[Nature Reviews Chemistry] 我室Pavlo O. Dral发表论文:Molecular excited states through a machine learning lens

发布日期:2021年05月25日   浏览次数:

我室Pavlo O. DralNature Reviews Chemistry 上发表论文:Molecular excited states through a machine learning lens

摘要:Theoretical simulations of electronic excitations and associated processes in molecules are indispensable for fundamental research and technological innovations. However, such simulations are notoriously challenging to perform with quantum mechanical methods. Advances in machine learning open many new avenues for assisting molecular excited-state simulations. In this Review, we track such progress, assess the current state of the art and highlight the critical issues to solve in the future. We overview a broad range of machine learning applications in excited-state research, which include the prediction of molecular properties, improvements of quantum mechanical methods for the calculations of excited-state properties and the search for new materials. Machine learning approaches can help us understand hidden factors that influence photo-processes, leading to a better control of such processes and new rules for the design of materials for optoelectronic applications.