jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenhao Zhang | Jean‐François Halet | Takao Mori
Source: npj Computational Materials (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Using orbital sensitivity analysis to pinpoint the role of orbital interactions in thermoelectric power factor Abstract The modification of the electronic band structure is of critical importance for thermoelectric materials whose heat to electricity power generation is related to carrier effective mass and alignment of semiconductor band edges. One approach to optimize the electronic band structure is by modification of orbital interactions through doping or alloying. While the current ab-initio quantum chemical calculations can help us to investigate orbital components of electronic bands, they reveal little information on the relative tunability of electronic states and transport properties with respect to orbital interactions. In this work, we present a method, based on a symmetry-adapted tight-binding model and sensitivity analysis, that can pinpoint the role of orbital interactions in determining electronic band structure and transport properties. As an application, a systematic theoretical analysis is used to show how the power factor of PbTe can/cannot be improved by playing with interatomic orbital interactions. The orbital interaction sensitivity analysis introduced in this work can easily be applied to other compounds or properties. Electronic structure ; Electronic band structure ; Seebeck coefficient ; Ab initio ; Effective mass (spring–mass system) ; Thermoelectric effect ; Molecular orbital ; Work (physics) ; Materials science ; Chemistry ; Condensed matter physics ; Physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Mohammad Rajabi-Sarkhani | Yousef Abbaspour‐Gilandeh | Abdolmajid Moinfar | Mohammad Tahmasebi | Eduardo De la Cruz Gámez | Mario Hernández-Hernández | José Luis Hernández-Hernández
Source: Preprints.org (preprint, 2023-11-27)
Field: Chemistry — Analytical Chemistry

Application of Convolutional Neural Network Coupled with Hyperspectral Imaging to Determine the Viability of Peanut Seeds and Its Comparison with Conventional Machine Learning Methods Based on UV-Vis Spectroscopy Seeds can maintain their quality for a limited time; after that, they will lose their germination ability and vigor. Some physiological and physicochemical changes in the structure of the seeds during storage can decrease the quality of the seeds which is known as aging. Therefore, detection of the strong young seeds from the old ones is a vital issue in the modern agriculture. Conventional methods of detection of the seed viability and germination are destructive, time-consuming and costly. In this research, two peanut cultivars, namely North Carolina 2 (NC-2) and Florispan were selected and three artificial aging levels were induced to them. Hyperspectral images (HSI) of the samples were acquired and the seed viability was evaluated using two pre-trained convolutional neural network (CNN) image processing models, AlexNet and VGGNet. The noise of the reflection spectra of the samples was relatively resolved and modified by combining Preprocessing techniques of moving average (MA) and standard normal variate (SNV). Using principal component analysis (PCA), the dimensions were declined and three principal components (PC) were extracted. These PCs were then used as variables in the classification of support vector machine (SVM) and linear discriminant analysis (LDA). The results showed the high capability of CNN architectures such as AlexNet and VGGNet in detection of the seed viability based on the HIS with no pre-processing and feature extraction. The mentioned architectures reached the accuracy of 0.985 and 0.986, respectively. The combination of feature extraction method of PCA with LDA and SVM classifiers showed that the use of a limited number of PCs instead of all wavelengths can decrease the complexity of modeling, while enhancing the efficiency of the models such that LDA and SVM classifiers achieved the accuracy of 0.983 and 0.986 in classification of peanut sees, respectively. Hyperspectral imaging ; Support vector machine ; Principal component analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Convolutional neural network ; Linear discriminant analysis ; Preprocessor ; Feature extraction ; Artificial neural network ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Štěpán Sršeň | O. Anatole von Lilienfeld | Petr Slavı́ček
Source: ChemRxiv (preprint, 2023-05-26)
Field: Materials Science — Materials Chemistry

