Yale researchers have developed a revolutionary AI-based method to significantly accelerate the calculation of electronic structures in materials, reducing computation time from up to a million CPU hours to just about an hour. This approach uses a variational autoencoder (VAE) to create a compact, unsupervised representation of the electron wave function, a quantum descriptor critical for understanding material properties. By reducing the computational complexity of exploring excited state properties, the method enables more efficient and accurate predictions of material behavior, especially in 2D materials. This breakthrough has immediate implications for discovering new materials with desirable properties, such as improved light interaction and conductivity, by making previously time-prohibitive calculations accessible for a broader range of applications.
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Technical Consultant and EU Representative of Virtual Lab Inc., the parent company of the Materials Square platform
Website: Home | Virtual Lab Inc.
Email: gabriele@simulation.re.kr
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