jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Prakash Muthudoss | Ishan Vijay Tewari | Rayce Lim Rui | Kwok Jia Young | Eddy Yii Chung Ann | Doreen Ng Sean Hui | Ooi Yee Khai | Ravikiran Allada | M. J. Manohar Rao | Saurabh Shahane | Samir Das | Irfan B. Babla | Sandeep Mhetre | Amrit Paudel
Source: AAPS PharmSciTech (article, 2022-10-13)
Field: Chemistry — Analytical Chemistry

Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model's performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Impact of various data learning approaches on NIR spectral data. Materials science ; Spectroscopy ; Nanotechnology ; Analytical Chemistry (journal) ; Engineering physics ; Computer science ; Chemistry ; Chromatography ; Physics Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies ; Mineral Processing and Grinding
Authors: Arijit Dutta | Prasenjit Sen
Source: Journal of Materials Chemistry C (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning assisted hierarchical filtering: a strategy for designing magnets with large moment and anisotropy energy Machine learning models are developed to hierarchically screen materials so that the ones with large magnetization and magnetic anisotropy energy are selected. Anisotropy ; Materials science ; Magnet ; Magnetic anisotropy ; Anisotropy energy ; Moment (physics) ; Magnetization ; Energy (signal processing) ; Condensed matter physics ; Magnetic moment ; Engineering physics ; Mechanical engineering ; Magnetic field ; Physics ; Optics ; Engineering ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; Magnetic properties of thin films ; Magnetic and transport properties of perovskites and related materials
Authors: Timothy Liao | Weiyi Xia | Masahiro Sakurai | Renhai Wang | Chao Zhang | Huaijun Sun | Kai‐Ming Ho | Cai‐Zhuang Wang | James R. Chelikowsky
Source: Physical Review Materials (article, 2022-02-07)
Field: Materials Science — Materials Chemistry

Predicting magnetic anisotropy energies using site-specific spin-orbit coupling energies and machine learning: Application to iron-cobalt nitrides The authors present a promising machine learning model, which focuses on site-magnetic-properties for rapid screening in materials design and accelerates computational screening of candidate materials that possess high magnetizations and large magnetic anisotropy energies. Materials science ; Anisotropy ; Cobalt ; Spin–orbit interaction ; Coupling (piping) ; Condensed matter physics ; Magnetic anisotropy ; Spin (aerodynamics) ; Nitride ; Magnetization ; Nanotechnology ; Metallurgy ; Magnetic field ; Thermodynamics ; Physics ; Layer (electronics) ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Elham Kiyani | Steven A. Silber | Mahdi Kooshkbaghi | Mikko Karttunen
Source: Physical review. E (article, 2022-12-09)
Field: Materials Science — Materials Chemistry

Machine-learning-based data-driven discovery of nonlinear phase-field dynamics One of the main questions regarding complex systems at large scales concerns the effective interactions and driving forces that emerge from the detailed microscopic properties. Coarse-grained models aim to describe complex systems in terms of coarse-scale equations with a reduced number of degrees of freedom. Recent developments in machine-learning algorithms have significantly empowered the discovery process of governing equations directly from data. However, it remains difficult to discover partial differential equations (PDEs) with high-order derivatives. In this paper, we present data-driven architectures based on a multilayer perceptron, a convolutional neural network (CNN), and a combination of a CNN and long short-term memory structures for discovering the nonlinear equations of motion for phase-field models with nonconserved and conserved order parameters. The well-known Allen-Cahn, Cahn-Hilliard, and phase-field crystal models were used as test cases. Two conceptually different types of implementations were used: (a) guided by physical intuition (such as the local dependence of the derivatives) and (b) in the absence of any physical assumptions (black-box model). We show that not only can we effectively learn the time derivatives of the field in both scenarios, but we can also use the data-driven PDEs to propagate the field in time and achieve results in good agreement with the original PDEs. Nonlinear system ; Computer science ; Artificial neural network ; Field (mathematics) ; Convolutional neural network ; Partial differential equation ; Artificial intelligence ; Perceptron ; Intuition ; Machine learning ; Statistical physics ; Theoretical computer science ; Physics ; Mathematics ; Mathematical analysis Solidification and crystal growth phenomena ; Model Reduction and Neural Networks ; Advanced Mathematical Modeling in Engineering
Authors: Upendra Kumar | Km Arti Mishra | Ajay Kushwaha | Sung Beom Cho
Source: Journal of Physics and Chemistry of Solids (article, 2022-08-31)
Field: Materials Science — Materials Chemistry

