Machine-learning approach to the design of OSDAs for zeolite beta

Daeyaert, Frits; Ye, Fengdan; Deem, Michael W.

doi:10.1073/pnas.1818763116

Cited by 63 publications

(54 citation statements)

References 31 publications

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“…16,25,26,31 In such situations, the mixing of different OSDAs (i.e., the use of more than one OSDA) 9 and the use of seed crystals 17 can lead to successful crystallization, which may economically outperform the existing preparation protocols. Further, the MD simulation, the most computationally expensive step in our ACO, can be replaced or performed together with simplied geometry optimization, 61 topological analysis of OSDAs, 62 and recently developed machine-learning models, 63 which is promising to accelerate OSDA design.…”

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confidence: 99%

Multi-objectivede novomolecular design of organic structure-directing agents for zeolites using nature-inspired ant colony optimization

2020

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Multi-objectivede novomolecular design of organic structure-directing agents for zeolites using nature-inspired ant colony optimization

2020

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“…Another approach was taken by Deem and co-workers, who addressed the design of organic structure directing agents (OSDAs). 534 Zeolites are all isomorphic structures, and OSDAs are used during the synthesis to favor the formation of the desired isomorph. Finding the right OSDA to synthesize a particular zeolite is seen as one of the bottlenecks.…”

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confidence: 99%

“…The figure shows the top-three OSDAs that Deem and co-workers discovered. 534 The scores in the figure are the binding energy in kJ/(mol Si). Figure adapted from ref ( 534 ).…”

Section: Applications Of Supervised Machine Learningmentioning

confidence: 99%

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

et al. 2020

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By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal–organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.

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“…[213] Previously, DFT-derived data sets have been used for the derivation of force fields, [231][232][233] however the application of ML to these challenges offers huge benefits, due to the relative ease of training the models, as well as the ability for machine learning models to identify nonlinear trends. A recent highlight of the application of machine learning techniques trained on computational data includes the design of organic SDAs for the synthesis of zeolite beta by Daeyaert et al, [242] determination of the most thermodynamically favorable aluminum distribution in a range of zeolite topologies by Evans et al, [213] and the reliable calculation of anisotropic properties toward the discovery of auxetic zeolite frameworks by Gaillac et al [243] Modeling of these features demonstrates the ability of machine learning to accelerate datadriven predictions that can help inform experiment, cementing their importance in the virtuous circle.…”

Section: Computational Datamentioning

confidence: 99%

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

et al. 2020

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Notwithstanding these impediments, in many fields of materials science, solutions are being designed to mitigate hindrances to the efficient sampling of chemical space and improving the robustness of computational screening models through a combination of high throughput synthesis (HTS), characterization, and machine learning. In this review, we highlight key developments in high throughput approaches pertinent to porous materials. Specifically, we focus on developments in the field of zeolitic materials, metal-organic frameworks (MOFs), and touch upon covalent organic frameworks (COFs). Although superficially these classes of materials have little in common, there are structural links such as topology which allow for the consideration of how high throughput methods contrast with one another. By contrasting developments in different fields, we identify some underutilized approaches which could be leveraged in the future. We target structurally ordered materials because the exploration of chemical and topological space is more straightforward to enumerate, though similar approaches could be applied to disordered materials. The scale of the problem faced in materials science of targeted structure and function [1] is by no means unique; in drug discovery, HTS and chemoinformatic approaches have been used for more than 40 years in an attempt to accelerate the discovery of druggable molecules. [2,3] Unlike the drug discovery process, where Lipinski's "rule of 5" [4] provides a reliable top level sift for viable targets, identifying the most promising material for a target application requires very particular properties that may be intertwined in complex and contradictory ways. For example, thermoelectrics require high electrical conductivity and low thermal conductivity and yet these properties are typically correlated unless they can be separated. [5] Given the large scope of potential applications, the advent of the Materials Genome Initiative (MGI) [6,7] in the United States has undoubtedly helped to promote ways to solve the aforementioned obstacles and numerous others. Similarly there are major initiatives within the EU (e.g., NOMAD [8] and BIGmax [9]) and Switzerland (MARVEL [10]). In the MGI, nanoporous materials, including zeolites and MOFs, have been specifically targeted [11] and in this review, we seek to highlight particular challenges in the field of porous materials and how researchers have sought to overcome these challenges. We focus primarily on the methods used in HTS and rapid throughput/screening computational approaches. Aspects of high throughput computation applied to porous materials have been reviewed before, [12-14] as has high throughput experimentation (HTE) [15,16] but here we focus on their combination within an integrated workflow. Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre-and post-synthetic computer simulations are mostly carried out in a piecemeal a...

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Machine-learning approach to the design of OSDAs for zeolite beta

Cited by 63 publications

References 31 publications

Multi-objectivede novomolecular design of organic structure-directing agents for zeolites using nature-inspired ant colony optimization

Multi-objectivede novomolecular design of organic structure-directing agents for zeolites using nature-inspired ant colony optimization

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

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