Data Science & Materials Conference

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This conference brought together foundational and applied data science experts to present the latest machine learning methods and their applications in molecular and materials science and engineering.

Foundational & Applied Data Science for Molecular & Material Science & Engineering Conference
Iacocca Hall, Lehigh University. (Held May 22-24, 2019)

Data science has become ubiquitous in science and engineering. There is a tremendous recent surge in the adoption of machine learning tools in physics, chemistry, chemical engineering, materials science, and related disciplines to elucidate and design complex processes (chemical/biological, engineered/natural) or material systems with wide ranging applications addressing grand challenges in energy, health, environment, and water. At this critical juncture, the “practitioners” of ML in these fields, in both academia and industry, will benefit from close interaction with “developers” of modern ML tools (i.e. data scientists) who, in turn, can learn the problems and the needs of specific domains. To foster this relationship in the spirit of the NSF TRIPODS+X program, this interdisciplinary conference brings together foundational data scientists and domain practitioners to give talks to a common audience. The specific focal area of the conference is the development and application of data science algorithms and tools to address problems in molecular and materials science and engineering (i.e. any problem spanning the length scale of atoms to bulk materials). 

Organizing Committee:
Srinivas Rangarajan and Jeetain Mittal, Chemical & Biomolecular Engineering.
Payel Das, IBM Research AI, T J Watson Research Center.
Joshua Agar, Materials Science & Engineering, Lehigh University.

Full Schedule (pdf)
Poster Titles & Abstracts (pdf)

Plenary Speaker
  • Paulette Clancy, Johns Hopkins University
    “Merging Physical Science and Machine Learning to Tackle Complexity and Combinatorics in Materials Processing.”

Guest Speakers

  • Joshua C. Agar, Lehigh University
    “Deducing Inference from Hyperspectral Imaging of Materials Using Recurrent Neural Networks.” Abstract (pdf)

  • Chi Chen, University of California San Diego
    “Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.” Abstract (pdf)

  • Lihua Chen, Ramprasad Group, Georgia Tech
    “Polymer Genome: An Informatics Platform for Rational Polymer Dielectrics Design and Beyond.” Abstract (pdf)

  • Payel Das, IBM Research AI, TJ Watson Research Center
    ” How Can AI Help In Material and Molecule Discovery?.” Abstract (pdf)

  • Alex Dunn, Lawrence Berkeley National Lab
    “Matminer and Automatminer: Software Tools for Accelerating Materials Discovery with Machine Learning.” Abstract (pdf)

  • Amir Barati Farimani, Carnegie Mellon University
    “Learning Spatio-temporal Convection with Deep Generative Models.” Abstract (pdf)

  • Habib Hajm, Sandia National Laboratories
    “Surrogate Modeling and Uncertainty Quantification in Models of Physical Systems.” Abstract (pdf)

  • Oles Isayev, University of North Carolina
    “Supercharging Computational Chemistry with Machine Learning.” Abstract (pdf)

  • Heather Kulik’s Group, MIT | Speaker Jon Paul Janet
    “Using Machine Learning and First-Principles Simulation to Automate the Design of Transition Metal Complexes.” Abstract (pdf)

  • Yingyu Liang, University of Wisconsin-Madison 
    “N-Gram Graph: A Simple Unsupervised Representation for Molecules.” Abstract (pdf)

  • Tim Mueller, Johns Hopkins University
    “Fast and Accurate Interatomic Potentials by Genetic Programming.” Abstract (pdf)

  • Paris Perdikaris, University of Pennsylvania
    “Data-driven Modeling of Stochastic Systems using Physics-aware Deep Learning.” Abstract (pdf)

  • Jim Pfaendtner, University of Washington
    “Fantastic Liquids and Where to Find Them: Optimization of Discrete Chemical Space.” Abstract (pdf)

  • Srinivas Rangarajan, Lehigh University
    “Towards Accurate Mechanistic Models of Catalytic Systems using Data-driven Techniques.” Abstract (pdf) 

  • Subramanian Sankaranarayanan, Argonne National Lab
    “Accelerating Materials Discovery and Design using AI and Machine Learning.” Abstract (pdf)

  • Katya Scheinberg, Cornell University (formerly Lehigh University)
    “Overview and Advances in Derivative Free and Black Box Optimization of Expensive Functions.” Abstract (pdf)

  • Tess Smidt, Lawrence Berkeley National Lab
    “Toward the Systematic Generation of Hypothetical Atomic Structures: Geometric Motifs and Neural Networks.” Abstract (pdf)

  • Martin Takac, Lehigh University
    “New Quasi-Newton Methods for Training Deep Learning Models.” Abstract (pdf)

  • Pratyush Tiwary, University of Maryland
    “Past-future Information Bottleneck Framework for Sampling Molecular Reaction Coordinate, Thermodynamics and Kinetics.” Abstract (pdf)

  • Zack Ulissi, Carnegie Mellon University
    “Enabling Catalyst Data Science with Active Optimization for Compositionally-Diverse Materials.” Abstract (pdf)

  • Venkat Viswanathan, Carnegie Mellon University
    “Data-Driven Discovery of Electrolytes for Next-Generation Batteries.” Abstract (pdf)

  • Edmund Webb III, Lehigh University
    “Predicting Domain Scale Mechano-reactivity in Human Blood Proteins via Coupled Machine Learning and Molecular Simulation”. Abstract (pdf)

  • Wenwei Zheng, Arizona State University
    “Methods for Predicting Dimensions of Intrinsically Disordered Proteins.” Abstract (pdf)