2020 MRS Spring Meeting

Call for Papers

Symposium EQ04-Machine Learning on Experimental Data for Emergent Quantum Materials

For the past decade, quantum materials, where complex phenomena emerge from complex orbital, charge, lattice and spin interactions, have been a source of enormous fundamental breakthroughs. Quantum materials are difficult if not impossible to understand solely using existing simulation and analytical techniques, therefore, insights from experimental data are of critical importance. Machine learning continues to advance as a powerful tool for understanding and designing materials. These methods have been highly successful in improving atomistic simulations, materials design and discovery, literature information extraction, and quantum information systems. However, the following challenges remain unresolved when merging machine learning methods with experimental data: 1) Experimental data can have noise from various sources. 2) In comparison to high-throughput computational results, experimental data is often small and scattered. 3) Experimental data often disagree with simulation or computational results.

Given the vast opportunities that machine learning may bring experimental investigations of quantum materials, we feel it obligatory and timely to organize a symposium to address the following challenges and bottlenecks of applying state-of-the-art machine learning architectures to address key open questions in quantum materials: How do we apply machine learning to noisy, scarce experimental data, especially when there is disagreement with computational data? How do we extract key insights on quantum materials from experimental data that cannot be accessed from conventional manual data analysis?

This symposium will highlight recent progress in applying machine learning to various types of materials characterization techniques: neutron and x-ray scattering, optical spectroscopies, angular-resolved photoemission spectroscopy (ARPES), free electron laser, among other emerging novel spectroscopies. We will cover diverse quantum materials, such as novel topological insulators, semimetals and metals, in bulk, thin film, 2D and 1D form. We will emphasize recent progress in machine learning techniques relevant to noise reduction and inferring missing or the corruption of data. We will focus on applications of machine learning to augment experimental data for novel quantum materials, as well as “hot off the press” characterization and analysis tools for quantum materials. This symposium will provide an interactive, widely-accessible forum for materials scientists to get up to speed on the exciting recent progress of machine learning and quantum materials. To ensure cross-fertilization of these new techniques and approaches, sessions will be organized by scientific theme rather than material category. Additional sessions will focus on recent methodological advances of the machine learning capabilities to probe the charge, spin or lattice degrees of freedom.

Topics will include:

  • Bridging the gap between computational and experimental data
  • Convolutional neural network-based architectures for 2D and higher-dimensional spectra
  • Recursive neural network-based architectures for time-resolved spectra
  • State-of-the-art X-ray scattering to explore the interplay between the charge, spin and orbital degrees of freedom
  • Neutron scattering measurement to study the magnetic properties and exotic excitations in materials
  • Femtosecond to attosecond ultrafast free electron laser for materials properties far away from equilibrium
  • Graphical models and other non-neural network methods in addressing experimental data
  • Machine learning methods for inverse design (properties - geometry) over experimentally accessible parameters / structures
  • A tutorial complementing this symposium is tentatively planned.

Invited Speakers:

  • Johua Agar (Lehigh University, USA)
  • Ilke Arslan (Argonne National Laboratory, USA)
  • Andrei Bernevig (Princeton University, USA)
  • Silvana Botti (Friedrich-Schiller-Universität Jena, Germany)
  • Maria Chan (Argonne National Laboratory, USA)
  • Cheng-Chien Chen (The University of Alabama at Birmingham, USA)
  • Nikolas Claussen (University of California, Santa Barbara, USA)
  • Anubhav Jain (Lawrence Berkeley National Laboratory, USA)
  • Heather Kulik (Massachusetts Institute of Technology, USA)
  • Sean Lubner (Lawrence Berkeley National Laboratory, USA)
  • Kelly Morrison (Loughborough University, United Kingdom)
  • Nicolas Regnault (École Normale Supérieure, France)
  • Homin Shin (National Research Council, Canada)
  • Robert-Jan Slager (University of Cambridge, United Kingdom)
  • Alan Tennant (Oak Ridge National Laboratory, USA)
  • Aika Terada (The University of Tokyo, Japan)
  • Koji Tsuda (The University of Tokyo, Japan)
  • Xijie Wang (Stanford University, USA)
  • Yaoi Wang (Clemson University, USA)
  • Wei Xu (Brookhaven National Laboratory, USA)

Symposium Organizers

Mingda Li
Massachusetts Institute of Technology

Maciej Haranczyk
IMDEA Materials Institute

Chris Rychroft
Harvard University

Tess Smidt
Lawrence Berkeley National Laboratory

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