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Esther Heid - Publications

Affiliations: 
2021 Department of Chemical Engineering Massachusetts Institute of Technology, Cambridge, MA, United States 
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13 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2024 Wanzenböck R, Heid E, Riva M, Franceschi G, Imre AM, Carrete J, Diebold U, Madsen GKH. Exploring inhomogeneous surfaces: Ti-rich SrTiO(110) reconstructions active learning. Digital Discovery. PMID 39364117 DOI: 10.1039/d4dd00231h  0.32
2024 Heid E, Schörghuber J, Wanzenböck R, Madsen GKH. Spatially Resolved Uncertainties for Machine Learning Potentials. Journal of Chemical Information and Modeling. 64: 6377-6387. PMID 39110874 DOI: 10.1021/acs.jcim.4c00904  0.477
2023 Heid E, Greenman KP, Chung Y, Li SC, Graff DE, Vermeire FH, Wu H, Green WH, McGill CJ. Chemprop: A Machine Learning Package for Chemical Property Prediction. Journal of Chemical Information and Modeling. 64: 9-17. PMID 38147829 DOI: 10.1021/acs.jcim.3c01250  0.565
2023 Heid E, Probst D, Green WH, Madsen GKH. EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions. Chemical Science. 14: 14229-14242. PMID 38098707 DOI: 10.1039/d3sc02048g  0.727
2023 Heid E, McGill CJ, Vermeire FH, Green WH. Characterizing Uncertainty in Machine Learning for Chemistry. Journal of Chemical Information and Modeling. PMID 37338239 DOI: 10.1021/acs.jcim.3c00373  0.651
2023 Carrete J, Montes-Campos H, Wanzenböck R, Heid E, Madsen GKH. Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning. The Journal of Chemical Physics. 158. PMID 37212411 DOI: 10.1063/5.0146905  0.372
2022 Zahrt AF, Mo Y, Nandiwale KY, Shprints R, Heid E, Jensen KF. Machine-Learning-Guided Discovery of Electrochemical Reactions. Journal of the American Chemical Society. PMID 36459170 DOI: 10.1021/jacs.2c08997  0.599
2022 Sankaranarayanan K, Heid E, Coley CW, Verma D, Green WH, Jensen KF. Similarity based enzymatic retrosynthesis. Chemical Science. 13: 6039-6053. PMID 35685792 DOI: 10.1039/d2sc01588a  0.689
2022 Bolcato G, Heid E, Boström J. On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods. Journal of Chemical Information and Modeling. 62: 1388-1398. PMID 35271260 DOI: 10.1021/acs.jcim.1c01535  0.313
2021 Heid E, Liu J, Aude A, Green WH. Influence of Template Size, Canonicalization, and Exclusivity for Retrosynthesis and Reaction Prediction Applications. Journal of Chemical Information and Modeling. PMID 34939786 DOI: 10.1021/acs.jcim.1c01192  0.686
2021 Heid E, Green WH. Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction. Journal of Chemical Information and Modeling. PMID 34734699 DOI: 10.1021/acs.jcim.1c00975  0.683
2021 Heid E, Goldman S, Sankaranarayanan K, Coley CW, Flamm C, Green WH. EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates. Journal of Chemical Information and Modeling. PMID 34587449 DOI: 10.1021/acs.jcim.1c00921  0.702
2020 Guan Y, Coley CW, Wu H, Ranasinghe D, Heid E, Struble TJ, Pattanaik L, Green WH, Jensen KF. Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors. Chemical Science. 12: 2198-2208. PMID 34163985 DOI: 10.1039/d0sc04823b  0.667
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