Virtual Winter School on Computational Chemistry
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Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Many compelling functional materials and highly selective catalysts have been discovered that are defined by their metal-organic bonding. The rational design of de novo transition metal complexes however remains challenging. First-principles (i.e., with density functional theory, or DFT) high-throughput screening is a promising approach but is hampered by high computational cost, particularly in the brute force screening of large numbers of ligand and metal combinations. In this talk, I will describe how automation[1-3], machine learning[4-6] autonomous tool development, sensitivity analysis, and multireference character detection can all be used to simultaneously overcome limitations in cost and accuracy of DFT-based screening in open shell transition metal chemistry. I will describe the basic approach of our automated workflow, molSimplify[1-3], for automating calculations as well as recent extensions of machine learning (ML) models that avoid direct calculation of not just energetic or structural properties but also the likelihood of a calculation to succeed or a complex to contain strong correlation. I will describe how unifying databases shed light on the sensitivity of discovery outcomes to underlying DFT functionals. I will discuss possibilities for accelerating discovery with ML with awareness of uncertainty when sampling new regions of chemical space. Time permitting, I will describe how this powerful toolkit has advanced our understanding of metal-organic bonding in materials far-ranging from functional spin crossover complexes to open-shell transition metal catalysts and metal-organic frameworks.
 Ioannidis, E. I.; Gani, T. Z. H.; Kulik, H. J. molSimplify: A Toolkit for Automating Discovery in Inorganic Chemistry. Journal of Computational Chemistry 2016, 37, 2106-2117.
 molSimplify lite web interface: https://molsimplify.mit.edu
 Kulik group github with molSimplify and MultirefPredict: https://github.com/hjkgrp
 Janet, J. P.; Kulik, H. J. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships. The Journal of Physical Chemistry A 2017, 121, 8939-8954.
 Janet, J. P.; Liu, F.; Nandy, A.; Duan, C.; Yang, T.; Lin, S.; Kulik, H. J. Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry. Inorganic Chemistry 2019, 58, 10592-10606.
 Janet, J. P.; Kulik, H. J. Machine Learning in Chemistry. ACS InFocus Book Series. 2020.
 Duan, C.; Janet, J. P.; Liu, F.; Nandy, A.; Kulik, H. J. Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models. Journal of Chemical Theory and Computation 2019, 15, 2331-2345.
 Liu, F.; Duan, C.; Kulik, H. J. Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening. The Journal of Physical Chemistry Letters 2020, 11, 8067-8076.
 Janet, J. P.; Ramesh, S.; Duan, C.; Kulik, H. J. Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization. ACS Central Science 2020, 6, 513-524.
 Janet, J. P.; Duan, C.; Yang, T.; Nandy, A.; Kulik, H. J. A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery. Chemical Science 2019, 10, 7913-7922.
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