Artificial Intelligence Led Selection of Crystal Conformations

本项目的主要目标是将深度神经网络方法应用于晶体构象的预测。为了实现这一目标，新的构象描述符将在CSD中的所有构象以及计算生成的其他未观察到的构象中开发和计算。然后将训练神经网络模型并用于晶体构象的预测。如果成功，我们将继续研究深度强化学习方法，以提高当前预测算法的预测能力。

The main objective for this project is to apply deep neural network methods to the prediction of crystal conformations. In order to achieve this, new conformational descriptors will be developed and computed in all conformations in the CSD as well as other unobserved conformations generated computationally. A neural network model then will be trained and used for the prediction of crystal conformations. If successful we will then go on to investigate deep reinforcement learning methods to enhance the predictive power of current prediction algorithms.