Bandgap analysis of transition-metal dichalcogenide and oxide via machine learning approach Band gap ; Computer science ; Oversampling ; Oxide ; Transition metal ; Machine learning ; Artificial intelligence ; Materials science ; Chemistry ; Metallurgy ; Optoelectronics Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Funan Sun | Xingyi Wang | Pengju Han | Bin He
Source: International Journal of Pressure Vessels and Piping (article, 2022-09-17)
Field: Materials Science — Materials Chemistry

Combined EIS and BAS-BP neural network analysis of electrochemical corrosion on pipeline steel in silty soil in a Salt–Temperature coupling environment Corrosion ; Nyquist plot ; Materials science ; Dielectric spectroscopy ; Cathodic protection ; Metallurgy ; Electrochemistry ; Chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Cheng Zeng | Xi Chen | Andrew A. Peterson
Source: The Journal of Chemical Physics (article, 2022-02-09)
Field: Materials Science — Materials Chemistry

A nearsighted force-training approach to systematically generate training data for the machine learning of large atomic structures A challenge of atomistic machine-learning (ML) methods is ensuring that the training data are suitable for the system being simulated, which is particularly challenging for systems with large numbers of atoms. Most atomistic ML approaches rely on the nearsightedness principle ("all chemistry is local"), using information about the position of an atom's neighbors to predict a per-atom energy. In this work, we develop a framework that exploits the nearsighted nature of ML models to systematically produce an appropriate training set for large structures. We use a per-atom uncertainty estimate to identify the most uncertain atoms and extract chunks centered around these atoms. It is crucial that these small chunks are both large enough to satisfy the ML's nearsighted principle (that is, filling the cutoff radius) and are large enough to be converged with respect to the electronic structure calculation. We present data indicating when the electronic structure calculations are converged with respect to the structure size, which fundamentally limits the accuracy of any nearsighted ML calculator. These new atomic chunks are calculated in electronic structures, and crucially, only a single force-that of the central atom-is added to the growing training set, preventing the noisy and irrelevant information from the piece's boundary from interfering with ML training. The resulting ML potentials are robust, despite requiring single-point calculations on only small reference structures and never seeing large training structures. We demonstrated our approach via structure optimization of a 260-atom structure and extended the approach to clusters with up to 1415 atoms. Atom (system on chip) ; Computer science ; Set (abstract data type) ; Position (finance) ; Training set ; Point (geometry) ; Cutoff ; Work (physics) ; Electronic structure ; Artificial intelligence ; Training (meteorology) ; Algorithm ; Machine learning ; Physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Yifan Lu | Jun Jiang | Jialiang Zhang | Rongru Zhang | Qiang Zhang | Yuanzi Zhou | Lei Wang | Honghao Yue
Source: Thin-Walled Structures (article, 2022-04-25)
Field: Materials Science — Materials Chemistry

A dynamic stiffness improvement method for thin plate structures with laminated/embedded shape memory alloy actuators Shape-memory alloy ; Actuator ; Flutter ; Materials science ; SMA* ; Stiffness ; Structural engineering ; Cantilever ; Finite element method ; Bending stiffness ; Specific modulus ; Acoustics ; Computer science ; Engineering ; Composite material ; Aerodynamics ; Elastic modulus ; Aerospace engineering ; Physics Shape Memory Alloy Transformations ; Composite Structure Analysis and Optimization ; Aeroelasticity and Vibration Control
Authors: A. I. Rusanov | O. Dmitrieva | Н. Ж. Мамардашвили | Igor V. Tetko
Source: International Journal of Molecular Sciences (article, 2022-01-21)
Field: Materials Science — Materials Chemistry

