Last week the CMM attended the 19th International Conference on Density Functional Theory and its Applications in Brussels (https://www.dft2022.be/). The conference covers a broad range of topics in the field, from the latest theoretical developments to cutting-edge applications in chemistry and physics, bringing together scientists from all over the world.
Stefaan Cottenier was invited to talk about his efforts on comparing different DFT methods and codes. If you use DFT to predict a property of a crystal, how confident can you be that the prediction is computed in a bug-free way? And if your DFT-code uses pseudopotentials, can you trust the pseudopotential does not modify your predictions? If you want an answer to these questions, feel free to (re)watch his talk here.
Tom Braeckevelt presented our recent work on metal halide perovskites (MHPs). In the past decade, MHPs have shown great potential for various optoelectronic applications. However, their spontaneous transition to an inactive yellow phase impedes the widespread adoption of, e.g., CsPbI3 and FAPbI3. Using RPA calculations and phase-transferable machine learning potentials we determined the transition temperature of MHPs.
YingXing Cheng elaborated on a new ACKS2ω model, which offers a solid connection between the quantum-mechanical description of frequency-dependent response and computationally efficient force-field models. With this methodology we want to propose an alternative for today’s quantum-mechanics-based methods, which are still computationally expensive for extended systems.
Finally the CMM was also represented with two posters. Liesbeth De Bruecker presented our computational study of the electronic structure of Co2+ Aqua-Complexes and Sander Vandenhaute introduced our work on Machine Learning Potentials for Activated Processes using Active Learning.