jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Madison Wenzlick | Osman Mamun | Ram Devanathan | Kelly Rose | Jeffrey A. Hawk
Source: JOM (article, 2022-05-19)
Field: Materials Science — Materials Chemistry

Assessment of Outliers in Alloy Datasets Using Unsupervised Techniques Outlier ; Anomaly detection ; Data mining ; Cluster analysis ; Principal component analysis ; Overfitting ; Computer science ; Identification (biology) ; Unsupervised learning ; Pattern recognition (psychology) ; Cluster (spacecraft) ; Artificial intelligence ; Machine learning ; Artificial neural network Machine Learning in Materials Science ; Fault Detection and Control Systems ; Computational Drug Discovery Methods
Authors: Abhishek Ojha | Megha | Satya Bulusu | Arup Banerjee
Source: Computational and Theoretical Chemistry (article, 2022-12-10)
Field: Materials Science — Materials Chemistry

Structure and dynamics of 38-atom Ag-Pt nanoalloys using ANN based-interatomic potential Chemistry ; Platinum ; Atom (system on chip) ; Interatomic potential ; Molecular dynamics ; Catalysis ; Chemical physics ; Metal ; Crystallography ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Electrocatalysts for Energy Conversion
Authors: Maksim Kulichenko | Kipton Barros | Nicholas Lubbers | Ying Wai Li | Richard A. Messerly | Sergei Tretiak | Justin S. Smith | Benjamin Nebgen
Source: Research Square (preprint, 2022-10-03)
Field: Materials Science — Materials Chemistry

Uncertainty Driven Dynamics for Active Learning of Interatomic Potentials Dynamics (music) ; Interatomic potential ; Statistical physics ; Molecular dynamics ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Process Optimization and Integration
Authors: Alireza Aghajamali | Amir Karton
Source: Micro and Nano Engineering (article, 2022-01-19)
Field: Chemistry — Organic Chemistry

Correlation between the energetic and thermal properties of C40 fullerene isomers: An accurate machine-learning force field study The isomerisation energies and thermal stabilities of the entire set of C40 fullerene isomers are investigated using molecular dynamics simulations with the recently developed machine-learning-based Gaussian Approximation Potential (GAP-20) force field. Our simulations predict high statistical correlation between the relative isomerisation energy distribution of all C40 isomers and their thermal fragmentation temperatures. The most energetically and thermally stable isomer is the symmetric C40-D2 isomer and its fragmentation temperature is 3875 ± 25 K. In contrast, the least stable isomer is the D5d nanorod-shaped isomer which is 146.8 kcal mol−1 higher in energy and decomposes at 2975 ± 25 K, i.e., around 900 K below the most stable isomer. In addition, we show that most isomers lie in the range between 20 and 60 kcal mol−1 above the most stable isomer, and nearly 60% of isomers decompose in the temperature range of 3425–3675 K. These computational results provide valuable insights into the synthesis of C40 fullerenes at high-temperatures. Isomerization ; Fullerene ; Force field (fiction) ; Molecular dynamics ; Fragmentation (computing) ; Chemistry ; Computational chemistry ; Physical chemistry ; Physics ; Organic chemistry ; Quantum mechanics Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites ; Machine Learning in Materials Science
Authors: Luigi Sbailò | Ádám Fekete | Luca M. Ghiringhelli | Matthias Scheffler
Source: arXiv (Cornell University) (preprint, 2022-05-31)
Field: Materials Science — Materials Chemistry

