Citrine’s External Research Department works on externally funded collaborations with academia, national labs, and industry, providing unique expertise and a multi-year track record of success. Our long-standing culture of sharing knowledge and scientific engagement has led to strong academic and government partnerships and dozens of published papers and conference talks on our innovations. We help the scientific community to benefit from the work we do through an open materials data standard, open sourcing of selected libraries, and frequent journal publications and conference presentations on cutting-edge research. Projects span varied material classes and involve collaborators across industry, government, and academia. In each case Citrine is providing expertise in data management, machine learning and analysis to enable an accelerated assessment of a broader field of material candidates. The ERD works on a wide range of fundamental and applied research programs. An ideal ERD research project involves some amount of non-proprietary and publishable work, and addresses one or more these themes: - Applications - Machine learning-guided materials and process design, with a feedback loop that involves experiments and/or simulations - Modeling materials phenomena across length and timescales, from atomistic to macroscopic - Machine learning-augmented materials characterization - Method Development - Materials informatics algorithm and method development - Integrating machine learning and physics-based simulations - Software - Automation of simulations and laboratory experiments - Materials data infrastructure deployment and utilization - Open-source software development - Data - Materials property database development and publication We first work to establish an external funding source (e.g., government research funding agencies or foundations) who will sponsor the project. ERD is unable to work on unfunded research projects. Citrine provides collaborative groups with unique combined expertise in materials-specific machine learning, physics-based simulations, state-of-the-art software infrastructure, and scientific software development.
