jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lewis Y. Geer | Stephen E. Stein | W. Gary Mallard | Douglas J. Slotta
Source: arXiv (Cornell University) (preprint, 2024-01-03)
Field: Chemistry — Spectroscopy

AIRI: Predicting Retention Indices and their Uncertainties using Artificial Intelligence The Kováts Retention index (RI) is a quantity measured using gas chromatography and commonly used in the identification of chemical structures. Creating libraries of observed RI values is a laborious task, so we explore the use of a deep neural network for predicting RI values from structure for standard semipolar columns. This network generated predictions with a mean absolute error of 15.1 and, in a quantification of the tail of the error distribution, a 95th percentile absolute error of 46.5. Because of the Artificial Intelligence Retention Indices (AIRI) network's accuracy, it was used to predict RI values for the NIST EI-MS spectral libraries. These RI values are used to improve chemical identification methods and the quality of the library. Estimating uncertainty is an important practical need when using prediction models. To quantify the uncertainty of our network for each individual prediction, we used the outputs of an ensemble of 8 networks to calculate a predicted standard deviation for each RI value prediction. This predicted standard deviation was corrected to follow the error between observed and predicted RI values. The Z scores using these predicted standard deviations had a standard deviation of 1.52 and a 95th percentile absolute Z score corresponding to a mean RI value of 42.6. Standard deviation ; Percentile ; Artificial neural network ; NIST ; Statistics ; Absolute deviation ; Mean absolute error ; Mathematics ; Mean absolute percentage error ; Standard error ; Artificial intelligence ; Mean squared error ; Pattern recognition (psychology) ; Computer science ; Speech recognition Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Hanfeng Zhai
Source: arXiv (Cornell University) (preprint, 2024-09-08)
Field: Materials Science — Materials Chemistry

Stress Predictions in Polycrystal Plasticity using Graph Neural Networks with Subgraph Training Numerical modeling of polycrystal plasticity is computationally intensive. We employ Graph Neural Networks (GNN) to predict stresses on complex geometries for polycrystal plasticity from Finite Element Method (FEM) simulations. We present a novel message-passing GNN that encodes nodal strain and edge distances between FEM mesh cells, and aggregates to obtain embeddings and combines the decoded embeddings with the nodal strains to predict stress tensors on graph nodes. The GNN is trained on subgraphs generated from FEM mesh graphs, in which the mesh cells are converted to nodes and edges are created between adjacent cells. We apply the trained GNN to periodic polycrystals with complex geometries and learn the strain-stress maps based on crystal plasticity theory. The GNN is accurately trained on FEM graphs, in which the $R^2$ for both training and testing sets are larger than 0.99. The proposed GNN approach speeds up more than 150 times compared with FEM on stress predictions. We also apply the trained GNN to unseen simulations for validations and the GNN generalizes well with an overall $R^2$ of 0.992. The GNN accurately predicts the von Mises stress on polycrystals. The proposed model does not overfit and generalizes well beyond the training data, as the error distributions demonstrate. This work outlooks surrogating crystal plasticity simulations using graph data. Plasticity ; Computer science ; Artificial intelligence ; Deep learning ; Induced subgraph isomorphism problem ; Materials science ; Theoretical computer science ; Composite material ; Graph Machine Learning in Materials Science ; Orthopaedic implants and arthroplasty
Authors: Gladys Ama Quartey | Peter Kessels Dadzie | Solomon Asante‐Okyere | John Frank Eshun
Source: Transactions on Engineering and Computing Sciences (article, 2024-12-12)
Field: Chemistry — Analytical Chemistry

