aimsnist

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Enabling Data Science Methods for Catalyst Design and Discovery
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A Framework and Infrastructure for Uncertainty Quantification and Management in Materials Design
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Predicting local atomic structures from X-ray absorption spectroscopy using theory and machine learning
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Smart Metrics for High Performance Material Design
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Graphs, Environments, and Machine Learning for Materials Science
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A Machine Learning Framework for Materials Knowledge Systems
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The MGI and AI
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Failing Fastest: What an Effective HTE and ML Workflow Enables for Functional Metallic Glasses
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How to Leverage Artificial Intelligence to Accelerate Data Collection and Analysis of Diffusion Multiples
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Coupling AI with HiTp experiments to Discover Metallic Glasses Faster
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“Materials Informatics and Big Data: Realization of 4th Paradigm of Science in Materials Science
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Data Mining to Discovery for Inorganic Solids: Software Tools and Applications
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Autonomous experimental phase diagram acquisition
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Classical force fields as physics-based neural networks
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Pathways Towards a Hierarchical Discovery of Materials
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