Formation enthalpies for transition metal alloys using machine learning

Ubaru, Shashanka; Międlar, Agnieszka; Saad, Yousef; Chelikowsky, James R.

doi:10.1103/physrevb.95.214102

Cited by 30 publications

(18 citation statements)

References 43 publications

(54 reference statements)

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“…The prediction of crystal structures and their stability [399,400] has also been performed for several materials such as perovskites [287,[401][402][403], superhard materials [404], bcc materials and Fe alloys [405], binary alloys [406], phosphor hosts [407], Heuslers [408,409], catalysts [410], amorphous carbon [411], high-pressurehydrogen-compressor materials [412], binary intermetallic compounds with transition metals [413], and multicomponent crystalline solids [414]. An atomic-position independent descriptor was able to reach a MAE of 70 meV/atom for formation energy predictions of a diverse dataset of more than 85 000 materials [415].…”

Section: Discovery Energies and Stabilitymentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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Section: Discovery Energies and Stabilitymentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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“…1,2 One common task in materials informatics is the use of machine learning (ML) for the prediction of materials properties. Examples of recent models built with ML include steel fatigue strength, 3 small molecule properties calculated from density functional theory, 4 thermodynamic stability, 5 Gibbs free energies, 6 band gaps of inorganic compounds, 7 alloy formation enthalpies, 8 and grain boundary energies. 9 Across all of these applications, a training database of simulated or experimentally-measured materials properties serves as input to a ML algorithm that predictively maps features (i.e., materials descriptors) to target materials properties.…”

Section: Materials Informatics (Mi)mentioning

confidence: 99%

Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

Meredig

Antono

Church

et al. 2018

Mol. Syst. Des. Eng.

210

180

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Traditional machine learning (ML) metrics overestimate model performance for materials discovery. We introduce (1) leave-onecluster-out cross-validation (LOCO CV) and (2) a simple nearestneighbor benchmark to show that model performance in discovery applications strongly depends on the problem, data sampling, and extrapolation. Our results suggest that ML-guided iterative experimentation may outperform standard high-throughput screening for discovering breakthrough materials like high-T c superconductors with ML.Materials informatics (MI), or the application of data-driven algorithms to materials problems, has grown quickly as a field in recent years. 9 Across all of these applications, a training database of simulated or experimentally-measured materials properties serves as input to a ML algorithm that predictively maps features (i.e., materials descriptors) to target materials properties. Ideally, the result of training such models would be the experimental realization of new materials with promising properties. The MI community has produced several such success stories, including thermoelectric compounds, 10,11 shapememory alloys, 12 superalloys, 13 and 3d-printable high-strength aluminum alloys. 14 However, in many cases, a model is itself the output of a study, and the question becomes: to what extent could the model be used to drive materials discovery? Typically, the performance of ML models of materials properties is quantified via cross-validation (CV). CV can be performed either in a single division of the available data into a training set (to build the model) and a test set (to evaluate its performance), or as an ensemble process known as k-fold CV wherein the data are partitioned into k nonoverlapping subsets of nearly equal size (folds) and model performance is averaged across each combination of k-1 training folds and one test fold. Leave-one-out crossvalidation (LOOCV) is the limit where k is the number of total examples in the dataset. Table 1 summarizes some examples of model performance statistics as reported in the aforementioned studies (some studies involved testing multiple algorithms across multiple properties).In Table 1, the reported model performance is uniformly excellent across all studies. A tempting conclusion is that any of these models could be used for one-shot high-throughput screening of large numbers of materials for desired properties. However, as we discuss below, traditional CV has critical shortcomings in terms of quantifying ML model performance for materials discovery. Issues with traditional crossvalidation for materials discoveryMany ML benchmark problems consist of data classification into discrete bins, i.e., pattern matching. For example, the Design, System, ApplicationMachine learning (ML) has become a widely-adopted predictive tool for materials design and discovery. Random k-fold cross-validation (CV), the traditional gold-standard approach for evaluating the quality of ML models, is fundamentally mismatched to the nature of materials discovery, and leads to ...

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“…SVR is a universal regression method inheriting merits from support vector machines [33][34][35], e.g., the minimization of the structural risk, superiority of generalization for future test data, and ease of handling nonlinear problems with kernel trick. SVR has been successfully used in many fields such as time series prediction [36], X-ray pulse properties prediction [37], and material thermodynamic property prediction [38].…”

Section: Spectrum Regression Analysis By Svrmentioning

confidence: 99%

Quantitative characterization of bovine serum albumin thin-films using terahertz spectroscopy and machine learning methods

Sun¹,

Du²,

Lu³

et al. 2018

Biomed. Opt. Express

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The development of new spectral analysis methods in bio thin-film detection has generated intense interest in terahertz (THz) spectroscopy and its application in a wide range of fields. In this paper, it is the first time that machine learning methods are applied to the quantitative characterization of bovine serum albumin (BSA) deposited thin-films detected by terahertz time-domain spectroscopy. The spectra data of BSA thin-films prepared by solutions with concentrations ranging from 0.5 to 35 mg/ml are analyzed using the support vector regression method to learn the underlying model of the frequency against the target concentration. The learned mode successfully predicts the concentrations of the unknown test samples with a coefficient of determination R = 0.97932. Furthermore, aiming to identify the relevance of each frequency to the concentration, the maximal information coefficient statistical analysis is used and the three most discriminating frequencies in THz frequency are identified at 1.2, 1.1 and 0.5 THz respectively, which means a good prediction for BSA concentration can be achieved by using the top three relevant frequencies. Moreover, the top discriminating frequencies are in good agreement with the frequencies predicted by a long-wavelength elastic vibration model for BSA protein.

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Formation enthalpies for transition metal alloys using machine learning

Cited by 30 publications

References 43 publications

From DFT to machine learning: recent approaches to materials science–a review

From DFT to machine learning: recent approaches to materials science–a review

Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

Quantitative characterization of bovine serum albumin thin-films using terahertz spectroscopy and machine learning methods

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