Estimating Young Modulus of Elasticity of Terminalia catappa: A Machine Learning Approach The purpose of this research was to evaluate the potential of Magnetic Resonance Spectroscopy (MRS) in estimating Young’s modulus of elasticity of wood species. To do so, Terminalia catappa, a wood species of common occurrence was chosen and its mechanical properties such as bending strength, compression parallel to the grain, and shear parallel to the grain properties were determined using testing methods for small and clear specimens of wood with the British (BS 373, 1957) and American Society of Testing Materials’ specifications (ASTM D143, 1983s. The results showed that at 18% moisture content the wood has a density of 520 kg/m3 with a mean modulus of rupture of 86.04 Mpa, compressive strength parallel to the grain of 42.02 Mpa, modulus of elasticity of 10,500 Mpa, and shear strength parallel to the grain of 16.42 N/mm2. This dataset was used on machine learning approaches such as decision tree and random forest. The estimated value of Young’s modulus using the machine learning models varies between 1000 to 13000 MPa. The obtained results indicated that the use of Magnetic Resonance Spectroscopy (MRS) is an efficient tool for predicting Wood-Young’s modulus. This research paves the way for further investigations on the application of MRS and machine learning for predicting a wider range of wood properties. By employing machine learning techniques such as decision trees and random forests, researchers can develop robust models for estimating Young's modulus in other wood species. This approach allows for leveraging large datasets that encompass various influencing factors, ultimately leading to more accurate predictions compared to traditional methods. Terminalia ; Young's modulus ; Elasticity (physics) ; Modulus ; Mathematics ; Econometrics ; Composite material ; Materials science ; Traditional medicine ; Medicine Spectroscopy and Chemometric Analyses
Authors: Ruben Goeminne | Véronique Van Speybroeck
Source: ChemRxiv (preprint, 2024-11-20)
Field: Chemistry — Inorganic Chemistry

Ab initio predictions of adsorption in flexible metal-organic frameworks for water harvesting applications. Recently, metal-organic frameworks like MOF-303 and MOF-LA2-1 have demonstrated exceptional performance for water harvesting applications. To enable a reticular design of such materials, an accurate prediction of the adsorption properties with chemical accuracy and fully accounting for the flexibility is crucial. The computational prediction of water adsorption properties of metal-organic frameworks (MOFs) has become standard practice. However their predictive power to design new materials is hindered by the limited accuracy of the used interatomic potential and the limitations on how to account for the framework flexibility. In this work, we showcase a methodology to obtain chemically accurate adsorption isotherms that fully account for the framework flexibility. The method is founded on very accurate and efficiently trained machine learning potentials and transition matrix Monte Carlo simulations to account for framework flexibility. By first benchmarking the reference electronic level of theory used for the training, quantitatively accurate adsorption isotherms are obtained for MOF-303, a highly topical MOF being investigated for its potential use in water harvesting applications. We show that both an accurate level of theory and a proper inclusion of local and global framework flexibility is vital in the prediction of the adsorption properties of MOF-303. The broader applicability of our methodology is demonstrated through the study of related linker-exchanged materials, MOF-333 and MOF-LA2-1. Analyses of the density profiles of water adsorbed in these MOFs yields deeper insight into the origins and differences of the observed isotherms. An optimal water harvester should have initial seeding sites with intermediate adsorption strength, to prevent detrimental low-pressure water uptake. To increase the working capacity, linker extension strategies can be used while maintaining the initial seeding sites, as was done in the MOF-LA2-1. The proposed methodology is applicable to other guest molecules and MOFs, paving the way to future rational design of MOFs with specific adsorption properties for the application at hand. Flexibility (engineering) ; Metal-organic framework ; Adsorption ; Computer science ; Materials science ; Ab initio ; Nanotechnology ; Chemistry ; Mathematics ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Boron and Carbon Nanomaterials Research
Authors: Hao Deng | Bin Liu | Dong Ding
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Computational Prediction of Proton-Conducting Perovskite Properties Under Realistic Conditions Measurements of proton-conducting Perovskite properties under extreme conditions remain challenging due to the limitation of experimental techniques. As a powerful tool, molecular dynamic (MD) simulation can provide abundant information on well-defined systems under realistic conditions. However, quantum-chemistry-based MD simulations are computationally expensive, while traditional potentials lack accuracy and training efficiency, especially for multi-element systems like Perovskite. Recently, machine learning potential (MLP) has attracted much attention since this tool empowers MD simulations with accuracy and efficiency. In this presentation, we will discuss the properties prediction of INL-developed benchmarking Perovskite, PrNi 1-δ Co δ O 3 (PNC), as an example based on density functional theory (DFT) and a well-trained sparse Gaussian process (SGP) potential. Properties, including expansion, diffusivity, and mechanical properties, which significantly influence the electrochemical cell efficiency and durability, were predicted under extreme conditions. The established protocol provides a versatile avenue for future proton-conducting perovskite investigations. Perovskite (structure) ; Proton ; Materials science ; Computer science ; Physics ; Engineering ; Chemical engineering ; Nuclear physics Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Perovskite Materials and Applications
Authors: Gladys Ama Quartey
Source: Transactions on Machine Learning and Artificial Intelligence (article, 2024-06-10)
Field: Chemistry — Analytical Chemistry

