Amir Taqieddin

Physics-Informed ML · Batteries · Thermal-Fluid & Electrochemical Systems · Materials Informatics

Materials Informatics Scientist, Solid Power · PhD, Mechanical Engineering

Multiscale AI-Driven & Physics-Based Solutions for Batteries and Thermal Systems

Machine Learning · Batteries · Thermal-Fluid & Electrochemical Systems

Integrating physics-informed AI, multiscale thermal-fluid simulations, and electrochemical engineering to accelerate the design, optimization, and control of next-generation battery technologies, with extensions to electrochemical water treatment and CO₂-removal systems.

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About

Uniting machine learning, thermal-fluid science, and electrochemical engineering to accelerate next-generation battery and energy systems

Current Position

Materials Informatics Scientist at Solid Power Operating Inc., leading AI-driven digital twins and multiscale simulations for next-generation solid-state and lithium-ion batteries. Previously, I worked at Nissan’s Advanced Technology Center – Silicon Valley on materials informatics and battery management systems for electric vehicles, and at Technip on thermal-fluid and process engineering for large-scale energy systems.

Academic Background

PhD in Mechanical & Industrial Engineering and MSc in Mechanical Engineering from Northeastern University, where my dissertation developed multiscale computational frameworks for carbon- and iron-based materials in electrochemical water treatment and ocean-based CO₂ removal. My training is rooted in thermal-fluid sciences and electrochemical engineering.

Research Vision

My research integrates multiscale physics-based modeling, thermal-fluid science, machine learning, and materials informatics to solve critical challenges in energy storage, water treatment, and climate technologies. I develop computational frameworks that connect molecular-scale phenomena to device- and system-level performance.

Industry & Research Experience

~5+ years of industrial R&D across automotive, batteries, energy, and computational science

Academic & Research Affiliations

Northeastern University logo
Northeastern University
PhD & MSc · Mechanical & Industrial Engineering
UIUC logo
University of Illinois Urbana–Champaign
Nanofluidics & 2D Materials Research
JUST logo
Jordan University of Science & Technology
BSc · Mechanical Engineering (1st of 128)

Research Program

Multiscale ML and computation for electrochemical materials, batteries, reactors, and thermal-fluid systems

Atomistic Scale

MD, DFT, force fields

Microstructure

Porous media, interfaces

Device/Cell

Batteries, sensors, reactors

System Level

Packs, grids, deployment

Atomistic View: Molecular Dynamics of Confined Fluids

Short molecular dynamics simulation showing how water and ions move at the nanoscale in confined geometries. These atomistic insights feed directly into microstructure- and reactor-scale models for electrochemical systems and nanofluidic devices.

Overview of multiscale electrochemical systems, reactive oxygen species design, and closed-loop materials informatics for energy storage applications.
Graphical overview of my research: (a) multiscale electrochemical cell modeling from quantum to continuum scales, (b) design features and applications of reactive oxygen species systems, and (c) closed-loop materials informatics connected to energy storage devices such as post-lithium and redox-flow batteries.
Three interconnected projects: closed-loop materials informatics, hybrid physics-informed machine learning using real-world data, and onboard control systems using reduced-order models.
Three interconnected research thrusts: (1) closed-loop materials informatics for accelerated predictions and informed design, (2) fusion of real-world data into hybrid physics-informed ML for prognosis and health management, and (3) onboard control systems using reduced-order models for diagnosis and optimized operation.

Energy Storage & Batteries

Developing digital twins and physics-informed ML for safe, fast-charging batteries. Integrating electro-chemo-mechanical and thermal-fluid modeling with real-time control strategies for solid-state and advanced lithium batteries.

  • Digital twins for battery management
  • Thermal-mechanical failure prediction
  • Safe fast-charging protocols
  • ML-accelerated design maps

Collaboration opportunities: AI-enabled battery digital twins, fast-charging protocol design, and thermal-mechanical safety modeling with OEMs, suppliers, and research labs.

