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Using deep learning (DL) to accelerate and/or improve scientific workflows can yield discoveries that are otherwise impossible.
Heat of formation models
Griessen, R. and Riesterer, T · 1988
Earlier work this paper cites.
Zinc 15 – ligand discovery for everyone
Sterling, T. and Irwin, J. J · 2015
Earlier work this paper cites.
Rdkit: Open-source cheminformatics software
Landrum, G · 2016
Earlier work this paper cites.
Grammar variational autoencoder
Kusner, M. J., Paige, B., and Hernández-Lobato, J. M · 2017
Earlier work this paper cites.
Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
Cited alongside, same era.
Zeroth-order stochastic variance reduction for nonconvex optimization
Liu, S., Kailkhura, B., Chen, P.-Y., Ting, P., Chang, S., and Amini, L · 2018
Cited alongside, same era.
Informed machine learning–a taxonomy and survey of integrating knowledge into learning systems
von Rueden, L., Mayer, S., Beckh, K., Georgiev, B., Giesselbach, S., Heese, R., Kirsch, B., Pfrommer, J., Pick, A., Ramamurthy, R., et al · 2019
Cited alongside, same era.
Combining differentiable pde solvers and graph neural networks for fluid flow prediction
Belbute-Peres, F. D. A., Economon, T., and Kolter, Z · 2020
Cited alongside, same era.
A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications
Liu, S., Chen, P.-Y., Kailkhura, B., Zhang, G., Hero III, A. O., and Varshney, P. K · 2020
Later among the works it cites.
Physics-informed machine learning
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., and Yang, L · 2021
Later among the works it cites.
Dqc: A python program package for differentiable quantum chemistry
Kasim, M. F., Lehtola, S., and Vinko, S. M · 2022
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