More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available. Molar absorptivity ; Mean squared error ; Extinction (optical mineralogy) ; Absorption (acoustics) ; Set (abstract data type) ; Biological system ; Computer science ; Series (stratigraphy) ; Data set ; Chemistry ; Mathematics ; Artificial intelligence ; Materials science ; Statistics ; Physics ; Optics ; Mineralogy Porphyrin and Phthalocyanine Chemistry ; Photodynamic Therapy Research Studies ; Photochemistry and Electron Transfer Studies
Authors: Ryo Tamura | Guillaume Deffrennes | Kwangsik Han | Taichi Abe | Haruhiko Morito | Yasuyuki Nakamura | Masanobu Naito | Ryoji Katsube | Yoshitaro Nose | Kei Terayama
Source: Science and Technology of Advanced Materials Methods (article, 2022-05-13)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based phase diagram construction for high-throughput batch experiments To know phase diagrams is a time saving approach for developing novel materials. To efficiently construct phase diagrams, a machine learning technique was developed using uncertainty sampling, which is called as PDC (Phase Diagram Construction) package [K. Terayama et al. Phys. Rev. Mater. 3, 033802 (2019).]. In this method, the most uncertain point in the phase diagram was suggested as the next experimental condition. However, owing to recent progress in lab automation techniques and robotics, high-throughput batch experiments can be performed. To benefit from such a high-throughput nature, multiple conditions must be selected simultaneously to effectively construct a phase diagram using a machine learning technique. In this study, we consider some strategies to do so, and their performances were compared when exploring ternary isothermal sections (two-dimensional) and temperature-dependent ternary phase diagrams (three-dimensional). We show that even if the suggestions are explored several instead of one at a time, the performance did not change drastically. Thus, we conclude that PDC with multiple suggestions is suitable for high-throughput batch experiments and can be expected to play an active role in next-generation automated material development. Throughput ; Phase diagram ; Construct (python library) ; Computer science ; Diagram ; Ternary operation ; Phase (matter) ; Automation ; Isothermal process ; Point (geometry) ; Artificial intelligence ; Machine learning ; Mathematics ; Mechanical engineering ; Engineering ; Thermodynamics ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Materials and Properties
Authors: Akihito Nakanishi | Hiroaki Fukunishi | Riri Matsumoto | Fumihito Eguchi
Source: BioTech (article, 2022-10-12)
Field: Chemistry — Analytical Chemistry

Development of a Prediction Method of Cell Density in Autotrophic/Heterotrophic Microorganism Mixtures by Machine Learning Using Absorbance Spectrum Data Microflora is actively used to produce value-added materials in industry, and each cell density should be controlled for stable microflora use. In this study, a simple system evaluating the cell density was constructed with artificial intelligence (AI) using the absorbance spectra data of microflora. To set up the system, the prediction system for cell density based on machine learning was constructed using the spectra data as the feature from the mixture of <i>Saccharomyces cerevisiae</i> and <i>Chlamydomonas reinhardti</i>i. As the results of predicting cell density by extremely randomized trees, when the cell densities of <i>S. cerevisia</i>e and <i>C. reinhardtii</i> were shifted and fixed, the coefficient of determination (<i>R</i><sup>2</sup>) was 0.8495; on the other hand, when the cell densities of <i>S. cerevisiae</i> and <i>C. reinhardtii</i> were fixed and shifted, the <i>R</i><sup>2</sup> was 0.9232. To explain the prediction system, the randomized trees regressor of the decision tree-based ensemble learning method as the machine learning algorithm and Shapley additive explanations (SHAPs) as the explainable AI (XAI) to interpret the features contributing to the prediction results were used. As a result of the SHAP analyses, not only the optical density, but also the absorbance of the Soret and Q bands derived from the chloroplasts of <i>C. reinhardtii</i> could contribute to the prediction as the features. The simple cell density evaluating system could have an industrial impact. Chlamydomonas reinhardtii ; Biological system ; Absorbance ; Artificial intelligence ; Computer science ; Saccharomyces cerevisiae ; Autotroph ; Machine learning ; Mathematics ; Chemistry ; Pattern recognition (psychology) ; Yeast ; Biology ; Chromatography ; Biochemistry ; Bacteria Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Metabolomics and Mass Spectrometry Studies
Authors: Fei Pan | Chia‐Chen Wu | Yu-Lin Chen | Po-Yen Kung | Yen‐Hsun Su
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed, <i>e.g.</i>, ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties. Surface plasmon resonance ; Plasmon ; Selection (genetic algorithm) ; Nanoparticle ; Colloidal gold ; Materials science ; Nanotechnology ; Resonance (particle physics) ; Optoelectronics ; Computer science ; Physics ; Artificial intelligence Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Nanoplatforms for cancer theranostics
Authors: Lei Zhou | Xuefei Wang | Chu Zhang | Nan Zhao | Mohamed Farag Taha | Yong He | Zhengjun Qiu
Source: Food and Bioprocess Technology (article, 2022-08-08)
Field: Chemistry — Analytical Chemistry

