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AI3SD Video: Audacity of huge: Machine Learning for the discovery of transition metal catalysts and materials

AI3SD Video: Audacity of huge: Machine Learning for the discovery of transition metal catalysts and materials
AI3SD Video: Audacity of huge: Machine Learning for the discovery of transition metal catalysts and materials
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 a family of dozens of functionals to more robustly uncover new materials; and iii) by the use of natural language processing to extract, learn, and directly predict experimental measures of stability on heterogeneous MOF materials.
AI, AI3SD Event, Artificial Intelligence, Big Data, Chemistry, Data Science, Data Sharing, Datasets, Machine Learning, Materials Discovery, ML
Kulik, Heather
ce599472-a454-44cf-ada8-8abed9762432
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Kulik, Heather
ce599472-a454-44cf-ada8-8abed9762432
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Kulik, Heather and Frey, Jeremy G. (2021) AI3SD Video: Audacity of huge: Machine Learning for the discovery of transition metal catalysts and materials. Kanza, Samantha and Niranjan, Mahesan (eds.) AI3SD Autumn Seminar Series 2021. 13 Oct - 15 Dec 2021. (doi:10.5258/SOTON/AI3SD0172).

Record type: Conference or Workshop Item (Other)

Abstract

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 a family of dozens of functionals to more robustly uncover new materials; and iii) by the use of natural language processing to extract, learn, and directly predict experimental measures of stability on heterogeneous MOF materials.

Video
AI3SDAutumnSeminar-081221-HeatherKulik - Version of Record
Available under License Creative Commons Attribution.
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More information

Published date: 8 December 2021
Additional Information: 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), MSDE Outstanding Early-Career Paper Award (2021), and a Sloan Fellowship (2021).
Venue - Dates: AI3SD Autumn Seminar Series 2021, 2021-10-13 - 2021-12-15
Keywords: AI, AI3SD Event, Artificial Intelligence, Big Data, Chemistry, Data Science, Data Sharing, Datasets, Machine Learning, Materials Discovery, ML

Identifiers

Local EPrints ID: 453344
URI: http://eprints.soton.ac.uk/id/eprint/453344
PURE UUID: 8c15cab7-7302-444c-af90-05a20be99cff
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 13 Jan 2022 17:48
Last modified: 14 Jan 2022 02:53

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Contributors

Author: Heather Kulik
Author: Jeremy G. Frey ORCID iD
Editor: Samantha Kanza ORCID iD
Editor: Mahesan Niranjan ORCID iD

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