Wright State University
Sensing, Intelligent Modeling & Multiscale Materials Analysis
SIGMA Lab studies how materials deform, react, and fail — from atoms to structures — and builds machine-learning models that sense, predict, and design around that behavior. Our work spans continuum & multiscale mechanics, defect physics, energy materials, and AI-enhanced sensing and manufacturing.
What we study
Research Areas
Six connected threads linking atomistic mechanisms to component-scale performance.
Multiscale & mechano-chemical modeling
Continuum, phase-field, molecular-dynamics and DFT models that couple mechanics with chemistry — oxidation, lithiation, and reacting solids.
Defect engineering & nanomechanics
Strain- and defect-mediated design of 1D/2D nanostructures for ultra-high strength, and irradiation damage in ceramics.
Phase-change & energy materials
Li-ion electrode mechanics, phase-change thin films, electrochemistry and corrosion for energy storage and thermal switching.
AI-enhanced sensing
Machine-learning models layered on optical and chemical sensors — including BTEX gas detection — to improve accuracy and speed.
Additive manufacturing & digital twins
Process models and CNN-based surrogates for laser powder-bed fusion — melt-pool and temperature prediction, synchronized multi-laser arrays.
Applied structural dynamics
Noise and vibration mitigation for infrastructure, including metamaterial concepts for rail and lab-scale acoustic testbeds.
Selected results
Research in Pictures
Swipe or use the arrows to page through representative figures from the lab’s work.
Atomistic simulations · defect engineering
Critical stress and stiffness of pristine versus defect-engineered nanowires in tension and compression, with the local strain field around a stacking-fault network (ρSF = 325 SF/µm).
Machine learning · optical sensing
Modeling pipeline for an ML-enhanced BTEX optical sensor — diffusion and absorption models feed a Beer–Lambert response that a convolutional network learns to read.
From “Optical Sensor for BTEX Detection: Integrating Machine Learning for Enhanced Sensing,” Advanced Sensor and Energy Materials, 2024.
Machine learning · additive manufacturing
A decoder-CNN surrogate trained to reproduce finite-element melt-pool temperature fields in laser powder-bed fusion — matching the physics at a fraction of the compute cost.
Part of our current LPBF melt-pool & temperature-prediction project.
Phase-field modeling · irradiation damage
Latent tracks from swift heavy-ion irradiation coalescing as fluence increases — from isolated damage zones to overlapping, ribbon-like defect regions.
From “Effect of stress and irradiation fluence on latent track formation in swift heavy ion irradiation: A case study on TiO₂,” Radiation Physics and Chemistry, 2025.
Underway
Current Projects
Active research and sponsored work in the lab right now.
CNN surrogate for LPBF melt-pool & temperature prediction
Convolutional-network models trained to predict temperature distribution and melt-pool behavior in laser powder-bed fusion, aimed at faster process control than direct simulation.
Machine-learning-enhanced optical sensing
Integrating ML with optical sensor platforms — including BTEX detection — to push sensitivity and response time beyond conventional signal processing.
Railroad noise & vibration mitigation
ODOT-sponsored work on the generation mechanisms of railroad noise and vibration and on practical mitigation strategies for track-adjacent infrastructure.
Metamaterial noise-mitigation testbed
An undergraduate capstone effort building lab-scale hardware to test noise mitigation using metamaterial panels and short acoustic barriers.
Selected bibliography
Publications
29 peer-reviewed journal articles, 2008–2026, listed most recent first.
Who we are
People
Hamed Attariani, Ph.D.
Principal Investigator
Wright State University | Dayton, OH 45435
Wright State University–Lake Campus | Celina, OH 45822
Hamed leads SIGMA Lab's work on multiscale mechanics and machine-learning-driven materials design. His research connects atomistic and continuum modeling — phase-field methods, molecular dynamics, and density-functional theory — to real sensing and manufacturing problems, from defect-engineered nanostructures to AI-assisted additive manufacturing. He is a member of the Society of Engineering Science (SES) and the Materials Research Society (MRS).
- Ph.D., Engineering Mechanics
- Iowa State University, 2014
- M.S., Mechanical Engineering
- Ferdowsi University of Mashhad, 2008
- B.S., Mechanical Engineering
- Ferdowsi University of Mashhad, 2005
Recognition & service
Awards & honors
- Outstanding Faculty Research Award, Wright State (2022)
- Outstanding Faculty Teaching Award, Wright State (2017)
- ACS Omega Editor’s Choice Award (2017)
- Aerospace Excellence Award & Research Excellence Award, Iowa State (2014)
- Excellence in Graduate Research Conference — 1st place (2010), 2nd place (2011)
Editorial & professional service
- Editorial Board, Journal of Materials: Design, Synthesis and Processing
- Guest Editor, special issue, Journal of Nanomaterials (2018)
- Organizer, mini-symposium on electronic materials under extreme conditions, Hopkins Extreme Materials Institute MACH Conference
Join the lab
We welcome inquiries from graduate students interested in computational mechanics (molecular dynamics and phase-field methods), materials discovery, and machine learning as applied to energy, additive manufacturing, corrosion, composites, microstructure prediction, and sensing — as well as from collaborators in fabrication and testing. Please send a CV and a short note on your research interests.
Get in touch
Contact
- hamed.attariani@wright.edu
- Phone
- 419–586–0373
- Address
- Russ Engineering Center 184
3640 Colonel Glenn Hwy
Dayton, OH 45435-0001
Trenary Hall 114
7600 Lake Campus Dr
Celina, OH 45822 - Affiliation
- Professor, Dept. of Mechanical and Materials Engineering
Wright State University
Engineering Program
Wright State University–Lake Campus