De Novo Crystal

De novo crystal design focuses on computationally predicting and designing novel crystal structures with desired properties, moving beyond modifications of existing materials. Current research emphasizes developing advanced machine learning models, including graph neural networks, variational autoencoders, and Bayesian optimization techniques, to efficiently explore the vast chemical and structural space of possible crystals and predict their properties like formation energy and stability. These efforts aim to accelerate materials discovery by identifying promising candidates for synthesis and characterization, ultimately impacting diverse fields such as energy storage, electronics, and pharmaceuticals. The accuracy and efficiency of these predictions are being rigorously evaluated using various metrics, including classification scores and comparisons against experimental data, with a focus on mitigating biases in existing datasets.