ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

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

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works

Open Babel: An open chemical toolbox

O'Boyle, N. M., Banck, M. S., James, C. A. J., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011) · Journal of Cheminformatics · doi:10.1186/1758-2946-3-33 · Cited by 10,705 · see citing works

Machine learning for molecular and materials science

Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968 · cited 1× · see citing works

Convolutional Networks on Graphs for Learning Molecular Fingerprints

Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1509.09292 · Cited by 2,628 · 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

Inverse molecular design using machine learning: Generative models for matter engineering

Sánchez-Lengeling, B., & Aspuru‐Guzik, A. (2018) · Science · doi:10.1126/science.aat2663 · Cited by 2,087 · see citing works

A general-purpose machine learning framework for predicting properties of inorganic materials

Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.28 · Cited by 1,831 · see citing works

Quantum-chemical insights from deep tensor neural networks

Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017) · Nature Communications · doi:10.1038/ncomms13890 · Cited by 1,445 · see citing works

KNIME: The Konstanz Information Miner

Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., Ohl, P., Sieb, C., Thiel, K., & Wiswedel, B. (2008) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-540-78246-9_38 · Cited by 1,404 · 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

The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid

Hachmann, J., Olivares‐Amaya, R., Atahan-Evrenk, Ş., Amador‐Bedolla, C., Sánchez‐Carrera, R. S., Gold‐Parker, A., Vogt-Maranto, L., Brockway, A. M., & Aspuru‐Guzik, A. (2011) · The Journal of Physical Chemistry Letters · doi:10.1021/jz200866s · Cited by 647 · see citing works

Universal fragment descriptors for predicting properties of inorganic crystals

Isayev, O., Oses, C., Toher, C., Gossett, E., Stefano Curtarolo, A., & Tropsha, A. (2017) · Nature Communications · doi:10.1038/ncomms15679 · Cited by 638 · see citing works

Active learning

Settles, B. (2012) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00429ed1v01y201207aim018 · Cited by 605 · see citing works

Machine learning methods in chemoinformatics

Mitchell, J. B. O. (2014) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1183 · Cited by 470 · see citing works

Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project

Hachmann, J., Olivares‐Amaya, R., Jinich, A., Appleton, A. L., Blood-Forsythe, M. A., Seress, L. R., Román-Salgado, C., Trepte, K., Atahan-Evrenk, Ş., Er, S., et al. (2013) · Energy & Environmental Science · doi:10.1039/c3ee42756k · Cited by 247 · see citing works

Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics

Olivares‐Amaya, R., Amador‐Bedolla, C., Hachmann, J., Atahan-Evrenk, Ş., Sánchez‐Carrera, R. S., Vogt-Maranto, L., & Aspuru‐Guzik, A. (2011) · Energy & Environmental Science · doi:10.1039/c1ee02056k · Cited by 204 · see citing works

The Harvard organic photovoltaic dataset

Lopez, S. A., Pyzer‐Knapp, E. O., Simm, G. N. C., Lutzow, T., Li, K., Seress, L. R., Hachmann, J., & Aspuru‐Guzik, A. (2016) · Scientific Data · doi:10.1038/sdata.2016.86 · Cited by 131 · see citing works

Advances of machine learning in molecular modeling and simulation

Haghighatlari, M., & Hachmann, J. (2019) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2019.02.009 · Cited by 131 · see citing works

Constant size descriptors for accurate machine learning models of molecular properties

Collins, C. R., Gordon, G. J., von Lilienfeld, O. A., & Yaron, D. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5020441 · Cited by 123 · see citing works

Perspective: Computational chemistry software and its advancement as illustrated through three grand challenge cases for molecular science

Krylov, A. I., Windus, T. L., Barnes, T., Marin‐Rimoldi, E., Nash, J. A., Pritchard, B. P., Smith, D. G. A., Altarawy, D., Saxe, P. W., Clementi, C., et al. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5052551 · Cited by 99 · see citing works

Batch Mode Active Learning for Regression With Expected Model Change

Cai, W., Zhang, M., & Zhang, Y. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2542184 · Cited by 96 · see citing works

Advances and challenges in deep generative models for de novo molecule generation

Xue, D., Gong, Y., Yang, Z., Chuai, G., Qu, S., Shen, A., Yu, J., & Liu, Q. (2018) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1395 · Cited by 87 · see citing works

Deep Generative Models for Molecular Science

Jørgensen, P. B., Schmidt, M. N., & Winther, O. (2018) · Molecular Informatics · doi:10.1002/minf.201700133 · Cited by 86 · see citing works

Simulation and design of energy materials accelerated by machine learning

Wang, H., Ji, Y., & Li, Y. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1421 · Cited by 74 · see citing works