Machine Learning & AI

Designing neural-network and physics-informed ML workflows for materials discovery, process control, and design space exploration. My work combines PINNs, neural operators, classical regression, and Bayesian/multi-objective optimization to build reliable surrogates for complex multiphysics systems.

  • Neural networks for surrogate modeling and digital twins
  • Physics-informed neural networks (PINNs) & neural operators for PDE-based systems
  • Uncertainty-aware regression and calibration (e.g., ensembles, Gaussian processes)
  • Bayesian optimization & active learning for experiments and simulations
  • Multi-objective optimization and Pareto analysis for competing design targets

Collaboration opportunities: physics-informed ML for materials and energy systems, Bayesian and multi-objective optimization for process design, and open-source tools for PINNs, regression, and neural-operator-based surrogates.

Materials Informatics

Translating microstructure into performance using ML and statistical modeling. From tomography images to actionable design rules for batteries and electrochemical systems.

  • Structure–property relationships from images, graphs, and simulations
  • Regression models (linear, regularized, tree-based, Gaussian processes)
  • Neural-network surrogates (MLPs, CNNs, GNNs) for microstructure-aware properties
  • Multi-objective optimization (Pareto fronts, trade-offs in performance/robustness)
  • Bayesian optimization to navigate large processing–structure design spaces

Collaboration opportunities: tomography-driven microstructure analysis, regression and optimization pipelines for materials design, and Bayesian optimization workflows for solid electrolytes and composite electrodes.

Electrochemical Water Treatment

Multiscale modeling of granular activated carbon (GAC) electrodes for reactive oxygen species generation. Connecting molecular-scale water adsorption to reactor-level performance and pollutant removal efficiency in multiphase, flow-through systems.

  • ROS generation mechanisms
  • Porous electrode design
  • Flow-through reactor optimization
  • Scale-up and MRV frameworks

Collaboration opportunities: scaling GAC-based reactors, integrating modeling with pilot systems, and developing MRV and techno-economic frameworks for decentralized treatment.

CO₂ Sequestration & Climate

Electrochemical strategies coupling iron chemistry, alkalinity enhancement, and carbon capture. Developing reactor designs and MRV tools for ocean iron fertilization and carbon dioxide removal technologies.

  • Ocean alkalinity enhancement
  • Iron-based electrochemical CDR
  • Monitoring & verification (MRV)
  • Techno-economic analysis

Collaboration opportunities: marine CDR consortia, model-development for field pilots, and independent MRV for electrochemical ocean iron fertilization and alkalinity enhancement.

Nanofluidics & 2D Materials

Transport in nanoscale channels and heterostructures, including electroosmotic coupling and high-sensitivity biosensors based on deformed graphene and MoS₂ for nucleic acid detection and biosensing applications.

  • Electroosmotic transport
  • Graphene field-effect biosensors
  • Nanoscale hydrodynamics
  • 2D heterostructure devices

Collaboration opportunities: joint projects on graphene/TMDC sensors, flexible electronics, and nanofluidic transport simulations for biosensing or separation technologies.

Research Philosophy

Building computational frameworks that integrate multiscale physics-based modeling, thermal-fluid science, and data-driven machine learning to enable both forward performance prediction and inverse, target-driven design of electrochemical materials, devices, and processes for sustainable energy and environmental applications — from ROS-generating activated carbon electrodes and ocean iron fertilization reactors to AI-designed solid-state batteries.

Mechanistic
Physics-first approach
Data-Driven
ML-accelerated design
Multi-Scale
Atoms to systems
Validated
Experiment-informed

Electrochemical Water Treatment & Reactive Oxygen Species

Multiscale models link water adsorption and redox chemistry in granular activated carbon to continuum transport, bubble dynamics, and reactor-scale ROS generation for efficient pollutant removal.

