Researchers at Lawrence Livermore National Laboratory (LLNL) have developed an innovative method combining generative AI and first-principles simulations to predict complex three-dimensional atomic structures. This approach, detailed in a study published in Machine Learning: Science and Technology, employs diffusion models conditioned on X-ray absorption near edge structure (XANES) spectroscopy data to accurately determine atomic arrangements, particularly for shapeless or disordered materials. The scalable model bridges nanoscale to microscale features, making it applicable to areas such as grain boundaries and phase interfaces. Beyond structural analysis, this technique supports inverse design, enabling the engineering of materials with desired properties, advancing energy and sustainability technologies.
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