Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project

By Johannes Hachmann, Roberto Olivares-Amaya, A. Jinich, A. Appleton, M. Blood-Forsythe, Laszlo R Seress, Carolina Román-Salgado, K. Trepte, Sule Atahan-Evrenk, S. Er and 5 more. 2014

203 citations recorded by the source corpus.

The virtual high-throughput screening framework of the Harvard Clean Energy Project allows for the computational assessment of candidate structures for organic electronic materials – in particular photovoltaics – at an unprecedented scale. We report the most promising compounds that have emerged after studying 2.3 million molecular motifs by means of 150 million density functional theory calculations. Our top candidates are analyzed with respect to their structural makeup in order to identify important building blocks and extract design rules for efficient materials. An online database of the results is made available to the community.

Subfields: Electrical and Electronic Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/61afe5d40d0a54b3e4ac46c00eef2a03a7028222

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Citations in the corpus

Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models (2017, background): “In the case of molecular design, besides generating valid molecules [7], one may want to optimize molecular properties to screen for their potential in solar cells [9] or batteries [17] or OLEDS [6].”
Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC) (2017, background): “• Organic photovoltaics , a subset of 15,000 non-fullerene acceptors proceeding from the Harvard Clean Energy Project [8].”
Machine-learning-assisted materials discovery using failed experiments (2016, background): “Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility 11 , photovoltaic properties 12 , gas adsorption capacity 13 or lithium-ion intercalation 14 ) to identify promising target candidates for synthetic efforts 11,15 ; determination of the…”
Variational Implicit Processes (2018, methodology): “Finally we perform experiments with data from the Harvard Clean Energy Project, the world’s largest materials high-throughput virtual screening effort [37].”
Constrained Bayesian Optimization for Automatic Chemical Design (2017, methodology): “SMILES strings Weininger [1988] are a means of representing molecules as a character sequence.”
Black-box $\alpha$-divergence Minimization (2015, methodology): “We perform additional experiments with data from the Harvard Clean Energy Project, which is the world’s largest materials high-throughput virtual screening effort (Hachmann et al., 2014).”