Multiscale schematic of molecular adsorption, continuum transport, bubble dynamics, and porosity-controlled ROS evolution leading to clean water.
Multiscale view of electrochemical water treatment: molecular adsorption and reactivity, continuum transport and bubble dynamics in porous carbon, and reactor-scale porosity effects on ROS generation.
Design-application arc for reactive oxygen species systems, showing design features, ROS chemistries, and application domains including water treatment and energy.
Design space for reactive oxygen species systems, connecting design features and materials chemistries with applications in water treatment, sterilization, energy storage, and fuel cells.

Bubble Dynamics in Gas-Evolving Electrochemical Cells

Multiphase simulations of bubble growth and detachment help quantify how gas evolution reshapes local hydrodynamics, transport, and reactive surface area in electrochemical water treatment reactors.

Multi-Bubble Growth and Interaction

Coupled level set–VOF simulation of many bubbles nucleating and growing on an electrode surface. The clip highlights how neighboring bubbles interact, coalesce, and restructure the local flow field in gas-evolving electrochemical systems.

3D Gas-Evolving Bubble Simulation

Three-dimensional view of a single gas bubble growing and detaching from an electrode. This type of model is used to study bubble shape, detachment dynamics, and the resulting changes in interfacial area and mass transport.

Ocean-Based CO₂ Removal & Marine Electrochemistry

Electrochemical platforms couple iron fertilization, alkalinity enhancement, and hydrogen production to enable controlled, monitorable marine carbon dioxide removal.

Two-panel illustration of electrochemical ocean iron fertilization and floating platform for ocean alkalinity enhancement and hydrogen production.
Concepts for electrochemical ocean iron fertilization and ocean alkalinity enhancement: (a) mobile systems that release bioavailable iron into the upper ocean, and (b) floating electrochemical platforms that generate alkalinity and hydrogen while enhancing long-term CO₂ sequestration.

Hybrid Physics-Informed Machine Learning & Digital Twins

Real-world data and physics-based models are fused into hybrid ML frameworks that learn hidden dynamics, accelerate PDE solvers, and enable prognosis and health management for electrochemical systems.

Five-step hybrid physics-informed ML pipeline including data collection, feature engineering, fusion model development, physics-informed training, and prognosis.
Hybrid physics-informed ML pipeline: collecting real-world data, feature engineering, fusion of data-driven surrogates and differential operators, physics-informed training with PDE models, and deployment for digital-twin prediction and health management.
Ground truth versus prediction for battery imaging, demonstrating the accuracy of neural surrogates.
Example application of hybrid surrogates: ML models predicting complex battery imaging outputs (ground truth vs prediction) to accelerate diagnostics and design.

Physics-Informed Control for Battery Safety & BMS

Reduced-order thermal and electrochemical models are embedded inside differential physics-informed controllers to compute safe, optimal control actions for batteries and energy storage systems.

Three-step diagram showing reduced-order models feeding a physics-informed neural-network controller for battery safety and security applications.
Differential physics-informed control: (1) reduced-order equivalent circuit and thermal models, (2) PINN-based controller that maps states to optimal responses with feedback, and (3) deployment in energy storage applications for battery safety and secure fast charging.

Selected Publications

High-impact research spanning electrochemistry, thermal-fluid science, materials, and computational methods

Amir Taqieddin, Stephanie Sarrouf, Muhammad Fahad Ehsan, Akram N. Alshawabkeh

Multiscale Insights into Structure–Porosity Interplay and Water Adsorption in Granular Activated Carbon for Enhanced Electrochemical Water Treatment

ACS ES&T Water, 2025

Paper Open Access

Maria del Mar Cerrillo Gonzalez, Amir Taqieddin, Stephanie Sarrouf, Nima Sakhaee, Juan Manuel Paz-García, Akram N. Alshawabkeh, Muhammad Fahad Ehsan

Enhancing H₂O₂ Generation Using Activated Carbon Electrocatalyst Cathode: Experimental and Computational Insights on Current, Cathode Design, and Reactor Configuration

Catalysts, 2025

Paper

Stephanie Sarrouf, Amir Taqieddin, Muhammad Fahad Ehsan, Akram N. Alshawabkeh

Engineering Electrode Polarity for Enhancing In Situ Generation of Hydroxyl Radicals Using Granular Activated Carbon

