Unraveling the role of bonding chemistry in connecting electronic and thermal transport by machine learning

2020 ◽  
Vol 8 (17) ◽  
pp. 8716-8721 ◽  
Author(s):  
Rinkle Juneja ◽  
Abhishek K. Singh
Keyword(s):  
Machine Learning ◽  
Thermal Transport ◽  
Ion Interactions ◽  
Bonding Chemistry

Electronic and thermal transport in materials originate from various forms of electron and ion interactions.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Role of machine learning algorithms over heart diseases prediction

2020 ◽  
Author(s):  
Siva Kumar Jonnavithula ◽  
Abhilash Kumar Jha ◽  
Modepalli Kavitha ◽  
Singaraju Srinivasulu
Keyword(s):  
Machine Learning ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

The role of machine learning analytics and metrics in retailing research

2020 ◽  
Author(s):  
Xin (Shane) Wang ◽  
Jun Hyun (Joseph) Ryoo ◽  
Neil Bendle ◽  
Praveen K. Kopalle
Keyword(s):  
Machine Learning ◽  
Learning Analytics

scholarly journals Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows

2021 ◽  
Author(s):  
Doris Xin ◽  
Eva Yiwei Wu ◽  
Doris Jung-Lin Lee ◽  
Niloufar Salehi ◽  
Aditya Parameswaran
Keyword(s):  
Machine Learning

scholarly journals 180 The role of social constructs in placental insufficiency outcomes: an unsupervised machine learning approach

2021 ◽  
Vol 224 (2) ◽  
pp. S121-S122
Author(s):  
Ramamurthy Siripuram ◽  
Nathan R. Blue ◽  
Robert M. Silver ◽  
William A. Grobman ◽  
Uma M. Reddy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Placental Insufficiency ◽  
Learning Approach ◽  
Social Constructs ◽  
Unsupervised Machine Learning ◽  
Machine Learning Approach
Sign in / Sign up

Export Citation Format

Share Document