The Institute for AI-Enabled Materials, Discovery, Design, and Synthesis (AIMS) hosts events to build collaborations among participating AIMS institutions.
None scheduled at this time.
April 9: NSF Workshop on AI & Materials
Join AIMS for the “NSF Workshop on Accelerating Materials Discovery, Design, and Synthesis: A Grand Challenge for Artificial Intelligence” from 10:00 a.m. to 4:00 p.m. ET on Friday, April 9. One of the main goals of the workshop is to identify opportunities for synergistic advances in AI (including machine learning, automated planning and optimization of experiments, multi-objective optimization, human-AI interaction, and more) and material science and engineering.
The workshop will include a keynote and three panel discussions, featuring researchers from 15 universities or government organizations.
March 26: “Bridging the Gap Between Literature Data Extraction and Domain Specific Materials Informatics”
Abstract: Data has become a fundamental ingredient for accelerating and optimizing materials design and synthesis. Advances in applying natural language processing (NLP) to material science text has greatly increased the size and acquisition speed of materials science data from the published literature. This presentation will describe work to extract information from peer reviewed academic literature across a range of materials. Applying NLP pipelines to these types of materials science systems can be challenging due to the general schema and the noisiness of automatically extraction data. Dr. Olivetti will present data engineering techniques and discuss an optimal balance between automatic and manual data extraction.
About the Speaker: Elsa Olivetti is the Esther and Harold E. Edgerton Career Development Professor in the Department of Materials Science and Engineering (DMSE) at the Massachusetts Institute of Technology. Her research focuses on improving the environmental and economic sustainability of materials in the context of rapid-expanding global demand. Dr. Olivetti received her B.S. degree in Engineering Science from the University of Virginia and her Ph.D. in Materials Science Engineering from MIT.
March 5: “Predicting Materials Properties at Scale with Machine Learning”
Abstract: Machine learning (ML) models have demonstrated human, or even superhuman, performance in many tasks, from playing traditional board games to image classification. In this webinar, I will discuss how ML is poised to have a similar transformative impact in materials science. Applied on large data sets, ML techniques can be used to discover novel technological materials, to model complex systems at an accuracy beyond the reach of traditional computational techniques, and to enhance the accuracy and speed of interpreting characterization data. A key focus of this talk will be on the heterogeneity and scarcity of materials data, the challenges these characteristics present for ML, and the potential approaches to overcome them.
About the Speaker: Dr. Shyue Ping Ong is an Associate Professor of NanoEngineering at the University of California, San Diego. He obtained his Ph.D. from the Massachusetts Institute of Technology in 2011. His group, the Materials Virtual Lab, is dedicated to the interdisciplinary application of machine learning and first principles computations to accelerate materials design. He is a key developer of the Materials Project and the globally used Python Materials Genomics (pymatgen) materials library. Dr. Ong is also a recipient of the U.S. Department of Energy Early Career Research Program and the Office of Naval Research Young Investigator Program awards.
February 26: “Physical Discovery by Machine Learning: from Symmetries and Chemical Reactions to Generative and Causal Models”
Abstract: Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy. The applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models have been demonstrated. In this presentation, I will discuss several applications of autoencoders and variational autoencoders for the analysis of image and spectral data in STEM and SPM. The special emphasis is made on the rotationally invariant variational autoencoders that allow to disentangle rotational degrees of freedom from other latent variables in imaging and spectral data. The analysis of the latent space of autoencoders further allows establishing physically relevant transformation mechanisms. Extension of encoder approach towards establishing structure-property relationships will be illustrated on the example of ferroelectric domain walls and plasmonic structures. I will further illustrate the applications of the Bayesian inference methods towards inferring the mesoscopic and atomistic physics of materials in terms of continuous and atomistic generative models, and illustrate the pathways towards incorporation of physical models as priors within Bayesian optimization towards effective sampling of experimental parameter spaces. Ultimately, we seek to answer the causal questions such as whether frozen atomic disorder drives the emergence of the local structural distortions or average shift of the Fermi level induces structural reconstruction that in turn drive cation distribution, whether the nucleation spot of phase transition can be predicted based on observations before the transition, and what is the driving forces controlling the emergence of unique functionalities in quantum materials.
This research is supported by the by the U.S. Department of Energy, Basic Energy Sciences, Materials Sciences and Engineering Division and the Center for Nanophase Materials Sciences, which is sponsored at Oak Ridge National Laboratory by the Scientific User Facilities Division, BES DOE.
About the Speaker: Sergei V. Kalinin is a corporate fellow and a group leader at the Center for Nanophase Materials Sciences at Oak Ridge National Laboratory. He received his MS degree from Moscow State University in 1998 and Ph.D. from the University of Pennsylvania (with Dawn Bonnell) in 2002. His research presently focuses on the applications of big data and artificial intelligence methods in atomically resolved imaging by scanning transmission electron microscopy and scanning probes for applications including physics discovery and atomic fabrication, as well as mesoscopic studies of electrochemical, ferroelectric, and transport phenomena via scanning probe microscopy.
Sergei has co-authored >650 publications, with a total citation of >33,000 and an h-index of >94. He is a fellow of MRS, APS, IoP, IEEE, Foresight Institute, and AVS; a recipient of the Blavatnik Award for Physical Sciences (2018), RMS medal for Scanning Probe Microscopy (2015), Presidential Early Career Award for Scientists and Engineers (PECASE) (2009); Burton medal of Microscopy Society of America (2010); 4 R&D100 Awards (2008, 2010, 2016, and 2018); and a number of other distinctions.
February 12: “Artificial Intelligence for materials design: advances and remaining challenges”
Use of artificial intelligence with materials data sets has significantly improved the speed of discovery of new materials with improved attributes. In the field of glass and polymer design, combinations of different regression methods, such as genetic algorithm, Gaussian processes, and neural networks, and data generation methods, such as molecular dynamics and density functional theory, have led to robust design methods and near defect free manufacturing processes. Yet, despite the many successes, many serious challenges for faster and more robust algorithms remain before the design and manufacture of materials with optimal characteristics.
Dr. Adama Tandia received his Ph.D. in Applied Mathematics/Applied Physics from Paul Sabatier University (France) in 1998. He worked at the Department of Applied Mathematics at Northwestern University before joining the Department of Modeling & Simulation at Corning Incorporated since 2000. Tandia is a recognized subject matter expert in applications of molecular modeling and machine learning for materials design and process optimization. In 2008, Tandia introduced the use of machine learning at Corning and led the uphill charge for its widespread across the company.
January 29 Seminar: “Domain-specific Considerations in Machine Learning for Materials Design”
Abstract: Machine learning (ML) offers a promising path to significantly accelerating the development of new materials. At Citrine, we have found that a materials-tailored approach (rather than domain-agnostic ML) is crucial to success. For example, uncertainty quantification (UQ) can help prioritize candidate materials within vast design spaces, and physics-based simulations can provide valuable training data for transfer learning when experiments are scarce. In this talk, I will outline several examples of materials-tailored ML method development, and also highlight promising areas for future research.
About the Speaker: Dr. Bryce Meredig is cofounder and Chief Science Officer of Citrine Informatics, a materials informatics platform company, where he leads the External Research Department (ERD). ERD conducts non-proprietary, publishable research with collaborators in academia, government, and industry. Dr. Meredig’s research interests include the development and validation of physics-informed machine learning methods specific to applications in materials science and chemistry; integration of physics-based simulations with machine learning; and data infrastructure for materials science. Dr. Meredig earned his PhD from Northwestern University and BAS and MBA from Stanford University.