The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid

By Johannes Hachmann, Roberto Olivares-Amaya, Sule Atahan-Evrenk, Carlos Amador-Bedolla, Roel S. Sánchez-Carrera, Aryeh Gold-Parker, Leslie Vogt, Anna M. Brockway, Alán Aspuru-Guzik. 2011

616 citations recorded by the source corpus.

This Perspective introduces the Harvard Clean Energy Project (CEP), a theory-driven search for the next generation of organic solar cell materials. We give a broad overview of its setup and infrastructure, present first results, and outline upcoming developments. CEP has established an automated, high-throughput, in silico framework to study potential candidate structures for organic photovoltaics. The current project phase is concerned with the characterization of millions of molecular motifs using first-principles quantum chemistry. The scale of this study requires a correspondingly large computational resource, which is provided by distributed volunteer computing on IBM’s World Community Grid. The results are compiled and analyzed in a reference database and will be made available for public use. In addition to finding specific candidates with certain properties, it is the goal of CEP to illuminate and understand the structure–property relations in the domain of organic electronics. Such insights can o...

Subfields: Electrical and Electronic Engineering, Materials Chemistry, Computational Chemistry & Drug Discovery, AI for Physical Sciences

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Citations in the corpus

Discriminative Embeddings of Latent Variable Models for Structured Data (2016, neutral): “2 Harvard Clean Energy Project(CEP) dataset The Harvard Clean Energy Project (Hachmann et al., 2011) is a theory-driven search for the next generation of organic solar cell materials.”
Covariant Compositional Networks For Learning Graphs (2018, background): “The Harvard Clean Energy Project (Hachmann et al., 2011), consisting of 2.3 million organic compounds that are candidates for use in solar cells.”
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (2016, methodology): “Current methods are either an exhaustive search through a fixed library, [8, 9] or the use of a discrete local search method such as a genetic algorithm [10–15] or a similar discrete interpolation technique.”
Machine learning of molecular electronic properties in chemical compound space (2013, methodology): “where φ : R→ [0, 1], the parameter θ controls the granularity of the transformation and sigm(x)= ex/(1 + ex) is a sigmoid function.”
Wavelet Scattering Regression of Quantum Chemical Energies (2016, background): “It has important industrial applications such as molecular structure screening for improved organic photovoltaic materials [24] and predicting the thermodynamics and kinetics of industrially relevant chemical reactions [17].”
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity (2017, methodology): “All ACNN core set models were trained with 15 atom types, 3 radial filters, and a (32, 32, 16) pyramidal atomistic fully-connected layer.”
Accelerating the discovery of materials for clean energy in the era of smart automation (2018, methodology): “For the Clean Energy Project for the discovery of OPVs 176 , the initial fragments that were used to generate the combinatorial library were recommended by the Bao group at Stanford University 180 (‘human intuition’ and ‘library generation’ blocks, Fig.”