Catalysts, 2024

Paper

Amir Taqieddin, Stephanie Sarrouf, Muhammad Fahad Ehsan, Ken Buesseler, Akram N. Alshawabkeh

Electrochemical Ocean Iron Fertilization and Alkalinity Enhancement Approach Toward CO₂ Sequestration

npj Ocean Sustainability, 2024

Paper

Amir Taqieddin, Stephanie Sarrouf, Muhammad Fahad Ehsan, Akram N. Alshawabkeh

New Insights on Designing the Next-Generation Materials for Electrochemical Synthesis of Reactive Oxidative Species Towards Efficient and Scalable Water Treatment: A Review and Perspectives

Journal of Environmental Chemical Engineering, 2023

Paper Open Access

Michael T. Hwang, Mohammad Heiranian, Yerim Kim, Juyoung Leem, Amir Taqieddin, et al.

Ultrasensitive Detection of Nucleic Acids Using Deformed Graphene Channel Field-Effect Biosensors

Nature Communications, 2020

Paper

Amir Taqieddin, Yuxuan Liu, Akram N. Alshawabkeh, Michael R. Allshouse

Computational Modeling of Bubbles Growth Using the Coupled Level Set—Volume of Fluid Method

Fluids, 2020

Paper

Amir Taqieddin

Review—Mathematical Formulations of Electrochemically Gas-Evolving Systems

Journal of The Electrochemical Society, 2018

Paper

Amir Taqieddin, Roya Nazari, Ljiljana Rajic, Akram N. Alshawabkeh

Review—Physicochemical Hydrodynamics of Gas Bubbles in Two Phase Electrochemical Systems

Journal of The Electrochemical Society, 2017

Paper
View Full Publication List

Patents & Inventions

Intellectual property spanning electrochemical water treatment, ocean-based CO₂ removal, and physics-informed control for advanced battery and energy systems.

I am an inventor or co-inventor on 3 patents / patent applications that are publicly visible and 6 additional filings in progress. The work spans ocean-based carbon dioxide removal, electrochemical water treatment, and AI-enabled battery management systems.

Electrochemical Water Treatment & ROS-Generating Electrodes

Invention and patent activity around flow-through electrochemical reactors and granular activated carbon electrodes that generate reactive oxygen species in situ for degradation of organic contaminants and emerging pollutants. These inventions build directly on my multiscale modeling and experimental work on ROS-generating electrodes and reactor design.

Themes: electro-Fenton(-like) processes, porous electrode engineering, high-throughput flow reactors, and scalable architectures for decentralized water treatment.

Ocean-Based CO₂ Removal & Marine Electrochemistry

Patented concepts that integrate electrochemical ocean iron fertilization, alkalinity enhancement, and hydrogen co-production, enabling controlled nutrient release and quantifiable, durable marine CO₂ sequestration. This includes offshore platforms that electrochemically generate bioavailable iron and alkalinity in seawater.

Themes: marine carbon dioxide removal (mCDR), electrochemical iron cycling, alkalinity management, and coupled MRV (monitoring, reporting, verification) frameworks.

Battery Management & Physics-Informed Control

Patents on vehicle battery management systems and differential physics networks that combine physics-based battery models with neural networks to compute optimal control targets for electric vehicles. These systems enable safer operation, extended lifetime, and improved fast-charging by leveraging physics-informed AI inside battery management controllers.

Themes: physics-informed ML, EV battery management, fast-charging, and integrated control of thermal, electrochemical, and degradation processes.

Selected Published Patents & Applications

Offshore Mobile Platform for Electrochemical Ocean Iron Fertilization and Hydrogen Gas Generation

US 2023/0183633 A1 · Published 2023

Inventors: Amir Taqieddin, Akram N. Alshawabkeh, Kenneth O. Buesseler

Concept: a self-operating offshore electrochemical platform that releases bioavailable iron and alkalinity into seawater while co-producing hydrogen gas, enabling controlled ocean iron fertilization and enhanced CO₂ sequestration.

