jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Iona Xia | Jian Ge | Kevin Willis | Yinan Zhao
Source: arXiv (Cornell University) (preprint, 2022-10-08)
Field: Chemistry — Analytical Chemistry

Discovering Ca II Absorption Lines With a Neural Network Quasar absorption line analysis is critical for studying gas and dust components and their physical and chemical properties as well as the evolution and formation of galaxies in the early universe. Ca II absorbers, which are one of the dustiest absorbers and are located at lower redshifts than most other absorbers, are especially valuable when studying physical processes and conditions in recent galaxies. However, the number of known quasar Ca II absorbers is relatively low due to the difficulty of detecting them with traditional methods. In this work, we developed an accurate and quick approach to search for Ca II absorption lines using deep learning. In our deep learning model, a convolutional neural network, tuned using simulated data, is used for the classification task. The simulated training data are generated by inserting artificial Ca II absorption lines into original quasar spectra from the Sloan Digital Sky Survey (SDSS) whilst an existing Ca II catalog is adopted as the test set. The resulting model achieves an accuracy of 96% on the real data in the test set. Our solution runs thousands of times faster than traditional methods, taking a fraction of a second to analyze thousands of quasars while traditional methods may take days to weeks. The trained neural network is applied to quasar spectra from SDSS's DR7 and DR12 and discovered 399 new quasar Ca II absorbers. In addition, we confirmed 409 known quasar Ca II absorbers identified previously by other research groups through traditional methods. Quasar ; Galaxy ; Redshift ; Convolutional neural network ; Astrophysics ; Absorption (acoustics) ; Sky ; Artificial neural network ; Physics ; Spectral line ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Astronomy ; Optics Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Austin McDannald | Howie Joress | Brian DeCost | Avery E. Baumann | A. Gilad Kusne | Kamal Choudhary | Taner Yildirim | Daniel W. Siderius | W. Wong‐Ng | Andrew J. Allen | Christopher M. Stafford | Diana L. Ortiz‐Montalvo
Source: arXiv (Cornell University) (preprint, 2022-07-25)
Field: Materials Science — Materials Chemistry

Reproducible Sorbent Materials Foundry for Carbon Capture at Scale We envision an autonomous sorbent materials foundry (SMF) for rapidly evaluating materials for direct air capture of carbon dioxide (CO2), specifically targeting novel metal organic framework materials. Our proposed SMF is hierarchical, simultaneously addressing the most critical gaps in the inter-related space of sorbent material synthesis, processing, properties, and performance. The ability to collect these critical data streams in an agile, coordinated, and automated fashion will enable efficient end-to-end sorbent materials design through machine learning driven research framework. Sorbent ; Agile software development ; Process engineering ; Foundry ; Materials science ; Computer science ; Nanotechnology ; Engineering ; Chemistry ; Metallurgy ; Adsorption Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Carbon Dioxide Capture Technologies
Authors: Jan G. Rittig | Martin Ritzert | Artur M. Schweidtmann | Stefanie Winkler | Jana M. Weber | Philipp Morsch | K. Alexander Heufer | Martin Grohe | Alexander Mitsos | Manuel Dahmen
Source: RWTH Publications (RWTH Aachen) (preprint, 2022-06-01)
Field: Materials Science — Materials Chemistry

Graph Machine Learning for Design of High-Octane Fuels Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further auto-ignition training data. Octane ; Graph ; Computer science ; Modular design ; Theoretical computer science ; Machine learning ; Artificial intelligence ; Programming language ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: I. А. Shamanaeva | Zhichao Yu | Daria A. Golodnova | Dmitry A. Sladkovskiy | Е. В. Пархомчук
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

The Way to Enhance Sapo-34 Activity and Stability in Methanol-to-Olefin Conversion Olefin fiber ; Methanol ; Stability (learning theory) ; Chemistry ; Business ; Chemical engineering ; Catalysis ; Organic chemistry ; Computer science ; Engineering Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis and Oxidation Reactions
Authors: Pablo Sánchez-Palencia | Said Hamad | Pablo Palacios | Ricardo Grau‐Crespo | Keith T. Butler
Source: arXiv (Cornell University) (preprint, 2022-05-20)
Field: Materials Science — Materials Chemistry