The NOMAD Artificial-Intelligence Toolkit: Turning materials-science data into knowledge and understanding We present the Novel-Materials-Discovery (NOMAD) Artificial-Intelligence (AI) Toolkit, a web-browser-based infrastructure for the interactive AI-based analysis of materials-science findable, accessible, interoperable, and reusable (FAIR) data. The AI Toolkit readily operates on the FAIR data stored in the central server of the NOMAD Archive, the largest database of materials-science data worldwide, as well as locally stored, users' owned data. The NOMAD Oasis, a local, stand alone server can be also used to run the AI Toolkit. By using Jupyter notebooks that run in a web-browser, the NOMAD data can be queried and accessed; data mining, machine learning, and other AI techniques can be then applied to analyse them. This infrastructure brings the concept of reproducibility in materials science to the next level, by allowing researchers to share not only the data contributing to their scientific publications, but also all the developed methods and analytics tools. Besides reproducing published results, users of the NOMAD AI toolkit can modify the Jupyter notebooks towards their own research work. Interoperability ; Computer science ; Analytics ; World Wide Web ; Data science ; Knowledge extraction ; Artificial intelligence Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Mineral Processing and Grinding
Authors: Mengfei Zhou | Yinchao Hu | Ruizhen Wang | Tian Guo | Qiqing Yu | Luyue Xia | Xiaofang Sun
Source: Journal of Chemometrics (article, 2022-12-12)
Field: Chemistry — Analytical Chemistry

An end‐to‐end deep learning approach for Raman spectroscopy classification Abstract Raman spectroscopy has numerous advantages as a means of analyzing materials and is widely used in petrochemical, material, food, biological, medical, and other fields. Its analysis process is fast, nondestructive, and requires no prepreparation. Meanwhile, the research on applying machine learning methods in Raman spectral recognition is becoming increasingly popular. In this study, an end‐to‐end deep learning method called deep residual shrinkage‐VGG (DRS‐VGG) is proposed, which is able to match Raman spectral features with model structure and reduces the reliance on feature engineering. The addition of identity shortcut and soft thresholding in the model eliminates redundant signals to achieve end‐to‐end spectral identification. The effectiveness of the proposed model is verified in three subsets of the RRUFF Raman database and bacterial Raman dataset from different perspectives without data augmentation, and the recognition accuracy is 97.84%, 92.81%, and 95.08%, respectively. Compared with other methods, the proposed DRS‐VGG model achieved a significant improvement in speed or accuracy. The model's understanding of the spectra is visualized by the gradient‐weighted class activation mapping (Grad‐CAM), which explains the excellent classification performance. Additionally, the weight pruning technique is used to achieve model compression and improve recognition accuracy by shrinking the weights and fine‐tuning the biases. Raman spectroscopy ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Thresholding ; Deep learning ; Residual ; Pruning ; Algorithm ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Leyla Nazari | Azim Khazaei | Ewa Ropelewska
Source: Cereal Chemistry (article, 2022-03-01)
Field: Chemistry — Analytical Chemistry

Prediction of tannin, protein, and total phenolic content of grain sorghum using image analysis and machine learning Abstract Background and Objectives Sorghum is an alternative crop where poor soil is a limiting factor for the production of corn. Laboratory measurements of chemical compounds of grains are expensive, time‐consuming predominantly done on powdered samples. Therefore, a rapid and reliable method integrating image processing and machine learning was evaluated for the prediction of total phenolic compounds (TPCs), tannin, and protein. Findings The highest TPC (0.83%) was obtained for genotype KGS36 (the brown pericarp) followed by KGS23 (0.54%). The concentration of tannin ranged from 0.008% to 0.616%. Mean comparison revealed variation for the protein concentration (15.17%–11.53%); the highest content obtained by KGS25 and the lowest by KGS36 ( p &lt; .01). Multilayer perceptron (MLP) as one of the common artificial neural networks (ANNs) applied to simulate the chemical of the grains was evaluated through the textural features extracted from grain images. For the MLP network, the minimum number of hidden networks was set to 8 and the maximum number was set to 25. For learning and saving, the number of networks was set to 20 and the number of saved networks was set to 5. The best MLP models were selected based on performance (R) and error values for the train, test, and validation sets. Conclusions An ANN for the prediction of the chemical concentration was suggested. The correlations between predicted and observed data were higher than .915. Significance and Novelty These models are of great significance for the prediction of chemical concentrations of grain sorghum in plant breeding and food industry. Tannin ; Sorghum ; Artificial neural network ; Artificial intelligence ; Chemistry ; Test set ; Biological system ; Multilayer perceptron ; Proanthocyanidin ; Perceptron ; Mathematics ; Machine learning ; Agronomy ; Food science ; Polyphenol ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Nuts composition and effects
Authors: Salma Salem | Stephen R. Byrn | Daniel T. Smith | Vadim J. Gurvich | Stephen W. Hoag | Feng Zhang | Robert O. Williams | Kari Clase
Source: Journal of Drug Delivery Science and Technology (article, 2022-04-13)
Field: Chemistry — Analytical Chemistry

