakayama, K., Takahashi, A., & Tanaka, I. (2017) · Physical review. B./Physical review. B · doi:10.1103/physrevb.95.144110 · Cited by 303 · see citing works

Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning

Balachandran, P. V., Kowalski, B. A., Sehirlioglu, A., & Lookman, T. (2018) · Nature Communications · doi:10.1038/s41467-018-03821-9 · Cited by 278 · see citing works

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

Kusne, A. G., Gao, T., Mehta, A., Ke, L., Nguyen, M. C., Ho, K., Antropov, V., Wang, C., Kramer, M. J., Long, C. J., et al. (2014) · Scientific Reports · doi:10.1038/srep06367 · Cited by 275 · see citing works

Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning

Liu, D., Zhou, J., Pan, D., Peng, Y., & Peng, X. (2015) · Measurement · doi:10.1016/j.measurement.2014.11.031 · Cited by 266 · cited 1× · see citing works

Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes

Ahmad, Z., Xie, T., Maheshwari, C., Grossman, J., & Viswanathan, V. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00229 · Cited by 248 · see citing works

Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties

Gaultois, M. W., Oliynyk, A. O., Mar, A., Sparks, T. D., Mulholland, G. J., & Meredig, B. (2016) · APL Materials · doi:10.1063/1.4952607 · Cited by 220 · see citing works

Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons

Graser, J., Kauwe, S. K., & Sparks, T. D. (2018) · Chemistry of Materials · doi:10.1021/acs.chemmater.7b05304 · Cited by 201 · see citing works

Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches

Oliynyk, A. O., & Mar, A. (2017) · Accounts of Chemical Research · doi:10.1021/acs.accounts.7b00490 · Cited by 144 · see citing works

Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats

Ghiringhelli, L. M., Carbogno, C., Levchenko, S. V., Mohamed, F., Huhs, G., Lüders, M., Oliveira, M. J. T., & Scheffler, M. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0048-5 · Cited by 128 · see citing works

Data mining our way to the next generation of thermoelectrics

Sparks, T. D., Gaultois, M. W., Oliynyk, A. O., et al. (2015) · Scripta Materialia · doi:10.1016/j.scriptamat.2015.04.026 · Cited by 126 · see citing works

Performance and resource considerations of Li-ion battery electrode materials

Ghadbeigi, L., Harada, J. K., Lettiere, B. R., et al. (2015) · Energy & Environmental Science · doi:10.1039/c5ee00685f · Cited by 121 · see citing works

Application of artificial neural networks in design of lithium-ion batteries

Wu, B., Han, S., Shin, K. G., & Lu, W. D. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.05.040 · Cited by 120 · see citing works

Machine Learning Prediction of Heat Capacity for Solid Inorganics

Kauwe, S. K., Graser, J., Vazquez, A., & Sparks, T. D. (2018) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-018-0108-9 · Cited by 107 · see citing works

Classifying Crystal Structures of Binary Compounds AB through Cluster Resolution Feature Selection and Support Vector Machine Analysis

Oliynyk, A. O., Adutwum, L. A., Harynuk, J. J., & Mar, A. (2016) · Chemistry of Materials · doi:10.1021/acs.chemmater.6b02905 · Cited by 93 · see citing works

Overcoming data scarcity with transfer learningPreprint

Hutchinson, M., Antono, E., Gibbons, B. M., Paradiso, S. P., Ling, J., & Meredig, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.05099 · Cited by 78 · see citing works

Experimental and Simulation Investigations of Porosity Graded Cathodes in Mitigating Battery Degradation of High Voltage Lithium-Ion Batteries

Liu, L., Guan, P., & Liu, C. (2017) · Journal of The Electrochemical Society · doi:10.1149/2.1021713jes · Cited by 74 · see citing works

Machine-learning models for combinatorial catalyst discovery

Landrum, G. A., Penzotti, J. E., & Putta, S. (2005) · Measurement Science and Technology · doi:10.1088/0957-0233/16/1/035 · Cited by 48 · see citing works

How to improve the stability and rate performance of lithium-ion batteries with transition metal oxide anodes

Wang, G., Leng, X., Han, S., et al. (2016) · Journal of materials research/Pratt's guide to venture capital sources · doi:10.1557/jmr.2016.330 · Cited by 46 · see citing works

Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning

Kauwe, S. K., Welker, T., & Sparks, T. D. (2020) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-020-00178-0 · Cited by 40 · see citing works

Spray pyrolysis of conductor- and binder-free porous FeS2 films for high-performance lithium ion batteries

Khateeb, S. A., & Sparks, T. D. (2018) · Journal of Materials Science · doi:10.1007/s10853-018-3055-0 · Cited by 27 · see citing works

Gd 12 Co 5.3 Bi and Gd 12 Co 5 Bi, Crystalline Doppelgänger with Low Thermal Conductivities

Oliynyk, A. O., Sparks, T. D., Gaultois, M. W., et al. (2016) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.6b00826 · Cited by 22 · see citing works

Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf−In System

Oliynyk, A. O., Gaultois, M. W., Hermus, M., et al. (2018) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.8b01122 · Cited by 8 · see citing works

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works

Opportunities and challenges of text mining in materials research

Kononova, O., He, T., Huo, H., Trewartha, A., Olivetti, E. A., & Ceder, G. (2021) · iScience · doi:10.1016/j.isci.2021.102155 · Cited by 182 · see citing works

An Analysis of General Component, Proper Component and Across-Genre Component in Genre

Yuan, H. (2005) · Journal of Yantai University

Research on Intelligent Agent Technology of Component Assembling Based on Active Services

Mei-fen, Z. (2009) · Journal of Wuhan University of Technology-Mater Sci Ed

Creation and application of component relations language

Jian-lin, Z. (2008) · Jisuanji gongcheng yu sheji

Driver Reaction Acceptance and Evaluation to Abnormal Driving Situations

Kim, K., Kim, B. W., Lee, J. W., et al. (2018) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc.2018.8539705 · Cited by 6 · see citing works

Study on self-adaption mechanism for component oriented internetware

Ju-hua, R. (2009) · Jisuanji yingyong yanjiu · Cited by 1 · see citing works