Powdery Food Identification Using NIR Spectroscopy and Extensible Deep Learning Model Artificial intelligence ; Metric (unit) ; Computer science ; Ranking (information retrieval) ; Deep learning ; Adaptability ; Identification (biology) ; Pattern recognition (psychology) ; Authentication (law) ; Extractor ; Machine learning ; Process engineering ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Stewart Pearson | Parinaz Naseri | Sean V. Hum
Source: IEEE Open Journal of Antennas and Propagation (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Beam-Splitting Bianisotropic Metasurface Designed by Optimization and Machine Learning Electromagnetic metasurfaces have attracted significant interest recently due to their low profile and advantageous applications. Practically, many metasurface designs start with a set of constraints for the radiated far-field, such as main-beam direction(s) and side lobe levels, and end with a non-uniform physical structure for the surface. This problem is quite challenging, since the required tangential field transformations are not completely known when only constraints are placed on the scattered fields. Hence, the required surface properties cannot be solved for analytically. Moreover, the translation of the desired surface properties to the physical unit cells can be time-consuming and difficult, as it is often a one-to-many mapping in a large solution space. Here, we divide the inverse design process into two steps: a macroscopic and microscopic design step. In the former, we use an iterative optimization process to find the surface properties that radiate a far-field pattern that complies with specified constraints. This iterative process exploits non-radiating currents to ensure a passive and lossless design. In the microscopic step, these optimized surface properties are realized with physical unit cells using machine learning surrogate models. The effectiveness of this end-to-end synthesis process is demonstrated through measurement results of a beam-splitting prototype. Surface (topology) ; Computer science ; Process (computing) ; Beam (structure) ; Iterative and incremental development ; Translation (biology) ; Field (mathematics) ; Inverse ; Algorithm ; Physics ; Optics ; Mathematics ; Geometry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Xiaonan Wang | Jie Li | Yingzhe Zheng | Jiali Li
Source: Frontiers of Chemical Science and Engineering (article, 2022-03-25)
Field: Materials Science — Materials Chemistry

Smart systems engineering contributing to an intelligent carbon-neutral future: opportunities, challenges, and prospects Big data ; Scale (ratio) ; Carbon neutrality ; Field (mathematics) ; Computer science ; Analytics ; Data science ; Internet of Things ; Intelligent decision support system ; Systems engineering ; Engineering management ; Engineering ; Knowledge management ; Artificial intelligence ; Renewable energy ; Computer security Machine Learning in Materials Science ; Digital Transformation in Industry ; Technology Assessment and Management
Authors: Xiaowei Zhang | Tianyuan Gong | Youcai Xiao | Yi Sun
Source: International Journal of Impact Engineering (article, 2022-11-21)
Field: Materials Science — Materials Chemistry

Dynamic mechanical properties and penetration behavior of reactive nano-inorganic cement-based composites Materials science ; Split-Hopkinson pressure bar ; Composite material ; Penetration (warfare) ; Ultimate tensile strength ; Projectile ; Compressive strength ; Penetration test ; Strain rate ; Cement ; Penetration depth ; Dynamic range compression ; Engineering High-Velocity Impact and Material Behavior ; Advanced ceramic materials synthesis ; Metal and Thin Film Mechanics