Impact assessment of the variables affecting the drug release and extraction of polyethylene oxide based tablets Polyethylene oxide ; Extraction (chemistry) ; Drug ; Materials science ; Chromatography ; Polyethylene ; Oxide ; Pharmacology ; Medicine ; Chemistry ; Composite material ; Polymer ; Metallurgy Analytical Methods in Pharmaceuticals ; Forensic Toxicology and Drug Analysis ; Drug Solubulity and Delivery Systems
Authors: Mathew J. Cherukara | Arun Mannodi‐Kanakkithodi
Source: Modelling and Simulation in Materials Science and Engineering (article, 2022-02-08)
Field: Materials Science — Materials Chemistry

Deep learning the properties of inorganic perovskites Abstract The ability to accurately and quickly predict the stability of materials and their structural and electronic properties remains a grand challenge in materials science. Density functional theory is widely used as a means of predicting these material properties, but is known to be computationally expensive and scales as the cube of the number of electrons in the material’s unit cell. In this article, for a previously published dataset of inorganic perovskites, we show that a single neural network model using only the elemental properties of the compounds’ constituents can predict lattice constants to within 0.1 Å, heat of formation to within 0.2 eV, and band gaps to within 0.7 eV RMSE. We also compare the performance of the trained network to two widely used regression techniques, namely random forest and Kernel ridge regression, and find that the neural network’s predictions are more accurate for each of the properties. The simultaneous accurate prediction of multiple key properties of technologically relevant materials is promising for rational design and optimization in known and novel chemical spaces. Materials science ; Artificial neural network ; Stability (learning theory) ; Band gap ; Regression ; Statistical physics ; Cube (algebra) ; Biological system ; Computer science ; Machine learning ; Mathematics ; Statistics ; Optoelectronics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Aileen Luo | Oleg Gorobtsov | Jocienne N. Nelson | Ding-Yuan Kuo | Tao Zhou | Ziming Shao | Ryan Bouck | Mathew J. Cherukara | Martin V. Holt | Kyle Shen | Darrell G. Schlom | Jin Suntivich | Andrej Singer
Source: Applied Physics Letters (article, 2022-10-10)
Field: Materials Science — Materials Chemistry

X-ray nano-imaging of defects in thin film catalysts via cluster analysis Functional properties of transition-metal oxides strongly depend on crystallographic defects; crystallographic lattice deviations can affect ionic diffusion and adsorbate binding energies. Scanning x-ray nanodiffraction enables imaging of local structural distortions across an extended spatial region of thin samples. Yet, localized lattice distortions remain challenging to detect and localize using nanodiffraction, due to their weak diffuse scattering. Here, we apply an unsupervised machine learning clustering algorithm to isolate the low-intensity diffuse scattering in as-grown and alkaline-treated thin epitaxially strained SrIrO3 films. We pinpoint the defect locations, find additional strain variation in the morphology of electrochemically cycled SrIrO3, and interpret the defect type by analyzing the diffraction profile through clustering. Our findings demonstrate the use of a machine learning clustering algorithm for identifying and characterizing hard-to-find crystallographic defects in thin films of electrocatalysts and highlight the potential to study electrochemical reactions at defect sites in operando experiments. Materials science ; Thin film ; Scattering ; Diffraction ; Cluster (spacecraft) ; Ionic bonding ; Crystallographic defect ; Crystallography ; Epitaxy ; Crystal structure ; Oxide ; Transition metal ; Cluster analysis ; Chemical physics ; Nanotechnology ; Optics ; Catalysis ; Chemistry ; Ion ; Computer science ; Machine learning ; Physics Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; X-ray Diffraction in Crystallography
Authors: Emil Thyge Skanning Kjær | Andy S. Anker | Marcus Nørgaard Weng | Simon J. L. Billinge | Raghavendra Selvan | Kirsten M. Ø. Jensen
Source: ChemRxiv (preprint, 2022-03-06)
Field: Materials Science — Materials Chemistry

