jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ayorinde Tayo Olanipekun | Daniel Mashao
Source:  (article, 2023-12-30)
Field: Materials Science — Materials Chemistry

HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API This research presents the HardnessTesterV app- a web application for predicting the Vicker hardness of Laser welded Metallic alloy using Flask API, HTML, and CSS to build the front end and back end. Vickers hardness is an important property in the manufacturing industry, and predicting it accurately is vital for material selection and design. The Web application takes some of the important processing parameters as the input features and uses a random forest Machine learning model to predict its Vickers hardness. Flask API was used to create a RESTful interface that provides a user-friendly platform for users to submit the combinations of the processing parameters and receive predictions. HTML and CSS were used to design the front end of the web application to provide a visually appealing interface for users. The application was deployed on a PythonAnywhere cloud server, making it easily accessible to users. The application was deployed on a cloud server, making it easily accessible to users worldwide. The accuracy of the predictions was evaluated using various metrics and the results showed that the developed model can accurately predict the Vickers hardness of metallic alloys. HardnessTesterV web app measures the Vickers hardness of 2507 Duplex stainless steel material. The web application is expected to be a valuable tool for engineers and researchers in the manufacturing industry for material selection and design. Vickers hardness test ; Alloy ; Materials science ; Metal ; Metallurgy ; Computer science ; Microstructure Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Materials Characterization Techniques
Authors: Timothy Roche | Aihua Wood | Philip Cho | Chancellor Johnstone
Source: Mathematics (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks This paper concerns the development of a machine learning tool to detect anomalies in the molecular structure of Gallium Arsenide. We employ a combination of a CNN and a PCA reconstruction to create the model, using real images taken with an electron microscope in training and testing. The methodology developed allows for the creation of a defect detection model, without any labeled images of defects being required for training. The model performed well on all tests under the established assumptions, allowing for reliable anomaly detection. To the best of our knowledge, such methods are not currently available in the open literature; thus, this work fills a gap in current capabilities. Convolutional neural network ; Gallium arsenide ; Computer science ; Anomaly detection ; Artificial intelligence ; Pattern recognition (psychology) ; Gallium ; Artificial neural network ; Anomaly (physics) ; Machine learning ; Materials science ; Physics ; Optoelectronics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection
Authors: Rachel K. Luu | Marcin Wysokowski | Markus J. Buehler
Source: arXiv (Cornell University) (preprint, 2023-04-24)
Field: Materials Science — Materials Chemistry

Generative Discovery of Novel Chemical Designs using Diffusion Modeling and Transformer Deep Neural Networks with Application to Deep Eutectic Solvents We report a series of deep learning models to solve complex forward and inverse design problems in molecular modeling and design. Using both diffusion models inspired by nonequilibrium thermodynamics and attention-based transformer architectures, we demonstrate a flexible framework to capture complex chemical structures. First trained on the QM9 dataset and a series of quantum mechanical properties (e.g. homo, lumo, free energy, heat capacity, etc.), we then generalize the model to study and design key properties of deep eutectic solvents. In addition to separate forward and inverse models, we also report an integrated fully prompt-based multi-task generative pretrained transformer model that solves multiple forward, inverse design, and prediction tasks, flexibly and within one model. We show that the multi-task generative model has the overall best performance and allows for flexible integration of multiple objectives, within one model, and for distinct chemistries, suggesting that synergies emerge during training of this large language model. Trained jointly in tasks related to the QM9 dataset and deep eutectic solvents (DESs), the model can predict various quantum mechanical properties and critical properties to achieve deep eutectic solvent behavior. Several novel combinations of DESs are proposed based on this framework. Transformer ; Computer science ; Deep learning ; Generative grammar ; Artificial intelligence ; Artificial neural network ; Eutectic system ; Inverse ; Materials science ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Weiyi Xia | Ling Tang | Huaijun Sun | Chao Zhang | Kai‐Ming Ho | Gayatri Viswanathan | Kirill Kovnir | Cai‐Zhuang Wang
Source: Journal of Materials Chemistry A (article, 2023-12-06)
Field: Materials Science — Materials Chemistry

Correction: Accelerating materials discovery using integrated deep machine learning approaches Correction for ‘Accelerating materials discovery using integrated deep machine learning approaches’ by Weiyi Xia et al. , J. Mater. Chem. A , 2023, 11 , 25973–25982, https://doi.org/10.1039/d3ta03771a. Deep learning ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Upendra Kumar | Hyeon Woo Kim | Sobhit Singh | Hyunseok Ko | Sung Beom Cho
Source: arXiv (Cornell University) (preprint, 2023-06-21)
Field: Materials Science — Materials Chemistry

