“…As a result, ANN can easily correlate engineering/chemical processes using non‐linear mathematical models that represent input–output relationships and make predictions based on that learning (Himmelblau, 2000). The ML technique was used to accurately predict the crystallization behavior of small organic molecules and to identify crystal defects (Klunnikova et al, 2020). ANN methods predicting the solubility of 30 different compounds in supercritical carbon dioxide were found to be more accurate (relative deviation = 5.3%) than semi‐empirical equations with a relative deviation of 15.96% (Mehdizadeh & Movagharnejad, 2011).…”
Caffeine co‐crystal formation with other compounds is investigated in this study using a variety of machine learning (ML) methods. A total of 140 caffeine co‐crystal data points are used to train classification learners using MATLAB ML models. Kernel neural network, ensemble tree‐based, logistic regression, and support vector machine algorithms were among the ML models tested. The logistic regression algorithm produced the most accurate predictions of caffeine‐co‐crystal formation, with a validation accuracy of 97.1%. Experiments and molecular interaction studies between caffeine and other tea compounds (catechin and catechol) are used to validate ML predictions. As part of the evaluation, a random forest classifier was applied to select 1440 known molecular descriptors, among them 30 descriptors identified as responsible for caffeine co‐crystal formation were used for training and validation purpose. The reliability of the trained logistic regression algorithm means that it is suitable for use in predicting possible co‐crystals between caffeine and other compounds, thereby providing an understanding of caffeine co‐crystals formation without recourse to rigorous experimental tests.
Practical applications
Caffeine co‐crystals formation with other tea components is crucial to understand the generation of tea cream, which is undesirable to customers. Using a data‐driven approach (or machine learning) to identify the possible molecular combinations involved in co‐crystal formation can significantly reduce experimental test requirements. Machine learning data can help with investigations aimed at detailed characterization of ready‐to‐drink concentrated tea formulations. Furthermore, this study will assist researchers and policymakers in meeting the Sustainable Development Goals of “Industry, Innovation, and Infrastructure.”
“…As a result, ANN can easily correlate engineering/chemical processes using non‐linear mathematical models that represent input–output relationships and make predictions based on that learning (Himmelblau, 2000). The ML technique was used to accurately predict the crystallization behavior of small organic molecules and to identify crystal defects (Klunnikova et al, 2020). ANN methods predicting the solubility of 30 different compounds in supercritical carbon dioxide were found to be more accurate (relative deviation = 5.3%) than semi‐empirical equations with a relative deviation of 15.96% (Mehdizadeh & Movagharnejad, 2011).…”
Caffeine co‐crystal formation with other compounds is investigated in this study using a variety of machine learning (ML) methods. A total of 140 caffeine co‐crystal data points are used to train classification learners using MATLAB ML models. Kernel neural network, ensemble tree‐based, logistic regression, and support vector machine algorithms were among the ML models tested. The logistic regression algorithm produced the most accurate predictions of caffeine‐co‐crystal formation, with a validation accuracy of 97.1%. Experiments and molecular interaction studies between caffeine and other tea compounds (catechin and catechol) are used to validate ML predictions. As part of the evaluation, a random forest classifier was applied to select 1440 known molecular descriptors, among them 30 descriptors identified as responsible for caffeine co‐crystal formation were used for training and validation purpose. The reliability of the trained logistic regression algorithm means that it is suitable for use in predicting possible co‐crystals between caffeine and other compounds, thereby providing an understanding of caffeine co‐crystals formation without recourse to rigorous experimental tests.
Practical applications
Caffeine co‐crystals formation with other tea components is crucial to understand the generation of tea cream, which is undesirable to customers. Using a data‐driven approach (or machine learning) to identify the possible molecular combinations involved in co‐crystal formation can significantly reduce experimental test requirements. Machine learning data can help with investigations aimed at detailed characterization of ready‐to‐drink concentrated tea formulations. Furthermore, this study will assist researchers and policymakers in meeting the Sustainable Development Goals of “Industry, Innovation, and Infrastructure.”
“…Machine learning and extracted features are crucial for a data-based approach decision. Klunnikova et al 26 define a clear chart of machine learning workflow for structural damage prediction shown in Figure 2 which declares the steps of machine learning applications. There are three steps as follows: data prepossessing and cleaning, train model, and test and evaluate the model.…”
Section: Introductionmentioning
confidence: 99%
“…This emphasises the importance of cleaning raw data, selecting and extracting sensitive features before training machine learning models could enhance the performance and accuracy of the model prediction. Figure 2. Machine learning workflow. 26 …”
Structural health monitoring and assessment (SHMA) is exceptionally essential for preserving and sustaining any mechanical structure’s service life. A successful assessment should provide reliable and resolute information to maintain the continuous performance of the structure. This information can effectively determine crack progression and its overall impact on the structural operation. However, the available sensing techniques and methods for performing SHMA generate raw measurements that require significant data processing before making any valuable predictions. Machine learning (ML) algorithms (supervised and unsupervised learning) have been extensively used for such data processing. These algorithms extract damage-sensitive features from the raw data to identify structural conditions and performance. As per the available published literature, the extraction of these features has been quite random and used by academic researchers without a suitability justification. In this paper, a comprehensive literature review is performed to emphasise the influence of damage-sensitive features on ML algorithms. The selection and suitability of these features are critically reviewed while processing raw data obtained from different materials (metals, composites and polymers). It has been found that an accurate crack prediction is only possible if the selection of damage-sensitive features and ML algorithms is performed based on available raw data and structure material type. This paper also highlights the current challenges and limitations during the mentioned sections.
“…As a typical engineering ceramic material, single crystal sapphire consisting of α-Al 2 O 3 has been widely used in many applications such as optics, electronics, and temperature sensing etc., and is the most common wafer used in light emitting diodes (LEDs) by virtue of its excellent mechanical and optical properties such as great hardness, good thermal stability, chemical inertness, and good light transmission [1][2][3][4]. The surface quality of processed sapphire wafer plays a critical role in these applications, as well as in shape accuracy.…”
A novel flexible polishing process has been developed for sapphire wafer by using a polishing plate with rigid-flexible composite structure to satisfy the demands of excellent surface shape accuracy and high surface topography quality simultaneously. This new polishing plate was fabricated by alternately casting and curing the ring structure of soft and hard unsaturated resins. It is found that the overall stiffness of the polishing plate is improved due to the “hard support frame” of rigid-flexible polishing plate, as well as the ability of removal selectivity of the polishing plate is strengthened. The topography quality and shape accuracy of sapphire wafer polished by presented novel polishing process have been compared with those polished by conventional flexible polishing, respectively. Both experiment and simulation results are shown that the surface roughness and topographical variations of sapphire wafer polished by the novel rigid-flexible composite structure polishing plate have been greatly improved. Comparing with the conventional flexible polishing, the surface shape accuracy of the sapphire wafer polished by the presented novel polishing process can be improved by 54.1%.
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