tag: material science

AlphaGo Game Influences Argonne’s New AI Tool For Materials Discovery
By Technical Paper Link - 10 Jun, 2022 - Comments: 0

Research paper titled "Learning in continuous action space for developing high dimensional potential energy models" from researchers at Argonne National Lab with contributions from Oak Ridge National Laboratory. Abstract "Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action ... » read more

Deep Learning Applications For Material Sciences: Methods, Recent Developments
By Technical Paper Link - 10 Jun, 2022 - Comments: 0

New technical paper titled "Recent advances and applications of deep learning methods in materials science" from researchers at NIST, UCSD, Lawrence Berkeley National Laboratory, Carnegie Mellon University, Northwestern University, and Columbia University. Abstract "Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning... » read more

Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamics
By Technical Paper Link - 01 Apr, 2022 - Comments: 0

New academic paper out of USC Viterbi School of Engineering: Abstract "Ferroelectric materials exhibit a rich range of complex polar topologies, but their study under far-from-equilibrium optical excitation has been largely unexplored because of the difficulty in modeling the multiple spatiotemporal scales involved quantum-mechanically. To study optical excitation at spatiotemporal scales w... » read more

Learning properties of ordered and disordered materials from multi-fidelity data
By Technical Paper Link - 14 Jan, 2021 - Comments: 0

Source: Chen, C., Zuo, Y., Ye, W. et al. Learning properties of ordered and disordered materials from multi-fidelity data. Nat Comput Sci 1, 46–53 (2021). https://doi.org/10.1038/s43588-020-00002-x Abstract: "Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a n... » read more

Planarization Challenges At 7nm And Beyond
By Ed Sperling - 10 Dec, 2018 - Comments: 0

Dan Sullivan, executive director of semiconductor technology at Brewer Science, digs into the challenges of planarizing a thin film on a wafer for etch and optical control. The problem becomes more difficult at advanced nodes because the films are thinner. https://youtu.be/iNA6EGpoYZU     _________________________________ See more tech talk videos here   » read more

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