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Adaptive Materials Design

An active learning paradigm for materials.

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Adaptive learning for accelerated materials discovery

Materials design and discovery guided by community-driven active user interface, where data from users is utilized to refine predictions for optimally guiding next experiments or computations.

This portal showcases some of our unique capabilities in advancing materials design within the adaptive design framework.

Selected Publications
  • D. Xue, P.V. Balachandran, J. Hogden, J. Theiler, D. Xue and T. Lookman, "Accelerated search for materials with targeted properties by adaptive design", Nature Communications, 7, 11241 (2016)
  • G. Pilania, P.V. Balachandran, C. Kim and T. Lookman, "Finding New Perovskite Halides via Machine learning", Frontiers in Materials, 3, 19 (2016)
  • P.V. Balachandran, D. Xue and T. Lookman, "Structure-Curie temperature relationships in BaTiO3-based ferroelectric perovskites: Anomalous behavior of (Ba,Cd)TiO3 from DFT, statistical inference, and experiments", Physical Review B, 93, 144111 (2016)
  • T. Lookman, F.J. Alexander and A.R. Bishop, "Perspective: Codesign for materials science: An optimal learning approach ", APL Materials, 4, 053501 (2016)
  • P.V. Balachandran, D. Xue, J. Theiler, J. Hogden and T. Lookman, "Adaptive Strategies for Materials Design using Uncertainties", Scientific Reports, 6, 19660 (2016)
  • T. Lookman, P.V. Balachandran, D. Xue, G. Pilania, T. Shearman, J. Theiler, J.E. Gubernatis, J. Hogden, K. Barros, E. BenNaim and F.J. Alexander, "A Perspective on Materials Informatics: State-of-the-Art and Challenges", Information Science for Materials Discovery and Design, eds. T. Lookman, F.J. Alexander and K. Rajan, Springer International Publishing (2016)
  • G. Pilania, P.V. Balachandran, J.E. Gubernatis and T. Lookman, "Classification of ABO3 perovskite solids: a machine learning study", Acta Crystallographica B, 71, 507-513 (2015)