Swift and significant gains against climate change require the creation of novel, environmentally benign, and energy-efficient materials. Considered one of the richest veins researchers hope to tap in creating such useful compounds is an unlimited chemical space where molecular combos that supply remarkable optical, conductive, magnetic, and warmth transfer properties await discovery.
But finding these recent materials has been slow going.
“While computational modeling has enabled us to find and predict properties of latest materials much faster than experimentation, these models aren’t at all times trustworthy,” says Heather J. Kulik PhD ’09, associate professor within the departments of Chemical Engineering and Chemistry. “As a way to speed up computational discovery of materials, we’d like higher methods for removing uncertainty and making our predictions more accurate.”
A team from Kulik’s lab set out to deal with these challenges with a team including Chenru Duan PhD ’22.
A tool for constructing trust
Kulik and her group give attention to transition metal complexes, molecules comprised of metals found in the course of the periodic table which can be surrounded by organic ligands. These complexes may be extremely reactive, which provides them a central role in catalyzing natural and industrial processes. By altering the organic and metal components in these molecules, scientists can generate materials with properties that may improve such applications as artificial photosynthesis, solar energy absorption and storage, higher efficiency OLEDS (organic light emitting diodes), and device miniaturization.
“Characterizing these complexes and discovering recent materials currently happens slowly, often driven by a researcher’s intuition,” says Kulik. “And the method involves trade-offs: You may find a cloth that has good light-emitting properties, however the metal at the middle could also be something like iridium, which is exceedingly rare and toxic.”
Researchers attempting to discover nontoxic, earth-abundant transition metal complexes with useful properties are likely to pursue a limited set of features, with only modest assurance that they’re on the fitting track. “People proceed to iterate on a selected ligand, and get stuck in local areas of opportunity, somewhat than conduct large-scale discovery,” says Kulik.
To deal with these screening inefficiencies, Kulik’s team developed a recent approach — a machine-learning based “recommender” that lets researchers know the optimal model for pursuing their search. Their description of this tool was the topic of a paper in in December.
“This method outperforms all prior approaches and might tell people when to make use of methods and after they’ll be trustworthy,” says Kulik.
The team, led by Duan, began by investigating ways to enhance the standard screening approach, density functional theory (DFT), which is predicated on computational quantum mechanics. He built a machine learning platform to find out how accurate density functional models were in predicting structure and behavior of transition metal molecules.
“This tool learned which density functionals were probably the most reliable for specific material complexes,” says Kulik. “We verified this by testing the tool against materials it had never encountered before, where it in actual fact selected probably the most accurate density functionals for predicting the fabric’s property.”
A critical breakthrough for the team was its decision to make use of the electron density — a fundamental quantum mechanical property of atoms — as a machine learning input. This unique identifier, in addition to the usage of a neural network model to perform the mapping, creates a robust and efficient aide for researchers who want to find out whether or not they are using the suitable density functional for characterizing their goal transition metal complex. “A calculation that might take days or even weeks, which makes computational screening nearly infeasible, can as an alternative take only hours to supply a trustworthy result.”
Kulik has incorporated this tool into molSimplify, an open source code on the lab’s website, enabling researchers anywhere on the planet to predict properties and model transition metal complexes.
Optimizing for multiple properties
In a related research thrust, which they showcased in a recent publication in , Kulik’s group demonstrated an approach for quickly homing in on transition metal complexes with specific properties in a big chemical space.
Their work springboarded off a 2021 paper showing that agreement in regards to the properties of a goal molecule amongst a gaggle of various density functionals significantly reduced the uncertainty of a model’s predictions.
Kulik’s team exploited this insight by demonstrating, in a primary, multi-objective optimization. Of their study, they successfully identified molecules that were easy to synthesize, featuring significant light-absorbing properties, using earth-abundant metals. They searched 32 million candidate materials, certainly one of the most important spaces ever looked for this application. “We took apart complexes which can be already in known, experimentally synthesized materials, and we recombined them in recent ways, which allowed us to take care of some synthetic realism,” says Kulik.
After collecting DFT results on 100 compounds on this giant chemical domain, the group trained machine learning models to make predictions on all the 32 million-compound space, with an eye fixed to achieving their specific design goals. They repeated this process generation after generation to winnow out compounds with the specific properties they wanted.
“In the long run we found nine of probably the most promising compounds, and discovered that the particular compounds we picked through machine learning contained pieces (ligands) that had been experimentally synthesized for other applications requiring optical properties, ones with favorable light absorption spectra,” says Kulik.
Applications with impact
While Kulik’s overarching goal involves overcoming limitations in computational modeling, her lab is taking full advantage of its own tools to streamline the invention and design of latest, potentially impactful materials.
In a single notable example, “We’re actively working on the optimization of metal–organic frameworks for the direct conversion of methane to methanol,” says Kulik. “It is a holy grail response that folk have desired to catalyze for a long time, but have been unable to do efficiently.”
The potential for a quick path for transforming a really potent greenhouse gas right into a liquid that is well transported and may very well be used as a fuel or a value-added chemical holds great appeal for Kulik. “It represents certainly one of those needle-in-a-haystack challenges that multi-objective optimization and screening of hundreds of thousands of candidate catalysts is well-positioned to unravel, an excellent challenge that’s been around for therefore long.”