Machine Learning Boosts the Design and Discovery of Nanomaterials

Jia, Yuying; Hou, Xuan; Wang, Zhongwei; Hu, Xiangang

doi:10.1021/acssuschemeng.1c00483

Cited by 59 publications

(47 citation statements)

References 158 publications

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“…However, the prediction accuracy depends highly on the descriptors, as descriptors have a certain uniqueness for various materials and properties as long as the algorithm is selected correctly and the data set is complete. [ 14 ] For catalysis, the descriptors contain the essence from the physicochemical nature. Based on effective descriptors, ML can uncover the relationship bridging structure and its activity, selectivity, and stability.…”

Section: Introductionmentioning

confidence: 99%

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Liu

Luo

Wang

et al. 2022

Adv Funct Materials

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(1 of 25)development, it is still far from meeting the increased demand. [3] The emergence of artificial intelligence (AI) has fundamentally changed the situation, which has significantly accelerated the discovery process, owing to greatly improved algorithms and developments in data science. [4] Machine learning (ML), a simple and practical AI framework based on computer and statistical science, is used to develop algorithms to learn from historic data without being explicitly programmed to obtain specific results. [5] It can investigate relationships that are hard to clearly and definitely model mathematically, providing insights for new scientific advancements related to highly complex with many uncertain twisted together factors. [6] There are usually three factors that govern the learning and prediction process of an ML: algorithms, data/database, and descriptors. [4] The algorithms involve data extraction, data filing, and propagation from mathematical derivation. [5,7] The data can be derived not only from experiments but also from theoretical calculations. [4] A number of databases based on experiments [8] and calculations [9] have already been established. The descriptors depend to a large extent on the predicted material or properties. Based on the algorithm, databases, and descriptors, the ML applications have been successfully implemented to support various energy materials with analysis tools (e.g., Python-based SciKit-Learn [10] and TensorFlow [11] ) in combination with workflow management tools (e.g., ASE [12] and Atomate [13] ). However, the prediction accuracy depends highly on the descriptors, as descriptors have a certain uniqueness for various materials and properties as long as the algorithm is selected correctly and the data set is complete. [14] For catalysis, the descriptors contain the essence from the physicochemical nature. Based on effective descriptors, ML can uncover the relationship bridging structure and its activity, selectivity, and stability. [5,15] Thus, suitable descriptors must be established to understand the structure-activity relationship. Although many efforts have been made to accelerate the rational design of homogeneous catalysts, [7b,16] heterogeneous catalysts, [7b,16b,17] and electrocatalysis, [3,18] the development of ML-assisted real catalysts is still in its infancy. Despite these considerable research efforts, the lack of universal selection tactics for descriptors bridging the gap between activity and structures impedes the application

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Section: Introductionmentioning

confidence: 99%

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Liu

Luo

Wang

et al. 2022

Adv Funct Materials

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“…As these fields continue to develop and intersect with other disciplines, we can expect the development of novel materials and hybrids to expand. Aided by machine learning-guided approaches to materials discovery and novel synthetic pathways, 105,111,112 continued research that leverages the power of sequencespecific polymers to approach new problems in experimental and applied settings will continue to grow.…”

Section: Outlook and Emerging Directionsmentioning

confidence: 99%

Self‐assembled free‐floating nanomaterials from sequence‐defined polymers

Babi

Zhu

Lin

et al. 2021

Journal of Polymer Science

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Sequence‐defined polymers can be programmed to self‐assemble into precise nanostructures for applications in biosensing, drug delivery, optics, and molecular computation. Inspired by the natural self‐assembly processes present in biological protein and DNA systems, sets of molecular design rules have emerged across materials classes as instructions to build a variety of tunable structures. This review highlights recent advances in self‐assembled sequence‐defined and sequence‐specific polymers across peptides, peptoids, DNA, and non‐biological synthetic materials, with a focus on synthesis, assembly processes and overall structure. Specifically, these self‐assembled structures are free‐floating, as such constructs can potentially serve as a platform for the aforementioned applications. Emphasis is placed on the molecular design of polymers that self‐assemble into zero‐dimensional, one‐dimensional, two‐dimensional, or three‐dimensional nanostructures. With the development of automated syntheses and increasing control over self‐assembly, future work may focus on emerging classes of compatible hybrid materials with exciting directions toward new architectures and applications.

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“…catalysis, batteries, metal-organic frameworks, two-dimensional (2D) materials, polymers, metals, alloys, and so on [30][31][32][33] . Data-driven ML and DL technologies have advantages in catching up the relations between targeted properties and input variables [34] . Coherently integrating the data-driven approach with domain knowledge will make the black box more transparent, enhance ML and DL technologies more efficiently and boost the quantum jump from data to knowledge, thereby paving the way for novel materials discovery [35] .…”

Section: Introductionmentioning

confidence: 99%

Recent progress in the data-driven discovery of novel photovoltaic materials

Liu¹,

Li²,

Lü³

et al. 2022

J Mater Inf

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The discovery of new photovoltaic materials can facilitate technological progress in clean energy and hence benefit overall societal development. Machine learning (ML) and deep learning (DL) technologies integrated with domain knowledge are revolutionizing the traditional trial-and-error research paradigm that is associated with high costs, inefficiency, and significant human effort. This review provides an overview of the recent progress in the data-driven discovery of novel photovoltaic materials for perovskite, dye-sensitized and organic solar cells. The integral workflow of the ML/DL training progress is briefly introduced, covering data preparation, feature engineering, model building and their applications. The cutting-edge challenges and issues in the ML/DL workflow are summarized specifically for photovoltaic materials. Real examples are emphasized to illustrate how to utilize ML/DL techniques in the discovery of novel photovoltaic materials. The prospects and future directions of the data-driven discovery of novel photovoltaic materials are also provided.

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Machine Learning Boosts the Design and Discovery of Nanomaterials

Cited by 59 publications

References 158 publications

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Self‐assembled free‐floating nanomaterials from sequence‐defined polymers

Recent progress in the data-driven discovery of novel photovoltaic materials

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