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HPC4EI Focus on Materials

SAVE THE DATE: April 8, 2022

8:00 AM - 12:45 PM PDT


Join us on April 8th for this special webinar event!

Materials enable many of the new energy and carbon reducing technologies. High temperature, corrosive resistant materials enable higher temperature, higher efficiency engines which save fuel in transportation and electricity generation. New materials enable higher energy density, safer batteries for transportation and grid energy storage. New materials will aid in the capture and sequestration of carbon on the road to a sustainable energy future. New material design is enabled by high performance computing to predict material performance in advance of the expensive experimental effort needed to test new material formulations. This event will feature discussions from DOE National Laboratory computational scientists leading efforts on computational material development. They will discuss the state of computational material design and show how they are working directly with industry to implement these techniques to advance US innovation in material development.


High Performance Computing for Energy Innovation (HPC4EI) is funded by the Department of Energy’s Energy Efficiency and Renewable Energy’s (EERE) Advance Manufacturing Office (AMO), Fossil Energy and Carbon Management Office (FECM), Hydrogen and Fuel Cell Technologies Office (HFTO), and Vehicle Technology Office (VTO). The HPC4EI program pairs industry engineers and scientists with national laboratory computational experts to solve difficult production and design problems aiming to reduce national energy consumption. Since its inception 2015, the HPC4EI program has funded over 140 projects with participation by 11 national laboratories. The world-class computational capabilities at the national laboratories are used to address problems in steel and aluminum manufacture, de-carbonization and electrification of manufacturing processes, turbine design, advanced materials for light weighting and high temperature, high corrosion applications, chemical processing and many more topic areas.

Event Speakers

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Rajeev Assary - April 2022


Keynote Speaker

Dr. Rajeev S. Assary

Group Leader,Molecular Materials at Materials Science Division
Argonne National Laboratory

Dr. Rajeev S. Assary obtained PhD degree in Computational Chemistry in 2005 from The University of Manchester UK. Dr. Assary held postdoctoral positions in University of Manchester and Northwestern University prior to joining Argonne National Laboratory in 2009. At present, he is a Group Leader, Molecular Materials at Materials Science Division of Argonne National Laboratory. Dr. Assary’s research interests include fundamental and applied aspects of computational modeling based on quantum chemistry in biomass catalysis and ‘beyond lithium ion’ energy storage systems. He has published over 100 papers in peer reviewed journals. He conducts research as part of Joint Center of Electric Energy Storage (JCESR) and Consortium for Computational Physics and Chemistry (CCPC).


Atomistic Modeling and AI for Energy Storage and Conversion Chemistry

A priori atomistic modeling provides accurate information to enable design and discovery of materials for energy and chemicals. In energy storage, beyond lithium-ion (BLI) research has the potential to revolutionize consumer electronics including portable and stationary power, transportation sector, and grid energy storage. Multi-valent energy storage or economically viable Na+ batteries, high-density metal-air, metal-sulfur batteries, or grid-storage systems are considered in the beyond lithium-ion research and development. All these research efforts require significant a priori computations for materials discovery, property prediction, and optimization using atom-atom and molecule by molecule approaches. Atomistic modeling can provide a priori data to accelerate discovery of electrolytes, electrodes, and membranes to reduce the cost and time of discovery. Coupled with data science and multi-scale techniques, atomistic modeling can address prediction of molecular level properties of materials (redox potentials, solvation, spectroscopic, and reactivity) to down-select optimal materials or material combinations. In this presentation, I will describe some of our recent efforts in active learning coupled with large scale first principles simulations to down select/optimize desired molecules for flow battery technology. I will describe some of our quantum chemistry-informed molecular property predictions of thousands of molecules and data driven approach to study longer time scale diffusion of ions for multivalent battery concepts. Finally, data-driven approach for catalytic property prediction using periodic density functional theory and graph neural network will also be described.

Scott Roberts - April 2022

Keynote Speaker

Dr. Scott A. Roberts

Distinguished R&D Chemical Engineer,Thermal/Fluid Component Sciences Department
Sandia National Laboratories

Scott A. Roberts is a Distinguished R&D Chemical Engineer in the Thermal/Fluid Component Sciences department. He has B.S and Ph.D. degrees in Chemical Engineering from the University of Kansas and University of Minnesota, respectively. He has spent much of the past decade developing coupled multi-physics, multi-scale computational models for a variety of Sandia applications, including battery materials and performance, reentry vehicle heat shields, railguns, and multiple Sandia components. Beginning with a LDRD in 2014, Scott began creating robust workflows for image-based simulation of battery materials. This transitioned into funding through DOE’s vehicle technologies office and a follow-on LDRD about credibility of the workflow. These techniques are being used throughout the Sandia complex. Scott has participated in one previous HPC4EI project and is PI of a current HPC4EI project.


