Great story from @Mechanophore and colleagues @UofIllinois. From skeptic to believer: the power of models. Let's take a look at some of these journeys and what we can learn.
@Mechanophore's story - seeing models make intriguing predictions into reaction behavior. "A key turning point for me was seeing how FEA of Frontal Ring-Opening Metathesis Polymerizations simulates autonomous curing processes by balancing reaction enthalpy with heat transport"
@MrBSussels Story - modeled complex reaction mechanisms using a simplified graphical abstraction to create a system of coupled linear equations and analytically solved them to provide an empirical rate law in agreement with the experimental observations. Skeptic → Believer
A thorough review of the MGI data-driven approach and successes in soft materials, glasses, topological materials, catalyst discovery and many more can be found in this excellent paper by Juan de Pablo and @TheJacksonLab. doi.org/10.1038/s41524…#MGI
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It's been a wild week already in #ML/#AI for science. Advancements in using diffusion models for protein folding, using learned potentials to discover new catalyst materials, a proposed battery data genome to speed energy storage material discovery, and so much more! 🧵 (1/8)
@KevinKaichuang et al use diffusion models to generate novel foldable protein structures. (2/8)
@_akhaliq presents NeuralPLexer to predict protein-ligand structures. This will help understand the interaction between small molecules and proteins. Also with diffusion models ✨ (3/8)