Computational Chemistry

Computational chemistry employs computational methods to model and predict the behavior of molecules and materials, aiming to accelerate scientific discovery and reduce reliance on expensive experiments. Current research heavily utilizes machine learning, particularly graph neural networks, generative adversarial networks, and diffusion models, to improve the accuracy and efficiency of simulations, develop more accurate force fields, and predict properties like toxicity and reactivity. These advancements are significantly impacting various fields, including drug discovery, materials science, and catalysis design, by enabling faster and more accurate predictions of molecular properties and reaction pathways.