Transition state search is key in chemistry for elucidating reaction mechanisms and
exploring reaction networks. The search for accurate 3D transition state structures, however …

Geometric deep learning models, which incorporate the relevant molecular symmetries
within the neural network architecture, have considerably improved the accuracy and data …

Identifying transition states—saddle points on the potential energy surface connecting
reactant and product minima—is central to predicting kinetic barriers and understanding …

Recently, generative models have been gradually emerging into the extended dataset field,
showcasing their advantages. However, when it comes to generating tabular data, these …

The success of diffusion probabilistic models in generative tasks, such as text-to-image
generation, has motivated the exploration of their application to regression problems …

Transporting between arbitrary distributions is a fundamental goal in generative modeling.
Recently proposed diffusion bridge models provide a potential solution, but they rely on a …

Diffusion generative models, a class of machine learning techniques, have shown
remarkable promise in materials science and chemistry by enabling the precise generation …

This paper takes the example of industrial architectural heritage in Dalian to explore design
scheme generation methods based on generative artificial intelligence (AIGC). The study …

Identifying transition states--saddle points on the potential energy surface connecting
reactant and product minima--is central to predicting kinetic barriers and understanding …

This study introduces a modified score matching method aimed at generating molecular
structures with high energy accuracy. The denoising process of score matching or diffusion …