Spinel nitride solid solutions: charting properties in the configurational space with explainable machine learning Ab initio prediction of the variation of properties in the configurational space of solid solutions is computationally very demanding. We present an approach to accelerate these predictions via a combination of density functional theory and machine learning, using the cubic spinel nitride GeSn$_2$N$_4$ as a case study, exploring how formation energy and electronic bandgap are affected by configurational variations. Furthermore, we demonstrate the utility of applying explainable machine learning to understand the crystal chemistry origins of the trends that we observe. Different configuration descriptors (Coulomb matrix eigenspectrum, many-body tensor representation, and cluster correlation function vectors) are combined with different models (linear regression, gradient-boosted decision tree, and multi-layer perceptron) to extrapolate the calculation of ab initio properties from a small set of configurations to the full space with thousands of configurations. We discuss the performance of different descriptors and models. SHAP (SHapley Additive exPlanations) analysis of the machine learning models highlights how values of formation energy are dominated by variations in local crystal structure (single polyhedral environments), while values of electronic bandgap are dominated by variations in more extended structural motifs. Finally, we demonstrate the usefulness of this approach by constructing structure-property maps, identifying important configurations of GeSn$_2$N$_4$ with extremal properties, as well as by calculating accurate equilibrium properties using configurational averaging. Ab initio ; Tensor (intrinsic definition) ; Cluster analysis ; Computer science ; Band gap ; Perceptron ; Statistical physics ; Artificial intelligence ; Machine learning ; Materials science ; Algorithm ; Physics ; Mathematics ; Artificial neural network ; Condensed matter physics ; Quantum mechanics ; Pure mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; 2D Materials and Applications
Authors: Shanshan Zheng | Wanqian Guo | Chao Li | Yongbin Sun | Qi Zhao | Hao Lu | Qishi Si | Huazhe Wang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Application of Machine Learning and Deep Learning Methods for Hydrated Electron Rate Constant Prediction Constant (computer programming) ; Artificial intelligence ; Machine learning ; Deep learning ; Computer science Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: М. Е. Бойко | М. Д. Шарков | A. M. Boĭko | A. V. Bobyl | В. И. Николаев
Source: Письма в журнал технической физики (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Studying LiFePO-=SUB=-4-=/SUB=- powder samples via X-ray diffraction techniques using artificial neural networks A set of LiFePO 4 samples has been studied by small-angle X-ray scattering (SAXS). Using the technique of artificial neural networks, the shape of SAXS curves has been reconstructed taking into account the hardware function. Estimates of homogeneitiessizes and shapes in the studied LiFePO 4 samples have been obtained. Keywords: Small-angle X-ray scattering, artificial neural network, lithium-ion rechargeable batteries, incorrect tasks regularization. Small-angle X-ray scattering ; Artificial neural network ; Materials science ; Scattering ; Diffraction ; Regularization (linguistics) ; X-ray ; X-ray crystallography ; Biological system ; Mineralogy ; Computer science ; Artificial intelligence ; Optics ; Physics ; Chemistry X-ray Diffraction in Crystallography
Authors: Lung-Yi Chen | Ting-Wei Hsu | Tsai-Chen Hsiung | Yi‐Pei Li
Source: ChemRxiv (preprint, 2022-06-15)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Increment Theory for Formation Enthalpy Predictions Quantitative predictions of molecular thermochemistry, such as formation enthalpy, have been limited for complicated species due to a lack of available training data. Such predictions would be important in predicting reaction thermodynamics and constructing kinetic models. We introduce a graph-based deep learning approach that can separately learn the enthalpy contribution of each atom in its local environment and consider the effect of overall molecular structure. Because this approach follows the additivity scheme of increment theory, it can be generalized to larger and more complicated species not present in the training data. We demonstrate that our model can outperform the conventional increment theory in the predictions of formation enthalpy. We expect this approach will also enable rapid prediction of other extensive properties of large molecules that are difficult to derive from experiments or ab initio calculation. Thermochemistry ; Enthalpy ; Standard enthalpy change of formation ; Thermodynamics ; Ab initio ; Standard enthalpy of formation ; Additive function ; Statistical physics ; Computer science ; Chemistry ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Chen-Di Han | Ying‐Cheng Lai
Source: arXiv (Cornell University) (preprint, 2022-12-13)
Field: Materials Science — Materials Chemistry

Generating extreme quantum scattering in graphene with machine learning Graphene quantum dots provide a platform for manipulating electron behaviors in two-dimensional (2D) Dirac materials. Most previous works were of the "forward" type in that the objective was to solve various confinement, transport and scattering problems with given structures that can be generated by, e.g., applying an external electrical field. There are applications such as cloaking or superscattering where the challenging problem of inverse design needs to be solved: finding a quantum-dot structure according to certain desired functional characteristics. A brute-force search of the system configuration based directly on the solutions of the Dirac equation is computational infeasible. We articulate a machine-learning approach to addressing the inverse-design problem where artificial neural networks subject to physical constraints are exploited to replace the rigorous Dirac equation solver. In particular, we focus on the problem of designing a quantum dot structure to generate both cloaking and superscattering in terms of the scattering efficiency as a function of the energy. We construct a physical loss function that enables accurate prediction of the scattering characteristics. We demonstrate that, in the regime of Klein tunneling, the scattering efficiency can be designed to vary over two orders of magnitudes, allowing any scattering curve to be generated from a proper combination of the gate potentials. Our physics-based machine-learning approach can be a powerful design tool for 2D Dirac material-based electronics. Scattering ; Cloaking ; Computer science ; Solver ; Inverse scattering problem ; Physics ; Quantum mechanics ; Mathematics ; Mathematical optimization ; Metamaterial Graphene research and applications ; Topological Materials and Phenomena ; Quantum and electron transport phenomena
Authors: Collin J. Wilkinson | Anthony V. DeCeanne | Marc Dittmer | Christian Ritzberger | Markus Rampf | John C. Mauro
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Ceramics and Composites

