Data-driven analysis and machine learning for energy prediction in distributed photovoltaic generation plants: A case study in Queensland, Australia

Machine-Learning (ML) methods, such as Artificial Neural Networks (ANN) bring the data-driven design of chemical reactions within reach. Simultaneously with the verification of the absence of any bias in the...

Download Full-text

Combining an Expert-Based Medical Entity Recognizer to a Machine-Learning System: Methods and a Case Study

Biomedical Informatics Insights ◽

10.4137/bii.s11770 ◽

2013 ◽

Vol 6s1 ◽

pp. BII.S11770 ◽

Cited By ~ 4

Author(s):

Pierre Zweigenbaum ◽

Thomas Lavergne ◽

Natalia Grabar ◽

Thierry Hamon ◽

Sophie Rosset ◽

...

Keyword(s):

Machine Learning ◽

Linear Chain ◽

Learning System ◽

Entity Recognition ◽

Supervised Machine Learning ◽

Data Driven ◽

Driven System ◽

Medical Entity ◽

F Measure

Medical entity recognition is currently generally performed by data-driven methods based on supervised machine learning. Expert-based systems, where linguistic and domain expertise are directly provided to the system are often combined with data-driven systems. We present here a case study where an existing expert-based medical entity recognition system, Ogmios, is combined with a data-driven system, Caramba, based on a linear-chain Conditional Random Field (CRF) classifier. Our case study specifically highlights the risk of overfitting incurred by an expert-based system. We observe that it prevents the combination of the 2 systems from obtaining improvements in precision, recall, or F-measure, and analyze the underlying mechanisms through a post-hoc feature-level analysis. Wrapping the expert-based system alone as attributes input to a CRF classifier does boost its F-measure from 0.603 to 0.710, bringing it on par with the data-driven system. The generalization of this method remains to be further investigated.

Download Full-text

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

10.26434/chemrxiv.9763094 ◽

2019 ◽

Author(s):

Siddhartha Laghuvarapu ◽

Yashaswi Pathak ◽

U. Deva Priyakumar

Keyword(s):

Machine Learning ◽

Density Functional ◽

Computational Cost ◽

Geometry Optimization ◽

Dft Methods ◽

Energy Prediction ◽

Machine Learning Model ◽

Equilibrium Structures ◽

High Level ◽

Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

Download Full-text