Materials informatics: a journey towards material design and synthesis

Takahashi, Keisuke; Tanaka, Yuzuru

doi:10.1039/c6dt01501h

Cited by 99 publications

(70 citation statements)

References 12 publications

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“…Over the last 20 years machine learning (ML) approaches have been increasingly used to accelerate material science discovery, [35][36][37][38][39][40][41][42][43][44] applications include screening crystal structure, [45] rapid searching for thermometric materials, [46,47] predicting material properties from their structure, [48] predicting crystal structure, [36] and screening polymers for energy harvesting applications. [49,50] Until recently however neural networks that mimic the learning process of biological neurons have been a relatively unsuccessful class of machine learning algorithm, as they underperformed most other techniques for machine learning and data classification.…”

Section: Methodsmentioning

confidence: 99%

Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells

Majeed

Saladina

Krompiec

et al. 2019

Adv Funct Materials

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There is currently a worldwide effort to develop materials for solar energy harvesting which are efficient and cost effective, and do not emit significant levels of CO2 during manufacture. When a researcher fabricates a novel device from a novel material system, it often takes many weeks of experimental effort and data analysis to understand why any given device/material combination produces an efficient or poorly optimized cell. It therefore takes the community tens of years to transform a promising material system to a fully optimized cell ready for production (perovskites are a contemporary example). Herein, developed is a new and rapid approach to understanding device/material performance, which uses a combination of machine learning, device modeling, and experiment. Providing a set of electrical device parameters (charge carrier mobilities, recombination rates, trap densities, etc.) in a matter of seconds thus offers a fast way to directly link fabrication conditions to device/material performance, pointing a way to further and more rapid optimization of light harvesting devices. The method is demonstrated by using it to understand annealing temperature and surfactant choice and in terms of charge carrier dynamics in organic solar cells made from the P3HT:PCBM, PBTZT‐stat‐BDTT‐8:PCBM, and PTB7:PCBM material systems.

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Section: Methodsmentioning

confidence: 99%

Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells

Majeed

Saladina

Krompiec

et al. 2019

Adv Funct Materials

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“…This approach has previously experienced success within the research fields of biology and chemistry, which transformed research practices and resulted in the establishment of the fields of bioinformatics and chemoinformatics. Additionally, the application of data science towards research has commenced within the field of materials science where material design is performed using materials data and data science, leading to the establishment of materials informatics . From observing these shifts in research practices, it is natural to assume that the movement from “X” science towards “X” informatics would also be applicable towards the field of catalysis, thereby resulting in its counterpart catalyst informatics.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the application of data science towards research has commenced within the field of materials science where material design is performed using materials data and data science, leading to the establishment of materials informatics. [8][9][10] From observing these shifts in research practices, it is natural to assume that the movement from "X" science towards "X" informatics would also be applicable towards the field of catalysis, thereby resulting in its counterpart catalyst informatics. Alongside this movement, "The Catalyst Genome" has been proposed for designing catalysts using catalyst data.…”

Section: Introductionmentioning

confidence: 99%

The Rise of Catalyst Informatics: Towards Catalyst Genomics

Takahashi

Miyazato

et al. 2019

ChemCatChem

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Catalysis research is on the verge of experiencing a paradigm shift regarding how catalysts are designed and characterized due to the rise of catalyst informatics. The details of catalyst informatics are reviewed where the following three key concepts are proposed: catalyst data, catalyst data to catalyst design via data science, and catalyst platform. Additionally, progress and opportunities within catalyst informatics are explored and introduced. If the field of catalyst informatics grows in the appropriate manner, the methods and approaches taken for catalyst design would be fundamentally altered, leading towards great advancement within catalysis research.

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“…As the amount of available material data grows rapidly, the implementation of data science becomes vital to the progression of material science as trends in material data can directly impact the design of materials . More precisely, hidden trends and periodicity of materials, defined as material descriptors, are key to understanding material properties .…”

Section: Introductionmentioning

confidence: 99%

“…Within the BCC materials, Fe and FeAl are commonly known magnets . Descriptors for determining the magnetic moment are explored using machine learning where potential material descriptors are searched for within the calculated results as well as corresponding information from the periodic table ,. Once descriptors for magnetic moments are determined, machine learning is applied to train the machine using the given dataset as its learning dataset, leading to the prediction of magnetic moment using trained machine.…”

Section: Introductionmentioning

confidence: 99%

Uncovering Periodicity and Hidden Trends Responsible for Predicting the Magnetic Moment of Body Centered Cubic Crystal

Takahashi

2018

ChemPhysChem

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Prediction of the magnetic moment of binary body centered cubic (BCC) is explored in terms of first principle calculations and data science. A dataset of 1,541 binary BCC materials constructed by first principle calculations is implemented for data mining. Descriptors for determining the magnetic moment are explored using machine learning, where classification and regression models are both implemented. Data mining reveals that two descriptors are responsible for classifying whether the materials have zero or nonzero magnetic moments and can also classify which groups of magnetic moments they belong to (μ <1, 1≤μ <2, or 2≤μ <3) where the average scores produced in cross validation indicate 80 % and 91 % accuracy, respectively. Furthermore, the direct prediction of magnetic moments is performed using a regression model where eight descriptors are revealed with an average score of 74 % accuracy. The inverse problem - from a given magnetic moment to corresponding material - is successfully addressed where the stability of the predicted materials are confirmed by further first principle calculations. Thus, descriptors for the magnetic moment in BCC materials are revealed and can be seen as the base descriptor set for the magnetic moments of further complex materials.

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Materials informatics: a journey towards material design and synthesis

Cited by 99 publications

References 12 publications

Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells

Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells

The Rise of Catalyst Informatics: Towards Catalyst Genomics

Uncovering Periodicity and Hidden Trends Responsible for Predicting the Magnetic Moment of Body Centered Cubic Crystal

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