Mohammad Atif Faiz Afzal

205 Furnas Hall • Buffalo, NY 14260
(716) 262-5115 • m27@buffalo.edu
www.linkedin.com/in/UBAfzal

EDUCATION

University at Buffalo, The State University of New York
PhD, Chemical and Biological Engineering, GPA: 3.98, Dean’s List

Indian Institute of Technology Kanpur
BTech, Materials Science and Engineering, First Distinction

PROFESSIONAL BACKGROUND

Graduate Research Assistant
University at Buffalo, Department of Chemical and Biological Engineering
Advisor: Prof. Johannes Hachmann
PhD Intern
Research Process Fundamentals, ExxonMobil Chemical
Graduate Teaching Assistant
University at Buffalo, Department of Chemical and Biological Engineering
Project Associate
IIT Kanpur, Nanosciences and Nanofabrication Laboratory
Advisor: Prof. Ashutosh Sharma
Undergraduate Research Assistant and Laboratory Coordinator
IIT Kanpur, Biomaterials Processing and Characterization Laboratory
Advisor: Prof. Kantesh Balani

HONORS & AWARDS

SKILLS

PUBLICATIONS

  1. J. Hachmann, M. Afzal, M. Haghighatlari, Y. Pal, Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space, Molecular Simulation 44 (2018).
  2. M. Afzal, S. Ganesh, C. Cheng, J. Hachmann, Benchmarking model chemistries for the calculation of polarizability and refractive index values of organic polymers, In-submission to Journal of Chemical Theory and Computation (2018).
  3. J. Hachmann, T. Windus, J. McLean, A. Schrimpe-Rutledge, M. Afzal, M. Haghighatlari, Framing the Role of Big Data and Modern Data Science in Chemistry NSF CHE Workshop , NSF CHE Workshop Report (2018).
  4. A. Tkatchenko, M. Afzal, C. Anderson, T. Baker, R. Banisch, S. Chiama, C. Draxl, M. Haghighatlari, F. Heidar-Zadeh, M. Hirn, J. Hoja, O. Isayev, R. Kondor, L. Li, Y. Li, G. Martyna, M. Meila, K.S. Ruiz, M. Rupp, H. Sauceda, A. Shapeev, M. Stöhr, K.-R. Müller, S. Shankar, IPAM Program on Machine Learning & Many-Particle Systems - Recent Progress and Open Problems, Institute for Pure and Applied Mathematics (2017).
  5. M. Afzal, C. Cheng, J. Hachmann, Combining First-Principles and Data Modeling for the Accurate Prediction of the Refractive Index of Organic Polymers, Journal of Chemical Physics 148 (2017).
  6. S.K. Das, M. Afzal, S. Patil, A. Sharma, Enhanced Electrical Conductivity of Suspended Carbon Nanofibers: Effect of Hollow Structure and Improved Graphitization, Carbon 108 (2016).
  7. M. Afzal, S. Kalmodia, P. Kesarwani, B.Basu, K.Balani, Bactericidal Effect of Silver Reinforced Carbon-Nanotube and Hydroxyapatite Composites, Journal of Biomaterials Applications 27 (2013).
  8. M. Afzal, P. Kesarwani, S. Kalmodia, M. Reddy, B. Basu, K. Balani, Functionally Graded Hydroxyapatite-Alumina-Zirconia Biocomposite: Synergy of Toughness and Biocompatibility , Journal of the Material Science and Engineering C 32 (2012).
  9. Summary: 105 citations; h-index: 3; i10-index: 2 (Source: Google Scholar)

PUBLICATIONS (In Preparation)

  1. M. Afzal, J. Hachmann, ChemLG – a Smart and Massively Parallel Code to Accelerate the Molecular Library Generation.
  2. M. Afzal, J. Younker, G. Rodriguez, The effect of tacticity and side chain structure on the chain dimensions of polyolefins in dilute solutions.
  3. M. Afzal, A. Sonpal, M. Haghighatlari, J. Hachmann, Neural networks for the prediction of packing density of 1.5 million organic molecules .
  4. M. Afzal, S. Ganesh, C. Cheng, J. Hachmann, Computational and Data-driven Discovery of Novel, High-refractive Index Polyimides.