DeepStruc: Towards structure solution from pair distribution function data using deep generative models Structure solution of nanostructured materials that have limited long-range remains a bottleneck in materials development. We present a deep learning algorithm, DeepStruc, that can solve a simple nanoparticle structure directly from a Pair Distribution Function obtained from total scattering data by using a conditional variational autoencoder (CVAE). We first apply DeepStruc to PDFs from seven different structure types of monometallic nanoparticles, and show that structures can be solved from both simulated and experimental PDFs, including PDFs from nanoparticles that are not present in the training distribution. We also apply DeepStruc to a system of hcp, fcc and stacking faulted nanoparticles, where DeepStruc recognizes stacking faulted nanoparticles as an interpolation between hcp and fcc nanoparticles and is able to solve stacking faulted structures from PDFs. Our findings suggests that DeepStruc is a step towards a general approach for structure solution of nanomaterials. Stacking ; Autoencoder ; Nanoparticle ; Materials science ; Interpolation (computer graphics) ; Function (biology) ; Distribution (mathematics) ; Simple (philosophy) ; Pair distribution function ; Range (aeronautics) ; Nanomaterials ; Biological system ; Statistical physics ; Algorithm ; Computer science ; Deep learning ; Nanotechnology ; Mathematics ; Physics ; Artificial intelligence ; Image (mathematics) ; Mathematical analysis ; Composite material Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Microstructure and mechanical properties
Authors: Prateek Malhotra | Juan C. Verduzco | Subhayan Biswas | Ganesh D. Sharma
Source: ACS Applied Materials & Interfaces (article, 2022-12-02)
Field: Materials Science — Materials Chemistry

Active Discovery of Donor:Acceptor Combinations For Efficient Organic Solar Cells The structural flexibility of organic semiconductors offers vast a search space, and many potential candidates (donor and acceptor) for organic solar cells (OSCs) are yet to be discovered. Machine learning is extensively used for material discovery but performs poorly on extrapolation tasks with small training data sets. Active learning techniques can guide experimentalists to extrapolate and find the most promising D:A combination in a significantly small number of experiments. This study uses an active learning technique with a predictive random forest model to iteratively find the most optimal D:A combinations in the search space using various acquisition functions. Active learning results with five different acquisition functions (MM, MEI, MLI, MU, and UCB) are compared. Results reveal that acquisition functions that combine exploitation and exploration (MEI, MLI, and UCB) perform far better than purely exploiting (MM) and purely exploring (MU) acquisition functions. Interestingly, the proposed model can overcome the bottleneck of extrapolating small training data sets and find most promising D:A combinations in relatively fewer experiments. Bottleneck ; Flexibility (engineering) ; Organic solar cell ; Active learning (machine learning) ; Computer science ; Extrapolation ; Machine learning ; Artificial intelligence ; Chemical space ; Materials science ; Drug discovery ; Mathematics ; Biology ; Bioinformatics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Force Microscopy Techniques and Applications
Authors: Jihyeon Park | Jaeyoung Lee
Source: Electrochimica Acta (article, 2022-09-05)
Field: Materials Science — Materials Chemistry

Gradient boosting algorithm for current-voltage prediction of fuel cells Boosting (machine learning) ; Algorithm ; Fuel cells ; Voltage ; Mean squared error ; Process (computing) ; Computer science ; Root mean square ; Gradient boosting ; Mean absolute error ; Mean squared prediction error ; Approximation error ; Square root ; Simulation ; Mathematics ; Statistics ; Machine learning ; Engineering ; Chemical engineering ; Electrical engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Andrés I. Bertoni | Cristián G. Sánchez
Source: Physical Chemistry Chemical Physics (article, 2022-12-28)
Field: Materials Science — Materials Chemistry

