ACED: Accelerated Computational Electrochemical systems Discovery

Two key efforts to curb global greenhouse gas emissions involve reducing CO2 into energy-dense liquid fuels and replacing the energy-intensive Haber-Bosch process for nitrogen reduction with an electrochemical alternative. In addition, efforts to electrify long-distance trucking or aviation are limited by the low-energy density of modern lithium-ion chemistries compared to petroleum fuels. Novel materials can address both of these challenges; however, systematic, theoretical evaluations of candidate material systems in-silico are limited to small design spaces and low-fidelity screening that fail to model realistic operating conditions.

The goal of this project is to alter this paradigm by enabling rapid, high-fidelity screening of large numbers of electrochemical functional materials for use in new energy technologies. The team brings together complementary expertise in advanced computing, numerical methods, and materials science. Overall, the team expects to accelerate the overall energy material development and optimization process by 80%.

Research

Project Overview

This project involves software development efforts for accelerated solution of differential and algebraic equations describing the kinetics of the electrochemical systems, integration of these solvers with machine learning approaches, and global optimization over the chemical design space. The high-value candidates will be tested experimentally, validating the entire approach.

Sequential learning for design of experiments

We will perform physics-aware machine learning to iteratively guide simulations towards candidate materials. This sequential learning approach will enable an efficient and uncertainty-driven exploration of the high-dimensional design spaces encompassed in this work while allowing for multi-objective optimization of catalyst parameters.

High-throughput density functional theory calculations

To rapidly generate a large-scale database of reaction intermediate structures, we are developing an automated DFT framework. This will simultaneously accelerate adsorption energy computations while systematically accumulating and manipulating output data in a manner for ease of integration into machine learning models. Moreover, it will facilitate the overall closed-loop sequential learning approach to explore new systems.

Graph convolutional methods for molecules and surfaces

Building on the success of Crystal Graph Convolutional Neural Networks in predicting properties of crystalline solids as well as software packages for molecular machine learning such as DeepChem (for which we are developing the official Julia language port), we are developing software to combine these techniques to rapidly and accurately predict energetics of the adsorption processes critical to catalysis of reactions such as nitrogen reduction.

Accelerated solution of microkinetic differential equations

Leveraging the advanced numerical techniques implemented in the Julia programming language, we are developing customized solutions to dramatically speed up the solutions of the challenging stiff differential algebraic equations (DAE’s) that describe the interactions of chemical species on catalytic surfaces.

People

Leadership

Venkat Viswanathan
Associate Professor of Mechanical Engineering, Carnegie Mellon University
Alan Edelman
Professor of Applied Mathematics, Massachusetts Institute of Technology
Viral Shah
Viral Shah
Co-Founder and CEO, Julia Computing, Inc.
Chris Rackauckas
Scientific Advisor, Julia Computing, Inc.
Bryce Meredig
Bryce Meredig
CSO and Co-Founder, Citrine Informatics
James Saal
James Saal
Manger of External Research Programs, Citrine Informatics

Postdoctoral Researchers

Rachel Kurchin
Manufacturing Future Initiatives Postdoctoral Fellow, Carnegie Mellon Mechanical Engineering and Materials Science and Engineering
Clara Nyby C
Clara Nyby
Postdoctoral Fellow, Citrine Informatics
Vinay Hegde
Vinay Hegde
Postdoctoral Fellow, Citrine Informatics

Research Staff

Eric Muckley
Eric Muckley
Research Scientist, Citrine Informatics
Dhairya Gandhi
Data Scientist, Julia Computing
Qingyang (Ernest) Zhang
Qingyang (Ernest) Zhang
Post-master's Researcher

Graduate Students

Lance Kavalsky
PhD Student, CMU Mechanical Engineering
Dilip Krishnamurthy
Mechanical Engineering
Xiaoyu (Sean) Sun
Xiaoyu (Sean) Sun
Master's Student, CMU Computer Science
Matthew Johnson
Matthew Johnson
PhD Student in MIT Chemical Engineering and Lead Developer for RMG and RMS

Collaborators

Bharath Ramsundar
Lead developer for DeepChem
Valentin Sulzer
Postdoctoral Research Fellow (U Michigan) and PyBaMM lead developer
Shaojie Bai S
Shaojie Bai
PhD Student, CMU Computer Science
Michael F. Herbst
Postdoctoral fellow at CERMICS lab, École des Ponts ParisTech, France; Lead developer of DFTK.jl
David Poole
David Poole
PhD Student from Iowa State University; lead developer of JuliaChem.jl

News

  • November 26, 2019
    The Scott Institute published a news piece announcing our award! Check it out here!
  • November 20, 2019
    See the Julia award announcement here!

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