Researchers at Lawrence Livermore National Laboratory have developed software to efficiently automate and analyze point defects in crystalline materials. This new framework, which utilizes high-throughput computing and modern databasing, enables systematic studies of various defects in materials such as gallium nitride, gallium oxide, and strontium titanate. The software’s ability to streamline defect property analysis opens new possibilities for machine learning applications in material science, significantly advancing the understanding and development of semiconductor technologies.
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