Screening of bimetallic electrocatalysts for water purification with machine learning.

Electrocatalysis provides a potential solution to NO3 - pollution in wastewater by converting it to innocuous N2 gas. However, materials with excellent catalytic activity are typically limited to expensive precious metals, hindering their commercial viability. In response to this challenge, we have conducted the most extensive computational search to date for electrocatalysts that can facilitate NO3 - reduction reaction, starting with 59 390 candidate bimetallic alloys from the Materials Project and Automatic-Flow databases. Using a joint machine learning- and computation-based screening strategy, we evaluated our candidates based on corrosion resistance, catalytic activity, N2 selectivity, cost, and the ability to synthesize. We found that only 20 materials will satisfy all criteria in our screening strategy, all of which contain varying amounts of Cu. Our proposed list of candidates is consistent with previous materials investigated in the literature, with the exception of Cu-Co and Cu-Ag based compounds that merit further investigation.

Agents for sequential learning using multiple-fidelity data.

Sequential learning for materials discovery is a paradigm where a computational agent solicits new data to simultaneously update a model in service of exploration (finding the largest number of materials that meet some criteria) or exploitation (finding materials with an ideal figure of merit). In real-world discovery campaigns, new data acquisition may be costly and an optimal strategy may involve using and acquiring data with different levels of fidelity, such as first-principles calculation to supplement an experiment. In this work, we introduce agents which can operate on multiple data fidelities, and benchmark their performance on an emulated discovery campaign to find materials with desired band gap values. The fidelities of data come from the results of DFT calculations as low fidelity and experimental results as high fidelity. We demonstrate performance gains of agents which incorporate multi-fidelity data in two contexts: either using a large body of low fidelity data as a prior knowledge base or acquiring low fidelity data in-tandem with experimental data. This advance provides a tool that enables materials scientists to test various acquisition and model hyperparameters to maximize the discovery rate of their own multi-fidelity sequential learning campaigns for materials discovery. This may also serve as a reference point for those who are interested in practical strategies that can be used when multiple data sources are available for active or sequential learning campaigns.

Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.

The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. However, predicting these properties for bimetallic or more complicated surfaces is an open challenge. Computing cleavage energy is the first step in calculating surface energy across a large space. Here, we present a workflow to predict cleavage energies ab initio using high-throughput DFT and a machine learning framework. We calculated the cleavage energy of 3033 intermetallic alloys with combinations of 36 elements and 47 space groups. This high-throughput workflow was used to seed a database of cleavage energies. The database was used to train a crystal graph convolutional neural network (CGCNN). The CGCNN model provides an accurate prediction of cleavage energy with a mean absolute test error of 0.0071 eV/Å2. It can also qualitatively reproduce nanoparticle surface distributions (Wulff constructions). Our workflow provides quantitative insights into unexplored chemical space by predicting which surfaces are relatively stable and therefore more realistic. The insights allow us to down-select interesting candidates that we can study with robust theoretical and experimental methods for applications such as catalyst screening and nanomaterials synthesis.

Dynamic Workflows for Routine Materials Discovery in Surface Science.

The rising application of informatics and data science tools for studying inorganic crystals and small molecules has revolutionized approaches to materials discovery and driven the development of accurate machine learning structure/property relationships. We discuss how informatics tools can accelerate research, and we present various combinations of workflows, databases, and surrogate models in the literature. This paradigm has been slower to infiltrate the catalysis community due to larger configuration spaces, difficulty in describing necessary calculations, and thermodynamic/kinetic quantities that require many interdependent calculations. We present our own informatics tool that uses dynamic dependency graphs to share, organize, and schedule calculations to enable new, flexible research workflows in surface science. This approach is illustrated for the large-scale screening of intermetallic surfaces for electrochemical catalyst activity. Similar approaches will be important to bring the benefits of informatics and data science to surface science research. Lastly, we provide our perspective on when to use these tools and considerations when creating them.