Konuşmacılar
Açıklama
Metal-organic frameworks (MOFs) can be utilized in various engineering applications as nanoporous materials, including catalysts, biotechnology, controlled drug release, gas separation, and sensors. Recent experimental studies have demonstrated that controlled and reversible structural changes occur in MOFs under the influence of externally applied stimuli. While classical molecular dynamics simulations have been commonly used to model MOFs, potential deformations under external stimuli may not be well captured with classical force field parameters. In this work, we have constructed machine learning potentials for MOFs and compared predicted energies and forces with counterparts calculated using electronic structure methods. We obtained significantly low root mean square errors, implying that MOF energies and forces can be accurately predicted using machine learning potentials, thereby opening the door for quantum-accurate modeling of MOFs in a shorter time.
This project has received funding from the European Union’s Horizon 2021-2027 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 101063496.