We welcome the new year 2026 with a fresh lineup of speakers for the Atomistic Modeling Seminar. Get ready for exciting insights into materials science and atomistic modeling! 

Bartolomeo Civalleri will open the series. He is a professor at the Department of Chemistry at the University of Turin, Italy, and is an expert in theoretical and computational chemistry. His current research is focused on ab initio modeling in solid-state chemistry. As head of the Theoretical Chemistry Group, Civalleri has played a key role in developing the CRYSTAL code, a widely used tool for solid-state simulations. His group’s research spans density functional theory, hybrid functionals, nanotechnology, and the computational design of metal-organic frameworks, with a focus on accurately predicting and understanding material properties from first principles.

Localized atomic orbitals offer a compact, physically intuitive framework to model solids and enable efficient hybrid HF/DFT calculations. In his presentation “Using localized basis sets for large scale hybrid HF/DFT solid state calculations”, Civalleri will showcase their implementation in the CRYSTAL code and highlight applications to large‑scale MOF simulations and high‑throughput, machine‑learning‑driven MOF screening.

Date: Tuesday, January 13, 2026, 3:30 pm

Location: MIBE Lecture Hall

Abstract:

Localized basis functions, such as atomic orbitals, provide a natural and physically motivated description of the atomic-like features of solids. This oFers several advantages related to the locality of the basis functions and the compactness of the basis set [1,2]. Furthermore, they enable efficient calculations using state-of-the-art hybrid HF/DFT functionals [3]. 
This talk will highlight the use of localized atomic orbitals by discussing their implementation in the CRYSTAL code [4]. Selected applications of recently proposed composite HF/DFT hybrid methods [5] to the large-scale modelling of metal-organic frameworks (MOFs) [6], along with high-throughput screening of MOFs for machine learning-derived properties [7], will then be presented. 

[1] C. Pisani ed. Quantum-Mechanical Ab-initio Calculation of the Properties of Crystalline Materials, Lecture Notes in Chemistry, Springer-Verlag Berlin, Heidelberg; 1996.
[2] R. Dovesi, B. Civalleri, R. Orlando, C. Roetti, V.R. Saunders. Ab initio quantum simulations in solid state chemistry, Rev. Comput. Chem. 21 (2005) 1-125
[3] R. Dovesi, F. Pascale, B. Civalleri, et al. J. Chem. Phys. 152 (2020) 204111
[4] A. Erba, J. Desmarais, S. Casassa, B. Civalleri, et al. J. Chem. Theory Comput. 19 (2023) 6891−6932
[5] L. Doná, J.G. Brandenburg, B. Civalleri, J. Chem. Phys. 151 (2019) 121101; L. Donà, J.G. Brandenburg, I.J. Bush, B. Civalleri. Faraday Discussions 224 (2020) 292-308; M. Raimondo, L. Donà, B. Civalleri, JCTC (2025) submitted
[6] L. Donà, J. G. Brandenburg, B: Civalleri. J. Chem. Phys. 156 (2022) 094706
[7] H. Paja, M. Raimondo, L. Donà, B. Civalleri, in preparation