Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening

Victor Trinquet; Matthew L. Evans; Gian‐Marco Rignanese

doi:10.1039/d5tc01335f

January 1, 2025Journal of Materials Chemistry COpen Access

Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening

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Key Points

Active learning enables faster identification of promising nonlinear optical materials.
The study provides an open dataset of second-harmonic generation tensors for both real and hypothetical materials.
High-throughput screening methods enhance the efficiency of material discovery in inorganic semiconductors.
This approach supports the broader field of data-driven discovery with practical applications in optics.

Implication

Active learning reveals new nonlinear optical materials through high-throughput screening of semiconductors.

Abstract

An open first-principles dataset of second-harmonic generation tensors for known and hypothetical inorganic semiconductors, constructed using active learning, propels the data-driven discovery of nonlinear optical materials.

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Cite This Study

Trinquet et al. (2025) studied this question.

synapsesocial.com/papers/68c1c62f54b1d3bfb60f1d48 https://doi.org/10.1039/d5tc01335f

View Full Paper

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Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening

Key Points

Implication

Abstract

Discussion

Cite This Study

Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening

Key Points

Implication

Abstract

Discussion

Cite This Study

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