View on Google Patents

Non-Transitory Differential Physics Network

US 2025/0277855 A1 · Published 2025

Inventors: Amir Taqieddin, Masanobu Uchimura, Balachandran Gadaguntla Radhakrishnan, Shigemasa Kuwata

Concept: a differential-physics-based neural network stored on a non-transitory computer-readable medium that estimates battery degradation and other state variables, then backpropagates to compute optimal control parameters for battery operation.

View on Google Patents

Vehicle Battery Management System

US 2025/0276610 A1 · Published 2025

Inventors: Amir Taqieddin, Masanobu Uchimura, Balachandran Gadaguntla Radhakrishnan, Shigemasa Kuwata

Concept: a battery management system for vehicles whose controller incorporates a differential physics network to map measured battery states to target control values (e.g., voltage, current, temperature), enabling safer and more efficient EV battery operation.

View on Google Patents

Talks, Videos & Media

Public-facing talks, explainers, and coverage of my work in batteries, water, and climate technologies.

These resources highlight how multiscale computation and AI translate into real-world systems, from ocean-based CO₂ sequestration and electrochemical water treatment to nanofluidic sensors and battery technology.

Featured Videos & Talks

Electrochemical Ocean Iron Fertilization & Alkalinity Enhancement

Animated explainer of a self-operating electrochemical system that combines ocean iron fertilization, alkalinity enhancement, and hydrogen production for durable CO₂ sequestration.

Watch on YouTube Related npj Ocean Sustainability Paper

Multiscale Simulations of Electronic and Fluidic Nanoscale Systems

Blue Waters Symposium talk on multiscale simulations for nanofluidics and electronic transport, using petascale supercomputing to connect atomistic models and continuum descriptions.

Watch on YouTube Talk Abstract

AI in Battery Technology: From Laboratory Testing to Field Applications

Invited presentation in Cambridge EnerTech’s AI for Energy Storage program at the International Battery Seminar, highlighting physics-informed ML and digital twins for battery development and deployment.

Session Page

Simulation Highlights

A few short clips illustrating how multiscale simulations capture dynamics from nanoscale transport and crumpling in 2D materials to multiphase bubble and vortex flows in electrochemical systems.

Single Bubble Growth Near an Electrode

High-resolution simulation of a single gas bubble forming and growing on an electrochemical surface. This type of model is used to validate bubble growth laws and benchmark continuum descriptions of gas-evolving systems.

Vortex Structures in Reactive Flows

Visualization of vortex motion and flow structures that emerge in reactive, forced flows. Understanding these coherent structures is key for mixing, transport, and reactor design in electrochemical and thermal-fluid systems.

Crumpling Graphene for Ultrasensitive Sensing

Graphene crumpling visualization linked to deformed-channel biosensors. Controlled crumpling enables bandstructure tuning and enhanced sensitivity in graphene-based nucleic acid detectors and flexible 2D-material devices.

Media Coverage

“Engineer the Ocean” Climate Story (WBUR/NPR)

Featured in a WBUR climate segment on ocean iron enrichment, demonstrating a prototype floating electrochemical device that delivers a steady dose of iron for marine carbon removal experiments.

Read the Article

PROTECT Trainee Spotlight (Northeastern)

Dissertation and trainee spotlight on multiscale computational design of carbon and iron materials for electrochemical water treatment and the transition to industry roles in battery R&D.

Read the Story

Crumpled Graphene Cancer DNA Detector

Coverage of the Nature Communications work on deformed graphene biosensors for ultrasensitive nucleic acid detection, including commentary on bandgap engineering and flexible electronics.

Read the Article Related Paper

Carbon Dioxide Removal Community Overview

Highlighted in carbon-removal community summaries discussing electrochemical ocean iron fertilization and alkalinity enhancement as a combined marine carbon dioxide removal pathway.

