Machine learning modeling of superconducting critical temperature

Superconductivity has been the focus of enormous research effort since its discovery more than a century ago.Yet,some features of this unique phenomenon remain poorly understood;prime among these is the connection between superconductivity and chemical/structural properties of materials.To bridge the gap,several machine learning schemes are developed herein to model the critical temperatures(T_(c))of the 12,000+known superconductors available via the SuperCon database.Materials are first divided into two classes based on their T_(c) values,above and below 10 K,and a classification model predicting this label is trained.The model uses coarse-grained features based only on the chemical compositions.It shows strong predictive power,with out-of-sample accuracy of about 92%.Separate regression models are developed to predict the values of T_(c) for cuprate,iron-based,and low-T_(c) compounds.These models also demonstrate good performance,with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials.To improve the accuracy and interpretability of these models,new features are incorporated using materials data from the AFLOW Online Repositories.Finally,the classification and regression models are combined into a single-integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database(ICSD)for potential new superconductors.We identify>30 non-cuprate and non-iron-based oxides as candidate materials.

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and machine learning(ML)techniques.JARVIS is motivated by the Materials Genome Initiative(MGI)principles of developing open-access databases and tools to reduce the cost and development time of materials discovery,optimization,and deployment.

Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

Machine learning techniques have proven invaluable to manage the ever growing volume of materials research data produced as developments continue in high-throughput materials simulation,fabrication,and characterization.In particular,machine learning techniques have been demonstrated for their utility in rapidly and automatically identifying potential composition-phase maps from structural data characterization of composition spread libraries,enabling rapid materials fabrication-structure-property analysis and functional materials discovery.A key issue in development of an automated phase-diagram determination method is the choice of dissimilarity measure,or kernel function.The desired measure reduces the impact of confounding structural data issues on analysis performance.The issues include peak height changes and peak shifting due to lattice constant change as a function of composition.In this work,we investigate the choice of dissimilarity measure in X-ray diffraction-based structure analysis and the choice of measure’s performance impact on automatic composition-phase map determination.Nine dissimilarity measures are investigated for their impact in analyzing X-ray diffraction patterns for a Fe-Co-Ni ternary alloy composition spread.The cosine,Pearson correlation coefficient,and Jensen-Shannon divergence measures are shown to provide the best performance in the presence of peak height change and peak shifting(due to lattice constant change)when the magnitude of peak shifting is unknown.With prior knowledge of the maximum peak shifting,dynamic time warping in a normalized constrained mode provides the best performance.This work also serves to demonstrate a strategy for rapid analysis of a large number of X-ray diffraction patterns in general beyond data from combinatorial libraries.

Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

Analyzing large X-ray diffraction(XRD)datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries.Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties.Here,we report a new method for pattern analysis and phase extraction of XRD datasets.The method expands the Nonnegative Matrix Factorization method,which has been used previously to analyze such datasets,by combining it with custom clustering and cross-correlation algorithms.This new method is capable of robust determination of the number of basis patterns present in the data which,in turn,enables straightforward identification of any possible peak-shifted patterns.Peak-shifting arises due to continuous change in the lattice constants as a function of composition and is ubiquitous in XRD datasets from composition spread libraries.Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns,which is necessary in order to decipher the contribution of each unique single-phase structure to the multi-phase regions.The process can be utilized to determine accurately the compositional phase diagram of a system under study.The presented method is applied to one synthetic and one experimental dataset and demonstrates robust accuracy and identification abilities.