Fast and accurate excited states predictions: Machine learning and diabatization The efficiency of machine learning algorithms for electronically excited states is far behind ground-state applications. One of the underlying problems is the insufficient smoothness of the fitted potential energy surfaces and other properties in the vicinity of state crossings and conical intersections, which is a prerequisite for an efficient regression. Smooth surfaces can be obtained by switching to the diabatic basis. However, diabatization itself is still an outstanding problem. We overcome these limitations by solving both problems at once. We use a machine learning approach combining clustering and regression techniques to correct for the deficiencies of property-based diabatization which, in return, provides us with smooth surfaces that can be easily fitted. Our approach extends the applicability of property-based diabatization to multidimensional systems. We show the performance of the proposed methodology by reconstructing global potential energy surfaces of excited states of nitrosyl fluoride and formaldehyde. While the proposed methodology is independent of the specific property-based diabatization and regression algorithm, we show its performance for kernel ridge regression and a very simple diabatization based on transition multipoles. Compared to most other algorithms based on machine learning, our approach needs only a small amount of training data. Smoothness ; Computer science ; Diabatic ; Conical surface ; Algorithm ; Property (philosophy) ; Cluster analysis ; Excited state ; Kernel (algebra) ; Regression ; Machine learning ; Artificial intelligence ; Mathematical optimization ; Mathematics ; Physics ; Mathematical analysis ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures
Authors: Weiyi Gong | Tong Sun | Hexin Bai | Shah Tanvir ur Rahman Chowdhury | Peng Chu | Anoj Aryal | Jie Yu | Haibin Ling | John P. Perdew | Qimin Yan
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Incorporation of density scaling constraint in density functional design <i>via</i> contrastive representation learning We demonstrate that contrastive representation learning is a computationally efficient and flexible method to incorporate physical constraints, especially those defined by equalities, in machine-learning-based density functional design. Representation (politics) ; Constraint (computer-aided design) ; Computer science ; Scaling ; Artificial intelligence ; Feature learning ; Theoretical computer science ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Wen‐Wei Li | Axin Liang | Zijia Lu | Yao Xie
Source:  (article, 2023-10-20)
Field: Chemistry — Analytical Chemistry

Application of Molecular Imprinting Technique in Separation and Detection of Natural Products The content of effective components of natural products is small, generally requiring to be extracted and purified using complex technology, and the detection method is both time-consuming and costly. Molecular imprinting technology is an effective means to prepare highly selective media. Molecularly imprinted polymers have the advantages of strong selectivity, simple preparation, reusability and have shown a good prospect in the separation and detection of various natural products. Based on the types of active ingredients of natural products, this paper reviews the separation and detection of natural products via molecular imprinting technology in recent years, and discusses the advantages and disadvantages of molecular imprinting technology, in order to provide reference for the subsequent application of molecular imprinting technology. Molecular imprinting ; Reusability ; Imprinting (psychology) ; Molecularly imprinted polymer ; Nanotechnology ; Computer science ; Selectivity ; Chemistry ; Materials science ; Catalysis ; Organic chemistry ; Gene Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: A. Kenbay | Oleg Golikov | A. Aldiyarov | Darkhan Yerezhep
Source: Scientific and technical journal of information technologies mechanics and optics (article, 2023-08-01)
Field: Chemistry — Spectroscopy

Low-temperature cell for IR Fourier spectrometric investigation of hydrocarbon substances A specialized low-temperature measuring cell with a cryogenic capillary system for infrared spectral analysis of ethanol developed by the authors is presented. The use of the created low-temperature cell is possible for further studies of the low-temperature properties of both pure ethanol and mixtures with its contents, which is currently an urgent task, and the data obtained with its help can be used for ice research. Two methods of ethanol research at low temperature are presented in comparison. In the first method proposed by the authors, a specially developed low-temperature measuring cell based on a diffuse reflection prefix of the Fourier spectrometer FSM 2203 with a cryogenic capillary system is used. This system allows you to achieve the required low-temperature regime at normal atmospheric pressure. The results of the experiment are compared with the traditional method of gas-phase condensation of the test sample under low temperature conditions at the pressure P = 1.0·10–5 Torr. Infrared spectra of low molecular weight amorphous and crystalline ethanol were obtained at a temperature of 150 K at normal atmospheric pressure and in vacuum. Comparison of experimental results confirmed the operability of the new installation. In the experiments, peaks were observed in the absorption bands from 2850 to 3000 cm–1 and from 2950 to 3100 cm–1, corresponding to the valence CH vibrations of ethanol as well as in the absorption bands from 3150 to 3400 cm–1 and from 3300 to 3500 cm–1, which corresponds to the valence vibrations of OH. The results of the study showed the prospects of the proposed method and can be useful by researchers in the field of low-temperature spectroscopy at normal pressure. Analytical Chemistry (journal) ; Infrared ; Infrared spectroscopy ; Atmospheric pressure ; Torr ; Chemistry ; Capillary action ; Fourier transform infrared spectroscopy ; Liquid nitrogen ; Materials science ; Optics ; Thermodynamics ; Chromatography ; Organic chemistry ; Composite material Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Chemical Thermodynamics and Molecular Structure
Authors: Abdolraouf Samadi‐Maybodi | Sahar Abaskhani Davanlo
Source: Research Square (preprint, 2023-10-30)
Field: Materials Science — Materials Chemistry

Removal of sarafloxacin from aqueous solution through Ni/Al-layered double hydroxide@ZIF-8. Aqueous solution ; Hydroxide ; Layered double hydroxides ; Chemical engineering ; Chemistry ; Nuclear chemistry ; Materials science ; Inorganic chemistry ; Organic chemistry ; Engineering Layered Double Hydroxides Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Stefan Blücher | Klaus‐Robert Müller | Stefan Chmiela
Source: Journal of Chemical Theory and Computation (article, 2023-05-08)
Field: Materials Science — Materials Chemistry

Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence Kernel machines have sustained continuous progress in the field of quantum chemistry. In particular, they have proven to be successful in the low-data regime of force field reconstruction. This is because many equivariances and invariances due to physical symmetries can be incorporated into the kernel function to compensate for much larger data sets. So far, the scalability of kernel machines has however been hindered by its quadratic memory and cubical runtime complexity in the number of training points. While it is known that iterative Krylov subspace solvers can overcome these burdens, their convergence crucially relies on effective preconditioners, which are elusive in practice. Effective preconditioners need to partially presolve the learning problem in a computationally cheap and numerically robust manner. Here, we consider the broad class of Nyström-type methods to construct preconditioners based on successively more sophisticated low-rank approximations of the original kernel matrix, each of which provides a different set of computational trade-offs. All considered methods aim to identify a representative subset of inducing (kernel) columns to approximate the dominant kernel spectrum. Kernel (algebra) ; Krylov subspace ; Computer science ; Convergence (economics) ; Scalability ; Algorithm ; String kernel ; Kernel method ; Quadratic equation ; Field (mathematics) ; Applied mathematics ; Mathematical optimization ; Mathematics ; Variable kernel density estimation ; Iterative method ; Support vector machine ; Artificial intelligence ; Discrete mathematics ; Pure mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Model Reduction and Neural Networks
Authors: Xiaoang Yuan | Enlai Gao
Source: Journal of Physics Condensed Matter (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Data-driven discovery of ultraincompressible crystals from a universal correlation between bulk modulus and volumetric cohesive energy Bulk modulus and cohesive energy are two important quantities of condensed matter. From the interatomic energy landscape, we here derived a correlation between the bulk modulus (<i>B</i>) and the volumetric cohesive energy (<i>ρ</i><sub>e</sub>), i.e.<i>B</i>= 2(ln2)<sup>2</sup><i>ρ</i><sub>e</sub>/9<i>ϵ</i><sub>s</sub><sup>2</sup>=<i>kρ</i><sub>e</sub>, where<i>ϵ</i><sub>s</sub>and<i>k</i>are the strain-to-failure of interatomic bonds and the factor of proportionality, respectively. By analyzing numerous crystals from first principles calculations, it was shown that this correlation is universally applicable to various crystals including simple substances and compounds. Most interestingly, it was found that<i>ϵ</i><sub>s</sub>of crystals with a similar structure are almost a constant, resulting in a linear relationship between<i>B</i>and<i>ρ</i><sub>e</sub>. Furthermore, we found that the value of<i>k</i>for any compound can be determined based on the rule of mixtures, i.e.<i>k</i>= ∑<i>x</i><sub>i</sub><i>k</i><sub>i</sub>, where<i>x</i><sub>i</sub>and<i>k</i><sub>i</sub>are the atomic fraction and the factor of proportionality for each element in this compound, respectively. Finally, this correlation was used to predict the bulk moduli for a vast number of crystals with known<i>ρ</i><sub>e</sub>in databases. After first principles verification of the top 50 crystals with the highest predicted bulk modulus, 25 ultraincompressible crystals with a bulk modulus greater than 400 GPa that can rival diamond (436 GPa) were discovered. Bulk modulus ; Modulus ; Moduli ; Materials science ; Thermodynamics ; Crystallography ; Chemistry ; Physics ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Andrew R Balog | Leixin Miao | Saiphaneendra Bachu | Jani Jesenovec | B. Dutton | John S. McCloy | Nasim Alem
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High Resolution Scanning Transmission Electron Microscopy (S/TEM) Investigation Of Common Defects In Scandium and Aluminum Alloyed β-Ga2O3 Journal Article High Resolution Scanning Transmission Electron Microscopy (S/TEM) Investigation Of Common Defects In Scandium and Aluminum Alloyed β-Ga2O3 Get access Andrew R Balog, Andrew R Balog Department of Materials Science and Engineering, The Pennsylvania State University, State College, PA, United States Search for other works by this author on: Oxford Academic Google Scholar Leixin Miao, Leixin Miao Department of Materials Science and Engineering, The Pennsylvania State University, State College, PA, United States Search for other works by this author on: Oxford Academic Google Scholar Saiphaneendra Bachu, Saiphaneendra Bachu Department of Materials Science and Engineering, The Pennsylvania State University, State College, PA, United States Search for other works by this author on: Oxford Academic Google Scholar Jani Jesenovec, Jani Jesenovec Materials Science and Engineering Program, Washington State University, Pullman, WA, United StatesInstitute of Materials Research, Washington State University, Pullman, WA, United States Search for other works by this author on: Oxford Academic Google Scholar Benjamin Dutton, Benjamin Dutton Materials Science and Engineering Program, Washington State University, Pullman, WA, United StatesInstitute of Materials Research, Washington State University, Pullman, WA, United States Search for other works by this author on: Oxford Academic Google Scholar John McCloy, John McCloy Materials Science and Engineering Program, Washington State University, Pullman, WA, United StatesInstitute of Materials Research, Washington State University, Pullman, WA, United States Search for other works by this author on: Oxford Academic Google Scholar Nasim Alem Nasim Alem Department of Materials Science and Engineering, The Pennsylvania State University, State College, PA, United StatesMaterials Research Institute, The Pennsylvania State University, State College, PA, United States Corresponding author: nua10@psu.edu Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 1779–1780, https://doi.org/10.1093/micmic/ozad067.921 Published: 22 July 2023 State (computer science) ; Library science ; Science and engineering ; Engineering physics ; Engineering ; Art history ; Art ; Computer science ; Engineering ethics Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; ZnO doping and properties
Authors: Victor V. Volkov | Toby J. Blundell | Stephen P. Argent | Carole C. Perry
Source: Dalton Transactions (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Quantitative Raman microscopy to describe structural organisation in hollow microcrystals built from silicon catecholate and amines Macroscopic scale hollow microcrystals are a promising group of materials for gas and liquid uptake as well as sensing. In this contribution we describe the structure of hollow hexagonal cross-section crystals formulated as salts of a silicon catecholate anion and a tetramethylenediamine (TEMED) cation. Using a combination of X-ray single crystal diffraction, Raman spectroscopy and quantum chemistry we explore the structural properties of the hollow microcrystals. With the X-ray structural data as a starting point and assisted with quantum chemistry we compute Raman tensors to fit polarisation sensitive spectral responses and predict the orientation and packing of unit cells in respect to the long and short axis of the synthesised microcrystals. Using these newly developed methods for predicting molecular Raman responses in space with dependence on local orientation, we present the quantitative analysis of experimental Raman images of both hexagonal and tetragonal cross section hollow microcrystals formed from silicon catecholate anions using different amines as counterions. We describe the distributions of chemical components at the surfaces and edges of microcrystals, address the effect of catcholate hydrophobicity on water uptake and discuss possible strategies in chemical and post-assembly modifications to widen the functional properties of this group of environmentally friendly silicon organic framework (SOF) materials. Raman spectroscopy ; Silicon ; Microscopy ; Materials science ; Nanotechnology ; Atomic force microscopy ; Chemical engineering ; Crystallography ; Chemistry ; Optoelectronics ; Optics ; Engineering ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Supramolecular Self-Assembly in Materials
Authors: Farideh Badichi Akher | Yinan Shu | Zoltán M. Varga | Suman Bhaumik | Donald G. Truhlar
Source: ChemRxiv (preprint, 2023-04-27)
Field: Materials Science — Materials Chemistry

