Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms

Choudhary, Kamal; Zhang, Qin; Reid, Andrew; Chowdhury, Sugata; Nguyen, Nhan V.; Trautt, Zachary; Newrock, Marcus; Congo, Faical Y.; Tavazza, Francesca

doi:10.1038/sdata.2018.82

Cited by 102 publications

(102 citation statements)

References 51 publications

Supporting

Mentioning

101

Contrasting

Order By: Relevance

“…In table 2 some examples of the largest databases are highlighted. These theoretical and experimental databases have been used for several applications: battery technologies, high entropy alloys, water splitting, high-performance optoelectronic materials [337], topological materials, and others. Here we show some examples of its usage.…”

Section: High-throughputmentioning

confidence: 99%

“…Identification of suitable optoelectronic materials [337] as well as solar absorbers [345,346] have also been possible via HT calculations. Another important study based on HT methods is the obtention of elastic properties of inorganic materials [18,158] and the subsequent structuring of the data into publicly available databases.…”

Section: Materials Discovery Design and Characterizationmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

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...

show abstract

Section: High-throughputmentioning

confidence: 99%

Section: Materials Discovery Design and Characterizationmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Examples of general‐purpose databases with high chemical diversity (typically based on large experimental databases of all known inorganic materials such as the ICSD as well as a subset of generated hypothetical structures) and property diversity (relaxed structures, energies, electronic structure properties, etc.) include the Materials Project, AFLOWLIB, the computational materials repository, open quantum materials data (OQMD), novel materials discovery (NOMAD) repository, AiiDA, JAVIS‐DFT, CatApp, etc. For example, the Materials Project, one of the most popular computed data sources for ML works, currently hosts ≈133 000 DFT‐relaxed crystal structures, at the time of writing, with energies and electronic structure properties such as the bandgap available for the majority of materials and other properties such as elastic constants, piezoelectric coefficients, etc., available for a subset of materials.…”

Section: Data Collectionmentioning

confidence: 99%

“…Using 256 GW calculations, the authors were able to identify in this group high‐SLME materials including almost all contemporary PV absorber materials. Choudhary et al have performed meta‐GGA calculations of bandgaps using the Tran–Blaha‐modified Becke–Johnson (TBmBJ) potential for 12 881 out of ≈30 000 materials in the JAVIS‐DFT database . The SLME metric was calculated on 5097 nonmetallic materials and the elemental distribution for high‐SLME materials is shown in Figure 8 a.…”

Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

422

281

View full text Add to dashboard Cite

Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

show abstract

“…Many-body perturbation theory approaches (such as GW 11 and GW-BSE 12 ) are generally considered to be necessary to obtain accurate efficiencies because they accurately predict band gaps and frequency-dependent dielectric functions. However, meta-GGA based methods, such as the Tran-Blaha modified Becke-Johnson (TBmBJ) potential 13 , have been recently shown to achieve comparable accuracy in evaluating the same quantities at a significantly reduced computational cost, enabling the calculation of the frequency-dependent dielectric function and bandgap for thousands of inorganic crystalline materials 14 . The next step is to investigate if this data can be used to identify novel high solar-efficiency 15 materials.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods

et al. 2019

Self Cite

View full text Add to dashboard Cite

Solar-energy plays an important role in solving serious environmental problems and meeting highenergy demand. However, the lack of suitable materials hinders further progress of this technology. Here, we present the largest inorganic solar-cell material search to date using density functional theory (DFT) and machine-learning approaches. We calculated the spectroscopic limited maximum efficiency (SLME) using Tran-Blaha modified Becke-Johnson potential for 5097 non-metallic materials and identified 1997 candidates with an SLME higher than 10%, including 934 candidates with suitable convex-hull stability and effective carrier mass. Screening for 2D-layered cases, we found 58 potential materials and performed G0W0 calculations on a subset to estimate the prediction-uncertainty. As the above DFT methods are still computationally expensive, we developed a high accuracy machine learning model to pre-screen efficient materials and applied it to over a million materials. Our results provide a general framework and universal strategy for the design of high-efficiency solar cell materials. The data and tools are publicly 2 distributed at:

show abstract

Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms

Cited by 102 publications

References 51 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

A Critical Review of Machine Learning of Energy Materials

Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods

Contact Info

Product

Resources

About