Data Analytics and What It Means to the Materials Community Proceedings of a Workshop (2021) / Chapter Skim
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5 Emerging Applications
5 Emerging Applications
Pages 41-52
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Complex and hierarchical materials are crucial to meeting future materials needs. There are tens of millions or even billions of unexplored complex materials composed from just 30 common, nontoxic metals and metalloids in the periodic table.
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to drive more discoveries. Mehta argued that the current discovery cycle is broken, and AI is a vital tool for addressing this problem because the data are becoming too complex and come too fast to be manageable.
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Mehta suggested that perhaps a good strategy may be to jet tison some of the raw data from reproducible experiments as the data age, but carefully preserve conditions under which the data were collected (workflow) and associated metadata, so that much higher quality data can be recollected quickly when needed on improved instruments of the future.
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Mehta agreed that this was possible, and noted that ML failures can also bring insights. EMPOWERING SCIENTISTS THROUGH DATA AND AI Paredes discussed Citrine's philosophy and approach to addressing materials data problems and enabling materials discovery.
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These data exist in small data sets, which could have a high sample bias; the data are sparse, lacking information about many measurements and properties; the data often do not span multiple materials; and the data require extrapolation, because they are rarely clas sified or labeled. To solve these problems, Citrine applies domain knowledge to small data sets, enriching them with equations, simulation data, and experimental data.
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Another participant suggested Citrine could learn from LexisNexis, which is successful as a global aggregator of information because it pays for the data, and NIST, which has created a trove of vetted information on alloys. WHAT THE FUTURE HOLDS: AI FOR ACCELERATING MATERIALS DISCOVERY Gomes discussed AI's transition from academic research to real-world applica tions, its potential for enabling discoveries, and examples of current efforts to capital ize on this potential, including CRYSTAL, a multiagent system for materials discovery.
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In addition, Gomes emphasized the need to move AI beyond interpolation by predicting unknown materials and finding the right representa tions. Doing so will require incorporating prior knowledge and physics constraints, understanding the underlying phenomena, and managing uncertainty.
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Global parameter settings must be continually tweaked to eliminate specific errors, and then tweaked again to avoid subsequent problems. To overcome this variability, NASA created an end-to-end ML solution where the model can learn how to print a desired material correctly by optimizing the local printing parameter settings to minimize flaws, especially on the surface.
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With Biorelate LTD, I ntellegens is creating a system that automates information extraction from images and scientific publications -- in particular, for graphs and tables -- to deliver high-quality, previously hidden data. Another effort, called Optibrium, has several aims: to extract all pos sible information about data, including insights on why data is missing and what is hidden in the noise; to develop ML models that better extrapolate data capabilities; and to merge models that have been trained on private data silos.
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OPEN DISCUSSION Participants and panelists had a lively discussion on needs for advancing AI and on the tension between trusting AI and understanding it. Needs for AI in Materials Design Participants discussed multiple elements that are needed to advance the use of AI in materials design, including data, computation, time, and instrumentation.
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Wadley noted that Bayesian methods could infer properties and statistical dis tributions, and wondered if they could be used to optimize ML for materials design. Gardner agreed, and Surya Kalidindi, Georgia Institute of Technology, added that design factors are often used to combat ignorance and increase safety.
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Lourdes Salamanca-Riba, University of Maryland, noted that the tension be tween understanding and trusting AI is at its core a cultural issue, and one that may be perceived differently by the emerging cohort of researchers as compared to the previous generation. Gardner noted the relationship between this issue and the broader cultural evolution in terms of how people decide what information is "real." Another participant asked what was more important: new tools to solve prob lems, or advancing science overall?
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