Expensive models and experiments
PIML can reduce repeated simulation or experimental cost while retaining the governing knowledge used in Chemical and Electro Chemical Engineering.
Engineering Research Community
Chemical and Electro Chemical Engineering & Physics-Informed Machine Learning
Physics-grounded modelling, learning and validation for Chemical and Electro Chemical Engineering
Chemical and Electro Chemical Engineering combines conventional reaction, thermodynamics, transport and separations with charge transfer, ionic transport, electrode materials and electrochemical devices. It spans batteries, fuel cells, electrolysers, corrosion, electrodeposition, sensors and electrochemical manufacturing.
PIML is valuable because electrochemical systems are governed by coupled electrical, chemical, thermal and fluid equations but contain uncertain kinetics, porous structures and ageing mechanisms. Hybrid models can infer hidden internal states without discarding conservation and electrochemical limits.
This page presents ten focused research areas, degree-level project pathways, selected publications and direct support through the PIMLS biweekly members meeting.
This Chemical and Electro Chemical Engineering guide covers Physics-Informed Neural Networks (PINNs), physics-guided machine learning, scientific machine learning, neural operators, hybrid models and engineering digital twins. Explore the research and project pathways below, then join the Physics-Informed Machine Learning Society to connect with the international PIMLS community.
The central ideaEstablished Chemical and Electro Chemical Engineering knowledge + measurements and simulation + machine learning
10focused research areas
3academic project pathways
6selected publications
Biweeklymember research meeting
Why this combination matters
Why Chemical and Electro Chemical Engineering Needs Physics-Informed Learning
Use available scientific knowledge to make limited data more useful, transparent and testable.
Incomplete engineering models
Learn uncertain parameters, closures or discrepancies around an inspectable mechanistic foundation.
Transfer across conditions
Test whether structured models generalize across geometries, materials, assets, operating regimes or sites.
Trustworthy evidence
Use physical residuals, independent measurements, uncertainty and conventional engineering baselines before deployment.
Ten focused directions
Major Chemical and Electro Chemical Engineering PIML Research Areas
Each card connects a meaningful Chemical and Electro Chemical Engineering question with suitable scientific knowledge, modelling choices and evidence needed to test it.
01
Lithium-Ion Battery States
SOC, internal concentration and temperature are indirectly observed. PIML opportunities: Use porous/single-particle electrochemistry and thermal states for estimation.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
02
Battery Ageing and RUL
Capacity and resistance evolve with cycling and storage. PIML opportunities: Learn bounded degradation rates around electrochemical health models.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
03
Redox-Flow Batteries
Flow, reaction, crossover and electrical transport interact. PIML opportunities: Embed 2D cell equations and boundary conditions in fast surrogates.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
04
Fuel Cells
Gas transport, hydration, reaction and thermal state determine voltage. PIML opportunities: Use multi-physics hybrids for hidden-state and degradation estimation.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
05
Electrolysers and Hydrogen
Current, bubbles, species and heat control efficiency. PIML opportunities: Develop charge/mass-conserving surrogates for operation and design.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
06
Corrosion Prediction
Environment and alloy chemistry drive electrochemical loss. PIML opportunities: Fuse kinetic laws and exposure data with monotonic uncertainty-aware prognosis.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
07
Electrodeposition
Current distribution and mass transport determine thickness/morphology. PIML opportunities: Use electrochemical transport PINNs for inverse control and uniformity.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
08
Electrochemical Sensors
Interface kinetics and diffusion shape measured current/impedance. PIML opportunities: Infer analyte or fouling parameters with calibrated circuit/transport models.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
09
Electrochemical Impedance
Spectra contain overlapping processes and non-unique fits. PIML opportunities: Use circuit/PDE priors with identifiability and posterior uncertainty.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
10
Electrocatalysis
Activity depends on surfaces, kinetics and transport. PIML opportunities: Combine mechanistic descriptors with small-data learning and uncertainty.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
Choose the right research depth
Projects for Every Academic Stage
Start with a scope that matches your time, mathematical background, experimental access and expected research contribution.
Project pathway 1
B.E./B.Tech
Learn the foundations with a bounded, measurable system.
