jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anna W. Sobańska
Source: Membranes (article, 2022-11-11)
Field: Chemistry — Spectroscopy

Affinity of Compounds for Phosphatydylcholine-Based Immobilized Artificial Membrane—A Measure of Their Bioconcentration in Aquatic Organisms The <b><i>BCF</i></b> (bioconcentration factor) of solutes in aquatic organisms is an important parameter because many undesired chemicals enter the ecosystem and affect the wildlife. Chromatographic retention factor log <b><i>k<sub>w</sub><sup>IAM</sup></i></b> obtained from immobilized artificial membrane (IAM) HPLC chromatography with buffered, aqueous mobile phases and calculated molecular descriptors obtained for a group of 120 structurally unrelated compounds were used to generate useful models of log <b><i>BCF</i></b>. It was established that log <b><i>k<sub>w</sub><sup>IAM</sup></i></b> obtained in the conditions described in this study is not sufficient as a sole predictor of bioconcentration. Simple, potentially useful models based on log <b><i>k<sub>w</sub><sup>IAM</sup></i></b> and a selection of readily available, calculated descriptors and accounting for over 88% of total variability were generated using multiple linear regression (MLR), partial least squares (PLS) regression and artificial neural networks (ANN). The models proposed in the study were tested on an external group of 120 compounds and on a group of 40 compounds with known experimental log <b><i>BCF</i></b> values. It was established that a relatively simple MLR model containing four independent variables leads to satisfying <b><i>BCF</i></b> predictions and is more intuitive than PLS or ANN models. Bioconcentration ; Chemistry ; Linear regression ; Partial least squares regression ; Chromatography ; Biological system ; Environmental chemistry ; Mathematics ; Bioaccumulation ; Statistics ; Biology Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Analytical Methods in Pharmaceuticals
Authors: Sander Vandenhaute | Maarten Cools‐Ceuppens | Simon DeKeyser | Toon Verstraelen | Véronique Van Speybroeck
Source: ChemRxiv (preprint, 2022-11-03)
Field: Materials Science — Materials Chemistry

Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach Computational modeling of physical processes in metal-organic frameworks (MOFs) is highly challenging. The intrinsic length and time scales often stretch far beyond the nanometer and picosecond range due to e.g. large spatial heterogeneities or complex phase transitions. Machine learning potentials (MLPs) can extend the applicability of density functional theory (DFT) towards such challenging systems, but the generation of a representative training set of atomic configurations still poses a major challenge. In this work, we present an incremental learning scheme that constructs accurate and transferable MLPs based on a minimal number of DFT evaluations. Key to the approach is a combination of an active learning scheme that generates systematically improved MLPs with efficient and parallelized enhanced sampling protocols that explore increasingly larger portions of the phase space and learn physical interactions on-the-fly. The method requires a single atomic structure and a collective variable as input, after which the incremental learning approach constructs accurate interatomic potentials based on as few as 1000 single point DFT evaluations, even for flexible frameworks with multiple structurally different phases. The accuracy of the obtained potentials is extensively validated in terms of structural and mechanical properties across a wide range of thermodynamic conditions, yielding thermodynamically transferable MLPs. Finally, it is demonstrated how the incremental learning approach shows great potential to train universal MLPs for a larger set of materials. A proof of principle based on 10 well-known aluminum- and zirconium-based MOFs is shown. The proposed incremental learning approach is universally applicable and may induce a paradigm shift in both the accuracy as well as the time and length scale of computational models for framework materials. Computer science ; Artificial intelligence ; Machine learning ; Chemical space ; Set (abstract data type) ; Range (aeronautics) ; Materials science ; Chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Anuroop Sriram | Abhishek Das | Brandon M. Wood | Siddharth Goyal | C. Lawrence Zitnick
Source: arXiv (Cornell University) (preprint, 2022-03-18)
Field: Materials Science — Materials Chemistry

Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat climate change. However, the GNNs that have proven most effective for this task are memory intensive as they model higher-order interactions in the graphs such as those between triplets or quadruplets of atoms, making it challenging to scale these models. In this paper, we introduce Graph Parallelism, a method to distribute input graphs across multiple GPUs, enabling us to train very large GNNs with hundreds of millions or billions of parameters. We empirically evaluate our method by scaling up the number of parameters of the recently proposed DimeNet++ and GemNet models by over an order of magnitude. On the large-scale Open Catalyst 2020 (OC20) dataset, these graph-parallelized models lead to relative improvements of 1) 15% on the force MAE metric for the S2EF task and 2) 21% on the AFbT metric for the IS2RS task, establishing new state-of-the-art results. Computer science ; Scaling ; Graph ; Metric (unit) ; Task (project management) ; Artificial neural network ; Theoretical computer science ; Machine learning ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Rui Hu | Wen Lei | Hongmei Yuan | Shihao Han | Huijun Liu
Source: Nanomaterials (article, 2022-07-04)
Field: Materials Science — Materials Chemistry

High-Throughput Prediction of the Band Gaps of van der Waals Heterostructures via Machine Learning Van der Waals heterostructures offer an additional degree of freedom to tailor the electronic structure of two-dimensional materials, especially for the band-gap tuning that leads to various applications such as thermoelectric and optoelectronic conversions. In general, the electronic gap of a given system can be accurately predicted by using first-principles calculations, which is, however, restricted to a small unit cell. Here, we adopt a machine-learning algorithm to propose a physically intuitive descriptor by which the band gap of any heterostructures can be readily obtained, using group III, IV, and V elements as examples of the constituent atoms. The strong predictive power of our approach is demonstrated by high Pearson correlation coefficient for both the training (292 entries) and testing data (33 entries). By utilizing such a descriptor, which contains only four fundamental properties of the constituent atoms, we have rapidly predicted the gaps of 7140 possible heterostructures that agree well with first-principles results for randomly selected candidates. Heterojunction ; van der Waals force ; Seebeck coefficient ; Band gap ; Computer science ; Electronic band structure ; Thermoelectric effect ; Materials science ; Machine learning ; Physics ; Condensed matter physics ; Optoelectronics ; Quantum mechanics ; Molecule 2D Materials and Applications ; MXene and MAX Phase Materials ; Machine Learning in Materials Science
Authors: Qinghe Zhao | Zifang Zhang | Yuchen Huang | Junlong Fang
Source: Agriculture (article, 2022-09-13)
Field: Chemistry — Analytical Chemistry

TPE-RBF-SVM Model for Soybean Categories Recognition in Selected Hyperspectral Bands Based on Extreme Gradient Boosting Feature Importance Values Soybeans with insignificant differences in appearance have large differences in their internal physical and chemical components; therefore, follow-up storage, transportation and processing require targeted differential treatment. A fast and effective machine learning method based on hyperspectral data of soybeans for pattern recognition of categories is designed as a non-destructive testing method in this paper. A hyperspectral-image dataset with 2299 soybean seeds in four categories is collected. Ten features are selected using an extreme gradient boosting algorithm from 203 hyperspectral bands in a range of 400 to 1000 nm; a Gaussian radial basis kernel function support vector machine with optimization by the tree-structured Parzen estimator algorithm is built as the TPE-RBF-SVM model for pattern recognition of soybean categories. The metrics of TPE-RBF-SVM are significantly improved compared with other machine learning algorithms. The accuracy is 0.9165 in the independent test dataset, which is 9.786% higher for the vanilla RBF-SVM model and 10.02% higher than the extreme gradient boosting model. Hyperspectral imaging ; Pattern recognition (psychology) ; Support vector machine ; Artificial intelligence ; Boosting (machine learning) ; Extreme learning machine ; Computer science ; Radial basis function ; Mathematics ; Machine learning ; Artificial neural network Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; GABA and Rice Research
Authors: S. Devotta | Asha B. Chelani
Source: International Journal of Refrigeration (article, 2022-05-02)
Field: Chemistry — Organic Chemistry