Designing Pr-based Advanced Photoluminescent Materials using Machine Learning and Density Functional Theory This work presents a machine learning approach to predict novel perovskite oxide materials in the Pr-Al-O and Pr-Sc-O compound families with the potential for photoluminescence applications. The predicted materials exhibit a large bandgap and high Debye temperature, and have remained unexplored thus far. The predicted compounds (Pr$_3$AlO$_6$, Pr$_4$Al$_2$O$_9$, Pr$_3$ScO$_6$ and Pr$_3$Sc$_5$O$_{12}$) are screened using machine learning approach, which are then confirmed by density functional theory calculations. The study includes the calculation of the bandgap and density of states to determine electronic properties, and the optical absorption and emission spectra to determine optical properties. Mechanical stability of the predicted compounds, as demonstrated by satisfying the Born-Huang criterion. By combining machine learning and density functional theory, this work offers a more efficient and comprehensive approach to materials discovery and design. Density functional theory ; Photoluminescence ; Band gap ; Materials science ; Debye model ; Stability (learning theory) ; Debye ; Perovskite (structure) ; Machine learning ; Work (physics) ; Artificial intelligence ; Condensed matter physics ; Optoelectronics ; Computer science ; Computational chemistry ; Thermodynamics ; Chemistry ; Physics ; Crystallography Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Roy T. Forestano | K. Matchev | Katia Matcheva | Eyup B. Unlu
Source: arXiv (Cornell University) (preprint, 2023-08-15)
Field: Chemistry — Analytical Chemistry

Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection The next generation of telescopes will yield a substantial increase in the availability of high-resolution spectroscopic data for thousands of exoplanets. The sheer volume of data and number of planets to be analyzed greatly motivate the development of new, fast and efficient methods for flagging interesting planets for reobservation and detailed analysis. We advocate the application of machine learning (ML) techniques for anomaly (novelty) detection to exoplanet transit spectra, with the goal of identifying planets with unusual chemical composition and even searching for unknown biosignatures. We successfully demonstrate the feasibility of two popular anomaly detection methods (Local Outlier Factor and One Class Support Vector Machine) on a large public database of synthetic spectra. We consider several test cases, each with different levels of instrumental noise. In each case, we use ROC curves to quantify and compare the performance of the two ML techniques. Exoplanet ; Planet ; Anomaly detection ; Flagging ; Computer science ; Anomaly (physics) ; Outlier ; Novelty detection ; Transit (satellite) ; Support vector machine ; Artificial intelligence ; Novelty ; Machine learning ; Astronomy ; Physics ; Engineering ; Geography Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Spectroscopy and Laser Applications
Authors: K. Rossi | Ruiz-Ferrando | S. Mitchell
Source: QRU Quaderns de Recerca en Urbanisme (article, 2023-03-09)
Field: Materials Science — Materials Chemistry

Sustainable chemistry and catalysis design meet digitalization Catalysts play a critical role in directing and accelerating the conversion of reagents into targeted products. The design of novel and/or improved catalysts is central to enable the sustainable production of fuels and value-added chemicals. This is a crucial challenge that requires a multi-disciplinary approach, and in turn, it will help address the complex socioeconomic and environmental issues of our time. In this talk we will introduce the challenges of developing improved catalytic processes, and the importance of large collaborative initiatives, such as NCCR Catalysis, a Swiss National Center of Competences in Research. Next, we will discuss the role of computational modeling, high performance computing, and machine learning, in advancing catalysis design. In particular, we will showcase two collaborative research projects where deep learning methods and quantum chemistry simulations are exploited to characterize the structure of low nuclearity catalysts, an emergent class of materials uniquely positioned to enable sustainable catalytic conversion processes. Sustainable production ; Catalysis ; Class (philosophy) ; Sustainable energy ; Sustainable development ; World class Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Ammonia Synthesis and Nitrogen Reduction
Authors: Takuya Taniguchi | Daisuke Takagi | Kōzō Ishizaki | Tsuyoshi Asahi
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

