jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Diego Serrano | Haoran Li | Thomas Guillod | Shukai Wang | Min Luo | Charles R. Sullivan | Minjie Chen
Source:  (article, 2022-06-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Network as Datasheet: Modeling B-H Loops of Power Magnetics with Sequence-to-Sequence LSTM Encoder-Decoder Architecture This paper presents the concept of “Neural Network as Datasheet” for B- H loop modeling of power magnetics with sequence-to-sequence machine learning. Instead of directly pre-senting the measured characteristics of magnetic core materials, we employ a neural network to capture the B-H loop mapping relationships of magnetic materials under different excitation waveforms at different temperatures. The training and inference process of the neural network are fully automated to minimize the impact of human error. Neural networks are also effective in compressing the information contained in the raw database to avoid data search or interpolation. The neural network can be used to rapidly predict B- H loops under different operating conditions and support circuit simulations. Based on a recently developed large-scale magnetic core loss database - MagNet - we demonstrate that a neural network datasheet can effectively compress and release information about power magnetics and can play important roles in power electronics converter design. Datasheet ; Artificial neural network ; Computer science ; Encoder ; Power (physics) ; Interpolation (computer graphics) ; Sequence (biology) ; Electronic engineering ; Inference ; Artificial intelligence ; Computer hardware ; Engineering Magnetic Properties and Applications ; Silicon Carbide Semiconductor Technologies ; Electric Motor Design and Analysis
Authors: Kan Hatakeyama‐Sato
Source: Polymer Journal (article, 2022-12-02)
Field: Materials Science — Materials Chemistry

Recent advances and challenges in experiment-oriented polymer informatics Informatics ; Materials informatics ; Engineering informatics ; Process (computing) ; Cheminformatics ; Computer science ; Data science ; Health informatics ; Management science ; Nanotechnology ; Engineering ; Chemistry ; Materials science ; Political science ; Health care Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Jan G. Rittig | Martin Ritzert | Artur M. Schweidtmann | Stefanie Winkler | Jana M. Weber | Philipp Morsch | Karl Alexander Heufer | Martin Grohe | Alexander Mitsos | Manuel Dahmen
Source: AIChE Journal (article, 2022-11-23)
Field: Materials Science — Materials Chemistry

Graph machine learning for design of high‐octane fuels Abstract Fuels with high‐knock resistance enable modern spark‐ignition engines to achieve high efficiency and thus low CO 2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer‐aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph‐ML) provide novel, promising tools for CAMD. We propose a modular graph‐ML CAMD framework that integrates generative graph‐ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph‐ML models. The graph‐ML CAMD framework successfully identifies well‐established high‐octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further autoignition training data. Octane ; Graph ; Computer science ; Modular design ; Octane rating ; Autoignition temperature ; Theoretical computer science ; Machine learning ; Ignition system ; Engineering ; Chemistry ; Combustion ; Aerospace engineering ; Programming language ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: A.A. Catal | E. Bedir | R. Yilmaz | D. Canadinç
Source: Journal of Alloys and Compounds (article, 2022-02-04)
Field: Materials Science — Materials Chemistry

Design of a NiTiHf shape memory alloy with an austenite finish temperature beyond 400 °C utilizing artificial intelligence Shape-memory alloy ; Austenite ; SMA* ; Ternary operation ; Materials science ; Martensite ; Artificial neural network ; Hysteresis ; Feed forward ; Alloy ; Feedforward neural network ; Ternary alloy ; Computer science ; Metallurgy ; Artificial intelligence ; Algorithm ; Microstructure ; Physics ; Engineering ; Control engineering Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials ; High Entropy Alloys Studies
Authors: Eugen Hruška | Ariel Gale | Xiao Huang | Fang Liu
Source: The Journal of Chemical Physics (article, 2022-03-22)
Field: Materials Science — Materials Chemistry

