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Metaxscaler

AI application published by Sinegas23. SDK: docker. Availability at last check: Sleeping.

Sinegas23
Not specifiedView resource
Tool

META

Reinforcement learning published by vasuganesha1. SDK: docker. Availability at last check: Sleeping.

vasuganesha1
Not specifiedView resource
Tool

Code collection for machine learning assisted materials screening in the search for novel half-metallic Heusler alloys

This code collection contains scripts that have been used to screen the Materials Project database for novel (nearly) half-metallic full (L21) and inverse (XA) Heusler alloys by employing machine learning-based methods in order to predict the spin-polarization of the density of states at the Fermi level. The models used have been trained on density-functional theory data collected by collaborators. Each step of the materials screening process is performed by the provided scripts, including Data processing, Hyperparameter Optimization, Model Training & Evaluation, Screening Data (The Materials Project) Download & Processing, Predictive Modelin

Hilgers, Robin, Wortmann, Daniel, Blügel, Stefan
MIT LicenseView resource
Tool

Data and Code underlying publication: Experiment-informed finite-strain inverse design of spinodal metamaterials

Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when designing mechanical metamaterials with extreme performance. While their elastic mechanical properties have been systematically determined, their large-deformation, nonlinear responses have been challenging to predict and design, in part due to limited data sets and the need for complex nonlinear simulations. This work presents a novel physics-enhanced machine learning (ML) and optimization framework tailored to address the challenges of designing intricate spinodal met

Thakolkaran, Prakash, Espinal, Michael, Dhulipala, Somayajulu, Kumar, Siddhant, Portela, Carlos M.
MIT LicenseView resource
Tool

WaveletML: A Scalable and Extensible Wavelet Neural Network Framework

WaveletML is an open-source Python framework designed for building, training, and evaluating Wavelet Neural Networks (WNNs) tailored for supervised learning tasks such as regression and classification. Leveraging the power of PyTorch and the modularity of scikit-learn, WaveletML provides a unified, extensible, and scalable platform for researchers and practitioners to explore wavelet-based neural architectures.Key features include:A flexible architecture supporting various WNN configurations.Built-in support for both gradient-based and metaheuristic optimization methods.Seamless integration with the scikit-learn API for model interoperability

Nguyen, Van Thieu
GPL 3.0+View resource
Tool

Metabolic Detection of Pancreatic Ductal Adenocarcinoma through Machine Learning and Lipidomics

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers characterized with rapid progression, metastasis and difficulty in diagnosis. However, there are no effective liquid-based testing methods available for PDAC detection. Here we introduce a minimally invasive approach that employs machine learning and lipidomics to establish a metabolic method to detect PDAC. Through greedy algorithm and mass spectrum feature selection, we optimized 17 characteristic metabolites as detection features and developed a LC-MS-based targeted assay. In this study, 1033 patients with PDAC at various stages were examined. This approach has achie

Wang, Guangxi, Hantao Yao
Creative Commons Attribution 4.0 InternationalView resource

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