AIMATER is a cutting-edge platform that leverages the power of supercomputing and state-of-the-art methods. It provides open web-based access to a vast repository of computed information on both known and predicted materials. With AIMATER, researchers, scientists, and engineers can explore a wealth of data and utilize powerful analysis tools to inspire and design novel materials. The platform’s flagship app, CollaborationHub, offers a collaborative environment where experts from various disciplines can come together, exchange ideas, and drive innovation in materials science. Harnessing the potential of AIMATER and CollaborationHub, the possibilities for groundbreaking discoveries and advancements in material design are limitless.
ML predicted energy landscape of a lead based and lead free perovskite.
A data sharing and querying platfrom for catalysis surface reactions.
A user-friendly interface designed to facilitate the seamless training of machine learning models.
Park, Heesoo, et al. “Data-driven enhancement of cubic phase stability in mixed-cation perovskites.” Machine Learning: Science and Technology 2.2 (2021): 025030.
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