- Butler–Volmer parameter inverse problem
- battery single-particle PINN
- equivalent-circuit plus thermal residual model
- corrosion kinetic predictor
Expected outcome
A reproducible implementation, clear baselines, a manageable dataset and physically meaningful validation.
Project pathway 2
M.Tech
Combine an engineering model, substantial data and rigorous comparison.
- redox-flow cell operator surrogate
- probabilistic battery ageing twin
- electrolyser multi-physics soft sensor
- electrodeposition inverse control
Expected outcome
A thesis-quality study with held-out regimes, mechanistic and data-only baselines, ablation and uncertainty.
Project pathway 3
Ph.D.
Address a publishable methodological, multiscale or deployment research gap.
- multi-scale electrochemical digital twins
- transferable battery models across chemistry
- certifiable hybrid electrochemical control
- physics-informed materials/device co-design
Expected outcome
New methodology or validated engineering insight, multi-regime evidence, reproducible software and journal publications.
From idea to evidence
A Strong PIML Project Workflow
01
Define
Choose one Chemical and Electro Chemical Engineering question and a measurable engineering output.
02
Model
State the governing relationships, constraints or validated domain knowledge you will retain.
03
Compare
Build mechanistic and data-only baselines before the hybrid model.
04
Validate
Hold out experiments, conditions, assets, sites or regimes at the deployment level.
05
Publish
Report uncertainty, ablation, limitations, data lineage and reproducible code.
Read before you modelDiscuss Your Literature
Selected Publications and Why They Matter
Use this focused reading list to understand the general PIML framework, direct Chemical and Electro Chemical Engineering evidence and suitable hybrid modelling methods.
Literature review advice
Do not list papers only. Compare the engineering question, incorporated knowledge, data, split strategy, baselines, uncertainty and evidence level.
This source is included in the Chemical and Electro Chemical Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Chemical and Electro Chemical Engineering.
Read publication or record This source is included in the Chemical and Electro Chemical Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Chemical and Electro Chemical Engineering.
Read publication or record This source is included in the Chemical and Electro Chemical Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Chemical and Electro Chemical Engineering.
Read publication or record This source is included in the Chemical and Electro Chemical Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Chemical and Electro Chemical Engineering.
Read publication or record This source is included in the Chemical and Electro Chemical Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Chemical and Electro Chemical Engineering.
Read publication or record This source is included in the Chemical and Electro Chemical Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Chemical and Electro Chemical Engineering.
Read publication or record Build an interdisciplinary team
Where Chemical and Electro Chemical Engineering Can Collaborate
Computer Science
Scientific ML, optimization, trustworthy AI and reproducible research software.
Applied Mathematics
Differential equations, numerical methods, inverse problems and uncertainty.
Sensing & Control
Instrumentation, data acquisition, state estimation and responsible deployment.
Domain Laboratories
Experiments, calibration, validation evidence and practical expertise for Chemical and Electro Chemical Engineering.
Before you begin
Frequently Asked Research Questions
These answers help students avoid common scope, terminology and validation mistakes.
Still have a question?
Use the biweekly meeting form for research guidance.
Request accessNo. Chemical and Electro Chemical Engineering projects may use physics-guided features, hybrid residual models, differentiable simulators, neural operators, constrained architectures or data assimilation. State exactly what knowledge is incorporated.
Choose one engineering question, a measurable output and a defensible mechanistic baseline. Add learning only where data can identify an uncertainty or discrepancy.
A meaningful question, justified prior knowledge, deployment-level holdouts, strong baselines, ablation, uncertainty, reproducibility and honest limitations.
Simulation can broaden coverage, but simulation-only evidence cannot establish real-system accuracy. Use calibrated experiments, field measurements or trusted independent references appropriate to the claim.
Submit the biweekly members meeting form to discuss your project level, branch, data, model, validation plan and possible collaborators.
Take the next step
Bring your Chemical and Electro Chemical Engineering research idea to PIMLS
Join the biweekly members meeting for project guidance, collaboration and publication planning—or contact the Society directly.
Meeting participation is requested through the Google form. Complete it carefully so the Society can understand your research interest.