Distributed representations of atoms and materials for machine learning

Antunes, L.; Grau‐Crespo, Ricardo; Butler, Keith T.

doi:10.1038/s41524-022-00729-3

Cited by 23 publications

(10 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, the symmetry‐related E g prediction was deteriorated but the E f and E h prediction quality never changed. Antunes et al [ 59 ] very recently revealed that it was successful to predict various material properties from chemical formulas only by employing brilliant atomic representations (e.g., SkipAtom, Atom2Vec, Bag‐of‐Atom, etc.). Nonetheless, we found that the XRD plays a significant role for the symmetry‐related E g prediction in the present investigation.…”

Section: Resultsmentioning

confidence: 99%

Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction

Lee

Park

et al. 2022

Advanced Intelligent Systems

View full text Add to dashboard Cite

Herein, data‐driven symmetry identification, property prediction, and low‐dimensional embedding from powder X‐Ray diffraction (XRD) patterns of inorganic crystal structure database (ICSD) and materials project (MP) entries are reported. For this purpose, a fully convolutional neural network (FCN), transformer encoder (T‐encoder), and variational autoencoder (VAE) are used. The results are compared to those obtained from a well‐established crystal graph convolutional neural network (CGCNN). A task‐specified small dataset that focuses on a narrow material system, knowledge (rule)‐based descriptor extraction, and significant data dimension reduction are not the main focus of this study. Conventional powder XRD patterns, which are most widely used in materials research, can be used as a significantly informative material descriptor for deep learning. Both the FCN and T‐encoder outperform the CGCNN for symmetry classification. For property prediction, the performance of the FCN concatenated with multilayer perceptron reaches the performance level of CGCNN. Machine‐learning‐driven material property prediction from the powder XRD pattern deserves appreciation because no such attempts have been made despite common XRD‐driven symmetry (and lattice size) prediction and phase identification. The ICSD and MP data are embedded in the 2D (or 3D) latent space through the VAE, and well‐separated clustering according to the symmetry and property is observed.

show abstract

Section: Resultsmentioning

confidence: 99%

Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction

Lee

Park

et al. 2022

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…A range of atomistic ML models has been introduced in recent years. The focus has mainly been on the regression of atom-resolved properties, or global properties as dependent on individual atomic environments . The construction of structural descriptors is often guided by physical ideas, encoding information about environments and symmetries, but this is not an indispensable practice, as complex neural networks have also been used to capture materials structures from raw data inputs.…”

Section: Intrinsically Interpretable Modelsmentioning

confidence: 99%

Interpretable and Explainable Machine Learning for Materials Science and Chemistry

et al. 2022

Self Cite

View full text Add to dashboard Cite

Conspectus Machine learning has become a common and powerful tool in materials research. As more data become available, with the use of high-performance computing and high-throughput experimentation, machine learning has proven potential to accelerate scientific research and technology development. Though the uptake of data-driven approaches for materials science is at an exciting, early stage, to realize the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive power. The predictions and inner workings of models should provide a certain degree of explainability by human experts, permitting the identification of potential model issues or limitations, building trust in model predictions, and unveiling unexpected correlations that may lead to scientific insights. In this work, we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific studies. We start by defining the fundamental concepts of interpretability and explainability in machine learning and making them less abstract by providing examples in the field. We show how interpretability in scientific machine learning has additional constraints compared to general applications. Building upon formal definitions in machine learning, we formulate the basic trade-offs among the explainability, completeness, and scientific validity of model explanations in scientific problems. In the context of these trade-offs, we discuss how interpretable models can be constructed, what insights they provide, and what drawbacks they have. We present numerous examples of the application of interpretable machine learning in a variety of experimental and simulation studies, encompassing first-principles calculations, physicochemical characterization, materials development, and integration into complex systems. We discuss the varied impacts and uses of interpretabiltiy in these cases according to the nature and constraints of the scientific study of interest. We discuss various challenges for interpretable machine learning in materials science and, more broadly, in scientific settings. In particular, we emphasize the risks of inferring causation or reaching generalization by purely interpreting machine learning models and the need for uncertainty estimates for model explanations. Finally, we showcase a number of exciting developments in other fields that could benefit interpretability in material science problems. Adding interpretability to a machine learning model often requires no more technical know-how than building the model itself. By providing concrete examples of studies (many with associated open source code and data), we hope that this Account will encourage all practitioners of machine learning in materials science to look deeper into their models.

show abstract

“…For all CraTENet and CrabNet models, the input, X in , consisted of n = 8 elements, and was zero-padded if the composition consisted of less than eight elements. Each element in the input was described with a SkipAtom distributed representation [108] with dimensions d in = 200. (We performed experiments, as described in supplementary note 1 and supplementary table 3, to determine the performance of different descriptors).…”

Section: Model Training and Evaluationmentioning

confidence: 99%

Predicting thermoelectric transport properties from composition with attention-based deep learning

Antunes

Butler

Grau‐Crespo

2023

Mach. Learn.: Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even toxic elements, which limits their widespread adoption. To enable deployment on global scales, new classes of effective thermoelectrics are thus required. Ab initio models of transport properties can help in the design of new thermoelectrics, but they are still too computationally expensive to be solely relied upon for high-throughput screening in the vast chemical space of all possible candidates. Here, we use models constructed with modern machine learning techniques to scan very large areas of inorganic materials space for novel thermoelectrics, using composition as an input. We employ an attention-based deep learning model, trained on data derived from ab initio calculations, to predict a material’s Seebeck coefficient, electrical conductivity, and power factor over a range of temperatures and n- or p-type doping levels, with surprisingly good performance given the simplicity of the input, and with significantly lower computational cost. The results of applying the model to a space of known and hypothetical binary and ternary selenides reveal several materials that may represent promising thermoelectrics. Our study establishes a protocol for composition-based prediction of thermoelectric behaviour that can be easily enhanced as more accurate theoretical or experimental databases become available.

show abstract

Distributed representations of atoms and materials for machine learning

Cited by 23 publications

References 42 publications

Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction

Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction

Interpretable and Explainable Machine Learning for Materials Science and Chemistry

Predicting thermoelectric transport properties from composition with attention-based deep learning

Contact Info

Product

Resources

About