Prediction of hydrogen uptake of metal organic frameworks using explainable machine learning

Machine learning analysis of alloying element effects on hydrogen storage properties of AB2 metal hydrides

(2022) Suwarno Suwarno et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Predicting the hydrogen uptake ability of a wide range of zeolites utilizing supervised machine learning methods

(2022) Seyed Mehdi Seyed Alizadeh et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Predicting effects of built environment on fatal pedestrian accidents at location-specific level: Application of XGBoost and SHAP

(2022) Iljoon Chang et al.   ACCIDENT ANALYSIS AND PREVENTION

State-of-health estimation of Li-ion batteries in the early phases of qualification tests: An interpretable machine learning approach

(2022) Gyumin Lee et al.   EXPERT SYSTEMS WITH APPLICATIONS

New insights into hydrogen uptake on porous carbon materials via explainable machine learning

(2021) Muhammad Irfan Maulana Kusdhany et al.   CARBON

Potential application of metal-organic frameworks (MOFs) for hydrogen storage: Simulation by artificial intelligent techniques

(2021) Yan Cao et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Machine learning approaches to rediscovery and optimization of hydrogen storage on porous bio-derived carbon

(2021) Mohammad Rahimi et al.   Journal of Cleaner Production

Empirical investigation of hyperparameter optimization for software defect count prediction

(2021) Meetesh Nevendra et al.   EXPERT SYSTEMS WITH APPLICATIONS

Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach

(2020) Sujith Mangalathu et al.   ENGINEERING STRUCTURES

Enhanced isosteric heat of adsorption and gravimetric storage density of hydrogen in GNP incorporated Cu based core-shell metal-organic framework

(2020) Archana K et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Machine learning based prediction of metal hydrides for hydrogen storage, part I: Prediction of hydrogen weight percent

(2019) Alireza Rahnama et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Investigating the impact of data normalization on classification performance

(2019) Dalwinder Singh et al.   APPLIED SOFT COMPUTING

Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks

(2019) Alauddin Ahmed et al.   Nature Communications

A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm

(2019) Qinghua Han et al.   CONSTRUCTION AND BUILDING MATERIALS

Correlation Coefficients

(2018) Patrick Schober et al.   ANESTHESIA AND ANALGESIA

Prediction of Carbon Dioxide Adsorption via Deep Learning

(2018) Zihao Zhang et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Prediction of gas storage capacities in metal organic frameworks using artificial neural network

(2015) Zeynep Yıldız et al.   MICROPOROUS AND MESOPOROUS MATERIALS

Deep learning

(2015) Yann LeCun et al.   NATURE

Computation-Ready, Experimental Metal–Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals

(2014) Yongchul G. Chung et al.   CHEMISTRY OF MATERIALS

Hydrogen Storage in Metal–Organic Frameworks

(2011) Myunghyun Paik Suh et al.   CHEMICAL REVIEWS

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

(2011) M. Galar et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND RE

Hydrogen Storage in Metal-Organic Frameworks

(2010) Yun Hang Hu et al.   ADVANCED MATERIALS

Pd Nanoparticles Embedded into a Metal-Organic Framework: Synthesis, Structural Characteristics, and Hydrogen Sorption Properties

(2010) Claudia Zlotea et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Hydrogen storage in metal–organic frameworks

(2009) Leslie J. Murray et al.   CHEMICAL SOCIETY REVIEWS

When will fossil fuel reserves be diminished?

(2008) Shahriar Shafiee et al.   ENERGY POLICY