Machine Learning based prediction of noncentrosymmetric crystal materials

Song, Yuqi; Lindsay, Joseph; Zhao, Yong; Nasiri, Alireza; Louis, Steph-Yves M.; Ling, Jie; Hu, Ming; Hu, Jianjun

doi:10.1016/j.commatsci.2020.109792

Cited by 23 publications

(12 citation statements)

References 30 publications

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“…Among many ML techniques, the ones based on decision trees are commonly used to estimate a variety of materials properties. 46 Unlike linear models, they map nonlinear relationships in the data and are versatile at solving distinct supervised learning problems. Treebased algorithms are robust to noisy data, 47 being an appropriate choice for problems where the output is subjected to considerable statistical fluctuations, such as for the phenomenon of fatigue.…”

Section: Modelmentioning

confidence: 99%

Machine learning and finite element analysis: An integrated approach for fatigue lifetime prediction of adhesively bonded joints

Silva

Beber

Pitz

2021

Fatigue Fract Eng Mat Struct

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Since fatigue investigations are expensive and time consuming, models capable of predicting lifetime by leveraging existing experimental data are desirable.Here, this task is accomplished by combining machine learning (ML) and finite element analysis (FEA). The dataset contains 365 points comprising four adhesives with four different joint types. The model is fed with four input parameters: stress ratio and stress amplitude (functions of the applied load), and stress concentration factor and multiaxiality, which are obtained from FEA. An extremely randomized trees (ERT) algorithm, capable of dealing with small and noisy datasets, is used to design the model. After calibration, the model's performance was assessed on unseen data and compared with a linear regression model. The ERT predictions yield a significantly smaller error factor (ER) of 2.13 than that of the linear model (ER = 5.89). A relevance analysis shows that at least one FEA-based parameter must be fed into the model.

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Section: Modelmentioning

confidence: 99%

Machine learning and finite element analysis: An integrated approach for fatigue lifetime prediction of adhesively bonded joints

Silva

Beber

Pitz

2021

Fatigue Fract Eng Mat Struct

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“…Machine-learning (ML) approach is a scientific model that can efficiently learn from existing results and is gaining increasing attention in material exploration. [18][19][20][21][22] With the assistance of ML, researchers can explore massive new materials (like lead-free perovskites), 23,24 develop efficient solar cells, 19,21,22,27 etc. Previously, using ML algorithms, we successfully predicted the bandgap of 3D lead halide perovskites from their compositions and proposed possible compositions of mixed halide perovskites, which can be used in tandem solar cells.…”

Section: Introductionmentioning

confidence: 99%

Predicting the photon energy of quasi-2D lead halide perovskites from the precursor composition through machine learning

Wang

Zou

et al. 2022

Nanoscale Adv.

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“…Ma and co-workers reported several ML models such as regression trees and neural networks to study the correlation between molecular properties and device parameters of organic photovoltaics and dye-sensitized solar cells. − The following ML-assisted virtual screening is helpful to extract chemical knowledge and explore potential functional materials. In the field of crystal design, ML models have been constructed to predict the symmetry type of crystals and gave some hypothetical samples through searching in a data set . It has a great potential to accelerate the discovery of new NLO materials because NLO crystals should belong to noncentrosymmetric space groups.…”

Section: Introductionmentioning

confidence: 99%

“…In the field of crystal design, ML models have been constructed to predict the symmetry type of crystals and gave some hypothetical samples through searching in a data set. 19 It has a great potential to accelerate the discovery of new NLO materials because NLO crystals should belong to noncentrosymmetric space groups. Recently, Lin and co-workers applied a random forest model to predict the second-order nonlinear coefficients of diamond-like NLO crystals.…”

Section: ■ Introductionmentioning

confidence: 99%

Machine Learning with Multilevel Descriptors for Screening of Inorganic Nonlinear Optical Crystals

et al. 2021

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Nonlinear optical (NLO) crystals are the key materials in modern laser technology and science because of their intrinsic capability to convert the wavelength of the light source. The search for new NLO materials is still very active in both scientific and industrial communities. Machine learning (ML) becomes a powerful tool to explore new candidates of NLO materials and to reveal the underlying relationship between structures and properties. In this work, we have proposed multilevel features that are relevant to the atomic properties, the characters of fundamental structural groups, and the crystal structures to describe inorganic NLO crystals for machine learning. The first-level and second-level descriptors can be obtained based on chemical compositions of crystals without prior knowledge about crystal structures. Several ML classifiers have been optimized using a database that consists of hundreds of NLO crystals to identify the samples with desired birefringence (Δn) and second-order nonlinear coefficients (d ij ). In particular, almost all of the ML models that only involve the first-level and second-level features, called as the crystal-structure-free model, exhibit good classification performance. It is still far from perfect but suitable to act as a filter in the first step of high-throughput materials discovery. Using the optimized ML models, feature importance analyses and virtual screening processes have been performed to understand the relationship between the features and targeted properties and to extract the statistical pictures on elements and fundamental structural groups. Several unexplored crystals are also picked out as ML-proposed candidates, and three of them are suggested as new potential NLO materials based on further first-principle calculations. The present ML models are expected to accelerate the inverse design for new NLO crystals with desired properties.

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Machine Learning based prediction of noncentrosymmetric crystal materials

Cited by 23 publications

References 30 publications

Machine learning and finite element analysis: An integrated approach for fatigue lifetime prediction of adhesively bonded joints

Machine learning and finite element analysis: An integrated approach for fatigue lifetime prediction of adhesively bonded joints

Predicting the photon energy of quasi-2D lead halide perovskites from the precursor composition through machine learning

Machine Learning with Multilevel Descriptors for Screening of Inorganic Nonlinear Optical Crystals

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