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Accelerating materials discovery with big data and machine learning
The document discusses the use of big data and machine learning to accelerate materials discovery, particularly in the context of phosphors for lighting applications. It highlights resources like the Materials Project and outlines ongoing efforts to automate materials development through AI, robotics, and data-driven methodologies. Challenges include making experimental data machine-learning ready and enhancing automated sample characterization and decision-making processes.
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Accelerating materials discovery with big data and machine learning
- 1. Accelerating materials discovery withbig data and machine learning Anubhav Jain ESDR / ETA AI/ML for Chemistry and Materials Science Workshop 9/26/2024
- 2. Today: Simulation dataforms structured materials databases for design and machine learning • The Materials Project (www.materialsproject.org) • Free resource of calculated and contributed materials properties • >150,000 inorganic compounds • >500,000 registered users • Most popular database for downstream machine learning (composition or structure à property) MP for phosphors References ✦ Wang, Z. et al. Mining Unexplored Chemistries for Phosphors for High-Color- Quality White-Light-Emitting Diodes. Joule 2, 914–926 (2018) ✦ Li, S. et al. Data-Driven Discovery of Full-Visible-Spectrum Phosphor. Chemistry of Materials 31, 6286–6294 (2019) ✦ Ha, J. et al. Color tunable single-phase Eu2+ and Ce3+ co-activated Sr2LiAlO4 phosphors. Journal of Materials Chemistry C 7, 7734–7744 (2019) Prediction Statistical analysis of existing materials that co-occur with word ‘phosphor’ followed by structure prediction for new materials Experiment Predicted first known Sr-Li- Al-N quaternary, showed green-yellow/blue emission with quantum efficiency of 25% (Eu), 40% (Ce), 55% (co-activated Eu, Ce) Sr2LiAlN4 ≈ç ≈ “matbench-discovery” benchmark task
- 3. Challenge: Make publishedexperimental data “ML-ready” like simulation data sets (e.g., LLMs) Dagdelen, J.; Dunn, A.; Lee, S.; Walker, N.; Rosen, A. S.; Ceder, G.; Persson, K. A.; Jain, A. Structured Information Extraction from Scientific Text with Large Language Models. Nat Commun 2024, 15 (1), 1418. Top: dopant database; Bottom: Au NP synthesis
- 4. Today/Ongoing: end-to-end materialsdevelopment with automation/robotics, theory, and AI “A-lab” Materials Project NERSC AI recipes based on “reading” literature Iterative AI refines recipe to synthesize target phase New materials can be virtually pre-screened with supercomputers and AI (“Materials Project”) Targets from computer models can be synthesized using robotic equipment and AI (“A-lab”)
- 5. Challenge: automated samplecharacterization Hypothesis generation & simulation Data collection Uncertainty and decision- making Theory & Simulation Instrumentation & Tools Machine learning Possible structures, vacancies, defects, competing polymorphs or phases & their probabilities (algorithmic decision- making and confidence assessment) Samples (e.g., from automated synthesis) Molecular Foundry Tools designed for rapid, automated analysis Automated sample characterization protocol that matches “team of experts” behavior for single samples… …yet also scales to hundreds of samples & hundreds of targets Data infrastructure