scholarly journals Machine learning activation energies of chemical reactions

2021 ◽  
Author(s):  
Toby Lewis‐Atwell ◽  
Piers A. Townsend ◽  
Matthew N. Grayson
Keyword(s):  
Machine Learning ◽  
Chemical Reactions ◽  
Activation Energies

scholarly journals Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

2021 ◽  
Author(s):  
Kjell Jorner ◽  
Tore Brinck ◽  
Per-Ola Norrby ◽  
David Buttar
Keyword(s):  
Machine Learning ◽  
Activation Energies ◽  
Accurate Prediction ◽  
Nucleophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Mechanistic Modelling

Hybrid reactivity models, combining mechanistic calculations and machine learning with descriptors, are used to predict barriers for nucleophilic aromatic substitution.

scholarly journals Predicting Glycosylation Stereoselectivity Using Machine Learning

2021 ◽  
Author(s):  
Soo-Yeon Moon ◽  
Sourav Chatterjee ◽  
Peter Seeberger ◽  
Kerry Gilmore
Keyword(s):  
Machine Learning ◽  
Chemical Reactions

Predicting the stereochemical outcome of chemical reactions is challenging in mechanistically ambiguous transformations. The stereoselectivity of glycosylation reactions is influenced by at least eleven factors across four chemical participants and...

Determination of Activation Energies of Chemical Reactions by Differential Thermal Analysis

Nature ◽  
1966 ◽  
Vol 212 (5067) ◽  
pp. 1229-1229 ◽  
Author(s):  
G. O. PILOYAN ◽  
I. D. RYABCHIKOV ◽  
O. S. NOVIKOVA
Keyword(s):  
Thermal Analysis ◽  
Differential Thermal Analysis ◽  
Chemical Reactions ◽  
Activation Energies

scholarly journals Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space

2021 ◽  
Vol 155 (6) ◽  
pp. 064105
Author(s):  
Stefan Heinen ◽  
Guido Falk von Rudorff ◽  
O. Anatole von Lilienfeld
Keyword(s):  
Machine Learning ◽  
Chemical Reactions ◽  
Learning Barriers

The application of wave-mechanics to reactions involving hydrogen and diplogen

1934 ◽  
Vol 30 (4) ◽  
pp. 508-513
Author(s):  
R. A. Smith
Keyword(s):  
Activation Energy ◽  
Quantum Theory ◽  
Chemical Reactions ◽  
Reaction Mechanisms ◽  
Reaction Rate ◽  
Theoretical Study ◽  
Electronic States ◽  
Activation Energies ◽  
Wave Mechanics

A considerable amount of work has recently been done on the application of wave-mechanics to the theoretical study of chemical reactions. This has consisted chiefly in calculating activation energies and strengths of various bonds by consideration of electronic states in molecules. Some work has also been done on actual reaction mechanisms. It is evident from the latter that, owing to the large masses of the particles concerned, the quantum theory and the classical treatment will give different results only for reactions involving hydrogen or diplogen. Previous attempts to deal with such reactions have consisted simply of calculating the permeabilityG(W) of a barrier of height equal to the activation energy for protons of energyW.The reaction rate is then assumed to be given by

scholarly journals When machine learning meets multiscale modeling in chemical reactions

2020 ◽  
Vol 153 (9) ◽  
pp. 094117
Author(s):  
Wuyue Yang ◽  
Liangrong Peng ◽  
Yi Zhu ◽  
Liu Hong
Keyword(s):  
Machine Learning ◽  
Multiscale Modeling ◽  
Chemical Reactions

scholarly journals Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

2020 ◽  
Author(s):  
Gabriel dos Passos Gomes ◽  
Robert Pollice ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Chemical Reactions ◽  
Catalyst Design ◽  
New Drugs ◽  
Homogeneous Catalyst ◽  
Chemical Bonds ◽  
Catalytic Systems ◽  
Data Intensive ◽  
The Past

<div><div><div><p>The ability to forge difficult chemical bonds through catalysis has transformed society on all fronts, from feeding our ever-growing populations to increasing our life-expectancies through the synthesis of new drugs. However, developing new chemical reactions and catalytic systems is a tedious task that requires tremendous discovery and optimization efforts. Over the past decade, advances in machine learning have revolutionized a whole new way to approach data- intensive problems, and many of these developments have started to enter chemistry. However, similar progress in the field of homogenous catalysis are only in their infancy. In this article, we want to outline our vision for the future of catalyst design and the role of machine learning to navigate this maze.</p></div></div></div>

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