Modeling lab

CH-315

2.14. Applications of Supervised Machine Learning

This page is part of the content downloaded from 2.14. Applications of Supervised Machine Learning on Wednesday, 25 December 2024, 15:27. Note that some content and any files larger than 50 MB are not downloaded.

Description

In this lecture we take a look at some recent applications of ML in chemistry and materials science. 

Page content

Notebook

please open it in Google Colab

https://github.com/jwchen25/Modeling_Lab_Class/blob/master/Simple_Tutorial_ML4Chem.ipynb


Lecture

https://youtu.be/BEZFSGnvng4

Slides

content/examples.pdf

Additional Resources/References

Word embeddings

(1) Huo, H.; Rong, Z.; Kononova, O.; Sun, W.; Botari, T.; He, T.; Tshitoyan, V.; Ceder, G. Semi-Supervised Machine-Learning Classification of Materials Synthesis Procedures. npj Comput Mater 2019, 5 (1), 1–7. https://doi.org/10.1038/s41524-019-0204-1.
(2) Tshitoyan, V.; Dagdelen, J.; Weston, L.; Dunn, A.; Rong, Z.; Kononova, O.; Persson, K. A.; Ceder, G.; Jain, A. Unsupervised Word Embeddings Capture Latent Knowledge from Materials Science Literature. Nature 2019, 571 (7763), 95. https://doi.org/10.1038/s41586-019-1335-8.

GPR

(3) Jinnouchi, R.; Karsai, F.; Kresse, G. On-the-Fly Machine Learning Force Field Generation: Application to Melting Points. Phys. Rev. B 2019, 100 (1), 014105. https://doi.org/10.1103/PhysRevB.100.014105.
(4) Jinnouchi, R.; Lahnsteiner, J.; Karsai, F.; Kresse, G.; Bokdam, M. Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference. Phys. Rev. Lett. 2019, 122 (22), 225701. https://doi.org/10.1103/PhysRevLett.122.225701.

Boltzmann Generators

(5) Noé, F.; Olsson, S.; Köhler, J.; Wu, H. Boltzmann Generators: Sampling Equilibrium States of Many-Body Systems with Deep Learning. Science 2019, 365 (6457), eaaw1147. https://doi.org/10.1126/science.aaw1147. 


Reinforcement Learning

There has been a recent discussion about this paper. 

(6) Zhavoronkov, A.; Ivanenkov, Y. A.; Aliper, A.; Veselov, M. S.; Aladinskiy, V. A.; Aladinskaya, A. V.; Terentiev, V. A.; Polykovskiy, D. A.; Kuznetsov, M. D.; Asadulaev, A.; Volkov, Y.; Zholus, A.; Shayakhmetov, R. R.; Zhebrak, A.; Minaeva, L. I.; Zagribelnyy, B. A.; Lee, L. H.; Soll, R.; Madge, D.; Xing, L.; Guo, T.; Aspuru-Guzik, A. Deep Learning Enables Rapid Identification of Potent DDR1 Kinase Inhibitors. Nat Biotechnol 2019, 37 (9), 1038–1040. https://doi.org/10.1038/s41587-019-0224-x.
(7) Zhavoronkov, A.; Aspuru-Guzik, A. Reply to ‘Assessing the Impact of Generative AI on Medicinal Chemistry.’ Nat Biotechnol 2020. https://doi.org/10.1038/s41587-020-0417-3.
(8) Walters, W. P.; Murcko, M. Assessing the Impact of Generative AI on Medicinal Chemistry. Nat Biotechnol 2020. https://doi.org/10.1038/s41587-020-0418-2.

 

Inverse design 

(9) Kim, B.; Lee, S.; Kim, J. Inverse Design in Porous Materials Using Artificial Neural Networks. 2019. https://doi.org/10.26434/chemrxiv.7987475.v1.
(10) Sanchez-Lengeling, B.; Aspuru-Guzik, A. Inverse Molecular Design Using Machine Learning: Generative Models for Matter Engineering. Science 2018, 361 (6400), 360–365. https://doi.org/10.1126/science.aat2663.
(11) Gómez-Bombarelli, R.; Wei, J. N.; Duvenaud, D.; Hernández-Lobato, J. M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera-Iparraguirre, J.; Hirzel, T. D.; Adams, R. P.; Aspuru-Guzik, A. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Cent. Sci. 2018, 4 (2), 268–276. https://doi.org/10.1021/acscentsci.7b00572.