Good news! Alchemy in the age of machine learning & AI! This is only the beginning!
How much will ML & AI accelerate material science?
"... Now, ... researchers have created an AI model that guides scientists through the process of making materials by suggesting promising synthesis routes. In a new paper, they showed the model delivers state-of-the-art accuracy in predicting effective synthesis pathways for a class of materials called zeolites, which could be used to improve catalysis, absorption, and ion exchange processes. Following its suggestions, the team synthesized a new zeolite material that showed improved thermal stability.
The researchers believe their new model could break the biggest bottleneck in the materials discovery process. ..."
From the abstract:
"The synthesis of crystalline materials, such as zeolites, remains a notable challenge owing to a high-dimensional synthesis space, intricate structure–synthesis relationships and time-consuming experiments.
Here, considering the ‘one-to-many’ relationship between structure and synthesis, we propose DiffSyn, a generative diffusion model trained on over 23,000 synthesis recipes that span 50 years of literature.
DiffSyn generates probable synthesis routes conditioned on a desired zeolite structure and an organic template.
DiffSyn a chieves state-of-the-art performance by capturing the multi-modal nature of structure–synthesis relationships. We apply Diffsny to differentiate among competing phases and generate optimal synthesis routes.
As a proof of concept, we synthesize a UFI material using DiffSyn-generated synthesis routes. These routes, rationalized by density functional theory binding energies, resulted in the successful synthesis of a UFI material with a high Si/AlICP of 19.0, which is expected to improve thermal stability."
DiffSyn: a generative diffusion approach to materials synthesis planning (no public access)
DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planning (preprint, open access)
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