Machine learning-guided strategies for reaction conditions design and optimization
LY Chen, YP Li - Beilstein Journal of Organic Chemistry, 2024 - beilstein-journals.org
This review surveys the recent advances and challenges in predicting and optimizing
reaction conditions using machine learning techniques. The paper emphasizes the …
reaction conditions using machine learning techniques. The paper emphasizes the …
Accurately Predicting Barrier Heights for Radical Reactions in Solution Using Deep Graph Networks
Quantitative estimates of reaction barriers and solvent effects are essential for developing
kinetic mechanisms and predicting reaction outcomes. Here, we create a new data set of …
kinetic mechanisms and predicting reaction outcomes. Here, we create a new data set of …
Application of the Digital Annealer Unit in Optimizing Chemical Reaction Conditions for Enhanced Production Yields
SC Li, PH Wang, JW Su, WY Chiang, SH Huang… - arXiv preprint arXiv …, 2024 - arxiv.org
Finding appropriate reaction conditions that yield high product rates in chemical synthesis is
crucial for the chemical and pharmaceutical industries. However, due to the vast chemical …
crucial for the chemical and pharmaceutical industries. However, due to the vast chemical …
Graph neural networks for predicting metal–ligand coordination of transition metal complexes
J Toney, RS Michel, A Garrison, I Kevlishvili, H Kulik - 2024 - chemrxiv.org
High-throughput virtual screening campaigns are invaluable for surveying the combinatorial
space of possible transition metal complexes (TMCs), but they rely on accurate metal–ligand …
space of possible transition metal complexes (TMCs), but they rely on accurate metal–ligand …
Multi-fidelity graph neural networks for predicting toluene/water partition coefficients
Accurate prediction of toluene/water partition coefficients of neutral species is crucial in drug
discovery and separation processes; however, data-driven modeling of these coefficients …
discovery and separation processes; however, data-driven modeling of these coefficients …
Geometric Learning for Quantum-Informed, Machine Learning and Analysis of Electrostatic Preorganization
S Vargas - 2024 - search.proquest.com
This thesis is organized in a slightly unconventional fashion: algorithms lead and
applications fill out the content. I think this emphasizes my interests during graduate school-I …
applications fill out the content. I think this emphasizes my interests during graduate school-I …