Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)

Sánchez-Lengeling, Benjamín; Outeiral, Carlos; Guimaraes, Gabriel L.; Aspuru‐Guzik, Alán

doi:10.26434/chemrxiv.5309668.v3

Cited by 212 publications

(240 citation statements)

References 12 publications

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“…While originally developed for applications such as speech and image recognition, there is now an intense interest in machine learning models, which are now combined with methods from Adv. [192,[196][197][198] However, the ultimate impact of these approaches on the development of new materials and devices has yet to be evaluated. 2019, 31, 1808256 Figure 6.…”

Section: Discussionmentioning

confidence: 99%

“…[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. [192,[196][197][198] However, the ultimate impact of these approaches on the development of new materials and devices has yet to be evaluated.…”

Section: Discussionmentioning

confidence: 99%

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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%

Section: Discussionmentioning

confidence: 99%

Toward Design of Novel Materials for Organic Electronics

et al. 2019

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“…In particular, to broaden the search space, ML methods that use probabilistic language models based on deep neural networks (DNNs) have proliferated intensively since 2017. Promising examples have included various types of varia-tional autoencoders, [12][13][14][15] generative adversarial networks, [16] recurrent neural networks, [17,18] and so on. [11] Models trained to recognize chemically realistic structures are then used to refine chemical strings in the molecular design calculation.…”

Section: Introductionmentioning

confidence: 99%

“…[11] Models trained to recognize chemically realistic structures are then used to refine chemical strings in the molecular design calculation. Promising examples have included various types of varia-tional autoencoders, [12][13][14][15] generative adversarial networks, [16] recurrent neural networks, [17,18] and so on. These methods have been able to produce diverse chemical structures; however, they often require large training datasets to obtain a DNN-based generator that can produce chemically realistic molecules with grammatically valid SMILES.…”

Section: Introductionmentioning

confidence: 99%

iQSPR in XenonPy: A Bayesian Molecular Design Algorithm

Lambard

Liu

et al. 2019

Molecular Informatics

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iQSPR is an inverse molecular design algorithm based on Bayesian inference that was developed in our previous study. Here, the algorithm is integrated in Python as a new module called iQSPR-X in the all-in-one materials informatics platform XenonPy. Our new software provides a flexible, easy-to-use, and extensible platform for users to build customized molecular design algorithms using pre-set modules and a pre-trained model library in XenonPy. In this paper, we describe key features of iQSPR-X and provide guidance on its use, illustrated by an application to a polymer design that targets a specific range of bandgap and dielectric constant.

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“…Their goal is to generate new lead compounds in silico, such that their medical and chemical properties are predicted in advance. Examples of this approach include Variational Auto-Encoders [2], Adversarial Auto-Encoders [3,4], Recurrent Neural Networks and Reinforcement Learning [5,6,7], eventually in combination with Sequential Generative Adversarial Networks [8,9].…”

Section: Introductionmentioning

confidence: 99%

Can AI reproduce observed chemical diversity?

Benhenda¹

2018

Preprint

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Generating diverse molecules with desired chemical properties is important for drug discovery. The use of generative neural networks is promising for this task. To facilitate evaluation of generative models, this paper introduces a metric of internal chemical diversity, and raises the following challenge: can a nontrivial AI model reproduce observed internal diversity for desired molecules? To illustrate this metric, a mini-benchmark is performed with two generative models: a Reinforcement Learning model and the recently introduced ORGAN. The aim of this paper is to encourage research about internal diversity metrics.

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Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)

Cited by 212 publications

References 12 publications

Toward Design of Novel Materials for Organic Electronics

Toward Design of Novel Materials for Organic Electronics

iQSPR in XenonPy: A Bayesian Molecular Design Algorithm

Can AI reproduce observed chemical diversity?

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