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Gaurav Vishwakarma, Aditya Sonpal & Johannes Hachmann. (2021) Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and Best Practices for Machine Learning in Chemistry. Trends in Chemistry 3:2, pages 146-156.
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Mojtaba Haghighatlari, Gaurav Vishwakarma, Doaa Altarawy, Ramachandran Subramanian, Bhargava U. Kota, Aditya Sonpal, Srirangaraj Setlur & Johannes Hachmann. (2020)
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Clyde A. DalyJrJr & Rigoberto Hernandez. (2020) Learning from the Machine: Uncovering Sustainable Nanoparticle Design Rules. The Journal of Physical Chemistry C 124:24, pages 13409-13420.
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Mohammad Atif Faiz Afzal, Aditya Sonpal, Mojtaba Haghighatlari, Andrew J. Schultz & Johannes Hachmann. (2019) A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules. Chemical Science 10:36, pages 8374-8383.
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Ian M. Pendleton, Gary Cattabriga, Zhi Li, Mansoor Ani Najeeb, Sorelle A. Friedler, Alexander J. Norquist, Emory M. Chan & Joshua Schrier. (2019) Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management. MRS Communications 9:3, pages 846-859.
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Mohammad Atif Faiz Afzal, Mojtaba Haghighatlari, Sai Prasad Ganesh, Chong Cheng & Johannes Hachmann. (2019)
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Rachael Mansbach, Timothy Travers, Benjamin McMahon, Jeanne Fair & S. Gnanakaran. (2019) Snails In Silico: A Review of Computational Studies on the Conopeptides. Marine Drugs 17:3, pages 145.
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Mojtaba Haghighatlari & Johannes Hachmann. (2019) Advances of machine learning in molecular modeling and simulation. Current Opinion in Chemical Engineering 23, pages 51-57.
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Mohammad Atif Faiz Afzal & Johannes Hachmann. (2019) Benchmarking DFT approaches for the calculation of polarizability inputs for refractive index predictions in organic polymers. Physical Chemistry Chemical Physics 21:8, pages 4452-4460.
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Zak E. Hughes, Joseph C. R. ThackerAlex L. WilsonPaul L. A. Popelier. (2018) Description of Potential Energy Surfaces of Molecules Using FFLUX Machine Learning Models. Journal of Chemical Theory and Computation 15:1, pages 116-126.
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