CONFERENCE PRESENTATIONS

  1. Large-Scale Exploration of Chemical Space to Identify Exceptional Molecular Targets for Optical Applications, 256th ACS National Meeting, COMP Division Poster Session, Boston (MA), Aug 2018. (poster)
  2. Neural Networks Approach to Predict the Packing Density of 1.5 Million Organic Molecules, 256th ACS National Meeting, COMP Division Symposium on Revolutionizing Chemical Sciences with Artificial Intelligence, Boston (MA), Aug 2018. (talk)
  3. Large-Scale Exploration of Chemical Moieties for the Design of Next-Generation High-Refractive-Index Polymers, 2018 Conference on Foundations of Molecular Modeling and Simulation (FOMMS 2018) – Innovations for Complex Systems, Delevan (WI), Jul 2018. (poster)
  4. Exploration of Chemical Space to Identify Exceptional Molecular Targets for Optical Applications, Reunion Conference for IPAM’s Workshop on Machine Learning for Many-Particle Systems, San Bernardino (CA), Jun 2018. (talk)
  5. Harnessing Virtual High-Throughput Screening and Machine Learning for the Discovery of Novel High-Refractive-Index Polymers, Machine Learning in Science and Engineering, Symposium on Predicting Molecular Properties and Molecular Design, Pittsburgh (PA), Jun 2018. (poster)
  6. From Virtual High-Throughput Screening and Machine Learning to the Discovery and Rational Design of Polymers for Optical Applications, University at Buffalo PhD Dissertation Defense Seminar, Buffalo (NY), May 2018. (talk)
  7. The effect of tacticity and side chain structure on the coil dimensions of polyolefins, ExxonMobil Research and Engineering Seminar, Baytown (TX), Feb 2018. (internship talk)
  8. Accelerated Discovery of Polymers using Molecular Modeling, Virtual High-Throughput Screening, and Machine Learning, ExxonMobil Research and Engineering Seminar, Clinton (NJ), Nov 2017. (invited talk)
  9. Accelerated Discovery of Polymers using Molecular Modeling, Virtual High-Throughput Screening, and Machine Learning, ExxonMobil Research and Engineering Seminar, Baytown (TX), Sep 2017. (invited talk)
  10. Virtual High-Throughput Infrastructure for the Accelerated Discovery of Organic Materials, 254th ACS National Meeting, COMP Division Poster Session, Washington (DC), Aug 2017. (poster)
  11. Discovering Polyimides with Exceptional Optical Properties using First-Principles Modeling, Virtual High-Throughput Screening, and Machine Learning, 254th ACS National Meeting, Sci-Mix Poster Session, Washington (DC), Aug 2017. (poster)
  12. Discovering Polyimides with Exceptional Optical Properties using First-Principles Modeling, Virtual High-Throughput Screening, and Machine Learning, 254th ACS National Meeting, COMP Division Poster Session, Washington (DC), Aug 2017. (poster)
  13. Machine Learning Approach for the Fast and Accurate Prediction of Optical Properties of Organic Molecules, 254th ACS National Meeting, CINF Division Scholarship for Scientific Excellence Poster Session, Washington (DC), Aug 2017. (poster)
  14. Discovering Polymers with Exceptional Optical Properties Using First-Principles Modeling, Virtual High-Throughput Screening, and Machine Learning, Samsung Research Seminar, Boston (MA), Jul 2017. (invited talk)
  15. Accelerated Discovery of High-Refractive-Index Polymers Using First-Principles Modeling, Virtual High-Throughput Screening, and Data Mining, APS March Meeting 2017, New Orleans (LA), Mar 2017. (poster)
  16. VMD for Molecular Visualization , Workshop, Computational Science Club, Buffalo (NY), Mar 2017. (invited talk)
  17. ChemHTPS – A Virtual High-Throughput Screening Program Suite for the Chemical and Materials Sciences, APS March Meeting 2017, DMP/DCOMP Division Symposium on Computational Discovery and Design of Novel Materials, New Orleans (LA), Mar 2017. (talk)
  18. Accelerated Discovery of High-Refractive Index Polymers, IPAM Workshop on Synergies between Machine Learning and Physical Models, Los Angeles (CA), Dec 2016. (poster)
  19. Accelerated Discovery of High-Refractive-Index Polyimides, 19th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2016. (poster)
  20. Accelerated Discovery of High-Refractive-Index Polymers using First-Principles Modeling, Virtual High-Throughput Screening, and Data Mining, 252nd ACS National Meeting, COMP Division Poster Session, Philadelphia (PA), Aug 2016. (poster)
  21. ChemLG – A Smart and Massively Parallel Molecule Library Generator, 252nd ACS National Meeting, COMP Division Symposium on Designing Chemical Libraries for Screening, Philadelphia (PA), Aug 2016. (talk)
  22. Accelerated Discovery of High-Refractive-Index Polymers Using First-Principles Modeling, Virtual High-Throughput Screening, and Data Mining, 48th Midwest Theoretical Chemistry Conference (MWTCC 2016), Pittsburgh (PA), Jun 2016. (poster)
  23. Accelerated Discovery of Organic Polymers for Optical Applications, 2016 UB CBE Graduate Student Seminar, Buffalo (NY), May 2016. (invited talk)
  24. Accurate Prediction of the Refractive Index of Polymers Using First-Principles and Data Modeling, APS March Meeting 2016, Baltimore (MD), Mar 2016. (poster)
  25. Accurate Prediction of the Refractive Index of Polymers Using First-Principles and Data Modeling, 18th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2015. (poster)
  26. Accelerated Discovery of High Refractive Index Polyimides, 33rd Annual UB Chemistry Graduate Student Symposium, Buffalo (NY), May 2015. (poster)
  27. Computational Approach to Discovering Novel Monomers for High Refractive Index Organic Materials, 17th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2014. (poster)
  28. Thermodynamics of Surfactant Solutions, 16th CBE Graduate Research Symposium, Buffalo (NY), Sep 2013. (poster)
  29. Development of HA/SiC Composites via Thermal Plasma Route, International Conference and Workshop on Nanostructured Ceramics and other Nanomaterials, Delhi (India), Mar 2012. (poster)
  30. Structural, Mechanical, and Bactericidal Properties of Ag-Reinforced CNT/HA Composites, International Conference on Nanoscience and Technology, Hyderabad (India), Jan 2012. (poster)
  31. Functionally-Stepped Hydroxyapatite-Alumina-Zirconia: Potential Bone-Implant, International Conference on Biomaterials and Implants, Kolkata (India), Jul 2013. (talk)

PROFESSIONAL AFFILIATIONS & SERVICE

REFERENCES

Prof. Johannes Hachmann
Department of Chemical and Biological Engineering
University at Buffalo, SUNY
Buffalo, NY 14260
hachmann@buffalo.edu
+1-716-645-1524
Prof. Michel Dupuis
Department of Chemical and Biological Engineering
University at Buffalo, SUNY
Buffalo, NY 14260
mdupuis2@buffalo.edu
+1-716-645-9062
Prof. Edward Furlani
Department of Chemical and Biological Engineering
University at Buffalo, SUNY
Buffalo, NY 14260
efurlani@buffalo.edu
+1-716-645-1194