Trusting our machines: validating machine learning models for single-molecule transport experiments
Publikation: Bidrag til tidsskrift › Review › Forskning › fagfællebedømt
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Trusting our machines : validating machine learning models for single-molecule transport experiments. / Bro-Jørgensen, William; Hamill, Joseph M; Bro, Rasmus; Solomon, Gemma C.
I: Chemical Society Reviews, Bind 51, Nr. 16, 2022, s. 6875-6892.Publikation: Bidrag til tidsskrift › Review › Forskning › fagfællebedømt
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TY - JOUR
T1 - Trusting our machines
T2 - validating machine learning models for single-molecule transport experiments
AU - Bro-Jørgensen, William
AU - Hamill, Joseph M
AU - Bro, Rasmus
AU - Solomon, Gemma C.
PY - 2022
Y1 - 2022
N2 - In this tutorial review, we will describe crucial aspects related to the application of machine learning to help users avoid the most common pitfalls. The examples we present will be based on data from the field of molecular electronics, specifically single-molecule electron transport experiments, but the concepts and problems we explore will be sufficiently general for application in other fields with similar data. In the first part of the tutorial review, we will introduce the field of single-molecule transport, and provide an overview of the most common machine learning algorithms employed. In the second part of the tutorial review, we will show, through examples grounded in single-molecule transport, that the promises of machine learning can only be fulfilled by careful application. We will end the tutorial review with a discussion of where we, as a field, could go from here.
AB - In this tutorial review, we will describe crucial aspects related to the application of machine learning to help users avoid the most common pitfalls. The examples we present will be based on data from the field of molecular electronics, specifically single-molecule electron transport experiments, but the concepts and problems we explore will be sufficiently general for application in other fields with similar data. In the first part of the tutorial review, we will introduce the field of single-molecule transport, and provide an overview of the most common machine learning algorithms employed. In the second part of the tutorial review, we will show, through examples grounded in single-molecule transport, that the promises of machine learning can only be fulfilled by careful application. We will end the tutorial review with a discussion of where we, as a field, could go from here.
U2 - 10.1039/d1cs00884f
DO - 10.1039/d1cs00884f
M3 - Review
C2 - 35686581
VL - 51
SP - 6875
EP - 6892
JO - Chemical Society Reviews
JF - Chemical Society Reviews
SN - 0306-0012
IS - 16
ER -
ID: 311119235