Symposium AAA-Big Data and Data Analytics in Materials Science

Recent developments in experimental and simulation tools have converged with advances in raw computing resources, necessitating the development of new algorithms for analyzing the data generated. The ability to collect more digital data at faster speeds with multiple signals, length scales and viewing angles, does not lead to improved materials characterization or modeling without the ability to process and understand the data. This is becoming increasingly apparent as sensors are capturing data at a rate which cannot effectively be analyzed by a human. Indeed, when Materials Science is viewed as a "Big Data" problem, it becomes immediately apparent that our field presents problems unique to Materials Science and that, because of the highly complex and non-linear relationships known to exist, a simple correlation is usually dissatisfying. Robust analytical methods for extraction of quantitative physical information from raw data, and not just correlations, are needed. This type of analysis is inaccessible by traditional methods, either due to massive data volumes or, conversely, missing data.

From an experimental perspective, all measurements become inverse problems: the end result of the interaction of a beam with a material is all that is observed and the material structure/composition which gave produced the result needs to be found. Similarly, computational design of materials to meet property targets can be framed as an inverse problem, using physics based models as forward models in the inversion. Experiments, by their very nature, have limitations such as indirect measurements, inefficient detectors, restricted field of view, sample damage, and noise. This makes for ambiguous interpretation from a machine's point of view- i.e. the inversion is ill-posed and the results from traditional analysis methods give uninterpretable or physically unrealistic results.

This symposium will cover advances in methods for data analytics, for both experimentally and computationally generated data, specifically as they have been applied to materials science problems. This symposium will focus on current challenges in data analytics for materials science such as high throughput data generation, inverse methods, three and four dimensional data, multimodal data, multi-physics model data, and others.

• Computational strategies for analysis of large data sets
• Mathematical inverse methods applied to materials
• Tomography and serial sectioning
• Segmentation
• Hyperspectral imaging
• Time-resolved characterization techniques
• Data sparsity
• Compressed sensing in microscopy
• Multimodal data and data fusion
• Anomaly testing, outliers, and extreme events
• Forward modeling

• AAA_Big Data and Data Analytics in Materials Science _0 (Sandia National Laboratories, USA)
• AAA_Big Data and Data Analytics in Materials Science _1 (Purdue University, USA)
• AAA_Big Data and Data Analytics in Materials Science _2 (Purdue University, USA)
• AAA_Big Data and Data Analytics in Materials Science _3 (Gatan Inc., USA)
• AAA_Big Data and Data Analytics in Materials Science _4 (Carnegie Mellon University, USA)
• AAA_Big Data and Data Analytics in Materials Science _5 (Deutsches Elektronen-Synchrotron, Germany)
• AAA_Big Data and Data Analytics in Materials Science _6 (University of Michigan, USA)
• AAA_Big Data and Data Analytics in Materials Science _7 (Lawrence Berkeley National Laboratory, USA)
• AAA_Big Data and Data Analytics in Materials Science _8 (Lawrence Berkeley National Laboratory, USA)
• AAA_Big Data and Data Analytics in Materials Science _9 (Lausanne, Switzerland)
• AAA_Big Data and Data Analytics in Materials Science _10 (Massachusetts Institute of Technology, USA)
• AAA_Big Data and Data Analytics in Materials Science _11 (Tufts University, USA)
• AAA_Big Data and Data Analytics in Materials Science _12 (Fritz Haber Berlin, Germany)
• AAA_Big Data and Data Analytics in Materials Science _13 (Lawrence Berkeley National Laboratory, USA)
• AAA_Big Data and Data Analytics in Materials Science _14 (National Institute of Standards and Technology, USA)
• AAA_Big Data and Data Analytics in Materials Science _15 (Air Force Research Laboratory, USA)
• AAA_Big Data and Data Analytics in Materials Science _16 (University of Pennsylvania, USA)
• AAA_Big Data and Data Analytics in Materials Science _17 (Brookhaven National Laboratory, USA)
• AAA_Big Data and Data Analytics in Materials Science _18 (Continuum Analytics, USA)
• AAA_Big Data and Data Analytics in Materials Science _19 (Lehigh University, USA)
• AAA_Big Data and Data Analytics in Materials Science _20 (University of Wisconsin, USA)