Estimating Young Modulus of Elasticity of Terminalia catappa: A Machine Learning Approach The purpose of this research was to evaluate the potential of Magnetic Resonance Spectroscopy (MRS) in estimating Young’s modulus of elasticity of wood species. To do so, Terminalia catappa, a wood species of common occurrence was chosen and its mechanical properties such as bending strength, compression parallel to the grain, and shear parallel to the grain properties were determined using testing methods for small and clear specimens of wood with the British (BS 373, 1957) and American Society of Testing Materials’ specifications (ASTM D143, 1983s. The results showed that at 18% moisture content the wood has a density of 520 kg/m3 with a mean modulus of rupture of 86.04 Mpa, compressive strength parallel to the grain of 42.02 Mpa, modulus of elasticity of 10,500 Mpa, and shear strength parallel to the grain of 16.42 N/mm2. This dataset was used on machine learning approaches such as decision tree and random forest. The estimated value of Young’s modulus using the machine learning models varies between 1000 to 13000 MPa. The obtained results indicated that the use of Magnetic Resonance Spectroscopy (MRS) is an efficient tool for predicting Wood-Young’s modulus. This research paves the way for further investigations on the application of MRS and machine learning for predicting a wider range of wood properties. By employing machine learning techniques such as decision trees and random forests, researchers can develop robust models for estimating Young's modulus in other wood species. This approach allows for leveraging large datasets that encompass various influencing factors, ultimately leading to more accurate predictions compared to traditional methods. Terminalia ; Computer science ; Elasticity (physics) ; Young's modulus ; Machine learning ; Artificial intelligence ; Composite material ; Materials science ; Traditional medicine ; Medicine Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement
Authors: Xingfu Yang | Jiajun Song | Xiang Zhai | Mao Liao
Source:  (article, 2024-05-13)
Field: Materials Science — Materials Chemistry

Partial discharge diagnosis method for electrical equipment based on PSO-GRNN Localized discharge refers to the phenomenon of insulation material discharge in electrical equipment under high electric field strength due to uneven distribution of electric field. The occurrence of localized discharge in equipment poses significant risks to the insulation layer, and a rapid and accurate identification of discharge types is crucial for the normal operation of industrial systems. In regard to the problem of identifying localized discharge types in electrical equipment, considering the timeliness and accuracy requirements of the monitoring system, a method is proposed that involves constructing a phase distribution spectrogram of localized discharge to extract statistical features. The particle swarm optimization (PSO) algorithm is utilized to optimize a generalized regression neural network (GRNN) model. Finally, the statistical features are used as input to the neural network for discharge type identification. The results demonstrate that the proposed diagnostic method achieves high accuracy and efficiency in discharge type identification. Partial discharge ; Particle swarm optimization ; Artificial neural network ; Computer science ; Identification (biology) ; Artificial intelligence ; Engineering ; Algorithm ; Voltage ; Electrical engineering High voltage insulation and dielectric phenomena ; Embedded Systems and FPGA Design ; Elevator Systems and Control
Authors: Ozge Ozbayram | Audrey Olivier | Lori Graham‐Brady
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Heteroscedastic Gaussian Process Regression for Material Structure-Property Relationship Modeling Heteroscedasticity ; Property (philosophy) ; Econometrics ; Regression analysis ; Gaussian process ; Regression ; Kriging ; Process (computing) ; Statistics ; Mathematics ; Computer science ; Gaussian ; Physics Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Quality Function Deployment in Product Design
Authors: Tanya Liyaqat | Tanvir Ahmad | Muhammad Kashif | Chandni Saxena
Source: arXiv (Cornell University) (preprint, 2024-09-03)
Field: Materials Science — Materials Chemistry