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

Afzal, M. A. F., Haghighatlari, M., Ganesh, S. P., et al. (2019) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.9b01147 · Cited by 65 · see citing works

Learning molecular energies using localized graph kernels

Ferré, G., Haut, T. S., & Barros, K. M. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4978623 · Cited by 60 · see citing works

Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers

Afzal, M. A. F., Cheng, C., & Hachmann, J. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5007873 · Cited by 55 · see citing works

Benchmarking DFT approaches for the calculation of polarizability inputs for refractive index predictions in organic polymers

Afzal, M. A. F., & Hachmann, J. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp05492d · Cited by 46 · see citing works

A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

Afzal, M. A. F., Sonpal, A., Haghighatlari, M., Schultz, A. J., & Hachmann, J. (2019) · Chemical Science · doi:10.1039/c9sc02677k · Cited by 46 · see citing works

NSF’s Inaugural Software Institutes: The Science Gateways Community Institute and the Molecular Sciences Software Institute

Wilkins‐Diehr, N., & Crawford, T. D. (2018) · Computing in Science & Engineering · doi:10.1109/mcse.2018.05329813 · Cited by 40 · see citing works

Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space

Hachmann, J., Afzal, M. A. F., Haghighatlari, M., & Pal, Y. (2018) · Molecular Simulation · doi:10.1080/08927022.2018.1471692 · Cited by 38 · see citing works

Organic Photovoltaics

Amador‐Bedolla, C., Olivares‐Amaya, R., Hachmann, J., et al. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-394399-6.00017-5 · Cited by 27 · see citing works

Formatting biological big data for modern machine learning in drug discovery

Duran‐Frigola, M., Fernández‐Torras, A., Bertoni, M., & Aloy, P. (2019) · WIREs Computational Molecular Science · doi:10.1002/wcms.1408 · Cited by 25 · see citing works

ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials DataPreprint

Haghighatlari, M., Vishwakarma, G., Altarawy, D., et al. (2019) · ChemRxiv · doi:10.26434/chemrxiv.8323271 · Cited by 18 · see citing works

From Virtual High-Throughput Screening and Machine Learning to the Discovery and Rational Design of Polymers for Optical Applications

Afzal, M. A. F. (2018) · Cited by 12 · see citing works

Machine Learning Model Selection for Predicting Properties of High Refractive Index Polymers

Vishwakarma, G. (2018) · Cited by 7 · see citing works

A Deep Neural Network Model for Packing Density Predictions and its Application in the Study of 1.5 Million Organic MoleculesPreprint

Afzal, M. A. F., Sonpal, A., Haghighatlari, M., et al. (2019) · ChemRxiv · doi:10.26434/chemrxiv.8217758 · Cited by 4 · see citing works

PREDICTING MELTING POINTS OF DEEP EUTECTIC SOLVENTS

Sonpal, A. (2018) · Cited by 3 · see citing works

Making Machine Learning Work in Chemistry: Methodological Innovation, Software Development, and Application Studies

Haghighatlari, M. (2019) · Cited by 3 · see citing works

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · see citing works

Machine Learning for the Discovery, Design, and Engineering of Materials.

Duan, C., Nandy, A., & Kulik, H. J. (2022) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092320-120230 · Cited by 69 · see citing works

Machine learning in materials design: Algorithm and application*

Song, Z., Chen, X., Meng, F., Cheng, G., Wang, C., Sun, Z., & Yin, W.-J. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abc0e3 · Cited by 63 · see citing works

Identification of the dominant recombination process for perovskite solar cells based on machine learning

Le Corre, V. M., Sherkar, T. S., Koopmans, M., & Koster, L. J. A. (2021) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2021.100346 · Cited by 61 · see citing works

SKilLed communication for toolchains

Felden, T. (2015) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner.2015.7081886

Stream machine learning on vehicle data

Jacob, T., Kubica, S., & Rocco, V. (2018) · doi:10.1109/cando-epe.2018.8601157 · Cited by 2 · see citing works

Finite Element Method Based Toolchain for Simulation of Proximity Estimation Using Electronic Skin

Ostaszewska-Liżewska, A., & Klimaszewski, J. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-37649-8_25

The LSTS toolchain for networked vehicle systems

Pinto, J., Dias, P. S., Martins, R., et al. (2013) · doi:10.1109/oceans-bergen.2013.6608148 · Cited by 142 · see citing works

Software Numerical Instability Detection and Diagnosis by Combining Stochastic and Infinite-Precision Testing

Tang, E., Zhang, X., Müller, N., et al. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2642956 · Cited by 11 · see citing works