Machine Learning Predictions of Knoop Hardness in Lithium Disilicate Glass-Ceramics Knoop hardness test ; Lithium disilicate ; Materials science ; Ceramic ; Glass-ceramic ; Lithium (medication) ; Composite material ; Indentation hardness ; Metallurgy ; Mineralogy ; Psychology ; Geology ; Microstructure Glass properties and applications ; Advanced materials and composites ; Advanced X-ray and CT Imaging
Authors: Khashayar Ghorbani | Pedram Mirchi | Saeed Arabha | Ali Rajabpour | Sebastian Volz
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Lattice Thermal Conductivity and Elastic Modulus of Xn4 (X=Be, Mg and Pt) Materials science ; Thermal conductivity ; Elastic modulus ; Composite material ; Condensed matter physics ; Lattice (music) ; Physics ; Acoustics Thermal properties of materials ; Thermodynamic and Structural Properties of Metals and Alloys ; Thermal Expansion and Ionic Conductivity
Authors: J.J. Devogelaer | H. Meekes | P. Tinnemans | E. Vlieg | R. De Gelder
Source: Acta Crystallographica Section A Foundations and Advances (article, 2022-08-23)
Field: Materials Science — Materials Chemistry

Cocrystal discovery with network science and machine learning Physicochemical and biopharmaceutical properties of pure compounds can be modified by the formulation of multicomponent crystals, such as salts, solvates and cocrystals, which are crystalline aggregates containing multiple ionic and/or neutral species in the crystal lattice [1].The formation of salts is rather straightforward but the design of cocrystals remains challenging.Experimental screening of cocrystals is time-consuming and therefore computational tools to understand and predict cocrystals can be crucial for an efficient and successful screening process.Recently we constructed a network of coformers from the information present in the Cambridge Structural Database (CSD).Analysis of this network shows that the coformers in the network behave primarily in a bipartite manner, demonstrating the importance of combining complementary coformers [2].Using link-prediction algorithms, missing links in the network can be identified: cocrystals that are not yet present in the CSD but are likely to exist based on the information in the network [3].For a target compound unknown to the CSD, in-house data on cocrystallization can be combined with the entire CSD coformer network and link-prediction may quite accurately predict new combinations for the target compound [4].Moreover, using link prediction an invalid cocrystal set can be generated that can serve as negative input data for an approach based on artificial intelligence.The cocrystals present in the CSD, together with the results from link prediction applied to the coformer network, were used as input to a machine learning approach.We trained artificial neural networks (ANNs) to recognize fruitful combinations of coformers.Validation of the ANN model showed that high accuracies are obtained and that the combination of network science and machine learning delivers a powerful prediction tool for discovering new cocrystals [5]. Cocrystal ; Drug discovery ; Computer science ; Data science ; Computational biology ; Chemistry ; Biology ; Bioinformatics ; Molecule Machine Learning in Materials Science
Authors: Waldemar Studziński | Maciej Przybyłek | Alicja Gackowska
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Application of Gas Chromatographic Data and 2d Molecular Descriptors for Accurate Global Mobility Potential Prediction Molecular descriptor ; Artificial neural network ; Feature selection ; Multivariate adaptive regression splines ; Usability ; Correlation coefficient ; Artificial intelligence ; Multivariate statistics ; Computer science ; Machine learning ; Regression analysis ; Data mining ; Pattern recognition (psychology) ; Quantitative structure–activity relationship ; Bayesian multivariate linear regression Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: R. Carriere | A. Cherniaev
Source: 8th European Congress on Computational Methods in Applied Sciences and Engineering (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