AutoSolvate: A toolkit for automating quantum chemistry design and discovery of solvated molecules The availability of large, high-quality datasets is crucial for artificial intelligence design and discovery in chemistry. Despite the essential roles of solvents in chemistry, the rapid computational dataset generation of solution-phase molecular properties at the quantum mechanical level of theory was previously hampered by the complicated simulation procedure. Software toolkits that can automate the procedure to set up high-throughput explicit-solvent quantum chemistry (QC) calculations for arbitrary solutes and solvents in an open-source framework are still lacking. We developed AutoSolvate, an open-source toolkit, to streamline the workflow for QC calculation of explicitly solvated molecules. It automates the solvated-structure generation, force field fitting, configuration sampling, and the final extraction of microsolvated cluster structures that QC packages can readily use to predict molecular properties of interest. AutoSolvate is available through both a command line interface and a graphical user interface, making it accessible to the broader scientific community. To improve the quality of the initial structures generated by AutoSolvate, we investigated the dependence of solute-solvent closeness on solute/solvent identities and trained a machine learning model to predict the closeness and guide initial structure generation. Finally, we tested the capability of AutoSolvate for rapid dataset curation by calculating the outer-sphere reorganization energy of a large dataset of 166 redox couples, which demonstrated the promise of the AutoSolvate package for chemical discovery efforts. Computer science ; Closeness ; Workflow ; Interface (matter) ; Software ; Chemistry ; Graphical user interface ; Computational science ; Molecule ; Database ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Weili Zhen | Xi Zhou | Shirui Weng | Wenka Zhu | Changjin Zhang
Source: ACS Applied Materials & Interfaces (article, 2022-03-02)
Field: Materials Science — Materials Chemistry

Ultrasensitive, Ultrafast, and Gate-Tunable Two-Dimensional Photodetectors in Ternary Rhombohedral ZnIn<sub>2</sub>S<sub>4</sub> for Optical Neural Networks The demand for high-performance semiconductors in electronics and optoelectronics has prompted the expansion of low-dimensional materials research to ternary compounds. However, photodetectors based on 2D ternary materials usually suffer from large dark currents and slow response, which means increased power consumption and reduced performance. Here we report a systematic study of the optoelectronic properties of well-characterized rhombohedral ZnIn<sub>2</sub>S<sub>4</sub> (R-ZIS) nanosheets which exhibit an extremely low dark current (7 pA at 5 V bias). The superior performance represented by a series of parameters surpasses most 2D counterparts. The ultrahigh specific detectivity (1.8 × 10<sup>14</sup> Jones), comparably short response time (τ<sub>rise</sub> = 222 μs, τ<sub>decay</sub> = 158 μs), and compatibility with high-frequency operation (1000 Hz) are particularly prominent. Moreover, a gate-tunable characteristic is observed, which is attributed to photogating and improves the photoresponse by 2 orders of magnitude. Gating technique can effectively modulate the photocurrent-generation mechanism from photoconductive effect to dominant photogating. The combination of ultrahigh sensitivity, ultrafast response, and high gate tunability makes the R-ZIS phototransistor an ideal device for low-energy-consumption and high-frequency optoelectronic applications, which is further demonstrated by its excellent performance in optical neural networks and promising potential in optical deep learning and computing. Materials science ; Optoelectronics ; Photocurrent ; Photodetector ; Photoconductivity ; Photodiode ; Ternary operation ; Dark current ; Ultrashort pulse ; Semiconductor ; Optical power ; Optics ; Laser ; Computer science 2D Materials and Applications ; Perovskite Materials and Applications ; MXene and MAX Phase Materials
Authors: Abdullahi Abbas Adam | Hassan Soleimani | Muhammad Fadhlullah Bin Abd. Shukur | John Ojur Dennis | Bashir Abubakar Abdulkadir | Yarima Mudassir Hassan | Jemilat Yetunde Yusuf | N. A. Shamsuri
Source: Journal of Non-Crystalline Solids (article, 2022-04-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A new approach to understanding the interaction effect of salt and plasticizer on solid polymer electrolytes using statistical model and artificial intelligence algorithm Response surface methodology ; Plasticizer ; Glycerol ; Polymer ; Chemical engineering ; Materials science ; Electrolyte ; Salt (chemistry) ; Ionic conductivity ; Conductivity ; Chemistry ; Chromatography ; Organic chemistry ; Composite material ; Electrode Supercapacitor Materials and Fabrication ; Advanced Battery Materials and Technologies ; Conducting polymers and applications
Authors: Samy Blusseau | Yanneck Wielhorski | Zyad Haddad | Santiago Velasco-Forero
Source: Composites Part B Engineering (article, 2022-10-05)
Field: Materials Science — Polymers and Plastics

Instance segmentation of 3D woven fabric from tomography images by Deep Learning and morphological pseudo-labeling Segmentation ; Artificial intelligence ; Yarn ; Convolutional neural network ; Deep learning ; Parametric statistics ; Computer science ; Pattern recognition (psychology) ; Woven fabric ; Computer vision ; Materials science ; Mathematics ; Composite material Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Optical measurement and interference techniques
Authors: Alfonso Pedone | Marco Bertani | Luca Brugnoli | Annalisa Pallini
Source: Journal of Non-Crystalline Solids X (article, 2022-08-23)
Field: Materials Science — Materials Chemistry

