LeMaterial Reading Group | Electron Flow Matching for Generative Reaction Mechanism Prediction
Автор: Entalpic
Загружено: 2026-01-26
Просмотров: 63
Описание:
Most recent ML models in reaction prediction often fail to preserve mass conservation and are unable to generate/recover potential mechanistic pathways towards products. Thus, we propose FlowER (Flow Matching for Electron Redistribution), which models chemical reactions as a generative process of electron redistribution and uniquely combines 3 principles in an elegant fashion: mass conservation, mechanistic insights, and generative modelling to better align ML models with physical reality while remaining inherently explainable.
Mun Hong is currently a first-year computer science graduate student at Duke University, advised by Rohit Singh and Alexander Tong. Previously, he was a software engineer/research assistant at MIT, working on AI for synthesis planning under the supervision of Connor W. Coley.
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