Charting a course towards new light-activated molecules

许多小分子吸收光。对于计算机模拟来说，预测它们接下来会发生什么是一项具有挑战性的任务，但如果我们能解决这个问题，我们就会有一条新的路线来设计用于医学诊断成像的新荧光团、用于绿色合成的新光催化剂，或者用于在太阳能电池中获取太阳能量的新发色团。在这个项目中，我们的目标是通过结合激发态电子结构计算和机器学习来建立新的预测模型来解决这一挑战，以帮助我们搜索整个有机小分子空间，以识别有用的(和以前未知的)光激活分子。

Many small molecules absorb light. Predicting what happens to them next is a challenging task for computer simulations, but if we could solve this problem we would have a new route to designing new fluorophores for medical diagnostic imaging, new photocatalysts for green synthesis, or new chromophores for harvesting the sun's energy in solar cells. In this project, we aim to address this challenge by combining excited-state electronic structure calculations and machine-learning to build new predictive models to help us search the entire space of small organic molecules to identify useful (and previously-unknown) light-activated molecules.