Overview

Teaching & Mentoring

Committed to excellence in education and fostering the next generation of researchers

Teaching Vision

My teaching connects core mechanical and materials engineering—thermal-fluids, heat transfer, energy storage, and materials science—with modern data and machine learning (ML) tools. Students link structure, processing, and performance from microstructure and constitutive behavior to device-level response in battery packs, thermal networks, and electrochemical reactors. AI in my courses is always physics-informed, reproducible, and ethical, organized around a practical cycle: Concept → Model → Code → Experiment → Communication.

Backward Design & Mastery

I use backward design with clear outcomes in conceptual, mathematical, computational, and experimental skills. Low-stakes retrieval and specifications-based grading keep rigor while letting students iterate and improve.

Physics-Based Modeling with Data

Students derive governing equations (e.g., Navier–Stokes, transport in porous media), build simulators from pore to device scale, and construct physics-aware surrogates (PINNs, Gaussian processes) that respect constraints and units.

Active & Inclusive Classrooms

Short mini-lectures alternate with think–pair–share, live coding, and whiteboard derivations. Structured teams and multiple modalities (analytical, computational, visual) support participation from a broad range of learners.

Inclusive & Accessible Learning

I commit to inclusive practices: accessible materials, flexible demonstrations (visual, analytical, and computational), transparent rubrics, and team norms that prevent inequitable task allocation. I routinely solicit anonymous feedback, adjust pacing and supports, and integrate societal context—safety, environmental impact, and ethics in AI-enabled design and control of mechanical and materials systems.

Teaching Experience

  • Mentored graduate and undergraduate students in computational methods, thermal-fluid science, and electrochemistry
  • Delivered research seminars on multiscale modeling, ML for materials, and ocean-based CDR
  • Supervised student projects in nanofluidics, battery modeling, and data-driven design
  • Guest lectures on transport phenomena and electrochemical engineering

Core Course Interests

  • Thermodynamics, Fluid Mechanics, Heat Transfer
  • Transport Phenomena & Energy Systems
  • Numerical Methods & Scientific Computing
  • Electrochemical Engineering
  • Energy Systems Design

Advanced/Elective Courses

  • Materials Informatics & ML for Design
  • Multiscale Modeling of Electrochemical Systems
  • Computational Transport in Energy Devices
  • Nanofluidics & 2D Materials
  • Physics-Informed ML for Thermo-Fluid & Energy Systems
Request Teaching Statement

Working With Me

Flexible collaboration models for industry, academia, and research organizations

Industry Consulting & R&D

Partner on battery digital twins, materials informatics, or electrochemical process optimization. I bring hands-on experience from Solid Power, Nissan, IBM, and Technip to accelerate your product development.

  • Physics-informed ML model development
  • Battery safety & fast-charging protocols
  • Materials design optimization
  • Technical due diligence

Academic Collaboration

Joint research projects, grant proposals, or student co-supervision in energy, water, and climate technologies. Open to sabbaticals, adjunct positions, and visiting researcher arrangements.

  • Co-authored publications & proposals
  • PhD/Postdoc co-supervision
  • Lab-industry partnerships
  • Equipment & facility sharing

Speaking & Training

Technical seminars, keynotes, panel discussions, or multi-day workshops on ML for materials, electrochemical modeling, and sustainable energy systems.

  • Conference keynotes & invited talks
  • Corporate training workshops
  • Graduate student short courses
  • Webinars & online sessions

Typical Engagement Timeline

Initial consultations are typically 1-2 hours. Short-term projects run 3-6 months. Long-term collaborations can span multiple years with flexible arrangements. Contact me to discuss your specific needs.

Contact & Collaboration

Open to research collaborations, speaking opportunities, and academic & industry partnerships

Email

taqieddin.a [at] northeastern [dot] edu

To reduce spam, I avoid printing a clickable email address in plain text. You can reach me via the collaboration button or the email link in the footer.

Location

Boulder, Colorado, USA

Professional Networks

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