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AbstractComputational methods and machine learning (ML) are reshaping materials science by accelerating their discovery, design, and optimization. Traditional approaches such as density functional theory and molecular dynamics have been instrumental in studying materials at the atomic level. However, their high computational cost and, in certain cases, limited accuracy can restrict the scope ofin silicoexploration. ML promises to accelerate material property prediction and design. However, in many areas, the volume and fidelity of the data are critical barriers. Active learning can reduce the reliance on large data sets, and simulation has emerged as a critical tool for generating data on the fly. Despite these advances, challenges remain, particularly in data quality, model interpretability, and bridging the gap between computational predictions and experimental validation. Future research should develop automated frameworks capable of designing and testing materials for specific applications, and integrating ML with traditional simulations and experiments can contribute to this goal. Graphic abstract
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Empirical modeling of dopability in diamond-like semiconductors
Abstract Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost. In this work, we explore the application of machine learning for high-throughput carrier concentration range prediction. Bounding the model within diamond-like semiconductors, the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary. The data were analyzed using various statistical and machine learning methods. Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across bothp- andn-type dopability. The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts. Finally, dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials.
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- PAR ID:
- 10153904
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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