Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

Segler, Marwin; Waller, Mark P.

doi:10.1002/chem.201605499

Cited by 457 publications

(510 citation statements)

References 27 publications

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“…Especially bottom-up processes such as self-assembly, phase separation of material mixtures, or crystallization of small molecules and polymers are challenging to describe in computational methods with atomistic resolution due to the large time and length scales involved. [147] Copyright 2017, Wiley-VCH. Quantitative models thus have to be sensitive to small variations in chemical composition [55] Copyright 2011, American Chemical Society.…”

Section: Morphologymentioning

confidence: 99%

“…[147] The generation of perpetuating machine learning models should be the most important aim to allow the implementation of robust systems that provide useful information for nonchemists or machines. [147] The generation of perpetuating machine learning models should be the most important aim to allow the implementation of robust systems that provide useful information for nonchemists or machines.…”

Section: Synthetic Accessibilitymentioning

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: Morphologymentioning

confidence: 99%

Section: Synthetic Accessibilitymentioning

confidence: 99%

Toward Design of Novel Materials for Organic Electronics

et al. 2019

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“…This is a challenging problem, but important progress has recently been made in this direction. [56–58,60–62] As such algorithms and broad access to the required computing power continue to advance, and new opportunities opened up by recent advances in artificial intelligence/machine-learning are leveraged, it is likely that a substantial portion of such customized synthetic planning can be achieved automatically. [60,62,63] …”

Section: One Machine – Many Small Moleculesmentioning

confidence: 99%

The Molecular Industrial Revolution: Automated Synthesis of Small Molecules

2018

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The eighteenth and nineteenth centuries marked a sweeping transition from manual to automated manufacturing on the macroscopic scale. This enabled an unmatched period of human innovation that helped drive the Industrial Revolution. The impact on society was transformative, ultimately yielding substantial improvements in living conditions and lifespan in many parts of the world. During the same time period, the first manual syntheses of organic molecules was achieved. Now, two centuries later, we are poised for an analogous transition from highly customized crafting of specific molecular targets by hand to the increasingly general and automated assembly of many different types of molecules with the push of a button. Automation of customized small molecule synthesis pathways is already enabling safer, more reproducible, and readily scalable production of specific targets, and general machines now exist for the synthesis of a wide range of different peptides, oligonucleotides, and oligosaccharides. Creating general machines that are similarly capable of making many different types of small molecules on-demand, akin to that which has been achieved on the macroscopic scale with 3D printers, has proven to be substantially more challenging. Yet important progress is being made toward this potentially transformative objective with two complementary approaches: (1) automation of customized synthesis routes to different targets via machines that enable use of many different reactions and starting materials, and (2) automation of generalized platforms that make many different targets using common coupling chemistry and building blocks. Continued progress in these exciting directions has the potential to shift the bottleneck in molecular innovation from synthesis to imagination, and thereby help drive a new industrial revolution on the molecular scale.

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“…1,2 With the increasing utility of machine learning, specifically in chemical synthetic route predictions, this information is evermore desired in order to enhance accuracy and usefulness of these predictions with more accurate data for model training. [3][4][5] In addition, lower computational costs are moving the bottle neck of reaction engineering to the collection of experimental data. Flow-based microreactors have risen as a technology capable of addressing this issue amongst others.…”

Section: Introductionmentioning

confidence: 99%

“…This definition combined with eqn (2) for measurement time (t m ) is used keep track of the temperature profile this fluid element experiences. The linear temperature ramp is simultaneously carried out according to eqn (4). Here b, the ramp rate, could be negative or positive depending on whether the temperature was being ramped down or up, respectively.…”

mentioning

confidence: 99%

Efficient kinetic experiments in continuous flow microreactors

Aroh

Jensen

2018

React. Chem. Eng.

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Flow chemistry is an enabling technology that can offer an automated and robust approach for the generation of reaction kinetics data. Recent studies have taken advantage of transient flows to quickly generate concentration profiles with various online analytical tools. In this work, we demonstrate an improved method where temperature and flow are transient throughout the reaction. It was observed that only two orthogonal temperature ramp experiments under the same transient flow condition were sufficient to characterize a Paal-Knorr (one step bimolecular) reaction within our chosen reaction space. This method further shortens the time and decreases the materials needed to collect sufficient kinetic data and provides a framework with which more complex kinetic studies could be performed.

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Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

Cited by 457 publications

References 27 publications

Toward Design of Novel Materials for Organic Electronics

Toward Design of Novel Materials for Organic Electronics

The Molecular Industrial Revolution: Automated Synthesis of Small Molecules

Efficient kinetic experiments in continuous flow microreactors

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