jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoyang Wang | Yinan Wang | LinFeng Zhang | Fu‐Zhi Dai | Handong Wang
Source: arXiv (Cornell University) (preprint, 2021-11-08)
Field: Materials Science — Materials Chemistry

A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the understanding of the macroscopic property deterioration of tungsten under harsh and complex service environments. The interatomic potential used in the MD simulations is required to accurately describe a wide spectrum of relevant defect properties, which is by far challenging to the existing interatomic potentials. In this paper, we propose a new three-body embedding descriptor and hybridize it into the Deep-Potential (DP) framework, an end-to-end deep learning interatomic potential model. Trained with the dataset generated by a concurrent learning method, the potential model for tungsten, named by DP-HYB, is able to accurately predict a wide range of properties including elastic constants, stacking fault energy, the formation energies of free surfaces and point defects, which are included in the training dataset, and formation energies of grain boundaries and prismatic loops, the core structure of screw dislocation, the Peierls barrier and the transition path of the screw dislocation migration, which are not explicitly included in the training dataset. The DP-HYB is a good candidate for the atomistic simulations of tungsten property deterioration, especially those involving the mechanical property degradation under the harsh fusion service environment. Tungsten ; Interatomic potential ; Molecular dynamics ; Materials science ; Dislocation ; Stacking-fault energy ; Crystallographic defect ; Fusion ; Property (philosophy) ; Atomic units ; Multiscale modeling ; Chemical physics ; Computer science ; Artificial intelligence ; Condensed matter physics ; Physics ; Computational chemistry ; Chemistry ; Composite material ; Metallurgy Fusion materials and technologies ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Satvik Lolla | Haotong Liang | A. Gilad Kusne | Ichiro Takeuchi | William Ratcliff
Source: arXiv (Cornell University) (preprint, 2021-11-01)
Field: Materials Science — Materials Chemistry

A Semi-Supervised Approach for Automatic Crystal Structure\n Classification The structural solution problem can be a daunting and time consuming task.\nEspecially in the presence of impurity phases, current methods such as indexing\nbecome more unstable. In this work, we apply the novel approach of\nsemi-supervised learning towards the problem of identifying the Bravais lattice\nand the space group of inorganic crystals. Our semi-supervised generative deep\nlearning model can train on both labeled data -- diffraction patterns with the\nassociated crystal structure -- and unlabeled data, diffraction patterns that\nlack this information. This approach allows our models to take advantage of the\ntroves of unlabeled data that current supervised learning approaches cannot,\nwhich should result in models that can more accurately generalize to real data.\nIn this work, we classify powder diffraction patterns into all 14 Bravais\nlattices and 144 space groups (we limit the number due to sparse coverage in\ncrystal structure databases), which covers more crystal classes than other\nstudies. Our models also drastically outperform current deep learning\napproaches for both space group and Bravais Lattice classification using less\ntraining data.\n Bravais lattice ; Artificial intelligence ; Diffraction ; Computer science ; Machine learning ; Supervised learning ; Crystal system ; Pattern recognition (psychology) ; Lattice (music) ; Crystal structure ; Artificial neural network ; Chemistry ; Physics ; Crystallography ; Optics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Joshua A. Rackers
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

equivariant_electron_density SAND2022-3548 O This code contains scripts that allow users to generate molecular electron densities, train a machine learning model to predict electron densities, and analyze the accuracy of machine learned electron densities. It accompanies a publication titled "Cracking the Quantum Scaling Limit with Machine Learned Electron Densities.” Sandia National Laboratories is a multimission laboratory managed and operated by National Technology &amp; Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. Equivariant map ; Linguistics ; Mathematics ; Physics ; Pure mathematics ; Philosophy Chemical Thermodynamics and Molecular Structure ; Chemistry and Stereochemistry Studies ; History and advancements in chemistry
Authors: Zhisen Meng | Xiaosi Zhu | Péter Kovács | En‐Wei Liang | Zhao Wang
Source: The Astrophysical Journal (preprint, 2021-11-24)
Field: Chemistry — Organic Chemistry

Machine-learning Interpretation of the Correlation between Infrared Emission Features of Interstellar Polycyclic Aromatic Hydrocarbons Abstract Supervised machine-learning models are trained with various molecular descriptors to predict infrared (IR) emission spectra of interstellar polycyclic aromatic hydrocarbons. We demonstrate that a feature importance analysis based on the random forest algorithm can be utilized to explore the physical correlation between emission features. Astronomical correlations between IR bands are analyzed as examples of demonstration by finding the common molecular fragments responsible for different bands, which improves the current understanding of the long-observed correlations. We propose a way to quantify the band correlation by measuring the similarity of the feature importance arrays of different bands, by which a correlation map is obtained for emissions in the out-of-plane bending region. Moreover, a comparison between the predictions using different combinations of descriptors underscores the strong prediction power of the extended-connectivity molecular fingerprint, and shows that the combinations of multiple descriptors of other types in general lead to improved predictivity. Infrared ; Correlation ; Pattern recognition (psychology) ; Feature (linguistics) ; Similarity (geometry) ; Random forest ; Fingerprint (computing) ; Artificial intelligence ; Polycyclic aromatic hydrocarbon ; Computer science ; Physics ; Biological system ; Mathematics ; Astrobiology ; Optics Chemical Thermodynamics and Molecular Structure ; Advanced Chemical Physics Studies ; Spectroscopy and Laser Applications
Authors: Lars Kotthoff | Sourin Dey | Vivek Jain | Alexander Scott Tyrrell | Hud Wahab | Patrick A. Johnson
Source: arXiv (Cornell University) (preprint, 2021-07-29)
Field: Materials Science — Materials Chemistry

