Can we predict materials that can be synthesised?

Szczypiński, Filip; Bennett, Steven; Jelfs, Kim E.

doi:10.1039/d0sc04321d

Cited by 45 publications

(41 citation statements)

References 103 publications

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“…However, molecules that are too different from any known one can be risky to predict, as they can correspond to structures that are not chemically sensible or cannot be synthesised. 359,391 It has been recently noted that generative models often ignore synthesisability. M. Sumita et al 392 prepared a platform to predict photofunctional organic molecules with excited states at a desired range using Monte Carlo tree search 393 and a recurrent neural network.…”

Section: Methods To Sample the Chemical Spacementioning

confidence: 99%

“…There are multiple ways to score the complexity or synthetic accessibility of molecules, these can range from expert opinion to neural networks, chemical similarity indices and structural descriptors based on graph theory. [357][358][359] Y. Wen et al considered synthetic accessibility in their DFT and ML combined approach to screen approximately 10 000 novel dyes for DSSC applications. 40 For 500 promising hit candidates, they provide the synthetic accessibility score by Ertl and Schuffenhauer.…”

Section: A Combinatorial Modificationsmentioning

confidence: 99%

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High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

et al. 2021

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Section: Methods To Sample the Chemical Spacementioning

confidence: 99%

Section: A Combinatorial Modificationsmentioning

confidence: 99%

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

et al. 2021

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“… 1 − 3 A key component in computational materials discovery needs to be a consideration of whether a hypothetical material with promising properties can actually be experimentally obtained. 4 There are many elements to that challenge, including the ability to obtain or synthesize the precursors, finding a successful synthetic method to form the material, and being able to control the solid-state form and assembly, for example, to enable incorporation into a device. For materials built from entirely or primarily organic components, the consideration of whether the precursor building blocks of the material can be easily and ideally cheaply synthesized is vital.…”

Section: Introductionmentioning

confidence: 99%

Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors

Bennett

Szczypiński

Turcani

et al. 2021

J. Chem. Inf. Model.

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Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realization. Attempts at experimental validation are often time-consuming, expensive, and frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realization. We trained a machine learning model by first collecting data on 12,553 molecules categorized either as “easy-to-synthesize” or “difficult-to-synthesize” by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our data set, producing a binary classifier able to categorize easy-to-synthesize molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias toward precursors whose easier synthesis requirements would make them promising candidates for experimental realization and material development. We found that even by limiting precursors to those that are easier-to-synthesize, we are still able to identify cages with favorable, and even some rare, properties.

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“…While one may design a material that has promising properties, unless the thermodynamic viability and thus the synthetic route to the material has been considered, then it is unlikely that the material will actually be obtained. 34 Instead, one must screen thousands of potential materials, considering their viability and then their properties. 35 Thus, in the case of organic semiconductors, if we wish to 'design' a molecule with a target property, we must consider the solid-state packing.…”

Section: Introductionmentioning

confidence: 99%

Computational Screening of Organic Semiconductors: Exploring Side-Group Functionalisation and Assembly to Optimise Charge Transport in Chiral Molecules

Schmidt¹,

Weatherby²,

Sugden³

et al. 2021

Preprint

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Molecular materials are challenging to design as their packing arrangement and hence properties are subject to subtle variations in the interplay of soft intermolecular interactions that are difficult to predict. The rational design of new molecular materials with tailored properties is currently hampered by the lack of knowledge of how a candidate molecule will pack in space and how we can control the polymorphs we can experimentally obtain. Here, we develop a simplified approach to aid the material design process, by the development of a screening process that is used to test 1344 helicene molecules that have potential as organic electronic materials. Our approach bridges the gap between single molecule design, molecular assembly, and the resulting charge-carrier mobilities. We find that fluorination significantly improves electron transport in the molecular material by up to 200%; the reference [6]helicene packing showed a mobility of 0.30 cm2 V-1 s-1, fluorination increased the mobility to up to 0.96 and 0.97 (13-fluoro[6]H and 4,13-difluoro[6]H), assuming an outer reorganisation energy of 0.30 eV. Side groups containing triple bonds largely lead to improved transfer integrals. We validate our screening approach through the use of crystal structure prediction to confirm the presence of favourable packing motifs to maximize charge mobility.

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Can we predict materials that can be synthesised?

Abstract: Materials discovery is a crucial yet experimentally slow and wasteful process. We discuss how discovery can be accelerated by focusing on making predictions that are synthetically realisable.

Cited by 45 publications

References 103 publications

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors

Computational Screening of Organic Semiconductors: Exploring Side-Group Functionalisation and Assembly to Optimise Charge Transport in Chiral Molecules

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