Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient. Many existing methods are purely data driven, focusing on exploiting the intrinsic topology and construct
Graph neural networks (GNNs) have been used previously for identifying new crystalline materials. However, geometric structure is not usually taken into consideration, or only partially. Here, we develop a geometric-information-enhanced crystal graph neu
Thus, the design process is moving from a sequence of atomic (inde- pendent) steps toward a deeper level of integration. Tab. 1.1 summarizes a timeline of key developments in circuit and physical design. Tab. 1.1 Timeline of EDA progress with respect to circuit and physical design. Time ...
Understanding the atomic scale dynamics in condensed phases is essential for the design of functional materials to tackle global energy and environmental challenges1,2,3. The performance of many materials depends on the dynamics of individual atoms or small molecules in complex local environments. Despi...