Day 1: Methods and Algorithms

Dr. Marcus M. Noack: Introduction to the Workshop and to Autonomous Discovery

Prof. James A. Sethian: The Center for Advanced Mathematics for Energy Research Applications (CAMERA)

UC Berkeley

Dr. Draguna Vrabie

Pacific Northwest National Laboratory

Autonomous control systems and learning

Autonomous control systems are those that accomplish their objectives in real-world scenarios and under uncertainty in the environments and can compensate for changes and failures without external intervention. In this talk, I will provide a brief overview of modern controls systems and their transition to incorporate data-driven knowledge. I will then introduce a data-driven predictive control approach that uses physics-informed deep learning representations for synthesizing control policies and models of the unknown dynamic systems.

Dr. Benji Maruyama

Air Force Research Laboratory

Autonomous Research Systems for Materials Development

The current materials research process is slow and expensive; taking decades from invention to commercialization. Researchers are now exploiting advances in artificial intelligence (AI), autonomy & robotics, along with modeling and simulation to create research robots capable of doing iterative experimentation orders of magnitude faster than today. We will discuss concepts and advances in autonomous experimentation in general, and

associated hardware, software, and autonomous methods.

We propose a “Moore’s Law for the Speed of Research,” where the rate of advancement increases exponentially, and the cost of research drops exponentially. We consider a renaissance in “Citizen Science” where access to online research robots makes science widely available. This presentation will highlight advances in autonomous research and consider the implications of AI-driven experimentation on the materials landscape.

Dr. Kevin G. Yager

Center for Functional Nanomaterials, Brookhaven National Laboratory

Autonomous Analytics and Control in X-ray Scattering

Autonomous control of complex scientific instruments--such as synchrotron beamlines--holds enormous promise for accelerating experiments and materials discovery. This talk will discuss the vision for autonomous experiments (AE) using a synchrotron x-ray scattering beamline as the key example. Deep learning is used to classify x-ray detector images, with performance improving when domain-specific data transformations are applied. To close the autonomous loop, we deploy a general-purpose algorithm based on gaussian processes. The underlying modeling can exploit system-specific information, while the objective function can be tailored to a particular experiment, balancing knowledge gain, cost, and search targets. Examples from recent autonomous experiments will be presented, including measuring nanoparticle ordering, 3D-printed materials, multi-dimensional combinatorial libraries, and real-time photo-thermal processing.

Prof. Kristofer Reyes

Department of Materials Design and Innovation, University of Buffalo

From Black-Box to Problem-Fluency: An overview of models and algorithms for Closed-Loop, Autonomous Materials Design

Closed-loop materials design platforms have grown in recent popularity, with many robot scientists coming online in the past few years. Key to their operation are the robots' "brains" -- the machine learning models and decision-making algorithms they use to explore experiment space without human intervention. Many generic black-box models have enabled the rapid development of prototype platforms that have shown the promise of autonomous materials development. While at a nascent state, the field is primed to explore more sophisticated, problem-fluent methods and models, hoping that such practices can further accelerate discovery at scale. We will walk down the path from such black-box models to more problem-aware ones in this talk, highlighting core concepts and seminal works and motivating the practical need for more complex models. We place many of the topics covered in this workshop's discussions and tutorials in context with one another and explore promising new directions to modeling materials experiments and broader systems, all in the context of autonomous discovery.

Dr. Marcus M. Noack

Computational Research Division, Lawrence Berkeley National Laboratory

Domain-Aware Gaussian Processes and High-Performance Mathematical Optimization for Optimal and Autonomous Data Acquisition

Gaussian Processes have shown to be a powerful tool for autonomous control of data acquisition due to their robustness, analytical tractability, and natural inclusion of uncertainty quantification. In this talk, I want to present our work on a general, flexible, and powerful GP-driven framework for autonomous data acquisition. The focus will lie on making a Gaussian process domain aware, how this awareness can be used for decision-making, and the challenges that come with domain awareness.

Dr. Francis J. Alexander

Computational Science Initiative, Brookhaven National Laboratory

Optimal Experimental Design via Mean Objective Cost of Uncertainty

I will discuss the concept of the Mean Objective Cost of Uncertainty (MOCU) in the context of optimal experimental design. The method will be described in detail, including variants and new directions, I will then describe how this concept is currently being used in the resource allocation and experimental design first for simple dynamical systems, then complex biological systems, and finally, in the context of ExaScale computing.

Dr. Maria K. Chan

Center for Nanoscale Materials, Argonne National Laboratory

Integrating Theory and Modeling in Autonomous Discovery

Achieving scientific understanding from high throughput and autonomous experimental frameworks often requires the incorporation of modeling and simulation data. We will discuss the relevant challenges for integrating theory and experiment using AI, and the roles played by atomistic and first principles modeling in deciphering characterization data for autonomous feedback and continuous space search for materials design.