Anubhav Jain
84 SlideShares 61 Abonné 3 Suivis
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Profession
Berkeley Lab
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www.anubhavjain.net
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materialsproject fireworks workflow software materials project
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FireWorks workflow software

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Software tools for calculating materials properties in high-throughput (pymatgen, atomate, FireWorks)

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Combining density functional theory calculations, supercomputing, and data-driven methods to understand and design new thermoelectric materials for waste heat recovery

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The Materials Project: overview and infrastructure

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The Materials Project: An Electronic Structure Database for Community-Based Materials Design

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Software tools to facilitate materials science research

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Application of the Materials Project database and data mining towards the design of thermoelectric and functional materials

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Machine learning for materials design: opportunities, challenges, and methods

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Computational materials design with high-throughput and machine learning methods

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Density functional theory calculations and data mining for new thermoelectrics discovery

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Combining density functional theory calculations, supercomputing, and data-driven methods to design new materials

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Combining density functional theory calculations, supercomputing, and data-driven methods to design new thermoelectric materials

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FireWorks overview

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The Materials Project and computational materials discovery

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Automating materials science workflows with pymatgen, FireWorks, and atomate

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Methods, tools, and examples (Part II): High-throughput computation and machine learning applied to materials design

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Automated Machine Learning Applied to Diverse Materials Design Problems

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How might machine learning help advance solar PV research?

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Combining High-Throughput Computing and Statistical Learning to Develop and Understand New Thermoelectric Compounds

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The Materials Project: Experiences from running a million computational science simulations and sharing the results with tens of thousands of researchers

il y a 6 ans • 559 Vues