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Physical and mechanical properties of materials and the underlying phenomena in any materials-related process arise from the interaction and structure of basic constituents of matter. Owing to the progress of computer power, numerical simulations have become one of the most fundamental and reliable tools to unravel interactions at the electronic and atomic levels. In the Computational Materials Research Lab (CMRL), our research focuses on developing innovative theoretical and computational frameworks to bridge quantities at the electronic and atomic levels to materials formation, stability, and processes at the macroscopic level. Throughout our research efforts, we discover new materials with desired properties for a wide range of applications and unravel the underlying mechanisms for many materials-related phenomena.

Most Recent News Heading link

New publication in Physical Review Materials

Read our article “Defect energy formalism for CALPHAD thermodynamics of dilute point defects” (Editor’s Suggestion) at https://doi.org/10.1103/PhysRevMaterials.8.113802  

New publication in Ceramics International

Read our article “Heat radiation mitigation in rare-earth pyrosilicate composites: A first principles investigation of refractive index mismatch” at https://doi.org/10.1016/j.ceramint.2024.01.417

New publication in Physical Review Materials

Congratulations to Farid for publishing their research article “Improving ab initio diffusion calculations in materials through Gaussian process regression” in Physical Review Materials !   See the article at https://doi.org/10.1103/PhysRevMaterials.8.013804

Congratulations to Ali Davariashtiyani on Successfully Defending His PhD Dissertation!

Dissertation title: “Deep Learning for Predicting the Formation Energy and Synthesizability of Crystalline Materials” Link: https://proxy.cc.uic.edu/login?url=https://www.proquest.com/dissertations-theses/deep-learning-predicting-formation-energy/docview/3020675995/se-2?accountid=14552 Davariashtiyani, A. (2023). Deep learning for predicting the formation energy and synthesizability of crystalline materials (Order No. 31229421). Available…

Experiment conducted in the workshop

Outreach Activity at the 2023 Futures Unlimited Workshop

Prof. Kadkhodaei, Devi Beswajeet, and Amir Orvati demonstrated an experiment on December 14 2023 at the Futures Unlimited Workshop. Organized by the Oakton Community Colleges the Futures workshop brought together more than 800…

New publication in Communications Materials

Congratulations to Ali for publishing their research article “Formation energy prediction of crystalline compounds using deep convolutional network learning on voxel image representation” in Communications Materials!   See the article at  https://doi.org/10.1038/s43246-023-00433-9  

Congratulations to Manisha Barse on Successfully Defending Her Master's Thesis!

Thesis title: “Analysis of Defects in Ce5O9 Through Density Functional Theory” Link: https://proxy.cc.uic.edu/login?url=https://www.proquest.com/dissertations-theses/analysis-defects-ce5o9-through-density-functional/docview/2872623247/se-2?accountid=14552 Barse, M. (2023). Analysis of defects in Ce5O9 through density functional theory (Order No. 30765230). Available from Dissertations & Theses @ Big…