A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds

Jong, M. de; Chen, Wei; Notestine, Randy; Persson, Kristin A.; Ceder, Gerbrand; Jain, Anubhav; Asta, Mark; Gamst, Anthony

doi:10.1038/srep34256

Cited by 230 publications

(183 citation statements)

References 71 publications

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“…S5). Recently, Jong et al [33] developed a statistical learning (SL) framework using multivariate local regression on crystal descriptors to predict elastic properties using the same data from the Materials Project. By using the same number of training data, our model achieves root mean squared error (RMSE) on test sets of 0.105 log(GPa) and 0.127 log(GPa) for the bulk and shear moduli, which is similar to the RMSE of SL on the entire data set of 0.0750 log(GPa) and 0.1378 log(GPa).…”

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confidence: 99%

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

2018

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The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks (CGCNN) framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of DFT calculated properties for 8 different properties of crystals with various structure types and compositions after trained with 10 4 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

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confidence: 99%

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

2018

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“…A significant body of work has demonstrated that ML models can be trained to predict the results of first principles DFT calculations without the need for actual computationally intensive computations beyond the calculation of the reference data set. To name just a few examples, Rupp et al used kernel ridge regression (KRR) to train a model for the prediction of DFT molecular atomization energies [39], Jong et al used a gradient boosting approach to train DFT elastic moduli [40], Faber et al used a KRR model to predict DFT formation energies of Elpasolite (ABC 2 D 6 ) compositions [41], and Ye et al trained a deep ANN to predict DFT crystal stability [42]. Isayev et al proposed a model trained to DFT references with the gradient boosting decision tree technique to predict various properties of crystal structures including band gaps, elastic properties, and heat capacities [43].…”

Section: Progress In Machine Learning Methods For Materials Simulationsmentioning

confidence: 99%

Machine learning for the modeling of interfaces in energy storage and conversion materials

Artrith

2019

J. Phys. Energy

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The properties and atomic-scale dynamics of interfaces play an important role for the performance of energy storage and conversion devices such as batteries and fuel cells. In this topical review, we consider recent progress in machine-learning (ML) approaches for the computational modeling of materials interfaces. ML models are computationally much more efficient than first principles methods and thus allow to model larger systems and extended timescales, a necessary prerequisites for the accurate description of many interface properties. Here we review the recent major developments of ML-based interatomic potentials for atomistic modeling and ML approaches for the direct prediction of materials properties. This is followed by a discussion of ML applications to solid-gas, solid-liquid, and solid-solid interfaces as well as to nanostructured and amorphous phases that commonly form in interface regions. We then highlight how ML has been used to obtain important insights into the structure and stability of interfaces, interfacial reactions, and mass transport at interfaces. Finally, we offer a perspective on the current state of ML potential development and identify future directions and opportunities for this exciting research field.

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“…Previous studies by de Jong et al [79] and Tehrani et al [71] applied statistical learning techniques to smaller -yet similarly diverse-elastic datasets, using sets of compositional and structural descriptors. In similar fashion, we generate basic statistics from the composition and structure of each material which we use as the optimization algorithm's z vector.…”

Section: Application To the Materials Science Domain: Photocatalysismentioning

confidence: 99%

Rocketsled: a software library for optimizing high-throughput computational searches

2019

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A major goal of computation is to optimize an objective for which a forward calculation is possible, but no inverse solution exists. Examples include tuning parameters in a nuclear reactor design, optimizing structures in protein folding, or predicting an optimal materials composition for a functional application. In such instances, directing calculations in an optimal manner is important to obtaining the best possible solution within a fixed computational budget. Here, we introduce Rocketsled, an open-source Pythonbased software framework to help users optimize arbitrary objective functions. Rocketsled is built upon the existing FireWorks workflow software, which allows its computations to scale to supercomputing centers and for its objective functions to be complex, long-running, and error-prone workflows. Other unique features of Rocketsled include its ability to easily swap out the underlying optimizer, the ability to handle multiple competing objectives, the possibility to inject domain knowledge into the optimizer through feature engineering, incorporation of uncertainty estimates, and its parallelization scheme for running in high-throughput at massive scale. We demonstrate the generality of Rocketsled by applying it to optimize several common test functions (Branin-Hoo, Rosenbrock 2D, and Hartmann 6D). We highlight its potential impact through two example use cases for computational materials science. In a search for photocatalysts for hydrogen production among 18 928 perovskites previously calculated with density functional theory, the untuned Rocketsled Random Forest optimizer explores the search space with approximately 6-28 times fewer calculations than random search. In a search among 7394 materials for superhard candidates, Rocketsled requires approximately 61 times fewer calculations than random search to discover interesting candidates. Thus, Rocketsled provides a practical framework for establishing complex optimization schemes with minimal code infrastructure and enables the efficient exploration of otherwise prohibitively large search spaces.

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A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds

Cited by 230 publications

References 71 publications

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Machine learning for the modeling of interfaces in energy storage and conversion materials

Rocketsled: a software library for optimizing high-throughput computational searches

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