Parametrically Managed Activation Function for a Fitting a Neural Network Potential with Physical Behavior Enforced by a Low-Dimensional Potential Machine-learned representations of potential energy surfaces generated in the output layer of a feedforward neural network are becoming increasingly popular. One difficulty with neural-network output is that it is often unreliable in regions where training data is missing or sparse. Human-designed potentials often build in proper extrapolation behavior by choice of functional form. Because machine learning is very efficient, it is desirable to learn how to add human intelligence to machine-learned potentials in a convenient way. One example is the well understood feature of interaction potentials that they vanish when subsystems are too far separated to interact. In this article, we present a way to add a new kind of activation function to a neural network to enforce low-dimensional constraints. In particular the activation function depends parametrically on all the input variables. We illustrate the use of this step by showing how it can force an interaction potential to go to zero at large subsystem separations with either inputting a specific functional form for the potential or adding data to the training set in the asymptotic region of geometries where the subsystems are separated. In the process of illustrating this, we present an improved set of potential energy surfaces for the 14 lowest 3A´ states of O3. The method is more general than this example, and it may be used to add other low-dimensional knowledge or lower-level knowledge to machine-learned potentials. In addition to the O3 example, we present a greater-generality method called parametrically managed diabatization by deep neural network (PM-DDNN) that is an improvement on our previously presented permutationally restrained diabatization by deep neural network (PR-DDNN). Artificial neural network ; Generality ; Extrapolation ; Function (biology) ; Computer science ; Set (abstract data type) ; Artificial intelligence ; Process (computing) ; Energy (signal processing) ; Feature (linguistics) ; Feed forward ; Overfitting ; Mathematics ; Control engineering ; Engineering Machine Learning in Materials Science ; Nuclear Physics and Applications ; Nuclear Materials and Properties