The Harvard organic photovoltaic dataset

Lopez, Steven A.; Pyzer‐Knapp, Edward O.; Hernández-Lobato, José Miguel; Lutzow, Trevor; Li, Kewei; Seress, Laszlo R.; Hachmann, Johannes; Aspuru‐Guzik, Alán

doi:10.1038/sdata.2016.86

Cited by 116 publications

(121 citation statements)

References 37 publications

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“…The Scharber model for calculating SCE from the frontier orbital energies has been widely used in the OPV field and has been optimized for [6,6]‐phenyl‐C61‐butyric acid methyl ester (PCBM) acceptors. Large data sources for the frontier orbital energies exist from prior high‐throughput databases, such as the Harvard Clean Energy Project Database and the Harvard OPV data set (HOPV15) …”

Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

420

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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Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

420

281

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“…A third example of computational efforts to find new materials for organic electronics applications was performed in the context of the Clean Energy Project (CEP), [36,[186][187][188] which aims to systematically improve the efficiency of organic solar cells by finding new materials with better suited electronic properties. Of 2.3 million organic molecules were designed and computationally characterized using DFT calculations.…”

Section: Materials Designmentioning

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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“…These frontier molecular orbital energies were then calibrated to correct for the systematic errors in our quantum chemical calculations. The calibration was performed using GP regression 52 based on a set of experimental HOMO and LUMO energies for 94 molecules, including those in the recently published Harvard Organic Photovoltaic Set 53 (see also Tables S1 and S2).…”

Section: Molecular Generation and Dft Calculationsmentioning

confidence: 99%

Design Principles and Top Non-Fullerene Acceptor Candidates for Organic Photovoltaics

Lopez

Sánchez-Lengeling

Soares

et al. 2017

Joule

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199

195

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The power conversion efficiencies of organic photovoltaics (OPVs) have grown tremendously over the last 20 years and represent a low-cost and sustainable solution for harnessing solar energy to power our residences, workplaces, and devices. Fullerene-containing OPVs are relatively expensive and have limited overlap absorbance with the solar spectrum. We used density functional theory calculation and Gaussian processes calibration to predict frontier molecular orbitals and power conversion efficiencies for over 51,000 non-fullerene acceptors with the polymeric electron-donor component, poly[N-9 0-heptadecanyl-2,7carbazole-alt-5,5-(4 0 ,7 0-di-2-thienyl-2 0 ,1 0 ,3 0-benzothiadiazole)].

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The Harvard organic photovoltaic dataset

Cited by 116 publications

References 37 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Toward Design of Novel Materials for Organic Electronics

Design Principles and Top Non-Fullerene Acceptor Candidates for Organic Photovoltaics

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