Novel machine learning workflow for rock property prediction in the geologically complex presalt Santos basin, Brazil

2021 ◽  
Author(s):  
Dan Clarke ◽  
Martijn Blaauw ◽  
Jaydip Guha ◽  
Altay Sansal ◽  
Muhlis Unaldi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Property Prediction ◽  
Rock Property

scholarly journals Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
M. Withnall ◽  
E. Lindelöf ◽  
O. Engkvist ◽  
H. Chen
Keyword(s):  
Machine Learning ◽  
Message Passing ◽  
A Priori ◽  
Molecular Graph ◽  
Model Performance ◽  
Learning Approaches ◽  
Property Prediction ◽  
Physical Chemical ◽  
Chemical Descriptor ◽  
Hyperparameter Selection

AbstractNeural Message Passing for graphs is a promising and relatively recent approach for applying Machine Learning to networked data. As molecules can be described intrinsically as a molecular graph, it makes sense to apply these techniques to improve molecular property prediction in the field of cheminformatics. We introduce Attention and Edge Memory schemes to the existing message passing neural network framework, and benchmark our approaches against eight different physical–chemical and bioactivity datasets from the literature. We remove the need to introduce a priori knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.

scholarly journals Materials Representation and Transfer Learning for Multi-Property Prediction

2021 ◽  
Author(s):  
Shufeng Kong ◽  
Dan Guevarra ◽  
Carla P. Gomes ◽  
John Gregoire
Keyword(s):  
Machine Learning ◽  
Optical Absorption ◽  
Transfer Learning ◽  
Materials Science ◽  
Training Data ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Property Prediction ◽  
Adversarial Network ◽  
Correlation Learning

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

scholarly journals Materials Representation and Transfer Learning for Multi-Property Prediction

2021 ◽  
Author(s):  
Shufeng Kong ◽  
Dan Guevarra ◽  
Carla P. Gomes ◽  
John Gregoire
Keyword(s):  
Machine Learning ◽  
Optical Absorption ◽  
Transfer Learning ◽  
Materials Science ◽  
Training Data ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Property Prediction ◽  
Adversarial Network ◽  
Correlation Learning

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

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