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

Suwarno, Suwarno; Dicky, Ghazy; Suyuthi, Abdillah; Effendi, Mohammad Khoirul; Witantyo, Witantyo; Noerochim, Lukman; Ismail, M.

doi:10.1016/j.ijhydene.2022.01.210

Cited by 40 publications

(24 citation statements)

References 36 publications

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“…A set of 314 pairs of AB 2 alloys were collected and presented in the Supplementary Information from the literature 44 . They include the information of constituent elements and ΔH absorption (in KJ/(molH 2 )).…”

Section: Methodsmentioning

confidence: 99%

“…The heat of formation, phase abundance, and hydrogen storage capacity of AB 2 metal hydrides were all estimated using these models. The random forest model showed the most outstanding performance among the three models, with an average R 2 value of 0.722 44 . Determining the pressure-composition-temperature (PCT) curve is an important issue in metal hydrides.…”

Section: Introductionmentioning

confidence: 99%

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Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach

Gheytanzadeh

Rajabhasani

Baghban

et al. 2022

Sci Rep

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Hydrogen is a promising alternative energy source due to its significantly high energy density. Also, hydrogen can be transformed into electricity in energy systems such as fuel cells. The transition toward hydrogen-consuming applications requires a hydrogen storage method that comes with pack hydrogen with high density. Among diverse methods, absorbing hydrogen on host metal is applicable at room temperature and pressure, which does not provide any safety concerns. In this regard, AB2 metal hydride with potentially high hydrogen density is selected as an appropriate host. Machine learning techniques have been applied to establish a relationship on the effect of the chemical composition of these hosts on hydrogen storage. For this purpose, a data bank of 314 data point pairs was used. In this assessment, the different A-site and B-site elements were used as the input variables, while the hydrogen absorption energy resulted in the output. A robust Gaussian process regression (GPR) approach with four kernel functions is proposed to predict the hydrogen absorption energy based on the inputs. All the GPR models' performance was quite excellent; notably, GPR with Exponential kernel function showed the highest preciseness with R2, MRE, MSE, RMSE, and STD of 0.969, 2.291%, 3.909, 2.501, and 1.878, respectively. Additionally, the sensitivity of analysis indicated that ZR, Ti, and Cr are the most demining elements in this system.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach

Gheytanzadeh

Rajabhasani

Baghban

et al. 2022

Sci Rep

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“…Machine learning offers a fresh approach to this long-standing problem of classifying Laves phases. It has been used recently to predict the effect of alloying on the hydrogen storage properties and formation enthalpies of Laves phases. , Here, we apply machine learning methods to predict whether a Laves phase is likely to be adopted for any given composition AB 2 , taking into account variations caused by the solid solubility. Because the data set of known Laves vs non-Laves phases is well balanced, numerous, and diverse, we hypothesize that machine learning models can be trained that will be capable and suitable for distinguishing between them.…”

Section: Introductionmentioning

confidence: 99%

Ahead by a Century: Discovery of Laves Phases Assisted by Machine Learning

Sikdar,

Roy,

Selvaratnam

et al. 2024

Inorg. Chem.

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Laves phases AB 2 form the most abundant group of intermetallic compounds, consisting of combinations of larger electropositive metals A with smaller metals B. Many practical applications of Laves phases depend on the ability to tune their physical properties through appropriate substitution of either the A or B component. Although simple geometrical and electronic factors have long been thought to control the formation of Laves phases, no single factor alone can make predictions accurately. Several machine learning models have been developed to discover new Laves phases, including variations caused by solid solubility, using elemental properties solely on the basis of chemical composition. These models were trained on a data set comprising about 3700 entries of experimentally known phases AB 2 with Laves and non-Laves structures. Among these models, a decision tree algorithm gave very good performance (average recall of 95%, precision of 94%, and accuracy of 96% on the test set) by using only a small set of descriptors, the most important of which relates to the electron density at the boundary of the Wigner−Seitz cell for the B component. This model provides guidance for new experiments by making predictions on >400000 candidates very quickly. A chemically unintuitive candidate Cd(Cu 1−x Sb x ) 2 with a limited solid solubility of Sb for Cu was targeted; it was successfully synthesized and confirmed to adopt a cubic MgCu 2 -type Laves structure.

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“…The development of machine learning (ML) and other surrogate models presents a promising alternative to expensive high-throughput ab initio search platforms. 34,35,52,53 Once sufficiently trained, regression models are many orders of magnitude faster to execute than experiments or traditional ab initio calculations. They can be used to quickly screen materials composition space and discover novel, high-performing candidates.…”

Section: Introductionmentioning

confidence: 99%

Simple local environment descriptors for accurate prediction of hydrogen absorption and migration in metal alloys

Korostelev,

Wagner,

Klyukin

2023

J. Mater. Chem. A

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

Cited by 40 publications

References 36 publications

Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach

Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach

Ahead by a Century: Discovery of Laves Phases Assisted by Machine Learning

Simple local environment descriptors for accurate prediction of hydrogen absorption and migration in metal alloys

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