Machine learning-based molecular screening of solid–solid transition of molecular crystals Materials science ; Solid solution ; Transition (genetics) ; Chemical physics ; Nanotechnology ; Chemistry ; Biochemistry ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; History and advancements in chemistry
Authors: Yangjun Wu | Chu Guo | Fan Yi | P. Zhou | Honghui Shang
Source: arXiv (Cornell University) (preprint, 2023-06-29)
Field: Materials Science — Materials Chemistry

NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampling and local energy calculation. We develop a high-performance NNQS method for \textit{ab initio} electronic structure calculations. The major innovations include: (1) A transformer based architecture as the quantum wave function ansatz; (2) A data-centric parallelization scheme for the variational Monte Carlo (VMC) algorithm which preserves data locality and well adapts for different computing architectures; (3) A parallel batch sampling strategy which reduces the sampling cost and achieves good load balance; (4) A parallel local energy evaluation scheme which is both memory and computationally efficient; (5) Study of real chemical systems demonstrates both the superior accuracy of our method compared to state-of-the-art and the strong and weak scalability for large molecular systems with up to $120$ spin orbitals. Computer science ; Scalability ; Ansatz ; Ab initio ; Quantum Monte Carlo ; Atomic orbital ; Quantum ; Artificial neural network ; Electronic structure ; Quantum computer ; Computational science ; Statistical physics ; Monte Carlo method ; Topology (electrical circuits) ; Quantum mechanics ; Physics ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Hyun-Jong Kim | Hyun‐Woo Oh | Suyoung Chi
Source:  (article, 2023-10-11)
Field: Materials Science — Materials Chemistry

A Study on the Material Properties and Process Conditions Reasoning using Regression and Classification of Machine Learning In this paper, we explain the property prediction and manufacturing process condition inference methodology of ceramic materials with the introduction of AI models and perform PoC of the proposed method. We selected the optimal model by applying data with various input characteristics related to material properties to regression/classification machine learning models with proven performance. The accuracy of the final selected model was more than 90%, showing the possibility of drastically reducing the material development period. Property (philosophy) ; Machine learning ; Computer science ; Artificial intelligence ; Process (computing) ; Inference ; Regression ; Regression analysis ; Linear regression ; Data modeling ; Mathematics ; Statistics ; Database Machine Learning in Materials Science ; Technology and Data Analysis
Authors: Ji Wei Yoon | Adithya Kumar | Pawan Kumar | Kedar Hippalgaonkar | J. Senthilnath | Vijila Chellappan
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Explainable Machine Learning to Enable High-Throughput Electrical Conductivity Optimization of Doped Conjugated Polymers Conjugated system ; Throughput ; Polymer ; Doping ; Conductivity ; Materials science ; Computer science ; Electrical resistivity and conductivity ; Nanotechnology ; Artificial intelligence ; Optoelectronics ; Electrical engineering ; Chemistry ; Engineering ; Composite material ; Telecommunications Conducting polymers and applications ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Sadman Sadeed Omee | Lai Wei | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2023-09-13)
Field: Materials Science — Materials Chemistry

Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm While crystal structure prediction (CSP) remains a longstanding challenge, we introduce ParetoCSP, a novel algorithm for CSP, which combines a multi-objective genetic algorithm (MOGA) with a neural network inter-atomic potential (IAP) model to find energetically optimal crystal structures given chemical compositions. We enhance the NSGA-III algorithm by incorporating the genotypic age as an independent optimization criterion and employ the M3GNet universal IAP to guide the GA search. Compared to GN-OA, a state-of-the-art neural potential based CSP algorithm, ParetoCSP demonstrated significantly better predictive capabilities, outperforming by a factor of $2.562$ across $55$ diverse benchmark structures, as evaluated by seven performance metrics. Trajectory analysis of the traversed structures of all algorithms shows that ParetoCSP generated more valid structures than other algorithms, which helped guide the GA to search more effectively for the optimal structures Benchmark (surveying) ; Algorithm ; Artificial neural network ; Genetic algorithm ; Computer science ; Crystal structure prediction ; Pareto optimal ; Evolutionary algorithm ; Mathematical optimization ; Multi-objective optimization ; Machine learning ; Artificial intelligence ; Crystal structure ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yashwant Kurmi | M. Vishwanathan | Zhongliang Zu
Source:  (preprint, 2023-06-06)
Field: Materials Science — Materials Chemistry

