MARVEL Distinguished Lecture — Heather Kulik
Event details
| Date | 13.12.2022 |
| Hour | 15:00 › 16:30 |
| Speaker | Heather Kulik (MIT) |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
https://epfl.zoom.us/j/62028983673
Passcode: 3249
Prof. Heather Kulik
Massachusetts Institute of Technology
Materials discovery in challenging spaces with machine learning: from transition metal complexes to metal-organic frameworks
I will discuss our efforts to use machine learning (ML) to accelerate the computational tailoring and design of transition metal complexes and metal-organic framework (MOF) materials. One limitation in a challenging materials space such as open shell, 3d transition metal chemistry is that ML models and ML-accelerated high-throughput screening traditionally rely on density functional theory (DFT) for data generation, but DFT is both computationally demanding and prone to errors that limit its accuracy in predicting new materials. I will describe three ways we’ve overcome these limitations: i) through efficient global optimization to minimize the numbers of calculations carried out to obtain design rules in weeks instead of decades while satisfying multiple objectives; ii) through machine-learned consensus from dozens of DFT functionals to more robustly uncover new materials; iii) through the development of a density functional "recommender" that identifies the most accurate mean field theory for a given compound; and iv) by the use of natural language processing to extract, learn, and directly predict experimental measures of stability on heterogeneous MOF materials.
About the speaker
Heather J. Kulik is an Associate Professor in Chemical Engineering at MIT. She received her B.E. in Chemical Engineering from Cooper Union in 2004 and her Ph.D. in Materials Science and Engineering from MIT in 2009. She completed postdocs at Lawrence Livermore (2010) and Stanford (2010−2013), prior to returning to MIT as a faculty member in 2013 and receiving tenure in 2021.
Her work has been recognized by a Burroughs Wellcome Fund Career Award at the Scientific Interface (2012-2017), Office of Naval Research Young Investigator Award (2018), DARPA Young Faculty Award (2018), AAAS Marion Milligan Mason Award (2019-2020), NSF CAREER Award (2019), the Industrial & Engineering Chemistry Research “Class of Influential Researchers”, the ACS COMP Division OpenEye Award for Outstanding Junior Faculty in Computational Chemistry, the JPCB Lectureship (ACS PHYS), the DARPA Director’s Fellowship (2020), and a Sloan Fellowship (2021).
Passcode: 3249
Prof. Heather Kulik
Massachusetts Institute of Technology
Materials discovery in challenging spaces with machine learning: from transition metal complexes to metal-organic frameworks
I will discuss our efforts to use machine learning (ML) to accelerate the computational tailoring and design of transition metal complexes and metal-organic framework (MOF) materials. One limitation in a challenging materials space such as open shell, 3d transition metal chemistry is that ML models and ML-accelerated high-throughput screening traditionally rely on density functional theory (DFT) for data generation, but DFT is both computationally demanding and prone to errors that limit its accuracy in predicting new materials. I will describe three ways we’ve overcome these limitations: i) through efficient global optimization to minimize the numbers of calculations carried out to obtain design rules in weeks instead of decades while satisfying multiple objectives; ii) through machine-learned consensus from dozens of DFT functionals to more robustly uncover new materials; iii) through the development of a density functional "recommender" that identifies the most accurate mean field theory for a given compound; and iv) by the use of natural language processing to extract, learn, and directly predict experimental measures of stability on heterogeneous MOF materials.
About the speaker
Heather J. Kulik is an Associate Professor in Chemical Engineering at MIT. She received her B.E. in Chemical Engineering from Cooper Union in 2004 and her Ph.D. in Materials Science and Engineering from MIT in 2009. She completed postdocs at Lawrence Livermore (2010) and Stanford (2010−2013), prior to returning to MIT as a faculty member in 2013 and receiving tenure in 2021.
Her work has been recognized by a Burroughs Wellcome Fund Career Award at the Scientific Interface (2012-2017), Office of Naval Research Young Investigator Award (2018), DARPA Young Faculty Award (2018), AAAS Marion Milligan Mason Award (2019-2020), NSF CAREER Award (2019), the Industrial & Engineering Chemistry Research “Class of Influential Researchers”, the ACS COMP Division OpenEye Award for Outstanding Junior Faculty in Computational Chemistry, the JPCB Lectureship (ACS PHYS), the DARPA Director’s Fellowship (2020), and a Sloan Fellowship (2021).
Practical information
- Informed public
- Free
Organizer
Contact
- Patrick Mayor, MARVEL Program Manager