Interatomic potentials for oxide glasses: Past, present, and future The continuous development and improvement of interatomic potential models for oxide glasses have made classical molecular dynamics a powerful computational technique routinely used for studying the structure and properties of such materials on a par with the more advanced experimental techniques. In this brief review, we retrace the development of the most used interatomic potential models from the earliest MD simulations up to now with a look at the possible future developments in this field due to the advent of the machine learning era and data-driven methods. Interatomic potential ; Molecular dynamics ; Oxide ; Field (mathematics) ; Nanotechnology ; Computer science ; Statistical physics ; Materials science ; Chemistry ; Computational chemistry ; Physics ; Mathematics ; Metallurgy Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Glass properties and applications
Authors: Papaorn Siribunbandal | Yong‐Hoon Kim | Tanakorn Osotchan | Zhigang Zhu | Rawat Jaisutti
Source: ACS Omega (article, 2022-05-26)
Field: Chemistry — Organic Chemistry

Quantitative Colorimetric Detection of Dissolved Ammonia Using Polydiacetylene Sensors Enabled by Machine Learning Classifiers Easy-to-use and on-site detection of dissolved ammonia are essential for managing aquatic ecosystems and aquaculture products since low levels of ammonia can cause serious health risks and harm aquatic life. This work demonstrates quantitative naked eye detection of dissolved ammonia based on polydiacetylene (PDA) sensors with machine learning classifiers. PDA vesicles were assembled from diacetylene monomers through a facile green chemical synthesis which exhibited a blue-to-red color transition upon exposure to dissolved ammonia and was detectable by the naked eye. The quantitative color change was studied by UV-vis spectroscopy, and it was found that the absorption peak at 640 nm gradually decreased, and the absorption peak at 540 nm increased with increasing ammonia concentration. The fabricated PDA sensor exhibited a detection limit of ammonia below 10 ppm with a response time of 20 min. Also, the PDA sensor could be stably operated for up to 60 days by storing in a refrigerator. Furthermore, the quantitative on-site monitoring of dissolved ammonia was investigated using colorimetric images with machine learning classifiers. Using a support vector machine for the machine learning model, the classification of ammonia concentration was possible with a high accuracy of 100 and 95.1% using color RGB images captured by a scanner and a smartphone, respectively. These results indicate that using the developed PDA sensor, a simple naked eye detection for dissolved ammonia is possible with higher accuracy and on-site detection enabled by the smartphone and machine learning processes. Naked eye ; RGB color model ; Ammonia ; Detection limit ; Artificial intelligence ; Chemistry ; Support vector machine ; Machine learning ; Materials science ; Computer science ; Chromatography ; Organic chemistry Polydiacetylene-based materials and applications ; Ocular Surface and Contact Lens ; Antimicrobial Peptides and Activities
Authors: Jakub Kubečka | Anders S. Christensen | Freja Rydahl Rasmussen | Jonas Elm
Source: Environmental Science & Technology Letters (article, 2022-01-31)
Field: Chemistry — Spectroscopy

Quantum Machine Learning Approach for Studying Atmospheric Cluster Formation Quantum chemical (QC) calculations can yield direct insight into an atmospheric cluster formation mechanism and cluster formation rates. However, such calculations are extremely computationally demanding as more than millions of cluster configurations might exist and need to be computed. We present an efficient approach to produce high quality QC data sets for applications in cluster formation studies and how to train an accurate quantum machine learning model on the generated data. Using the two-component sulfuric acid─water system as a proof of concept, we demonstrate that a kernel ridge regression machine learning model with Δ-learning can be trained to accurately predict the binding energies of cluster equilibrium configurations with mean absolute errors below 0.5 kcal mol–1. Additionally, we enlarge the training data set with nonequilibrium configurations and show the possibility of predicting the binding energies of new structures of clusters several molecules larger than those in the training set. Applying the trained machine learning model leads to a drastic reduction in the number of relevant clusters that need to be explicitly evaluated by QC methods. The presented approach is directly transferable to clusters of arbitrary composition and will lead to faster and more efficient exploration of the configurational space of new cluster systems. Cluster (spacecraft) ; Computer science ; Artificial intelligence ; Psychology Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Statistical Mechanics and Entropy
Authors: Viviana Dovale-Farelo | Pedram Tavadze | Logan Lang | A. Bautista Hernández | A. Romero
Source: Scientific Reports (article, 2022-12-28)
Field: Materials Science — Materials Chemistry

