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Abstract: This paper considers the nature of a curriculum as presented in formal curriculum documents, and the inherent difficulties of representing formal disciplinary knowledge in a prescription for teaching and learning. The...

article Chemistry education Curricular models Disciplinary knowledge Canonical concepts Chemical reactions

Abstract: This paper considers the nature of a curriculum as presented in formal curriculum documents, and the inherent difficulties of representing formal disciplinary knowledge in a prescription for teaching and learning. The...

article Chemistry education Curricular models Disciplinary knowledge Canonical concepts Chemical reactions

Application of machine learning (ML) to the prediction of reaction activation barriers is a new and exciting field for these algorithms. The works covered here are specifically those in which ML is trained to predict the...

activation barriers Chemical reactions data Machine Learning reactivity prediction /dk/atira/pure/subjectarea/asjc/1300/1303 name=Biochemistry /dk/atira/pure/subjectarea/asjc/1700/1706 name=Computer Science Applications /dk/atira/pure/subjectarea/asjc/1600/1606 name=Physical and Theoretical Chemistry /dk/atira/pure/subjectarea/asjc/2600/2605 name=Computational Mathematics /dk/atira/pure/subjectarea/asjc/2500/2505 name=Materials Chemistry

Application of machine learning (ML) to the prediction of reaction activation barriers is a new and exciting field for these algorithms. The works covered here are specifically those in which ML is trained to predict the...

activation barriers Chemical reactions data Machine Learning reactivity prediction /dk/atira/pure/subjectarea/asjc/1300/1303 name=Biochemistry /dk/atira/pure/subjectarea/asjc/1700/1706 name=Computer Science Applications /dk/atira/pure/subjectarea/asjc/1600/1606 name=Physical and Theoretical Chemistry /dk/atira/pure/subjectarea/asjc/2600/2605 name=Computational Mathematics /dk/atira/pure/subjectarea/asjc/2500/2505 name=Materials Chemistry