인공지능 및 계산화학 연구실

4) developing fast and accurate electronic structure methods that can treat large molecules and materials more reliably. One great challenge in digital discovery is that many of the molecules and materials that are computationally designed are often discarded in the laboratories since they are not synthesizable. There are some widely employed approximate approaches to estimate synthesizability, such as the thermodynamic stability, but they could produce too many potentially synthesizable candidates or miss important metastable materials. The synthesis being a complex phenomenon, simple thermodynamic stability alone or other heuristic rules may not be able to yield reliable prediction capability. Our work in this area is related to predict the materials synthesizability, molecular and inorganic retrosynthesis, organic reactivity prediction, and synthesis-aware molecule generation. Automating the process of synthesis by artificial intelligence would accelerate synthesis design in future digital laboratories. Accuracy and feasibility of modern computational modelling techniques based on quantum mechanics has progressed recently to a level that allows one to make reliable predictions ahead of experiments. However, these methods, such as those based on density functional theory, still scale unfavorably to treat large systems, or their accuracy is well-known to be insufficient for certain molecular or materials properties compared to correlated wavefunction-based methods. Our group develops new density functional methods that are fast and accurate to accelerate the computational material discovery even further.