Practical combinatorial chemistry boosted by materials informatics

Practical combinatorial chemistry boosted by materials informatics

Combinatorial chemistry has been applied effectively to identify novel functional materials in the past. However, the chemical space is arbitrary and the regions in which new materials may be found are not known in advance. Thus, in practice, high-throughput technology has been used for compositional optimization of known materials, and materials discovery is hindered by a limited ability to search a wide chemical composition space. On the other hand, recent development of materials informatics has attracted a great deal of interest over the past decade in efforts to accelerate materials discovery. A large amount of training data is required to build a good model; however, the available materials data are in general quite limited, typically less than 100 experimental dataset for a target property.

Figure 1. Overview of the high-throughput screening approach

In our recent article, we present an effective way of paring combinatorial chemistry with materials informatics. In this scheme, a machine learning model is able to identify a finite chemical search space to explore using a high-throughput experimental system for materials discovery. The machine learning model predicts a target property of materials registered in database such as ICSD; in addition, it predicts elemental combinations to narrow the practical search space for subsequent combinatorial synthesis (Figure 1). This newly developed recommendation model could minimize problems associated with a small amount of training data for the machine learning and a limited search space of combinatorial chemistry.

We applied the present scheme to explore oxide ion conductors, which is a key material in solid oxide fuel cells. Our machine learning model recommended a chemical space spanned with Bi, Nb, Ta, and alkaline earth metals. The high-throughput combinatorial chemistry experiments were then performed to explore the best materials which could have the both high conductivity and thermal stability. Implementations of high-throughput conductivity and high-throughput x-ray diffraction (XRD) measurements were particularly effective to increase the total experimental throughput (Figure 2). As a result, we successfully discovered certain compositions in the Ca-(Nb,Ta)- Bi-O system which exhibited a higher conductivity than that of the conventional conductor, yttria-stabilized zirconia, and a high durability even at 873K.

Figure 2. High-throughput measurement system

In performing high-throughput XRD analysis, we identified one particular phase, referred to as m-BC, which showed a strong correlation with high conductivity. More surprisingly, the m-BC phase is not stable at high temperature but stabilized with Ca and Nb/Ta coexisted in crystal. This is a kind of experimental serendipity as we could not predict such effects of compositional complexity and materials stability from our machine learning model. However, the discovery indeed demonstrates the power of our high-throughput screening approach.

For more information, please see our recent publication in Communications Materials:

Masato Matsubara, Akitoshi Suzumura, Nobuko Ohba, Ryoji Asahi, “Identifying superionic conductors by materials informatics and high-throughput synthesis,” Communications Materials 1, 5 (2020).

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Electrical and Electronic Engineering
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