Accelerating the discovery of materials for clean energy in the era of smart automation

Tabor, Daniel P.; Roch, Loı̈c M.; Saikin, Semion K.; Kreisbeck, Christoph; Sheberla, Dennis; Montoya, Joseph H.; Dwaraknath, Shyam; Aykol, Muratahan; Ortíz, C.; Tribukait, Hermann; Amador‐Bedolla, Carlos; Brabec, Christoph J.; Maruyama, Benji; Persson, Kristin A.; Aspuru‐Guzik, Alán

doi:10.1038/s41578-018-0005-z

Cited by 577 publications

(436 citation statements)

References 275 publications

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“…Recent years have shown the emergence of a novel class of methods that are not based on physical models but on learning systematic relations in large datasets and on methods of statistical inference. [189][190][191][192][193] Examples include the use of regression and classification models such as neural networks for the prediction of molecular or materials properties [194,195] and for synthesis planning [146,149,150] as well as the use of generative models such as variational autoencoders and generative adversarial networks for inverse molecular design. Mater.…”

Section: Discussionmentioning

confidence: 99%

Toward Design of Novel Materials for Organic Electronics

et al. 2019

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organic/inorganic perovskites solar cells [6] to printable electronic circuits based on organic field-effect transistors (OFETs). [7] While OLED displays outperform their inorganic counterparts in terms of energy efficiency, [8] scientific and technical challenges concerning the stability and processability of the organic materials used in large-area OLEDs and organic solar cells remain. New challenges arise from applications, such as displays on flexible substrates, OLED lightning, large area displays as well as for printable or solution processable larger area solar cells. [8] Many of the remaining challenges are material related, e.g., the low mobility of charge carriers in organic materials in general and in amorphous organic semiconductors in particular. There are other materials related issues, such as limited OLED life times due to unstable blue hosts and emitters, [9] low fill factors, and therefore reduced power conversion efficiencies of organic solar cells, [10,11] low conductivity and high costs of organic charge transport layers of perovskite solar cells [6,12,13] and low conductivity and hard processability of crystalline OFET materials. [14] Conductivity and injection can be improved by doping the organic thin films with molecular dopants with high electron affinities (p-type) [15] or low ionization energies (n-type). [16] The doping mechanism of organic materials is in many cases not well understood, [17] making material and device optimization a costly experimental endeavor.The development of better materials is presently based on chemical insight, in part guided by theoretical understanding, or the experimental screening of large numbers of compounds. Given the size of the potentially available chemical space this remains a costly and time-consuming approach. Recent successes in experimental design of novel materials and concepts include the development of a stable strong molecular n-type dopant, [16] a study about the quantitative relation between interaction parameter, miscibility, and function of conjugated polymer donors and small-molecule acceptors for bulk heterojunctions as used in organic solar cells [18] the development of a universal strategy for ohmic hole injection into organic semiconductors with high ionization energies [19] and many others. [20][21][22][23][24][25] Another example is the development of strategies to harvest triplet excitons in organic light emitting diodes. For this purpose, novel classes of emitter molecules were developed, which include thermally activated delayed fluorescence (TADF)-based molecules, [26][27][28][29][30][31][32] rotationally accessed spin-state inversion, [33] and radical-based emitters. [34] Materials for organic electronics are presently used in prominent applications, such as displays in mobile devices, while being intensely researched for other purposes, such as organic photovoltaics, large-area devices, and thin-film transistors. Many of the challenges to improve and optimize these applications are material related and there is a nearly inf...

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Section: Discussionmentioning

confidence: 99%

Toward Design of Novel Materials for Organic Electronics

et al. 2019

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“…This ability of ML to generalize from a set of training data to explore unknown spaces makes it a tantalizing panacea to many challenges in materials science . Take, for example, the problem of novel materials discovery.…”

Section: Introductionmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

373

281

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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.

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“…Recently, the artificial intelligence based tools have been demonstrated to be a promising alternative approach to accelerate the discovery and development of novel materials . The machine learning approach, a subfield of artificial intelligence, pertains to the creation of models that can effectively learn from past data and situations .…”

Section: Introductionmentioning

confidence: 99%

Efficient Optimization of the Performance of Mn²⁺‐Doped Kesterite Solar Cell: Machine Learning Aided Synthesis of High Efficient Cu₂(Mn,Zn)Sn(S,Se)₄ Solar Cells

Hou

Gao

et al. 2018

Solar RRL

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Isoelectronic cation substitution is a potential method to decrease the density of Cu‐Zn anti‐site defects in CZTSSe, thus improving the VOC and performance of CZTSSe solar cells. The proper doping concentration is determined traditionally by the trial and error approach, costing much time, and materials. How to shorten the time to find the proper doping concentration is a big challenge for the development of solar cells. Here, by utilizing the machine learning model, the authors carry out an adaptive design for predicting the optimal doping ratio of Mn2+ ions in CZTSSe solar cells for improved solar cell efficiency. With the help of machine learning prediction, the authors rapidly and efficiently find the optimal doping ratio of Mn2+ in CZTSSe solar cells to be 0.05, achieving a highest solar cell efficiency of 8.9% in experiment. Further experimental characterizations of Mn‐doped CZTSSe show that the defect in CZTSSe after Mn doping is changed from an anti‐site CuZn defect to VCu defect. Our findings suggest that machine learning is a very powerful and efficient approach to aid the development of solar cell materials for its application in the photovoltaic field.

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Accelerating the discovery of materials for clean energy in the era of smart automation

Cited by 577 publications

References 275 publications

Toward Design of Novel Materials for Organic Electronics

Toward Design of Novel Materials for Organic Electronics

A Critical Review of Machine Learning of Energy Materials

Efficient Optimization of the Performance of Mn²⁺‐Doped Kesterite Solar Cell: Machine Learning Aided Synthesis of High Efficient Cu₂(Mn,Zn)Sn(S,Se)₄ Solar Cells

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Accelerating the discovery of materials for clean energy in the era of smart automation

Cited by 577 publications

References 275 publications

Toward Design of Novel Materials for Organic Electronics

Toward Design of Novel Materials for Organic Electronics

A Critical Review of Machine Learning of Energy Materials

Efficient Optimization of the Performance of Mn2+‐Doped Kesterite Solar Cell: Machine Learning Aided Synthesis of High Efficient Cu2(Mn,Zn)Sn(S,Se)4 Solar Cells

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Efficient Optimization of the Performance of Mn²⁺‐Doped Kesterite Solar Cell: Machine Learning Aided Synthesis of High Efficient Cu₂(Mn,Zn)Sn(S,Se)₄ Solar Cells