Addressing reproducibility challenges in high-throughput photochemistry
Light-mediated reactions have emerged as an indispensable tool in organic synthesis and
drug discovery, enabling novel transformations and providing access to previously …
drug discovery, enabling novel transformations and providing access to previously …
Teaching a neural network to attach and detach electrons from molecules
Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural
Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods …
Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods …
Integrating machine learning and large language models to advance exploration of electrochemical reactions
Electrochemical C‐H oxidation reactions offer a sustainable route to functionalize
hydrocarbons, yet identifying suitable substrates and optimizing synthesis remain …
hydrocarbons, yet identifying suitable substrates and optimizing synthesis remain …
nanoNET: machine learning platform for predicting nanoparticles distribution in a polymer matrix
Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are
linked to their compositions. However, it is challenging to establish a universal composition …
linked to their compositions. However, it is challenging to establish a universal composition …
Multi-instance learning approach to the modeling of enantioselectivity of conformationally flexible organic catalysts
Computational design of chiral organic catalysts for asymmetric synthesis is a promising
technology that can significantly reduce the material and human resources required for the …
technology that can significantly reduce the material and human resources required for the …
Roadmap to pharmaceutically relevant reactivity models leveraging high-throughput experimentation
The merger of High-Throughput Experimentation (HTE) and data science presents an
opportunity to both accelerate and inspire innovations in synthetic chemistry. Similarly …
opportunity to both accelerate and inspire innovations in synthetic chemistry. Similarly …
MACHINE LEARNING ESTIMATION OF REACTION ENERGY BARRIERS AND ITS APPLICATIONS IN ASTROCHEMISTRY
H Ji - 2024 - yorkspace.library.yorku.ca
We developed a machine learning model for fast estimating reaction energy barriers. The
model was trained on data for 11,730 elementary reactions and barriers computed with an …
model was trained on data for 11,730 elementary reactions and barriers computed with an …