Rapid Exploration of a 32.5M Compound Chemical Space with Active
Learning to Discover Density Functional Approximation Insensitive and
Synthetically Accessible Transitional Metal Chromophores
- URL: http://arxiv.org/abs/2208.05444v1
- Date: Wed, 10 Aug 2022 16:55:33 GMT
- Title: Rapid Exploration of a 32.5M Compound Chemical Space with Active
Learning to Discover Density Functional Approximation Insensitive and
Synthetically Accessible Transitional Metal Chromophores
- Authors: Chenru Duan, Aditya Nandy, Gianmarco Terrones, David W. Kastner, and
Heather J. Kulik
- Abstract summary: Two challenges for machine learning (ML) accelerated chemical discovery are the synthesizability of candidate molecules or materials and the fidelity of the data used in ML model training.
To address the first challenge, we construct a hypothetical design space of 32.5M transition metal complexes (TMCs), in which all of the constituent fragments are synthetically accessible.
To address the second challenge, we search for consensus in predictions among 23 functional density approximations across multiple rungs of Jacob's ladder.
- Score: 0.4063872661554894
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Two outstanding challenges for machine learning (ML) accelerated chemical
discovery are the synthesizability of candidate molecules or materials and the
fidelity of the data used in ML model training. To address the first challenge,
we construct a hypothetical design space of 32.5M transition metal complexes
(TMCs), in which all of the constituent fragments (i.e., metals and ligands)
and ligand symmetries are synthetically accessible. To address the second
challenge, we search for consensus in predictions among 23 density functional
approximations across multiple rungs of Jacob's ladder. To accelerate the
screening of these 32.5M TMCs, we use efficient global optimization to sample
candidate low-spin chromophores that simultaneously have low absorption
energies and low static correlation. Despite the scarcity (i.e., $<$ 0.01\%) of
potential chromophores in this large chemical space, we identify transition
metal chromophores with high likelihood (i.e., $>$ 10\%) as the ML models
improve during active learning. This represents a 1,000 fold acceleration in
discovery corresponding to discoveries in days instead of years. Analyses of
candidate chromophores reveal a preference for Co(III) and large, strong-field
ligands with more bond saturation. We compute the absorption spectra of
promising chromophores on the Pareto front by time-dependent density functional
theory calculations and verify that two thirds of them have desired excited
state properties. Although these complexes have never been experimentally
explored, their constituent ligands demonstrated interesting optical properties
in literature, exemplifying the effectiveness of our construction of realistic
TMC design space and active learning approach.
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