Prof. José Luis Mendoza Cortes

 

Fundamentals of Materials by Design and Machine Learning

in the Materials Genome/Atlas for Artificial

Photosynthesis and Energy Storage

Jose L. Mendoza1,2,3

1Department of Chemical & Biomedical Engineering, FAMU-FSU Joint College of Engineering, Tallahassee, FL

2Department of Physics, Scientific Computing, Materials Science and Engineering, Florida State University, Tallahassee, FL

 3Condensed Matter Theory - National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA

 

Email: mendoza@eng.famu.fsu.edu; mendoza@magnet.fsu.edu

 

Computational chemistry and materials science algorithms are now powerful enough that they can predict many properties of materials and molecules before they are synthesized. By implementing and developing new approaches to calculate materials and chemical properties in supercomputers, we have predicted over 100,000 materials for energy storage and catalysis [1-10]. The computations predicted several new materials which were made and tested in the lab [7-10]. The creation of our large amount of materials in-silico, has prompted to create our own type of materials genomes or materials Atlas for different purposes. We have implemented different machine learning methods using these databases to find further materials design principles. Some of the applications of the design principles of materials has been used towards developing an alternative way to generate and store energy; specifically artificial photosynthesis, i.e. conversion of sunlight into chemical fuels and other ways to storage energy [11-17].

References

[1] El-Kaderi, H. M.; Hunt, J. R.; Mendoza-Cortes, J. L.; Cote, A. P.; Taylor, R. E.; O’Keeffe, M.; Yaghi, O. M. Science 2007, 316, 268. 

[2] Furukawa, H.; El-Kaderi, H. M.; Park, K. S.; Hunt, J. R.; Mendoza-Cortes, J. L.; Cote, A. P.; Yaghi, O. M. Abstr. Pap. Am. Chem. Soc. 2007, 233, 291. 

[3] Mendoza-Cortes, J. L., Dissertation (B.Sc.), ITESM-UCLA-Caltech, 2008. 

[4] Han, S. S.; Mendoza-Cortes, J. L.; Goddard, W. A. Chem. Soc. Rev. 2009, 38, 1460. 

[5] Tranchemontagne, D. J.; Mendoza-Cortes, J. L.; O’Keeffe, M.; Yaghi, O. M. Chem. Soc. Rev. 2009, 38, 1257. 

[6] Mendoza-Cortes, J. L.; et al. J. Phys. Chem. A 2010, 114, 10824. 

[7] Mendoza-Cortes, J. L. et al. J. Phys. Chem. A 2011, 115, 13852. 

[8] Mendoza-Cortes, J. L. Dissertation (Ph.D.), California Institute of Technology, 2012. 

[9] Mendoza-Cortes, J. L. et al. J. Phys. Chem. A 2012, 116, 1621. 

[10] Mendoza-Cortes, J. L. et al. J. Phys. Chem. Lett. 2012, 3, 2671. 

[11] Mendoza-Cortes, J. L. et al. J. of Comp. Chem. 2015, 37, 163. 

[12] Mendoza-Cortes, J. L. et al. J. Am. Chem. Soc., 2013, 1073-1082. 

[13] Mendoza-Cortes, J. L. et al. Metalloproteins, 2015, CRC press. 

[14] Mendoza-Cortes, J. L. et al. J. Phys. Chem. C, 2015, 119, 22838. 

[15] Mendoza-Cortes, J. L. et al. J. Phys. Chem. C, 2015, 119, 4645. 

[16] Mendoza-Cortes, J. L. et al. J. Am. Chem. Soc., 2016, 138, 15204. 

[17] Mendoza-Cortes, J. L. et al. ACS Nano, 2017, 11, 5103.