Study on growth rate of TiO <sub>2</sub> nanostructured thin films: simulation by molecular dynamics approach and modeling by artificial neural network
importance 3/10
ai assisted
Machine Learning in Materials Science thin-film coatings stage: research
Problem
Predicting the thickness of TiO2 nanostructured thin films based on sol-gel dip-coating process parameters.
Why it matters
Accurate prediction of film thickness reduces experimental cost and improves coating quality for optical and electronic applications.
What the AI did
An artificial neural network was used to model and analyze experimental data on film thickness alongside molecular dynamics simulations.
Results
MD simulations consistent with AFM, SEM, and Dektak surface profiler measurements
Record
| Venue | Surface and Interface Analysis |
|---|---|
| Published | 2013 |
| Work type | article |
| Authors | Alireza Bahramian |
| Citations | 20 (OpenAlex, at import time) |
| References | 39 |
| Model or method | artificial neural network (ANN) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1002/sia.5314 |
| Open access | hybrid |
| Keywords | TiO2 thin films, artificial neural network, molecular dynamics, sol-gel, film thickness |
Importance rating
3
incremental / niche
Incremental niche advance: ANN modeling of TiO2 film thickness for sol-gel dip-coating; abstract gives no scale or broader impact evidence.
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