Carrete, J., Montes-Campos, H., Wanzenböck, R., Heid, E., & Madsen, G. K. H. (2023). Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning. Journal of Chemical Physics, 158(20), Article 204801. https://doi.org/10.1063/5.0146905 ( reposiTUm)

Heid, E., McGill, C. J., Vermeire, F., & Green, W. H. (2023). Characterizing uncertainty in machine learning for chemistry. Journal of Chemical Information and Modeling, 63(13), 4012–4029. https://doi.org/10.1021/acs.jcim.3c00373 ( reposiTUm)

Heid, E., Probst, D., Green, W. H., & Madsen, G. K. H. (2023). EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions. Chemical Science, 14(48), 14229–14242. https://doi.org/10.1039/d3sc02048g ( reposiTUm)

Heid, E., Greenman, K. P., Chung, Y., Li, S.-C., Graff, D. E., Vermeire, F., Wu, H., Green, W. H., & McGill, C. J. (2023). Chemprop: A Machine Learning Package for Chemical Property Prediction. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.3c01250 ( reposiTUm)

Heid, E. C. (2023, September 6). Deep learning of reaction properties via graph-convolutional neural nets [Presentation]. AI4ChemMat Hands-On Series 2023, United States of America (the). ( reposiTUm)

Heid, E. C., McGill, C., Vermeire, F., Green, W. H., & Madsen, G. K. H. (2023, September 25). Errors and Uncertainty in Machine Learning Models [Poster Presentation]. Joint TACO-NanoCat Conference 2023, Wien, Austria. http://hdl.handle.net/20.500.12708/191856 ( reposiTUm)