Predicting d-Band Center of Transition Metals Using Machine Learning
· Machine Learning · 10 minute read
Predict the d-band center of transition-metal catalysts with machine learning: electronic descriptors, model training, and DFT validation.
· Machine Learning · 10 minute read
Predict the d-band center of transition-metal catalysts with machine learning: electronic descriptors, model training, and DFT validation.
· Machine Learning · 6 minute read
Large-scale machine learning: stochastic and mini-batch gradient descent, online learning, and map-reduce parallelism for big data.
· Machine Learning · 6 minute read
Anomaly detection and recommender systems: Gaussian density estimation, collaborative filtering, and evaluation with precision and recall.
· Machine Learning · 8 minute read
Predict bulk modulus with machine learning and materials informatics: dataset assembly, descriptor selection, and model comparison.
· Machine Learning · 6 minute read
Clustering and dimensionality reduction: k-means, principal component analysis, and working with high-dimensional datasets.
· Machine Learning · 5 minute read
Support vector machines: maximum-margin classification, the kernel trick, and practical tuning of C and Gaussian kernel parameters.
· Machine Learning · 7 minute read
Machine learning-aided design of aluminum alloys: composition-property modeling, feature selection, and validation of predicted alloy candidates.
· Machine Learning · 7 minute read
Neural networks explained: architectures, activation functions, forward propagation, and backpropagation for multi-class classification.
· Machine Learning · 6 minute read
Logistic regression and regularization: decision boundaries, the sigmoid cost function, and L1/L2 penalties to prevent overfitting.
· Machine Learning · 6 minute read
Machine learning foundations: supervised and unsupervised learning, linear regression, cost functions, and gradient descent with worked examples.
· Machine Learning · 6 minute read
Predict band gap and band alignment of nitride-based semiconductors with machine learning: descriptor engineering, model training, and DFT validation.