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Ongoing, rapid innovations in fields ranging from microelectronics, aerospace, and automotive to defense, energy, and health demand new advanced materials at even greater rates and lower costs. Traditional materials R&D methods offer few paths to achieve both outcomes simultaneously. Materials informatics, while a nascent field, offers such a promise through screening, growing databases of materials for new applications, learning new relationships from existing data resources, and building fast predictive models. We highlight key materials informatics successes from the atomic-scale modeling community, and discuss the ecosystem of open data, software, services, and infrastructure that have led to broad adoption of materials informatics approaches. We then examine emerging opportunities for informatics in materials science and describe an ideal data ecosystem capable of supporting similar widespread adoption of materials informatics, which we believe will enable the faster design of materials.
Vertical hetero-structures made from stacked monolayers of transition metal dichalcogenides (TMDC) are promising candidates for next-generation optoelectronic and thermoelectric devices. Identification of optimal layered materials for these applications requires the calculation of several physical properties, including electronic band structure and thermal transport coefficients. However, exhaustive screening of the material structure space using ab initio calculations is currently outside the bounds of existing computational resources. Furthermore, the functional form of how the physical properties relate to the structure is unknown, making gradient-based optimization unsuitable. Here, we present a model based on the Bayesian optimization technique to optimize layered TMDC hetero-structures, performing a minimal number of structure calculations. We use the electronic band gap and thermoelectric figure of merit as representative physical properties for optimization. The electronic band structure calculations were performed within the Materials Project framework, while thermoelectric properties were computed with BoltzTraP. With high probability, the Bayesian optimization process is able to discover the optimal hetero-structure after evaluation of only ∼20% of all possible 3-layered structures. In addition, we have used a Gaussian regression model to predict not only the band gap but also the valence band maximum and conduction band minimum energies as a function of the momentum.
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