Deductive machine learning models for product identification
Deductive solution strategies are required in prediction scenarios that are under determined,
when contradictory information is available, or more generally wherever one-to-many non …
when contradictory information is available, or more generally wherever one-to-many non …
Machine Learning Models Capable of Chemical Deduction for Identifying Reaction Products
Deductive solution strategies are required in prediction scenarios that are under determined,
when contradictory information is available, or more generally wherever one-to-many non …
when contradictory information is available, or more generally wherever one-to-many non …
Deductive Machine Learning Challenges and Opportunities in Chemical Applications
T Jin, BM Savoie - Annual Review of Chemical and Biomolecular …, 2023 - annualreviews.org
Contemporary machine learning algorithms have largely succeeded in automating the
development of mathematical models from data. Although this is a striking accomplishment …
development of mathematical models from data. Although this is a striking accomplishment …
Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset
Mechanistic understanding of organic reactions can facilitate reaction development, impurity
prediction, and in principle, reaction discovery. While several machine learning models have …
prediction, and in principle, reaction discovery. While several machine learning models have …
When machine learning meets molecular synthesis
The recent synergy of machine learning (ML) with molecular synthesis has emerged as an
increasingly powerful platform in organic synthesis and catalysis. This merger has set the …
increasingly powerful platform in organic synthesis and catalysis. This merger has set the …
Machine learning strategies for reaction development: toward the low-data limit
Machine learning models are increasingly being utilized to predict outcomes of organic
chemical reactions. A large amount of reaction data is used to train these models, which is in …
chemical reactions. A large amount of reaction data is used to train these models, which is in …
Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large‐Scale Mechanistic Dataset
Mechanistic understanding of organic reactions can facilitate reaction development, impurity
prediction, and in principle, reaction discovery. While several machine learning models have …
prediction, and in principle, reaction discovery. While several machine learning models have …
Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability
GO Kayode, MM Montemore - JACS Au, 2023 - ACS Publications
Machine learning has been successfully applied in recent years to screen materials for a
variety of applications. However, despite recent advances, most screening-based machine …
variety of applications. However, despite recent advances, most screening-based machine …
Predicting reaction yields via supervised learning
AM Zuranski, JI Martinez Alvarado… - Accounts of chemical …, 2021 - ACS Publications
Conspectus Numerous disciplines, such as image recognition and language translation,
have been revolutionized by using machine learning (ML) to leverage big data. In organic …
have been revolutionized by using machine learning (ML) to leverage big data. In organic …
Putting Chemical Knowledge to Work in Machine Learning for Reactivity
K Jorner - Chimia, 2023 - research-collection.ethz.ch
Machine learning has been used to study chemical reactivity for a long time in fields such as
physical organic chemistry, chemometrics and cheminformatics. Recent advances in …
physical organic chemistry, chemometrics and cheminformatics. Recent advances in …