Experimental design for the highly accurate prediction of material properties using descriptors obtained by measurement

Takei, Yoshinori; Imai, Shin‐ichiro; Nakahara, Maki; Shibata, Satoshi; Nakanishi, Takashi; Demura, Masahiko

doi:10.1080/27660400.2021.1963641

Cited by 8 publications

(12 citation statements)

References 33 publications

(30 reference statements)

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“…Advances in Machine Learning (ML) have enabled high accuracies in molecule prediction, chemical reaction prediction, classifications, recommendations, natural language processing, etc. , The success of modern deep neural networks (DNNs) is mainly dependent on the availability of advanced computing power and a large amount of training data. Machine-Learning-As-A-Service (MLaaS) providers such as Amazon, Microsoft, and Google have access to both.…”

Section: Machine Learning Backgroundmentioning

confidence: 99%

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KE: A Knowledge Enhancing Framework for Machine Learning Models

Wang,

Shah,

Soliman

et al. 2023

J. Phys. Chem. A

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Machine learning models are widely used in science and engineering to predict the properties of materials and solve complex problems. However, training large models can take days and fine-tuning hyperparameters can take months, making it challenging to achieve optimal performance. To address this issue, we propose a Knowledge Enhancing (KE) algorithm that enhances knowledge gained from a lower capacity model to a higher capacity model, enhancing training efficiency and performance. We focus on the problem of predicting the bandgap of an unknown material and present a theoretical analysis and experimental verification of our algorithm. Our experiments show that the performance of our knowledge enhancement model is improved by at least 10.21% compared to current methods on OMDB datasets. We believe that our generic idea of knowledge enhancement will be useful for solving other problems and provide a promising direction for future research.

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Section: Machine Learning Backgroundmentioning

confidence: 99%

“…We demonstrate our approach in the domain of materials genomics. Machine learning is helping to accelerate the discovery of molecules and materials with desired properties. , An important problem in this domain is predicting the properties of an unknown material. Density functional theory (DFT) is often used to make property predictions.…”

Section: Introductionmentioning

confidence: 99%

KE: A Knowledge Enhancing Framework for Machine Learning Models

Wang,

Shah,

Soliman

et al. 2023

J. Phys. Chem. A

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“…The Materials Genome Initiative accelerated materials discovery through efforts such as the Materials Project, which combines supercomputing and density functional theory (DFT) to theoretically predict new materials and their properties before they are made. , While this “materials by design” approach successfully identified vast numbers of materials with a wide range of targeted properties, a significant bottleneck now exists at the next step of the process: the Edisonian nature of materials synthesis. Unlike the vast majority of related reports from previous studies that develop approaches to discover new materials with specific properties, − there is no robust predictive framework that can help map the reaction coordinate from precursors to the final crystalline solid when attempting to synthesize materials. Moreover, the compositions and structure types of crystalline inorganic solids are so disparate that it is exceptionally challenging to apply the lessons learned from one materials system to another.…”

Section: Introductionmentioning

confidence: 99%

Predictive Synthesis of Copper Selenides Using a Multidimensional Phase Map Constructed with a Data-Driven Classifier

et al. 2023

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Copper selenides are an important family of materials with applications in catalysis, plasmonics, photovoltaics, and thermoelectrics. Despite being a binary material system, the Cu–Se phase diagram is complex and contains multiple crystal structures in addition to several metastable structures that are not found on the thermodynamic phase diagram. Consequently, the ability to synthetically navigate this complex phase space poses a significant challenge. We demonstrate that data-driven learning can successfully map this phase space in a minimal number of experiments. We combine soft chemistry (chimie douce) synthetic methods with multivariate analyses via classification techniques to enable predictive phase determination. A surrogate model was constructed with experimental data derived from a design matrix of four experimental variables: C–Se bond strength of the selenium precursor, time, temperature, and solvent composition. The reactions in the surrogate model resulted in 11 distinct phase combinations of copper selenide. These data were used to train a classification model that predicts the phase with 95.7% accuracy. The resulting decision tree enabled conclusions to be drawn about how the experimental variables affect the phase and provided prescriptive synthetic conditions for specific phase isolation. This guided the accelerated phase targeting in a minimum number of experiments of klockmannite CuSe, which could not be isolated in any of the reactions used to construct the surrogate model. The reaction conditions that the model predicted to synthesize klockmannite CuSe were experimentally validated, highlighting the utility of this approach.

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“…For instance, a cross-coupling reaction was optimized using a small number of synthetic data by varying the concentrations and catalysts . In another study, material properties were optimized using molecular descriptors and analytical data . Similarly, solubility data were predicted using a combination of analytical data and molecular descriptors .…”

Section: Introductionmentioning

confidence: 99%

Identification of the Contributing Factors to the Photoelectric Conversion Efficiency for Hematite Photoanodes by Using Machine Learning

Idei,

Nagai,

Pan

et al. 2023

ACS Appl. Mater. Interfaces

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Experimental design for the highly accurate prediction of material properties using descriptors obtained by measurement

Cited by 8 publications

References 33 publications

KE: A Knowledge Enhancing Framework for Machine Learning Models

KE: A Knowledge Enhancing Framework for Machine Learning Models

Predictive Synthesis of Copper Selenides Using a Multidimensional Phase Map Constructed with a Data-Driven Classifier

Identification of the Contributing Factors to the Photoelectric Conversion Efficiency for Hematite Photoanodes by Using Machine Learning

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