Upcoming Invited Conference Talks
ESMI's researchers frequently appear as invited guest speakers on topics related to energy storage materials, high-throughput experimentation for materials, and autonomous materials discovery. You can meet with ESMI's researchers at the following conferences.
Materials Research Society (MRS) 2024 Spring Meeting
April 22–26 | Seattle, Washington
May 7–9 | Virtual
Scientists from PNNL will be joining their peers from around the world to discuss topical clusters including Broader Impact (BI); Characterization (CH); Electronics, Optics and Photonics (EL); Energy Generation and Conversion (EN); Energy Storage (ES); Manufacturing (MF); Materials Theory, Computation and Data Science (MT); Nanomaterials (NM); Quantum Materials and Materials Physics (QT); Soft Materials and Biomaterials (SB); and Structural and Functional Materials (SF). Learn More.
American Chemical Society (ACS) Spring 2024 Meeting
March 17-21, 2024 | New Orleans, Louisiana & Virtual
Many Flavors of Chemistry, the theme of the American Chemical Society (ACS) Spring 2024 conference, features a mix of hybrid, in-person, and live virtual sessions, along with keynote events, expo hours, and poster events. Many researchers from the Energy Storage Materials Initiative at PNNL will be participating in this year's conference. Learn More.
11th International Workshop on Combinatorial Materials Science and Technology
September 26-30, 2022 | Golden, Colorado
International Workshop on Combinatorial Materials Science and Technology is the major conference in the field of high-throughput experimentation for materials. Traditional topics include High Throughput Experimental Methods (combinatorial synthesis, spatially resolved characterization, data management) on various materials classes (Metals, Oxides, Semiconductors, Catalysis, Polymers, Biomaterials).This year, the special highlighted topic would be Autonomous Experimentation. Talks by ESMI researchers:
High throughput solubility determination for data-driven material design and discovery in redox flow battery research | Yangang Liang, Heather Job, Aaron Hollas, Ruozhu Feng, Wei Wang
Materials Discovery Accelerated by Artificial Intelligence-Guided Microscopy | Steven R. Spurgeon
NSF/Telluride Workshop: Materials Chemistry in Electrochemical Energy Storage
September 26-30, 2022 | Zoom
ESMI researcher Ruozhu Feng will be participating in the Telluride Science Research Center workshop focusing on mechanistic understanding and exploration of materials chemistry in electrochemical energy storage including batteries, solar cells, electrolyzers, and supercapacitors. Both organic and inorganic materials play critical roles in energy storage devices. The primary task of the workshop is to invoke the in-depth discussion on how electronic and steric factors, solvents, and additives affect electrochemical characteristics of organic and inorganic materials in energy storage devices. Learn More.
Organic Battery Days
October 13-14 | University of Houston, Texas
Organic Battery Days aims to strengthen communication and cooperation among experts as well as to exchange knowledge and experience on the most recent developments in the field of organic batteries. Speakers present on the design and synthesis of organic electroactive materials, study of these materials in various electrochemical systems (e.g., rechargeable batteries, flow cells), and electrolytes for organic electrodes. ESMI Director, Wei Wang, is one of the event organizers and ESMI researcher Ruozhu Feng will be giving a talk. Learn More.
Energy Materials & Data Science Seminar Series
How Robots Can Teach Us to Trust A.I.
September 20, 2021 | Dr. Jason R. Hattrick-Simpers is a Professor at the Department of Materials Science and Engineering, University of Toronto, and a Research Scientist at CanmetMATERIALS.
 | There has been an explosion of interest in the field of artificial intelligence (A.I.) to guide materials science, this has resulted in the discovery of exciting new phase change materials, amorphous alloys, and catalysts. But our A.I.’s are powered by data and the scientific literature largely consists of one-off experiments without quantified uncertainties, sufficient metadata to ensure reproducibility, or access to the primary data used to draw conclusions. Here I will discuss the tenuousness of ground truth, the need for openly preserving expert disagreement within scientific data sets, and challenges associated with aggregating data from the open literature. |
Towards Autonomous Materials Research: Recent Progress and Future Challenges
November 15, 2021 | Dr. Jens Hummelshoj works in the Toyota Research Institute’s AMDD department since 2016 where he guides research and development efforts and grows organizational health and innovation capacity.
 | The modus operandi in materials research and development is combining existing data with an understanding of the underlying physics to create and test new hypotheses via experiments or simulations. Since the early 2000s, there has been notable progress in automation of each component of the scientific process. With recent advances in machine learning and artificial intelligence for decision-making, the opportunity to automate the entire closed-loop process is emerging as an exciting research frontier. Autonomous systems are poised to make the search for new materials, properties, or parameters more efficient under budget and time constraints, and in effect accelerate materials innovation. This talk provides an overview of our work at Toyota Research Institute related to closed-loop research systems and applications across different materials challenges. |
Designing electrolyte materials with ML-accelerated simulations
February 14, 2022 | Dr. Rafael Gomez-Bombarelli is the Jeffrey Cheah Assistant Professor at MIT’s Department of Materials Science and Engineering since 2018.
 | Designing novel electrolytes is key to improving electrical energy storage technologies. Replacing the liquid electrolytes currently used with solid materials is a promising solution to improve the safety of lithium-ion batteries. However, the enormous design space of potential materials to apply, and the multitude of properties they need to satisfy (stability, cost, electrochemical properties, solubility, conductivity, etc.) limit the speed of discovery. In this presentation, Gomez-Bombarelli describes how the use of machine-learning techniques, including ML interatomic potentials trained from quantum mechanical simulations, can be applied to discovering electrolyte materials. |
Autonomous Materials Research and Discovery at NIST
May 9, 2022 | Dr. Aaron Gilad Kusne is a Staff Scientist with the National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, and an adjunct professor with the University of Maryland.
 | The last few decades have seen significant advancements in materials research tools, allowing scientists to rapidly synthesis 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. In this talk Gilad Kusne discusses autonomous systems being developed at NIST with a particular focus on autonomous control over user facility measurement systems for materials characterization, exploration, and discovery. |
Energy Storage Webinars
The Energy Storage @PNNL webinar series explores how current research efforts are driving increased adoption of energy storage technologies. As your host, Vince Sprenkle, PNNL's Energy Process and Materials Senior Advisor, brings you topics ranging from energy storage safety/reliability and flow batteries, to energy storage for social equity and hydrogen as a long-duration storage asset. All webinars are placed on our PNNL YouTube channel dedicated to Energy Storage. You can watch them here.
Developing a Flow Battery
April 28, 2022 | Wei Wang, Materials Scientist and Director for the Energy Storage Materials Initiative
This presentation describes the development of new electrolyte chemistries at PNNL. Solvation chemistry of the different electrolyte systems will be discussed, which provides a greater understanding of dynamic interactions between solvent-solvent, ion-solvent, and ion-ion at the molecular level. CLICK TO WATCH THE RECORDING
Machine Learning for Energy Storage Materials
September 22, 2022 | Emily Saldanha, Data Scientist
This presentation will highlight work performed under Pacific Northwest National Laboratory’s Energy Storage Materials Initiative to leverage such machine learning techniques to support the development process for electrolyte materials. In particular, the presentation will also discuss machine learning approaches for data extraction from literature, molecular property prediction, experimental design, and molecular inverse design. Recording will be available soon.