Vickers hardness prediction from machine learning methods The search for new superhard materials is of great interest for extreme industrial applications. However, the theoretical prediction of hardness is still a challenge for the scientific community, given the difficulty of modeling plastic behavior of solids. Different hardness models have been proposed over the years. Still, they are either too complicated to use, inaccurate when extrapolating to a wide variety of solids or require coding knowledge. In this investigation, we built a successful machine learning model that implements Gradient Boosting Regressor (GBR) to predict hardness and uses the mechanical properties of a solid (bulk modulus, shear modulus, Young's modulus, and Poisson's ratio) as input variables. The model was trained with an experimental Vickers hardness database of 143 materials, assuring various kinds of compounds. The input properties were calculated from the theoretical elastic tensor. The Materials Project's database was explored to search for new superhard materials, and our results are in good agreement with the experimental data available. Other alternative models to compute hardness from mechanical properties are also discussed in this work. Our results are available in a free-access easy to use online application to be further used in future studies of new materials at www.hardnesscalculator.com . Vickers hardness test ; Shear modulus ; Computer science ; Materials science ; Poisson's ratio ; Bulk modulus ; Indentation hardness ; Elastic modulus ; Modulus ; Boosting (machine learning) ; Young's modulus ; Machine learning ; Poisson distribution ; Composite material ; Mathematics ; Statistics Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics ; Advanced materials and composites
Authors: Bhuvanesh Sridharan | Sarvesh Mehta | Yashaswi Pathak | U. Deva Priyakumar
Source: The Journal of Physical Chemistry Letters (article, 2022-05-30)
Field: Chemistry — Spectroscopy

Deep Reinforcement Learning for Molecular Inverse Problem of Nuclear Magnetic Resonance Spectra to Molecular Structure Spectroscopy is the study of how matter interacts with electromagnetic radiation. The spectra of any molecule are highly information-rich, yet the inverse relation of spectra to the corresponding molecular structure is still an unsolved problem. Nuclear magnetic resonance (NMR) spectroscopy is one such critical technique in the scientists' toolkit to characterize molecules. In this work, a novel machine learning framework is proposed that attempts to solve this inverse problem by navigating the chemical space to find the correct structure given an NMR spectra. The proposed framework uses a combination of online Monte Carlo tree search (MCTS) and a set of graph convolution networks to build a molecule iteratively. Our method can predict the structure of the molecule ∼80% of the time in its top 3 guesses for molecules with <10 heavy atoms. We believe that the proposed framework is a significant step in solving the inverse design problem of NMR spectra. Spectral line ; Inverse problem ; Nuclear magnetic resonance spectroscopy ; Inverse ; Monte Carlo method ; NMR spectra database ; Computer science ; Spectroscopy ; Molecule ; Statistical physics ; Physics ; Nuclear magnetic resonance ; Mathematics ; Quantum mechanics ; Mathematical analysis Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Kevin M. Dalton | Jack B. Greisman | Doeke R. Hekstra
Source: Nature Communications (article, 2022-12-15)
Field: Materials Science — Materials Chemistry

A unifying Bayesian framework for merging X-ray diffraction data Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering. Diffraction ; Monochromatic color ; Femtosecond ; Reflection (computer programming) ; Electron diffraction ; Electron density ; Computer science ; Bayesian probability ; Merge (version control) ; Rotation formalisms in three dimensions ; Physics ; Optics ; Algorithm ; Computational physics ; Statistical physics ; Electron ; Artificial intelligence ; Laser ; Mathematics ; Quantum mechanics Enzyme Structure and Function ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Gerard G. Dumancas | Indra Adrianto
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2022-04-06)
Field: Chemistry — Analytical Chemistry

A stacked regression ensemble approach for the quantitative determination of biomass feedstock compositions using near infrared spectroscopy Partial least squares regression ; Ensemble learning ; Lasso (programming language) ; Random forest ; Regression ; Support vector machine ; Chemistry ; Linear regression ; Artificial intelligence ; Regression analysis ; Elastic net regularization ; Mean squared error ; Cross-validation ; Statistics ; Mathematics ; Computer science Spectroscopy and Chemometric Analyses ; Biofuel production and bioconversion ; Meat and Animal Product Quality