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Machine-Learning Models for Combinatorial Catalyst Discovery

Published online by Cambridge University Press:  01 February 2011

Gregory A. Landrum ,
Julie Penzotti  and
Santosh Putta
Gregory A. Landrum
Affiliation:
Rational Discovery LLC, 555 Bryant St. #467, Palo Alto, CA 94301, USA
Julie Penzotti
Affiliation:
Rational Discovery LLC, 555 Bryant St. #467, Palo Alto, CA 94301, USA
Santosh Putta
Affiliation:
Rational Discovery LLC, 555 Bryant St. #467, Palo Alto, CA 94301, USA
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Abstract

Standard machine-learning algorithms were used to build models capable of predicting the molecular weights of polymers generated by a homogeneous catalyst. Using descriptors calculated from only the two-dimensional structures of the ligands, the average accuracy of the models on an external validation data set was approximately 70%. Because the models show no bias and perform significantly better than equivalent models built using randomized data, we conclude that they learned useful rules and did not overfit the data.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 804: Symposium JJ – Combinatorial and Artificial Intelligence Methods in Materials Science II , 2003 , JJ11.5
Copyright
Copyright © Materials Research Society 2004

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References

REFERENCES

1. Mülhaupt, R., Macromol. Chem. Phys. 204 (2), 289 (2003);Google Scholar
Rappé, A. K., Skiff, W. M., and Casewit, C. J., Chem. Rev. 100, 1435 (2000);Google Scholar
Gladysz, J.A., Chem. Rev. 100, 1167 (2000);Google Scholar
Angermund, K., Fink, G., Jensen, V.R. et al., Chem. Rev. 100, 1457 (2000);Google Scholar
Woo, T.K., Margl, P.M., Deng, L. et al., Catalysis Today 50, 479 (1999).Google Scholar
2. Landrum, G. A. and Genin, H., Mat. Res. Soc. Symp. Proc. 700 (2002).Google Scholar
3. Landrum, G. A. and Genin, H., Journal of Solid State Chemistry 176 (2), 587 (2003).Google Scholar
4. Villars, P., Brandenburg, K., Berndt, M. et al., Engineering Applications of Artificial Intelligence 13, 497 (2000).Google Scholar
5. Ioffe, I. I., Application of Pattern Recognition to Catalytic Research. (John Wiley & Sons, New York, 1988).Google Scholar
6. Beyreuther, S., Hunger, J., Huttner, G. et al., Chem. Ber. 129, 745 (1996).Google Scholar
7. Cundari, T. R., Deng, J., Pop, H. F. et al., J Chem Inf Comput Sci 40 (4), 1052 (2000);Google Scholar
Cundari, T. R. and Russo, M., J Chem Inf Comput Sci 41 (2), 281 (2001);Google Scholar
Cundari, T. R., Sarbu, C., and Pop, H. F., J Chem Inf Comput Sci 42 (6), 1363 (2002).Google Scholar
8. Murphy, V., Bei, X., Boussie, T. R. et al., Chem Rec 2 (4), 278 (2002);Google Scholar
Wennemers, H., Comb Chem High Throughput Screen 4 (3), 273 (2001).Google Scholar
9. Boussie, T. R., Diamond, G. M., Goh, C. et al., J Am Chem Soc 125 (14), 4306 (2003).Google Scholar
10. Gasteiger, J and Marsili, M, Tetrahedron 36, 3219 (1980).Google Scholar
11. Hall, L.H. and Kier, L. B., in Reviews in Computational Chemistry, edited by Boyd, D.B. (VCH Publishers, New York, 1991), Vol. 2, pp. 367.Google Scholar
12. Stehling, U., Diebold, J., Kirsten, R. et al., Organometallics 13, 964 (1994);Google Scholar
Spaleck, W., Küber, F., Winter, A. et al., Organometallics 13, 954 (1994).Google Scholar
13. Labute, P., J Mol Graph Model 18 (4–5), 464 (2000).Google Scholar
14. Romesburg, H. C., Cluster Analysis for Researchers. (Krieger, Malabar, 1990).Google Scholar
15. Kumar, Vipin, An Introduction to Cluster Analysis for Data Mining. http://www-users.cs.umn.edu/~han/dmclass/cluster_survey_10_02_00.pdf, (2000).Google Scholar
16. Mitchell, T., Machine Learning. (McGraw-Hill, New York, 1997).Google Scholar
17. Murthy, Sreerama K., Data Mining and Knowledge Discovery 2, 345 (1998).Google Scholar
18. Shannon, C. E., Bell System Tech. J. 27, 379 (1948).Google Scholar
19. Fayyad, U.M. and Irani, K.B., Machine Learning 8, 87 (1992).Google Scholar
20. Fayyad, U.M. and Irani, K.B., Proceedings of the 13th International Joint Conference on Artificial Intelligence, 1022 (1993).Google Scholar
21. Suggesting that descriptors encoding the electron-withdrawing/donating properties of ring substituents might be useful.Google Scholar