The interplay of kinetics and thermodynamics governs reactive processes, and their control is key in synthesis efforts. While sophisticated numerical methods for studying equilibrium states have well advanced, quantitative predictions of kinetic behavior remain challenging. We introduce a reactant-to-barrier (R2B) machine learning model that rapidly and accurately infers activation energies and transition state geometries throughout the chemical compound space. R2B exhibits improving accuracy as training set sizes grow and requires as input solely the molecular graph of the reactant and the information of the reaction type. We provide numerical evidence for the applicability of R2B for two competing text-book reactions relevant to organic s...
We present a method to predict products, transition states, and reaction paths of unimolecular chemi...
Proposing and testing mechanistic hypotheses stands as one of the key applications of contemporary c...
The synthesis of molecules with desired properties is an important part of some areas of science and...
Application of machine learning (ML) to the prediction of reaction activation barriers is a new and ...
The scope of this thesis is the application of quantum machine learning (QML) methods to problems in...
Chemical compound space refers to the vast set of all possible chemical compounds, estimated to cont...
The discovery of chemical reactions is an inherently unpredictable and time-consuming process1. An a...
Reaction barriers are key to our understanding of chemical reactivity and catalysis. Certain reactio...
Modern quantum mechanical modelling methods, such as Density Functional Theory (DFT), have provided ...
Being able to predict the course of arbitrary chemical reactions is essential to the theory and appl...
Machine learning has been used to study chemical reactivity for a long time in fields such as physic...
Synthetic organic chemists face a dearth of challenges in the efficient construction of functional m...
Of the various factors influencing kinetically controlled product ratios, the role of nonstatistical...
Discovering new reactions, optimizing their performance, and extending the synthetically accessible ...
The search for new molecules often involves cycles of design-make-test-analyze steps, where new mole...
We present a method to predict products, transition states, and reaction paths of unimolecular chemi...
Proposing and testing mechanistic hypotheses stands as one of the key applications of contemporary c...
The synthesis of molecules with desired properties is an important part of some areas of science and...
Application of machine learning (ML) to the prediction of reaction activation barriers is a new and ...
The scope of this thesis is the application of quantum machine learning (QML) methods to problems in...
Chemical compound space refers to the vast set of all possible chemical compounds, estimated to cont...
The discovery of chemical reactions is an inherently unpredictable and time-consuming process1. An a...
Reaction barriers are key to our understanding of chemical reactivity and catalysis. Certain reactio...
Modern quantum mechanical modelling methods, such as Density Functional Theory (DFT), have provided ...
Being able to predict the course of arbitrary chemical reactions is essential to the theory and appl...
Machine learning has been used to study chemical reactivity for a long time in fields such as physic...
Synthetic organic chemists face a dearth of challenges in the efficient construction of functional m...
Of the various factors influencing kinetically controlled product ratios, the role of nonstatistical...
Discovering new reactions, optimizing their performance, and extending the synthetically accessible ...
The search for new molecules often involves cycles of design-make-test-analyze steps, where new mole...
We present a method to predict products, transition states, and reaction paths of unimolecular chemi...
Proposing and testing mechanistic hypotheses stands as one of the key applications of contemporary c...
The synthesis of molecules with desired properties is an important part of some areas of science and...