Finding Novel Materials for Practical Devices

Estimated reading time: 10 minutes

Performing the ANN analysis of all 5,600 complexes took just an hour. But in the real world, the number of complexes to be explored could be thousands of times larger — and any promising candidates would require a full DFT calculation. The researchers therefore needed a method of evaluating a big data set to identify any unacceptable candidates even before the ANN analysis. To that end, they developed a genetic algorithm — an approach inspired by natural selection — to score individual complexes and discard those deemed to be unfit. 

To prescreen a data set, the genetic algorithm first randomly selects 20 samples from the full set of complexes. It then assigns a “fitness” score to each sample based on three measures. First, is its spin-crossover energy low enough for it to be a good SCO? To find out, the neural network evaluates each of the 20 complexes. Second, is the complex too far away from the training data? If so, the spin-crossover energy from the ANN may be inaccurate. And finally, is the complex too close to the training data? If so, the researchers have already run a DFT calculation on a similar molecule, so the candidate is not of interest in the quest for new options. 

Based on its three-part evaluation of the first 20 candidates, the genetic algorithm throws out unfit options and saves the fittest for the next round. To ensure the diversity of the saved compounds, the algorithm calls for some of them to mutate a bit. One complex may be assigned a new, randomly selected ligand, or two promising complexes may swap ligands. After all, if a complex looks good, then something very similar could be even better — and the goal here is to find novel candidates. The genetic algorithm then adds some new, randomly chosen complexes to fill out the second group of 20 and performs its next analysis. By repeating this process a total of 21 times, it produces 21 generations of options. It thus proceeds through the search space, allowing the fittest candidates to survive and reproduce, and the unfit to die out. 

Performing the 21-generation analysis on the full 5,600-complex data set required just over five minutes on a standard desktop computer, and it yielded 372 leads with a good combination of high diversity and acceptable confidence. The researchers then used DFT to examine 56 complexes randomly chosen from among those leads, and the results confirmed that two-thirds of them could be good SCOs. 

While a success rate of two-thirds may not sound great, the researchers make two points. First, their definition of what might make a good SCO was very restrictive: For a complex to survive, its spin-splitting energy had to be extremely small. And second, given a space of 5,600 complexes and nothing to go on, how many DFT analyses would be required to find 37 leads? As Janet notes, “It doesn’t matter how many we evaluated with the neural network because it’s so cheap. It’s the DFT calculations that take time.” 

Best of all, using their approach enabled the researchers to find some unconventional SCO candidates that wouldn’t have been thought of based on what’s been studied in the past. “There are rules that people have — heuristics in their heads — for how they would build a spin-crossover complex,” says Kulik. “We showed that you can find unexpected combinations of metals and ligands that aren’t normally studied but can be promising as spin-crossover candidates.” 

Sharing the new tools 

To support the worldwide search for new materials, the researchers have incorporated the genetic algorithm and ANN into "molSimplify," the group’s online, open-source software toolkit that anyone can download and use to build and simulate transition metal complexes. To help potential users, the site provides tutorials that demonstrate how to use key features of the open-source software codes. Development of molSimplify began with funding from the MIT Energy Initiative in 2014, and all the students in Kulik’s group have contributed to it since then. 

The researchers continue to improve their neural network for investigating potential SCOs and to post updated versions of molSimplify. Meanwhile, others in Kulik’s lab are developing tools that can identify promising compounds for other applications. For example, one important area of focus is catalyst design. Graduate student in chemistry Aditya Nandy is focusing on finding a better catalyst for converting methane gas to an easier-to-handle liquid fuel such as methanol — a particularly challenging problem. “Now we have an outside molecule coming in, and our complex — the catalyst — has to act on that molecule to perform a chemical transformation that takes place in a whole series of steps,” says Nandy. “Machine learning will be super-useful in figuring out the important design parameters for a transition metal complex that will make each step in that process energetically favorable.” 

This research was supported by the U.S. Department of the Navy’s Office of Naval Research, the U.S. Department of Energy, the National Science Foundation, and the MIT Energy Initiative Seed Fund Program. Jon Paul Janet was supported in part by an MIT-Singapore University of Technology and Design Graduate Fellowship. Heather Kulik has received a National Science CAREER Award (2019) and an Office of Naval Research Young Investigator Award (2018), among others.