Stacked ensemble\-based mutagenicity prediction model using multiple modalities with graph attention network Mutagenicity is a concern due to its association with genetic mutations which can result in a variety of negative consequences, including the development of cancer. Earlier identification of mutagenic compounds in the drug development process is therefore crucial for preventing the progression of unsafe candidates and reducing development costs. While computational techniques, especially machine learning models have become increasingly prevalent for this endpoint, they rely on a single modality. In this work, we introduce a novel stacked ensemble based mutagenicity prediction model which incorporate multiple modalities such as simplified molecular input line entry system (SMILES) and molecular graph. These modalities capture diverse information about molecules such as substructural, physicochemical, geometrical and topological. To derive substructural, geometrical and physicochemical information, we use SMILES, while topological information is extracted through a graph attention network (GAT) via molecular graph. Our model uses a stacked ensemble of machine learning classifiers to make predictions using these multiple features. We employ the explainable artificial intelligence (XAI) technique SHAP (Shapley Additive Explanations) to determine the significance of each classifier and the most relevant features in the prediction. We demonstrate that our method surpasses SOTA methods on two standard datasets across various metrics. Notably, we achieve an area under the curve of 95.21\% on the Hansen benchmark dataset, affirming the efficacy of our method in predicting mutagenicity. We believe that this research will captivate the interest of both clinicians and computational biologists engaged in translational research. Modalities ; Computer science ; Graph ; Artificial intelligence ; Machine learning ; Theoretical computer science Machine Learning in Materials Science
Authors: I. A. Viklenko | V. V. Srabionyan | В. А. Дурыманов | Ya. N. Gladchenko-Dzhevelekis | V. N. Razdorov | L. A. Avakyan | Л. А. Бугаев
Source: Поверхность Рентгеновские синхротронные и нейтронные исследования (article, 2024-04-15)
Field: Materials Science — Materials Chemistry

Compact representation of the local atomic structure of matter for machine learning in XANES spectroscopy data processing A method for representing data on the local structure of atoms in the form of histograms of paired radial distribution functions is proposed. This method is used to construct a structure descriptor needed to determine the structure of materials using machine learning and artificial intelligence techniques. A special feature of the method is the use of two sets of paired radial distribution functions simultaneously: for pairs of all types of atoms and for pairs with a selected absorbing atom. The developed approach was tested on the problem of determining the local atomic structure of the environment of the silver color center in sodium silicate glasses using data from X-ray absorption near-edge structure for the Ag K-edge. The information content of the proposed structure descriptor is demonstrated by the ability to reconstruct the three-dimensional structure of a silver color center model from the corresponding pairwise distance histograms. Using several machine learning methods, it was shown that the proposed descriptor allows to achieve high-quality reproduction (mean square error ~10–3) of X-ray absorption near-edge structure spectra for silver color centers in glass, which makes it possible to reduce the time for calculating X-ray absorption near-edge structure spectra by 4 orders of magnitude. The resulting machine learning model allows us to establish a fundamental connection between the atomic structure of silver color centers in glasses and the Ag X-ray absorption near-edge structure spectrum, which is necessary for determining the structure of glasses. XANES ; Spectroscopy ; Representation (politics) ; Computer science ; Artificial intelligence ; Materials science ; Pattern recognition (psychology) ; Physics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Siu Ting Tai | Chen Wang | Ruihuan Cheng | Yue Chen
Source: arXiv (Cornell University) (preprint, 2024-11-02)
Field: Materials Science — Materials Chemistry

