Accelerating materials discovery using machine learning

Juan, Yongfei; Dai, Yong; Yang, Yang; Zhang, Jiao

doi:10.1016/j.jmst.2020.12.010

Cited by 127 publications

(54 citation statements)

References 127 publications

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“…It is usually supported by magnanimous lab experiments that are demanding both in terms of time and technology. Accordingly, the exploration of the structure-composition-property relationship is very difficult [3][4][5] . Machine learning is intensively applied in the field of advanced materials exploration and discovery for almost a decade [6][7][8][9] , becoming a high-efficient approach to investigate inorganic materials [5] .…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, the exploration of the structure-composition-property relationship is very difficult [3][4][5] . Machine learning is intensively applied in the field of advanced materials exploration and discovery for almost a decade [6][7][8][9] , becoming a high-efficient approach to investigate inorganic materials [5] . However, the complicacy of machine-learning processes and the inability to comprehend models make it hard to obtain good rules for describing connections between structure, composition and property of materials, which impedes their deeper comprehension [10] .…”

Section: Introductionmentioning

confidence: 99%

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Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Qin¹,

Lin²

2022

Energy Mater

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Qin¹,

Lin²

2022

Energy Mater

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“…Due to its strong data processing capacity and relatively low research threshold, machine learning can effectively reduce the cost of human and material resources in industrial development and shorten the research and development cycle [27]. By replacing or cooperating with traditional experiments and computational simulation, it can analyze material structure and predict material properties more quickly and accurately, so as to develop new functional materials more effectively [28,29]. Selecting different machine learning methods to predict material performance parameters from existing large data sets can effectively improve the prediction accuracy of material performance, so as to select materials with reasonable performance for experimental research [21].…”

Section: Introductionmentioning

confidence: 99%

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

Qin

Tian

et al. 2021

Crystals

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Perovskites have attracted increasing attention because of their excellent physical and chemical properties in various fields, exhibiting a universal formula of ABO3 with matching compatible sizes of A-site and B-site cations. In this work, four different prediction models of machine learning algorithms, including support vector regression based on radial basis kernel function (SVM-RBF), ridge regression (RR), random forest (RF), and back propagation neural network (BPNN), are established to predict the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy of perovskite materials. Combined with the fitting diagrams of the predicted values and DFT calculated values, the results show that SVM-RBF has a smaller bias in predicting the crystal volume. RR has a smaller bias in predicting the thermodynamic stability. RF has a smaller bias in predicting the formation energy, crystal volume, and thermodynamic stability. BPNN has a smaller bias in predicting the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy. Obviously, different machine learning algorithms exhibit different sensitivity to data sample distribution, indicating that we should select different algorithms to predict different performance parameters of perovskite materials.

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“…The main advantage of ML models is that they allow predictions of molecular properties with improved efficiency at a lower computational cost compared to traditional quantum chemistry approaches. Method development in the field of QML is progressing rapidly and it is increasingly influencing traditional methods [6,[15][16][17][18]. Developments in molecular representations and QML models have paved the way for predicting energetic, electronic, and thermodynamic properties, such as atomization energies, dipole moments, polarizabilities, and harmonic frequencies [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning of Quasiparticle Energies in Molecules and Clusters

Çaylak¹,

Baumeier

2021

Preprint

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<div> <div> <div> <p>We present a ∆-Machine Learning approach for the prediction of GW quasiparticle energies (∆MLQP) and photoelectron spectra of molecules and clusters, using orbital-sensitive graph-based representations in kernel ridge regression based supervised learning. Coulomb matrix, Bag-of-Bonds, and Bonds-Angles-Torsions representations are made orbital-sensitive by augmenting them with atom-centered orbital charges and Kohn–Sham orbital energies, which are both readily available from baseline calculations on the level of density-functional theory (DFT). We first illustrate the effects of different constructions of the orbital-sensitive representations (OSR) on the prediction of frontier orbital energies of 22K molecules of the QM8 dataset, and show that is is possible to predict the full photoelectron spectrum of molecules within the dataset using a single model with a mean-absolute error below 0.1eV. We further demonstrate that the OSR-based ∆MLQP captures the effects of intra- and intermolecular conformations in application to water monomers and dimers. Finally, we show that the approach can be embedded in multiscale simulation workflows, by studying the solvatochromic shifts of quasiparticle and electron-hole excitation energies of solvated acetone in a setup combining Molecular Dynamics, DFT, the GW approximation and the Bethe–Salpeter Equation. Our findings suggest that the ∆MLQP model allows to predict quasiparticle energies and photoelectron spectra of molecules and clusters with GW accuracy at DFT cost. </p> </div> </div> </div>

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Accelerating materials discovery using machine learning

Cited by 127 publications

References 127 publications

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

Machine Learning of Quasiparticle Energies in Molecules and Clusters

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