Machine learning accelerates the materials discovery

Fang, Jiheng; Xie, Mingxing; He, Xingqun; Zhang, Jiming; Hu, Jiyuan; Chen, Yongtai; Yue, Yang; Jin, Qinglin

doi:10.1016/j.mtcomm.2022.104900

Cited by 43 publications

(18 citation statements)

References 388 publications

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“…Many papers describe the process of ML algorithms. [84][85][86][87] Depending on the nature and characteristics of the problem, we need to choose the appropriate model architecture.…”

Section: Model Selection and Trainingmentioning

confidence: 99%

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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Optoelectronic materials are essential for today's scientific and technological development, and machine learning provides new ideas and tools for their research. In this paper, we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices. Then, we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods. We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices, including the methods related to crystal structure, properties (defects, electronic structure) research, materials and devices optimization, material characterization, and process optimization. In summarizing the algorithms and feature representations used in different studies, it is noted that prior knowledge can improve optoelectronic materials design, research, and decision-making processes. Finally, the prospect of machine learning applications in optoelectronic materials is discussed, along with current challenges and future directions. This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field.

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“…Many papers describe the process of ML algorithms. [84][85][86][87] Depending on the nature and characteristics of the problem, we need to choose the appropriate model architecture.…”

Section: Model Selection and Trainingmentioning

confidence: 99%

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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“…To face this problem, there is a need to produce a lot of experimental data of this massive interest in researching carbon-based SCs which provides us with the chance to apply machine learning (ML) algorithms to take advantage of these data and use them to educate ourselves and identify quantitative relations between input and output variables from existing data before introducing extrapolation predictions. In recent years, ML techniques have become an effective alternative to physics-based strategies [30][31][32]. ML approaches allow us to create effective connections between material qualities and performance without evoking the physical specifics if enough training data is supplied.…”

Section: Introductionmentioning

confidence: 99%

Machine learning models for capacitance prediction of porous carbon-based supercapacitor electrodes

Tawfik,

Mohammad,

Rahouma

et al. 2024

Phys. Scr.

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Energy storage devices (ESD) and systems (ESS) with better performance, higher reliability, longer life, and wiser management strategies are needed for daily technology advancement. Among these devices, the supercapacitor is the most preferable due to its high-limit capacitance that esteems more than different capacitors. Today, it is considered a significant challenge to design high-performance materials for supercapacitors by exploring the interaction between characteristics and structural features of materials. Advanced control strategies depend on the status of numerous indicators, but creating such systems requires making trade-offs between a vast array of elements. From an engineering perspective, Specific capacity is regarded as the most crucial performance-related characteristic of supercapacitor electrodes. Because of this, it is essential to predict capacitance when assessing a material's potential for use in constructing supercapacitor-electrode applications. Machine learning (ML) can significantly speed up computation, capture complex mechanisms to enhance the accuracy of the prediction and make the best choices based on detailed status data. We aimed to develop a new strategy for the assisted design of high-performance supercapacitor materials by applying ML to analyze the relationship between capacitance and structural features of porous carbon materials (PCMs) using hundreds of experimental data in the literature. To predict the associated capacitance performance for the porous carbon electrode, structural characteristics of the electrode material, Several elements of the electrochemical test, including specific surface area, Potential Window, pore volume, and Electrolyte material, as well as data extracted from nearly 100 practical research papers that were published, were used. In the present study, Linear Regression (LR), Regression Tree (RT), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to estimate the SC’s capacitance. The effectiveness of the ML models was evaluated in terms of the root mean square error (RMSE), mean absolute error (MAE), and the correlation between expected yield and system-provided yield. The developed ANFIS model, with RMSE, MAE,and R values of 22.8, 39.7647, and 0.90004, respectively, compares favourably regarding prediction performance to other models built for this purpose.

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“…Due to the storing and querying of connected data, the advancement of graph databases and graph database management systems has been advantageous for machine learning applications . Machine learning can be used to understand and predict specific material properties by mapping the relationships between final material properties and synthetic variables through the drawing of information from recorded data . The use of machine learning in the design and property prediction of materials has recently gained attention for implementation in many material systems such as inorganic oxides, metals, metallic glasses, battery materials, and electrolyte materials, expediting the design and development lifecycle of these materials. , In this work, following the development of the SiAGDB and data cleaning methods, we use a neural network model to predict the surface area of silica aerogels from synthetic and processing conditions.…”

Section: Introductionmentioning

confidence: 99%

Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning

et al. 2023

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Silica aerogels are mesoporous high surface area materials with extensive synthetic and processing conditions. To effectively synthesize aerogels, the impact of synthetic pathways on the resulting aerogel properties must be understood prior to experimental investigation. We develop an information architecture, the silica aerogel graph database (10 3 ), and a supervised machine learning neural network regression model to examine these relationships. The property graph database enables rapid queries and visualization of the impact of the synthesis and processing conditions on the final aerogel properties. The model maps from silica aerogel synthetic and processing conditions to predict the aerogel BET surface area with an average error of 109 ± 84 m 2 /g. Following a validation experiment, the model was shown to predict the aerogel surface area from new synthetic and processing conditions with an error of less than 5%. The experiment demonstrates the usefulness of the model in surface area prediction through the compatibility between computational and experimental results. Both in its current form and with further expansion, the developed graph database could reduce experimental dimensionality, time, and resources, enabling the successful synthesis of high surface area silica aerogels, which are advantageous for applications including thermal insulation, sorption media, and catalysis.

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Machine learning accelerates the materials discovery

Cited by 43 publications

References 388 publications

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Machine learning models for capacitance prediction of porous carbon-based supercapacitor electrodes

Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning

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