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.

New Publication in Machine Learning: Science and Technology

Read our article “Impact of data bias on machine learning for crystal compound synthesizability predictions” at https://iopscience.iop.org/article/10.1088/2632-2153/ad9378 Ali Davariashtiyani et al 2024 Mach. Learn.: Sci. Technol. 5 040501

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…