Unified artificial neural network-group contribution method for predictions of normal boiling point and critical temperature of refrigerants and related compounds Boiling point ; Refrigerant ; Artificial neural network ; Thermodynamics ; Group contribution method ; Group (periodic table) ; Point (geometry) ; Boiling ; Computer science ; Artificial intelligence ; Chemistry ; Mathematics ; Physics ; Organic chemistry ; Heat exchanger Chemical Thermodynamics and Molecular Structure ; Crystallography and molecular interactions ; Phase Equilibria and Thermodynamics
Authors: Haikuan Dong | Zheyong Fan | Ping Qian | Yanjing Su
Source: Physica E Low-dimensional Systems and Nanostructures (article, 2022-07-04)
Field: Materials Science — Materials Chemistry

Exactly equivalent thermal conductivity in finite systems from equilibrium and nonequilibrium molecular dynamics simulations Thermal conductivity ; Molecular dynamics ; Non-equilibrium thermodynamics ; Periodic boundary conditions ; Equivalence (formal languages) ; Boundary value problem ; Thermal ; Domain (mathematical analysis) ; Conductivity ; Thermal conduction ; Materials science ; Statistical physics ; Thermal equilibrium ; Physics ; Condensed matter physics ; Thermodynamics ; Mathematics ; Mathematical analysis ; Quantum mechanics Thermal properties of materials ; Graphene research and applications ; Carbon Nanotubes in Composites
Authors: Cong Zhu | Jianyue Ni | Zhe Yang | Ye Sheng | Jiong Yang | Wu Zhang
Source: Computational and Theoretical Chemistry (article, 2022-09-07)
Field: Materials Science — Materials Chemistry

Bandgap prediction on small thermoelectric material dataset via instance-based transfer learning Transfer of learning ; Robustness (evolution) ; Artificial intelligence ; Machine learning ; Transfer (computing) ; Graph ; Knowledge transfer ; Computer science ; Chemistry ; Theoretical computer science Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Sergey N. Pozdnyakov | Michael J. Willatt | Albert P. Bartók | Christoph Ortner | Gábor Cśanyi | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2022-10-07)
Field: Materials Science — Materials Chemistry

Comment on “Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions” [J. Chem. Phys. 156, 034302 (2022)] The "quasi-constant" smooth overlap of atomic position and atom-centered symmetry function fingerprint manifolds recently discovered by Parsaeifard and Goedecker [J. Chem. Phys. 156, 034302 (2022)] are closely related to the degenerate pairs of configurations, which are known shortcomings of all low-body-order atom-density correlation representations of molecular structures. Configurations that are rigorously singular-which we demonstrate can only occur in finite, discrete sets and not as a continuous manifold-determine the complete failure of machine-learning models built on this class of descriptors. The "quasi-constant" manifolds, on the other hand, exhibit low but non-zero sensitivity to atomic displacements. As a consequence, for any such manifold, it is possible to optimize model parameters and the training set to mitigate their impact on learning even though this is often impractical and it is preferable to use descriptors that avoid both exact singularities and the associated numerical instability. Transferability ; Constant (computer programming) ; Manifold (fluid mechanics) ; Gravitational singularity ; Degenerate energy levels ; Sensitivity (control systems) ; Subspace topology ; Topology (electrical circuits) ; Mathematics ; Physics ; Statistical physics ; Computer science ; Mathematical analysis ; Quantum mechanics ; Combinatorics ; Machine learning ; Engineering ; Electronic engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yuewei Zheng | Xuan Wang | Zhihao Guan | Junjie Deng | Xinghai Liu | Houbin Li | Pingping Zhao
Source: Particle & Particle Systems Characterization (article, 2022-08-09)
Field: Materials Science — Materials Chemistry

Application of CD and Eu<sup>3+</sup> Dual Emission MOF Colorimetric Fluorescent Probe Based on Neural Network in Fe<sup>3+</sup> Detection Abstract A new highly fluorescent hybrid material (CD@Eu‐MOF) is synthesized by encapsulating carbon dots (CD) prepared from citric acid and ethylenediamine on the basis of a metal‐organic framework (MOF) prepared from Eu 3+ and 1,2,4‐benzenetricarboxylic acid. The prepared composite not only maintains the excellent fluorescence properties of CD and Eu 3+ , respectively, but also forms a dual‐emission fluorescence system, and the system has good stability in an aqueous solution. It is further used as a novel fluorescent probe for the detection of Fe 3+ , which can effectively exclude the interference of other metal ions from the detection, and the intensity ratio of I Eu /I CD of CD@Eu‐MOF material has a good linear relationship with Fe 3+ in the range of 1–200 µ m . In this study, computer vision and backpropagation (BP) neural networks are used to train and fit the sample data, and it is verified that the actual fluorescence color of CD has a good linear relationship with Fe 3+ concentration. In addition, the BP neural network also verifies that the fluorescence spectrum data of CD@Eu‐MOF also have a good linear relationship with Fe 3+ concentration. This study provides a new method for the fabrication of ratio and colorimetric Fe 3+ fluorescence sensors. Fluorescence ; Ethylenediamine ; Citric acid ; Metal ions in aqueous solution ; Aqueous solution ; Metal-organic framework ; Luminescence ; Chemistry ; Metal ; Linear range ; Materials science ; Analytical Chemistry (journal) ; Inorganic chemistry ; Detection limit ; Physical chemistry ; Optoelectronics ; Adsorption ; Organic chemistry ; Optics ; Chromatography Carbon and Quantum Dots Applications ; Molecular Sensors and Ion Detection ; Advanced Nanomaterials in Catalysis
Authors: Dhanunjaya Naidu Vangapandu | Moutusi Paul | Palash Mishra | R. Sarathi | Stefan Kornhuber | Shivraman Thapliyal
Source: Polymer Engineering and Science (article, 2022-09-27)
Field: Materials Science — Materials Chemistry

Performance evaluation of thermally aged <scp>ATH</scp> and <scp>BN</scp> co‐doped silicone rubber <scp>nano‐micro</scp> composites for power cable applications Abstract Power cables are intended to operate at high temperatures over a long period which may change the performance of silicone rubber insulation used in high voltage cable accessories. Therefore, in the current study, to enhance the properties of silicone rubber, alumina try‐hydrate (ATH) and boron nitride (BN) micro and nanoparticles were added to the silicone rubber matrix. The synthesized undoped silicone rubber, silicone rubber with 40 wt% micro ATH, 7 wt% nano BN, and 10 wt% micro ATH + 5 wt% nano BN composites were thermally aged for 600 h at 180 º C. The composite properties were analyzed through electrical, thermal, and physicochemical techniques. To assess the electrical characteristics, water droplet‐initiated corona discharge studies as per IEC 60112 were conducted and leakage current, corona inception voltage, and erosion resistance were evaluated. A deep learning technique also was employed for the prediction of erosion resistance of the samples. Further, to examine the surface texture and chemical changes on the surface of the material, 3‐D microscopy and Fourier transform infrared spectroscopy (FTIR) was adopted. Finally, thermal distribution analysis was performed for all the composites by infrared thermography. The results demonstrated the suitability of nano‐micro co‐doped composites over nanocomposites and micro composites for power cable applications. Silicone rubber ; Materials science ; Composite material ; Nanocomposite ; Fourier transform infrared spectroscopy ; Natural rubber ; Nano- ; Silicone ; Power cable ; Boron nitride ; Chemical engineering High voltage insulation and dielectric phenomena ; Aerosol Filtration and Electrostatic Precipitation ; Thermal Analysis in Power Transmission
Authors: Yingjie Zhao | Jianshu Qin | Shijun Wang | Zhiping Xu
Source: Patterns (article, 2022-04-22)
Field: Materials Science — Materials Chemistry

Unraveling the morphological complexity of two-dimensional macromolecules 2D macromolecules, such as graphene and graphene oxide, possess a rich spectrum of conformational phases. However, their morphological classification has only been discussed by visual inspection, where the physics of deformation and surface contact cannot be resolved. We employ machine learning methods to address this problem by exploring samples generated by molecular simulations. Features such as metric changes, curvature, conformational anisotropy and surface contact are extracted. Unsupervised learning classifies the morphologies into the quasi-flat, folded, crumpled phases and interphases using geometrical and topological labels or the principal features of the 2D energy map. The results are fed into subsequent supervised learning for phase characterization. The performance of data-driven models is improved notably by integrating the physics of geometrical deformation and topological contact. The classification and feature extraction characterize the microstructures of their condensed phases and the molecular processes of adsorption and transport, comprehending the processing-microstructures-performance relation in applications. Surface (topology) ; Curvature ; Metric (unit) ; Materials science ; Graphene ; Macromolecule ; Contact angle ; Characterization (materials science) ; Deformation (meteorology) ; Artificial intelligence ; Biological system ; Nanotechnology ; Computer science ; Topology (electrical circuits) ; Chemistry ; Geometry ; Mathematics ; Engineering Machine Learning in Materials Science ; Graphene research and applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Grayson H. Harrington | Conlain Kelly | Vahid Attari | Raymundo Arróyave | Surya R. Kalidindi
Source: Integrating materials and manufacturing innovation (article, 2022-09-14)
Field: Materials Science — Materials Chemistry

Application of a Chained-ANN for Learning the Process–Structure Mapping in Mg2SixSn1−x Spinodal Decomposition Spinodal decomposition ; Spinodal ; Microstructure ; Curse of dimensionality ; Phase (matter) ; Artificial neural network ; Computer science ; Dimensionality reduction ; Process (computing) ; Materials science ; Artificial intelligence ; Physics Machine Learning in Materials Science ; High Temperature Alloys and Creep ; X-ray Diffraction in Crystallography
Authors: Mahmudul Hasan | Yuwei Mao | Kamal Choudhary | Francesca Tavazza | Alok Choudhary | Ankit Agrawal | Pınar Acar
Source: Integrating materials and manufacturing innovation (article, 2022-04-06)
Field: Materials Science — Materials Chemistry

Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures Microstructure ; Materials science ; Nonlinear system ; Crystallite ; Composite material ; Metallurgy ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Aluminum Alloy Microstructure Properties
Authors: Lili Li | Yulong Wang | Junguo Gao | Ning Guo | Gang Han
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Pattern Recognition of Growth Characteristics Based on UHF PD Signals of Electrical Tree In insulation system of power equipment, electrical tree grown under the effect of partial discharges (PD) is one of the main degradation processes leading to failure of high voltage polymeric insulation. PD signals are measured and analyzed for condition monitoring of electrical insulation. In this paper, the combine detection system of Ultra-High Frequency (UHF) PD and electrical tree was used to test PE samples. Based on the energy amplitude variation, the average energy and the average energy distribution of PD signals to identify the growth process of electrical tree, twenty-eight PD characteristic parameters were used as input parameters, such as the energy percentage of 16-layer wavelet decomposition, the wavelet coefficient parameter and the time-frequency domain parameter, which entered the three-layer Back Propagation Neural Network (BPNN) of forty-five hidden neurons, and then the accuracy of pattern recognition during the growth process of electrical tree can reach 99.93% after 5000 steps of training cycle. The consistency of calculation results and experimental results presents that the calculation method can reveal the relationship between PD signals and the growth of electrical tree, which lays the foundation for the process analysis of electrical tree and insulation state monitoring. Ultra high frequency ; Partial discharge ; Wavelet ; Electric potential energy ; Tree (set theory) ; Energy (signal processing) ; Electrical treeing ; Wavelet transform ; Voltage ; Computer science ; Waveform ; Materials science ; Acoustics ; Electronic engineering ; Electrical engineering ; Mathematics ; Engineering ; Artificial intelligence ; Statistics ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Meltem Y. Imamoglu | İskender Akkurt | Seher Arslankaya | Roya Boodaghi Malidarre | Işık Yeşim Dicle Erdamar
Source: The European Physical Journal Plus (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Prediction of radiation shielding properties for concrete by artificial neural networks Electromagnetic shielding ; Attenuation ; Radiation shielding ; Artificial neural network ; Radiation ; Materials science ; Computational physics ; Computer science ; Nuclear engineering ; Physics ; Optics ; Engineering ; Composite material ; Artificial intelligence Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Advanced X-ray and CT Imaging