Data-driven approach for benchmarking DFTB-approximate excited state methods We propose a chemically-informed data-driven approach to benchmark approximate density-functional tight-binding (DFTB) excited state (ES) methods that are currently available within the DFTB+ suite, using data from a machine learning (ML) dataset. Excited state ; Benchmark (surveying) ; Coupled cluster ; Singlet state ; Tight binding ; Computer science ; Statistical physics ; Formalism (music) ; Benchmarking ; Excitation ; Density functional theory ; Algorithm ; Chemistry ; Molecule ; Physics ; Computational chemistry ; Quantum mechanics ; Electronic structure Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; CO2 Reduction Techniques and Catalysts
Authors: Zihao Chen | Xiaoli Luan | Fei Liu
Source: Vibrational Spectroscopy (article, 2022-10-08)
Field: Chemistry — Analytical Chemistry

Deep learning near-infrared quality prediction based on multi-level dynamic feature Computer science ; Feature (linguistics) ; Artificial intelligence ; Pattern recognition (psychology) ; Redundancy (engineering) ; Collinearity ; Artificial neural network ; Data mining ; Mathematics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ruifeng Zhang
Source: Materials Lab (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Perspectives in the new era of materials intelligent design The launching integrated computational materials engineering (ICME) and materials genome engineering (MGE) has led the transformation of empirical and theoretical design paradigm into the rational computational one that further provides the basis for the data-driven design paradigm by integrating the high-throughput techniques in experiments and computations, the big data science with general principles, the informatics with knowledge discovery based on data mining and machine learning, and ultimately enabling the possibility of materials intelligence design (MID) via artificial intelligence. In this perspective article, we highlight the intelligent solution to acquire the property-structure-process-performance relationship of multilevel-structured materials by emphasizing modularization, automation, standardization, integration and intelligence, following the hierarchical relationship of data, information, knowledge and wisdom, which is essentially different from the past empirical, theoretical and computational paradigms. The new era of MID is expected to fundamentally reform the material innovation mode through an integrated infrastructure guided by novel concepts that is radically distinguished from the way of thinking and doing in the past, providing a perspective scientific vision and direction for future materials design. Big data ; Computer science ; Data science ; Materials informatics ; Standardization ; Automation ; Modular programming ; Artificial intelligence ; Management science ; Engineering informatics ; Engineering ; Health informatics ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: D.G. Walters | Nirmal Rai | Oishik Sen | W. Lee Perry
Source: Journal of Applied Physics (article, 2022-06-17)
Field: Materials Science — Materials Chemistry

Toward a machine-guided approach to energetic material discovery In this article, we trained a machine learning (ML) model to connect microstructural details of an energetic material formulation to its performance for the purpose of guiding the discovery of new explosive formulations. Our hypothesis was that the algorithm would robustly learn the training data and produce an accurate surrogate model. Specifically, the algorithm learned the relationship between details of the void size distribution (VSD), initiating shock pressure, and the energetic material performance. We used realistic constraints on the VSD and a range of cases were ingested by a physically informed reactive flow model working within a hydrodynamic solver running on high-performance computing resources. The ML algorithm produced a surrogate model that accurately predicted known test points around the parameter space. In addition to the utility of the model and the process used for its development, we noted interesting comparisons between what we, the authors—subject matter experts, would heuristically conclude from the training data and the surrogate model predictions. We detected nuanced details that were missed by the surrogate model; however, these details are not important to an energetic material formulator. We concluded that the algorithm did indeed robustly learn the training data and produce an accurate surrogate model. We further concluded that the surrogate model is a powerful tool to guide the formulator in the absence of subject matter experts and limited-access computing resources. Surrogate model ; Computer science ; Machine learning ; Solver ; Artificial intelligence ; Explosive material ; Process (computing) ; Range (aeronautics) ; Data mining ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Energetic Materials and Combustion