Quantitative CEST Molecular MR Imaging Using Deconvolution and Parallel Deep Learning Models &lt;p&gt;Chemical Exchange Saturation Transfer (CEST) imaging is a promising MRI molecular imaging technique that allows for multiple parameter quantification (MPQ) of various molecules. However, the presence of overlapping confounding signals and poor data quality pose challenges in accurately quantifying the CEST effect and the underlying exchange parameters. In this paper, we propose a deconvolution-based preprocessing and a deep learning architecture called Deep Multiple Z-spectrum Quantification (DMUZQ) to address these limitations and enable automatic MPQ in CEST imaging. Our approach begins with a deconvolution kernel that minimizes interference from overlapping pools in CEST Z-spectra. This preprocessing step enhances the features of the solute of interest and improves data quality, enabling more specific molecular information to be extracted.&lt;/p&gt; &lt;p&gt;The DMUZQ model utilizes multiple power level signals and provides a weighted average of the quantified outcome, leading to enhanced accuracy and robustness in parameter quantification. We perform model optimization at both the individual and complete model levels. To estimate the accurate range of solute exchange rate, we introduce explicit regularization using the Lyapunov stability criteria, which considers the functional relationship between solute concentration, relaxation time, and exchange rate. We validate the DMUZQ model through comprehensive analysis and comparisons with existing deep learning models, variants, and a conventional CEST quantification method based on data fitting. Simulated data experiments demonstrate that our DMUZQ method achieves higher accuracy when compared to ground truth. Evaluation on animal tumor models further confirms its superior performance over the conventional fitting approach.&lt;/p&gt; Deconvolution ; Computer science ; Artificial intelligence ; Preprocessor ; Algorithm ; Deep learning ; Robustness (evolution) ; Ground truth ; Pattern recognition (psychology) ; Chemistry Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Yoshitaka Miyaji | Hirotaku Ishikawa | Kaisei Enoki | Kazuki Endo | Hiroaki Miyake | Yasuhiro Tanaka
Source:  (article, 2023-09-24)
Field: Materials Science — Materials Chemistry

Estimation of Material Properties Through Charge Transport Simulation Using Machine Learning The increasing use of power electronics equipment in various operating environments has raised concerns about the space charge accumulation in insulation materials, especially in high-radiation environments. In order to ensure more robust reliability of power electronics equipment, it is important to have a better understanding of the space charge phenomena in insulation materials. In this study, we investigated the estimation of material properties related to space charge accumulation through the space charge transport simulation using machine learning. As results, the inverse analysis technique for estimating material properties related to charging through the space charge transport simulation was developed. However, some estimated values of material properties with low sensitivity to the space charge simulation had errors as large as approximately 10% compared to the true values. Therefore, it is necessary to consider improving the mathematical model of the space charge transport simulation as well as applying the inverse analysis technique to actual insulation materials in the future. Space charge ; Charge (physics) ; Electronics ; Reliability (semiconductor) ; Inverse ; Computer science ; Power (physics) ; Space environment ; Mechanical engineering ; Simulation ; Electrical engineering ; Engineering ; Physics ; Mathematics ; Electron High voltage insulation and dielectric phenomena ; Radiation Effects in Electronics ; Electrical Fault Detection and Protection
Authors: Irea Mosquera‐Lois | Seán R. Kavanagh | Johan Klarbring | Kasper Tolborg | Aron Walsh
Source: arXiv (Cornell University) (preprint, 2023-07-19)
Field: Materials Science — Materials Chemistry

Imperfections are not 0 K: free energy of point defects in crystals Defects determine many important properties and applications of materials, ranging from doping in semiconductors, to conductivity in mixed ionic-electronic conductors used in batteries, to active sites in catalysts. The theoretical description of defect formation in crystals has evolved substantially over the past century. Advances in supercomputing hardware, and the integration of new computational techniques such as machine learning, provide an opportunity to model longer length and time-scales than previously possible. In this Tutorial Review, we cover the description of free energies for defect formation at finite temperatures, including configurational (structural, electronic, spin) and vibrational terms. We discuss challenges in accounting for metastable defect configurations, progress such as machine learning force fields and thermodynamic integration to directly access entropic contributions, and bottlenecks in going beyond the dilute limit of defect formation. Such developments are necessary to support a new era of accurate defect predictions in computational materials chemistry. Metastability ; Semiconductor ; Ionic bonding ; Crystallographic defect ; Electrical conductor ; Point (geometry) ; Materials science ; Doping ; Engineering physics ; Nanotechnology ; Computer science ; Chemical physics ; Statistical physics ; Chemistry ; Physics ; Condensed matter physics ; Ion ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions