CENSAI is busy planning more events for the community. Check back soon for more information.
September 12, 2022
“Autonomous Materials Research and Discovery at the National Institute of Standards and Technology (NIST)”
Presenter: Dr. A. Gilad Kusne, staff scientist, National Institute of Standards and Technology, and adjunct professor, University of Maryland
Abstract: The last few decades have seen significant advancements in materials research tools, allowing scientists to rapidly synthesize and characterize large numbers of samples – a major step toward high-throughput materials discovery. Autonomous research systems take the next step, placing synthesis and characterization under control of machine learning. For such systems, machine learning controls experiment design, execution, and analysis, thus accelerating knowledge capture while also reducing the burden on experts. Furthermore, physical knowledge can be built into the machine learning, reducing the expertise needed by users, with the promise of eventually democratizing science. In this talk I will discuss autonomous systems being developed at NIST with a particular focus on autonomous control over user facility measurement systems for materials characterization, exploration and discovery.
March 14, 2022
“Machine Learning for Materials Property Prediction and Materials Characterization”
Presenter: Dane Morgan, the Harvey D. Spangler Professor of Engineering, University of Wisconsin-Madison
Abstract: Machine learning methods are playing an increasing role in materials research, from predicting properties to accelerating characterization to extracting data from text. In this talk I will give a brief overview of recent activities and opportunities for machine learning in materials. Then I will discuss recent efforts in my group in three areas. The first is assessing model domains and uncertainties in materials property prediction, where I will share assessments of common Bayesian (Gaussian process regression) and ensemble methods. We show that Gaussian process regression is better at determining model domain than ensembles, but that the ensembles error bars are more accurate when properly calibrated. I will also discuss our recent efforts to automate deep learning object detection approaches to find the location and geometry of different defect types in electron microscopy images of irradiated steels. We show that an accuracy comparable to human analysis can be achieved, suggesting a future where defect analysis is more standardized and orders of magnitude faster than today, but that training data requirements, transferability, and feature sizes are a challenge. Finally, I will discuss some successes and challenges fitting machine learning models to represent interactions between atoms, called machine learning potentials, which have the potential to dramatically accelerate a wide range of materials simulations. In all these examples I will try to illustrate where I think open challenges create opportunities for AI experts to help materials researchers integrate more effective machine learning approaches.
November 15, 2021
“Digital transformation in biologics drug discovery: challenges and opportunities”
Presenter: Maria Wendt, Head of Biologics Research US and Global Head of Digital Biologics Platform (ML/AI), Large Molecule Research, at Sanofi US.
Abstract: More, better, faster… and now smarter. Sanofi’s Large Molecule Research (LMR) (i.e. biologics drug discovery and development organization) is on its journey towards the promise of advanced computation. At Sanofi LMR we aim to turn innovative science into novel biologic candidates to improve the health of patients. We need to provide a robust engine to discover and deliver innovative biologics to conquer complex diseases via multi-targeting, smart biologics, and intelligent drug design. In this talk, I will provide an introduction to the challenges of discovering and developing large-molecule drugs (biologics) focusing on molecule design and describe the application landscape of AI/ML in this domain. I will provide an overview of our efforts to intensify the use of data and computation in all aspects of biologics discovery and optimization; and to transform to next-gen processes driven by ML/AI innovations.
October 18, 2021
“Computational Sustainability: Computing for a Better World and a Sustainable Future”
Presenter: Carla Gomes, the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science and the director of the Institute for Computational Sustainability at Cornell University
Abstract: Artificial Intelligence (AI) is a rapidly advancing field. Novel machine learning methods combined with reasoning and search techniques have led us to reach new milestones: from computer vision, machine translation, and Go world-champion level play, to self-driving cars. These ever-expanding AI capabilities open up new avenues for advances in new domains. I will discuss our AI research for advancing scientific discovery for a sustainable future. In particular, I will talk about our research in a new interdisciplinary field, Computational Sustainability, which has the overarching goal of developing computational models and methods to help manage the balance between environmental, economic, and societal needs for a sustainable future. I will provide examples of computational sustainability problems, ranging from biodiversity and wildlife conservation, to multi-criteria strategic planning of hydropower dams in the Amazon basin and materials discovery for renewable energy materials. I will also highlight cross-cutting computational themes and challenges for AI at the intersection of constraint reasoning, optimization, machine learning, multi-agent reasoning, and citizen science