Multifunctional structural design of graphene thermoelectrics by Bayesian optimization

Yamawaki, Masaki; Ohnishi, Masato; Ju, Shenghong; Shiomi, Junichiro

doi:10.1126/sciadv.aar4192

Cited by 120 publications

(77 citation statements)

References 73 publications

(92 reference statements)

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“…For this class of materials, not only the electronic conductivity σ (or resistivity ρ=σ −1 ) are the properties of interest, but also thermal conductivity κ p and Seebeck coefficient S need to be predicted in order to obtain the figure of merit ZT. Thermoelectric efficiency, along with the aforementioned properties could be predicted by decision trees [466] as well as Bayesian optimization [467,468] for example. Gaultois et al proposed a recommendation engine for best thermoelectric materials [469] based on a combination of ML methods.…”

Section: Electronic Propertiesmentioning

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: Electronic Propertiesmentioning

confidence: 99%

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

et al. 2019

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“…Bayesian optimisation has recently become an established and sample-efficient approach for the optimisation of such black-box functions and found several interesting applications in miscellaneous domains: such as reducing the extensive number of experiments that are usually required for a good material design [1], designing highstrength alloys [2], designing graphene thermoelectrics [3], optimisation of short polymer fiber synthesis [4], designing renewable energy systems and real-time control [5], optimisation of robot gait parameters [6,7], environmental monitoring and sensor set selection [8] and hyperparameter tuning of machine learning models [9].…”

Section: Introductionmentioning

confidence: 99%

Incorporating expert prior in Bayesian optimisation via space warping

Ramachandran

Gupta

Rana

et al. 2020

Knowledge-Based Systems

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Bayesian optimisation is a well-known sample-efficient method for the optimisation of expensive black-box functions. However when dealing with big search spaces the algorithm goes through several low function value regions before reaching the optimum of the function. Since the function evaluations are expensive in terms of both money and time, it may be desirable to alleviate this problem. One approach to subside this cold start phase is to use prior knowledge that can accelerate the optimisation. In its standard form, Bayesian optimisation assumes the likelihood of any point in the search space being the optimum is equal. Therefore any prior knowledge that can provide information about the optimum of the function would elevate the optimisation performance. In this paper, we represent the prior knowledge about the function optimum through a prior distribution. The prior distribution is then used to warp the search space in such a way that space gets expanded around the high probability region of function optimum and shrinks around low probability region of optimum. We incorporate this prior directly in function model (Gaussian process), by redefining the kernel matrix, which allows this method to work with any acquisition function, i.e. acquisition agnostic approach. We show the superiority of our method over standard Bayesian optimisation method through optimisation of several benchmark functions and hyperparameter tuning of two algorithms: Support Vector Machine (SVM) and Random forest.

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“…Often, process optimization is performed using black-box optimization methods, (e.g., Design of Experiments 1 , Bayesian Optimization 2,3 , Particle Swarm Optimization 4 ), in which selected variables are modified systematically within a range and the system's response surface is mapped to reach an optimum. These methods have shown potential for inverse design of materials and systems in a cost-effective manner, and are usually postulated as ideal methods for future selfdriving laboratories [5][6][7][8][9][10][11][12] . However, traditional black-box optimization approaches have limitations: the maximum achievable performance improvement is limited by the designer's choice of variables and their ranges, the parameter space is artificially constrained, and insights into the root causes of underperformance are severely limited, often requiring secondary characterization methods or batches composed of combinatorial variations of the base samples.…”

Section: Introductionmentioning

confidence: 99%

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

et al. 2020

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Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, without full transparency of the underlying physics, and with user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond global black-box 2 optimization. Our Bayesian-network approach links process conditions to materials descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device-performance parameters (e.g., cell efficiency), using a Bayesian inference framework with an autoencoder-based surrogate device-physics model that is 100x faster than numerical solvers. With the trained surrogate model, our approach is robust and reduces significantly the time-consuming experimentalist intervention, even with small numbers of fabricated samples. To demonstrate our method, we perform layerby-layer optimization of GaAs solar cells. In a single cycle of learning, we find an improved growth temperature for the GaAs solar cells without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above baseline and traditional black-box optimization methods.

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Multifunctional structural design of graphene thermoelectrics by Bayesian optimization

Abstract: Efficient multifunctional materials informatics enables the design of optimal graphene thermoelectrics.

Cited by 120 publications

References 73 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

Incorporating expert prior in Bayesian optimisation via space warping

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

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