Closing the Design Cycle, Image Processing for As-Built Process Simulations

Image-based simulations are a key enabler of digital twins and can represent a digital engineering workflow for the analysis of as-built materials and components. In this talk, I detail the development of key enabler technologies for credible and automated image-based simulation workflows. Machine learning techniques are created for 3D image segmentation with uncertainty, while automated tetrahedral meshing schemes are used for numerical simulation mesh development. This entire workflow is subjected to rigorous uncertainty quantification. Our processes are demonstrated on three key exemplar applications, thermal protection system materials, electrodes for lithium-ion batteries, and exploding bridge wire detonators.

Sneha Akhade - April 2022

Keynote Speaker

Dr. Sneha Akhade

Staff Scientist, Materials Sciences Division
Lawrence Livermore National Laboratory

Dr. Sneha Akhade is currently a Staff Scientist in the Materials Sciences Division at Lawrence Livermore National Laboratory. Dr. Akhade earned her Ph.D. in Chemical Engineering from Pennsylvania State University in 2016 and M.S. in Chemical Engineering from Carnegie Mellon University in 2011 and held a prior postdoctoral position at Pacific Northwest National Laboratory. Dr. Akhade’s research interests include sustainable catalytic conversion of carbon and hydrogen and HPC enabled rational design of materials for alternative energy storage and conversion technologies. She works at the intersection of several domains with partner national laboratories, start-ups, and academic institutions and has over 30 peer-reviewed publications and over 25 conference talks.


Multiscale Modeling of Catalyst Materials for Carbon Conversion

With growing emphasis on decarbonization and increasing energy demand, the efficient capture and utilization of CO2 can potentially pave the path forward for sustainable, modular, energy efficient, and decentralized production of platform chemicals using renewable energy inputs while simultaneously abating carbon emissions. Industrial application of CO2 electrochemical conversion technology is currently hindered by poor efficiency, selectivity, and durability of the catalysts. While existing efforts place significant emphasis on improving activity and selectivity of CO2 capture and conversion, they often fail to address the critical durability of the material under real-time operating conditions. The structure, composition, and electronic properties of the material are all key factors that impact the extent of degradation. Moreover, material evolution often is a multiscale problem; necessitating the need to develop multi-physics modeling frameworks for realistic operational understanding of materials for CO2 conversion and eventual design of more durable systems. In this talk, I will provide an overview of some of our efforts to examine various factors contributing to performance, morphology evolution and degradation of electrochemical CO2 conversion catalysts. I will discuss how HPC enabled hybrid atomistic and mesoscale level modeling can be leveraged towards realizing the interfacial complexities of carbon conversion. To this end, our efforts at LLNL in developing multiscale models and integrating electrolytic transport, catalytic conversion and catalyst stability over multiple length and time scales will be showcased.



7:30 AM

Login Period

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8:00 AM


Robin Miles, HPC4EnergyInnovation Director, Lawrence Livermore National Laboratory

8:10 AM

HPC4EnergyInnovation Program Overview: National Laboratories Partner with U.S. Manufacturers to Increase Innovation and Energy Efficiency

Aaron Fisher, HPC4EnergyInnovation Project Manager, Lawrence Livermore National Laboratory

8:30 AM

Atomistic Modeling and AI for Energy Storage and Conversion

Keynote Speaker

Dr. Rajeev S. Assary, Molecular Materials Group Leader, Argonne National Laboratory

9:15 AM

Closing the Design Cycle, Image Processing for As-Built Process Simulations

Keynote Speaker

Dr. Scott A. Roberts, Distinguished R&D Chemical Engineer, Sandia National Laboratories

10:00 AM

Multiscale Modeling of Catalyst Materials for Carbon Conversion

Keynote Speaker

Dr. Sneha Akhade, Staff Scientist Materials Science Division, Lawrence Livermore National Laboratory

HPC4EI Projects Featuring Materials Design

11:00 AM

Phase-Field Simulations of Direct Aging of AM-Processed 718 Alloy

Balasubramaniam Radhakrishnan, Distinguished Research Staff, Oak Ridge National Laboratory

11:20 AM

Modeling the Antiphase Boundary Energy in Ni3Al-based Alloys using Density Functional Theory and Machine Learning

Timofey Frolov, Staff Scientist, Lawrence Livermore National Laboratory

11:40 AM

Accelerating High Temperature Operation Development of High Entropy Alloys via High Performance Computation

Michael Gao, Physical Scientist, National Energy Technology Laboratory

12:00 PM

Ab-initio Guided Design and Materials Informatics for Accelerated Product Development of Next Generation Advanced High Strength Steels

Sylvie Aubry, Reaction Sorption and Transport Team Lead, Lawrence Livermore National Laboratory

12:20 PM

Why does material qualification take so long and how can microstructural modeling help?

Mark Messner, Principal Mechanical Engineer, Argonne National Laboratory

12:40 PM

Closing Remarks

Robin Miles, HPC4EnergyInnovation Director, Lawrence Livermore National Laboratory

12:45 PM


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Event Coordinator

Profile picture of Michelle Herawi

Michelle Herawi
HPC4EnergyInnovation Administrator


(925) 423-4964
hpc4ei [at]