IEEE Photonics Society

Boston Photonics Society Chapter

Boston Chapter of the IEEE Photonics Society

Machine Learning and Optical Systems PDF

Wednesday, October 7, 14, 21, 28 and November 4, 2020, 7:00-9:30 PM
Located at Online Seminar

October 21, 2020
7 PM

Online Seminar

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Automatic Design of Optoelectronic Materials with Atomistic Simulations and Deep Learning

Prof. Rafael Gomez-Bombarelli, Massachusetts Institute of Technology, Cambridge, MA


Prof. Rafael Gomez-Bombarelli, Massachusetts Institute of Technology, Cambridge, MA

Abstract:  The chemical space of organic optoelectronic materials is extremely vast. This allows exquisite fine-tuning of molecular designs to achieve desired properties, but hinders the systematic exploration of structure and property space. Although physics-based simulations can screen candidates much faster than chemical synthesis and device fabrication, autonomous chemical design is still a challenge. The discrete, graph-like nature of molecules presents a difficult optimization challenge; simulations may not capture all the experimental design and performance parameters and often ignore the vast amounts of pre-existing data.

Recent machine learning advances have allowed progress in many of these issues. Here, I will describe an ML-accelerated design cycle of optically active compounds that combines (i) data extraction from the literature: (ii) unsupervised and semisupervised deep learning to generate discrete molecules from continuous vectors so that numerical optimization methods can be applied to chemical design; (iii) ML-based calibration of theoretical results with respect to experiment; (iv) neural simulators that replace expensive atomistic simulations. The domains of application range from organic light emitting diodes, to photodiodes or optical switches.


Biography:  Rafael Gomez-Bombarelli is the Toyota Assistant Professor in the Department of Materials Science and Engineering (DMSE). Rafa joined the MIT faculty in January 2018. He received a B.S., M.S., and Ph.D. in Chemistry from Universidad de Salamanca in Spain, followed by postdoctoral work at Heriot-Watt University and Harvard University after which he was a senior researcher at Kyulux NA applying Harvard-licensed technology to create real-life commercial organic light-emitting diode (OLED) products. At MIT, his research focus on the interplay between atomistic simulations and machine learning for materials design.


Advance registration required, but there is no fee (Open to all IEEE members as well as non-members)

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The workshop expenses have
been generously supported by:



For more information on the technical content of the workshop, contact either:
1) Keisuke Kojima, (, Chair
2) Ajay Garg, (, Co-Chair
3) Dean Tsang, (, Co-Chair
4) Bill Nelson, (, Co-Chair