The Role of Machine Learning Algorithms in Materials Science: A State of Art Review on Industry 4.0

Choudhury, Amitava

doi:10.1007/s11831-020-09503-4

Cited by 24 publications

(16 citation statements)

References 103 publications

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“…Our dataset consisted of 34 observations, which is a typically small dataset in ML. The small dataset presents quite a high risk of overfitting or underfitting [ 28 , 29 ]. A less complex ML model is more friendly to a small dataset.…”

Section: Modeling and Datasetmentioning

confidence: 99%

Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning

Chen

Liao

et al. 2023

Materials

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Graphene has attracted significant interest due to its unique properties. Herein, we built an adsorption structure selection workflow based on a density functional theory (DFT) calculation and machine learning to provide a guide for the interfacial properties of graphene. There are two main parts in our workflow. One main part is a DFT calculation routine to generate a dataset automatically. This part includes adatom random selection, modeling adsorption structures automatically, and a calculation of adsorption properties. It provides the dataset for the second main part in our workflow, which is a machine learning model. The inputs are atomic characteristics selected by feature engineering, and the network features are optimized by a genetic algorithm. The mean percentage error of our model was below 35%. Our routine is a general DFT calculation accelerating routine, which could be applied to many other problems. An attempt on graphene/magnesium composites design was carried out. Our predicting results match well with the interfacial properties calculated by DFT. This indicated that our routine presents an option for quick-design graphene-reinforced metal matrix composites.

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Section: Modeling and Datasetmentioning

confidence: 99%

Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning

Chen

Liao

et al. 2023

Materials

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“…Scientific experimentation and discovery is teetering on the precipice of a new industrial revolution. Acceleration of science by combining automation and artificial intelligence (AI) has begun to revolutionize the structure of scientific experiments across physics, 1 chemistry, 2–8 materials science, 9–13 and biology. 14 The integration of high-throughput experimentation, AI, data science, and multi-scale modeling have spawned great interest, 15 notable results, 16 and substantive expectations.…”

Section: Introductionmentioning

confidence: 99%

What is missing in autonomous discovery: open challenges for the community

Maffettone,

Friederich,

Baird

et al. 2023

Digital Discovery

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“…[1][2][3][4][5] The challenges of AI for materials science are extensive, making acceleration of materials discovery a formidable task that requires advancing the frontier of AI. [6][7][8][9][10][11][12][13][14] Materials discovery embodies the convergence of limited data, data dispersed over multiple domains, and multi-property prediction, motivating commensurate integration of AI methods to collectively address these challenges, as demonstrated by the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP, pronounced "H-Clamp") framework introduced herein.…”

Section: Introductionmentioning

confidence: 99%

Materials Representation and Transfer Learning for Multi-Property Prediction

Kong¹,

Guevarra²,

Gomes³

et al. 2021

Preprint

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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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The Role of Machine Learning Algorithms in Materials Science: A State of Art Review on Industry 4.0

Cited by 24 publications

References 103 publications

Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning

Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning

What is missing in autonomous discovery: open challenges for the community

Materials Representation and Transfer Learning for Multi-Property Prediction

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