New predictive models for ballistic limit of spacecraft honeycomb-core sandwich panels subjected to hypervelocity impact Parameters of the honeycomb core (such as cell size and foil thickness), as well as the material of the core, influence the ballistic performance of honeycomb-core sandwich panels in cases of hypervelocity impact (HVI) by orbital debris. Two predictive models capable of accounting for this influence have been developed in this study: one utilized a conventional approach based on a dedicated ballistic limit equation, while the other employed an artificial neural network trained to predict the outcomes of HVI on HCSP. BLE fitting and ANN training were conducted using a database composed of 46 numerical experiments, performed with a validated numerical model and ten physical tests derived from the literature. The new ballistic limit equation is based on the Whipple shield BLE, in which the standoff distance between the facesheets was replaced by a function of the honeycomb cell size, foil thickness, and yield strength of the HC material. The corresponding fit factors were determined by minimizing the sum of squared errors between the BLE predictions and the results of HVI tests listed in the database. The BLE was then tested against a new set of simulation data and demonstrated an excellent predictive accuracy, with the discrepancy ranging from 1.13% to 5.58% only. The artificial neural network was developed using MATLAB&#39;s Deep Learning Toolbox framework and was trained utilizing the same HCSP HVI database as was employed for the BLE fitting. The ANN demonstrated a very good predictive accuracy, when tested against a set of simulation data not previously used in the training of the network, with the discrepancy ranging from 0.67% to 7.27%. Both of the developed predictive models (the BLE and the ANN) are recommended for use in the design of orbital debris shielding for spacecraft, involving honeycomb-core sandwich panels. Hypervelocity ; Ballistic limit ; Honeycomb ; Artificial neural network ; Core (optical fiber) ; Space debris ; Spacecraft ; Limit (mathematics) ; Structural engineering ; Computer science ; Algorithm ; Materials science ; Machine learning ; Physics ; Aerospace engineering ; Engineering ; Mathematics ; Projectile ; Mathematical analysis ; Composite material High-Velocity Impact and Material Behavior ; Nuclear and radioactivity studies ; Transportation Safety and Impact Analysis
Authors: I. А. Shamanaeva | Zhichao Yu | Daria A. Golodnova | Dmitry A. Sladkovskiy | К. А. Babina | Е. В. Пархомчук
Source: Journal of Porous Materials (article, 2022-11-10)
Field: Chemistry — Inorganic Chemistry

The way to enhance SAPO-34 activity and stability in methanol-to-olefin conversion Crystallinity ; Catalysis ; Olefin fiber ; Methanol ; Materials science ; Sonication ; Yield (engineering) ; Mixing (physics) ; Amorphous solid ; Chemical engineering ; Phase (matter) ; Selectivity ; Nuclear chemistry ; Chemistry ; Organic chemistry ; Composite material Zeolite Catalysis and Synthesis ; Chemical Synthesis and Characterization ; Catalysis and Oxidation Reactions
Authors: Kevin Ryczko | Jaron T. Krogel | Isaac Tamblyn
Source: arXiv (Cornell University) (preprint, 2022-05-09)
Field: Materials Science — Materials Chemistry

Machine Learning Diffusion Monte Carlo Energies We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small datasets (~60 DMC calculations in total). The first uses voxel deep neural networks (VDNNs) to predict DMC energy densities using Kohn-Sham density functional theory (DFT) electron densities as input. The second uses kernel ridge regression (KRR) to predict atomic contributions to the DMC total energy using atomic environment vectors as input (we used atom centred symmetry functions, atomic environment vectors from the ANI models, and smooth overlap of atomic positions). We first compare the methodologies on pristine graphene lattices, where we find the KRR methodology performs best in comparison to gradient boosted decision trees, random forest, gaussian process regression, and multilayer perceptrons. In addition, KRR outperforms VDNNs by an order of magnitude. Afterwards, we study the generalizability of KRR to predict the energy barrier associated with a Stone-Wales defect. Lastly, we move from 2D to 3D materials and use KRR to predict total energies of liquid water. In all cases, we find that the KRR models are more accurate than Kohn-Sham DFT and all mean absolute errors are less than chemical accuracy. Statistical physics ; Monte Carlo method ; Density functional theory ; Computer science ; Artificial intelligence ; Machine learning ; Diffusion Monte Carlo ; Algorithm ; Mathematics ; Physics ; Hybrid Monte Carlo ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Gülsah Gül | Roland Faller | Nazar İleri
Source: Biophysical Journal (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Molecular dynamics modeling of polystyrene functionalized carbon nanotubes Carbon nanotube ; Polystyrene ; Solubility ; Polymer ; Nanoparticle ; Materials science ; Nanotechnology ; Molecular dynamics ; Surface modification ; Cytotoxicity ; Carbon fibers ; Chemical engineering ; Chemistry ; Organic chemistry ; Computational chemistry ; In vitro ; Composite material Carbon Nanotubes in Composites ; Nanotechnology research and applications ; Computational Drug Discovery Methods