Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing
By Juan‐Pablo Correa‐Baena, K. Hippalgaonkar, J. V. Duren, Shaffiq Jaffer, Vijay Ramaseshan Chandrasekhar, V. Stevanovi'c, Cyrus Wadia, Supratik Guha, Tonio Buonassisi. Joule, 2018
312 citations recorded by the source corpus.
Successful materials innovations can transform society. However, materials research often involves long timelines and low success probabilities, dissuading investors who have expectations of shorter times from bench to business. A combination of emergent technologies could accelerate the pace of novel materials development by 10x or more, aligning the timelines of stakeholders (investors and researchers), markets, and the environment, while increasing return-on-investment. First, tool automation enables rapid experimental testing of candidate materials. Second, high-throughput computing (HPC) concentrates experimental bandwidth on promising compounds by predicting and inferring bulk, interface, and defect-related properties. Third, machine learning connects the former two, where experimental outputs automatically refine theory and help define next experiments. We describe state-of-the-art attempts to realize this vision and identify resource gaps. We posit that over the coming decade, this combination of tools will transform the way we perform materials research. There are considerable first-mover advantages at stake, especially for grand challenges in energy and related fields, including computing, healthcare, urbanization, water, food, and the environment.
Subfields: Electrical and Electronic Engineering, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/85e6f089e3b11976c8a42a2c5b9f33e6d0bee62e
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In Electrical and Electronic Engineering
In Materials Chemistry
Citations in the corpus
- Opportunities and Challenges for Machine Learning in Materials Science (2020, background): “Author Publication Year Reference Title Rajan 2015 176 Materials Informatics: The Materials “Gene” and Big Data Broderick and Rajan 2015 177 Informatics derived materials databases for multifunctional properties Kalidindi 2015 178 Data science and cyberinfrastructure: critical enablers for accelerat”