By how much can closed-loop frameworks accelerate computational materials discovery?

The implementation of automation and machine learning surrogatization within closed-loop computational workflows is an increasingly popular approach to accelerate materials discovery. However, the scale of the speedup associated with this paradigm shift from traditional manual approaches remains an open question. In this work, we rigorously quantify the acceleration from each of the components within a closed-loop framework for material hypothesis evaluation by identifying four distinct sources of speedup: (1) task automation, (2) calculation runtime improvements, (3) sequential learning-driven design space search, and (4) surrogatization of expensive simulations with machine learning models. This is done using a time-keeping ledger to record runs of automated software and corresponding manual computational experiments within the context of electrocatalysis. From a combination of the first three sources of acceleration, we estimate that overall hypothesis evaluation time can be reduced by over 90%, i.e., achieving a speedup of $\sim$$10\times$. Further, by introducing surrogatization into the loop, we estimate that the design time can be reduced by over 95%, i.e., achieving a speedup of $\sim$$15$-$20\times$. Our findings present a clear value proposition for utilizing closed-loop approaches for accelerating materials discovery.Comment: added Supplementary Informatio

George C. Marshall Space Flight Center Research and Technology Report 2014

Many of NASA's missions would not be possible if it were not for the investments made in research advancements and technology development efforts. The technologies developed at Marshall Space Flight Center contribute to NASA's strategic array of missions through technology development and accomplishments. The scientists, researchers, and technologists of Marshall Space Flight Center who are working these enabling technology efforts are facilitating NASA's ability to fulfill the ambitious goals of innovation, exploration, and discovery

2020 roadmap on solid-state batteries

Li-ion batteries have revolutionized the portable electronics industry and empowered the electric vehicle (EV) revolution. Unfortunately, traditional Li-ion chemistry is approaching its physicochemical limit. The demand for higher density (longer range), high power (fast charging), and safer EVs has recently created a resurgence of interest in solid state batteries (SSB). Historically, research has focused on improving the ionic conductivity of solid electrolytes, yet ceramic solids now deliver sufficient ionic conductivity. The barriers lie within the interfaces between the electrolyte and the two electrodes, in the mechanical properties throughout the device, and in processing scalability. In 2017 the Faraday Institution, the UK's independent institute for electrochemical energy storage research, launched the SOLBAT (solid-state lithium metal anode battery) project, aimed at understanding the fundamental science underpinning the problems of SSBs, and recognising that the paucity of such understanding is the major barrier to progress. The purpose of this Roadmap is to present an overview of the fundamental challenges impeding the development of SSBs, the advances in science and technology necessary to understand the underlying science, and the multidisciplinary approach being taken by SOLBAT researchers in facing these challenges. It is our hope that this Roadmap will guide academia, industry, and funding agencies towards the further development of these batteries in the future