Revisit Many-body Interaction Heat Current and Thermal Conductivity Calculation in Moment Tensor Potential/LAMMPS Interface The definition of heat current operator for systems for non-pairwise additive interactions and its impact on related lattice thermal conductivity ($κ_{L}$) via molecular dynamics simulation (MD) are ambiguous and controversial when migrating from conventional empirical potential models to machine learning potential (MLP) models. Empirical model descriptions are often limited to three- to four-body interaction while a sophisticated representation of the many-body physics could be resembled in MLPs. Herein, we study and compare the significance of many-body interaction to the heat current computation in one of the most popular MLP models, the Moment Tensor Potential (MTP). Non-equilibrium MD simulations and equilibrium MD simulations among four different materials, $PbTe$, amorphous $Sc_{0.2}Sb_{2}Te_{3}$, graphene, and $BAs$, were performed. We found inconsistency between the simulation thermostat and its implemented heat current operator in our non-equilibrium MD results which violate law of energy conservation and suggest a need for revision. We revisit the virial stress tensor expression within the calculator and identified the lack of a generalised many-body heat current description in it. We uncover the influence of the modified heat current formula that could alter the $κ_{L}$ results 29% to 64% using the equilibrium MD computational approach. Our work demonstrates the importance of a many-body description during thermal analysis in MD simulations when MLPs are in concern. This work sheds light on a better understanding of the relationship between interatomic interaction and its heat transport mechanism. Moment (physics) ; Interface (matter) ; Current (fluid) ; Thermal conductivity ; Tensor (intrinsic definition) ; Conductivity ; Heat current ; Physics ; Materials science ; Condensed matter physics ; Thermodynamics ; Classical mechanics ; Mathematics ; Geometry ; Quantum mechanics Thermal properties of materials ; Advanced Thermodynamics and Statistical Mechanics ; Quantum, superfluid, helium dynamics
Authors: J. Vijaya | Khuman Singh Sonwani | Arman Singh Kshatri | Aryash Shrivastava | Aryan Chandrakar
Source:  (article, 2024-05-03)
Field: Materials Science — Polymers and Plastics

Bhess Engine: A Chess Engine in Rust Utilizing NNUE and Zobrist Hashing for Material Evaluation In the rapidly evolving landscape of artificial intelligence, the Chess Engine stands out as a pioneering creation, meticulously crafted with Rust, a programming language revered for its exceptional performance, safety protocols, and concurrent processing capabilities. This project embarks on a comprehensive exploration of the strategic underpinnings behind the choice of Rust as the development platform. Delving deep into the core methodologies, the study intricately examines the adoption of cutting-edge Neural Network Evaluation Updates (NNUE) instead of conventional Alpha-Beta pruning. Additionally, it dissects the intricate implementation of Zobrist hashing, an efficient technique crucial for precise position representation. Furthermore, the research underscores the importance of material evaluation algorithms, unraveling their profound impact on elevating chess engine performance to unprecedented heights. This abstract offers a glimpse into the innovative strides taken, showcasing the fusion of advanced programming, neural networks, and algorithmic finesse in reshaping the landscape of chess engine development. Rust (programming language) ; Computer science ; Programming language Textile materials and evaluations
Authors: Hui Liu | Jiao Chen | Xiaolong Xiong | Jingru Ye | Xinjia Shuai | Jun Zhou | Qingqing Liu | Cheng Zhi Huang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning-Assisted Three-Dimensional Fluorescence for Heavy Metal Multi-Sensing Fluorescence ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Optics Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Machine Learning in Materials Science
Authors: Xianteng Zhou | Yuanji Xu | Yue Chen | Fuyang Tian
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Mechanism on Thermal Conductivity of Carbon-Vacancy and Porous Medium Entropy Ceramics Thermal conductivity ; Materials science ; Vacancy defect ; Porous medium ; Ceramic ; Porosity ; Mechanism (biology) ; Thermal ; Entropy (arrow of time) ; Conductivity ; Thermodynamics ; Condensed matter physics ; Composite material ; Physics ; Chemistry ; Physical chemistry Thermal properties of materials ; Advanced materials and composites ; High-pressure geophysics and materials