Prediction of new Ti-N phases using machine learned interatomic potential

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Publication date: 10 March 2026

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We can see that the threshold map distributes perturbations more optimally than purely random noise, resulting in a clearer and more detailed final image. The algorithm itself is extremely simple and trivially parallelisable, requiring only a few operations per pixel.