Modeling and Optimizing Laser-Induced Graphene A lot of technological advances depend on next-generation materials, such as graphene, which enables a raft of new applications, for example better electronics. Manufacturing such materials is often difficult; in particular, producing graphene at scale is an open problem. We provide a series of datasets that describe the optimization of the production of laser-induced graphene, an established manufacturing method that has shown great promise. We pose three challenges based on the datasets we provide -- modeling the behavior of laser-induced graphene production with respect to parameters of the production process, transferring models and knowledge between different precursor materials, and optimizing the outcome of the transformation over the space of possible production parameters. We present illustrative results, along with the code used to generate them, as a starting point for interested users. The data we provide represents an important real-world application of machine learning; to the best of our knowledge, no similar datasets are available. Graphene ; Computer science ; Production (economics) ; Process (computing) ; Scale (ratio) ; Electronics ; Space (punctuation) ; Transformation (genetics) ; Artificial intelligence ; Nanotechnology ; Industrial engineering ; Materials science ; Engineering Machine Learning in Materials Science ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: Keisuke Teramoto | Kei Hirose
Source: International Journal for Numerical Methods in Engineering (preprint, 2021-11-01)
Field: Materials Science — Materials Chemistry

Sparse multivariate regression with missing values and its application to the prediction of material properties Abstract In the field of materials science and engineering, statistical analysis and machine learning techniques have recently been used to predict multiple material properties from an experimental design. These material properties correspond to response variables in the multivariate regression model. In this study, we conduct a penalized maximum likelihood procedure to estimate model parameters, including the regression coefficients and covariance matrix of response variables. In particular, we employ ‐regularization to achieve a sparse estimation of The regression coefficients and inverse covariance matrix of response variables. In some cases, there may be a relatively large number of missing values in the response variables, owing to the difficulty of collecting data on material properties. We therefore propose a method that incorporates a correlation structure among the response variables into a statistical model to improve the prediction accuracy under the situation with missing values. The expectation maximization algorithm is also constructed, which enables application to a dataset with missing values in the responses. We apply our proposed procedure to real data consisting of 22 material properties. Missing data ; Multivariate statistics ; Covariance matrix ; Covariance ; Regression analysis ; Expectation–maximization algorithm ; Mathematics ; Statistics ; Regression ; Design matrix ; Computer science ; Maximum likelihood Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Fatigue and fracture mechanics
Authors: D. M. Tennant
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Revealing exotic magnetic states through machine learning assisted modeling of neutron diffuse scattering data Neutron scattering has a number of distinctive features. It is isotope sensitive, and readily detects light elements, as well as having a magnetic scattering cross section. The scattering formulae are relatively simple but measurements involve large amounts of noisy data. The data includes background and artifacts that are difficult to handle and in the case of diffuse and inelastic scattering 3D and 4D data that are challenging to visualize. Further, there is no solution to the inverse scattering problem. Experiments conventionally are limited by the lack of theory to interpret the scattering making it hard to optimally perform experiments and analyze results. Data science approaches are needed to solve these challenges. Computational simulations on wide ranges of cases provide the fundamental knowledge to interpret and guide the experiments. Integration of machine learning into the neutron scattering pipeline provides a way to handle the large numbers of simulations and volume of data and integrate these together. We have recently demonstrated integration of machine learning into neutron scattering for diffuse and i32rnelastic scattering. These involve 3D and 4D data sets and require sophisticated modeling. These are providing new insights into exotic magnetic states and I will show two examples of frustrated magnetic materials where we have applied these new approaches allowing the solution of difficult science problems. Neutron scattering ; Small-angle neutron scattering ; Materials science ; Neutron ; Scattering ; Nuclear physics ; Nuclear engineering ; Physics ; Optics ; Engineering Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Eric Hedlund | Keith G. Hedlund | Adam Green | Ravin Chowdhury | Cheol S. Park | Joseph E. Maclennan | Noel A. Clark
Source: arXiv (Cornell University) (preprint, 2021-10-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Detection of Islands and Droplets on Smectic Films Using Machine Learning Machine learning techniques have been developed to identify inclusions on the surface of freely suspended smectic liquid crystal films imaged by reflected light microscopy. The experimental images are preprocessed using Canny edge detection and then passed to a radial kernel support vector machine (SVM) trained to recognize circular islands and droplets. The SVM is able to identify these objects of interest with an accuracy that far exceeds that of conventional tracking software, especially when the background image is non-uniform or when the target features are in close proximity to one another. This method could be applied to tracking objects in a variety of visually inhomogeneous biological and soft matter environments, in order to study growth dynamics, the development of spatial order, and hydrodynamic behavio Tracking (education) ; Support vector machine ; Canny edge detector ; Artificial intelligence ; Kernel (algebra) ; Computer vision ; Enhanced Data Rates for GSM Evolution ; Computer science ; Optical microscope ; Edge detection ; Biological system ; Optics ; Liquid crystal ; Pattern recognition (psychology) ; Materials science ; Image (mathematics) ; Image processing ; Mathematics ; Physics ; Biology Liquid Crystal Research Advancements
Authors: Iratxe Barbolla | Leidi Hernández-Suárez | Viviana Quevedo-Tumailli | Deyani Nocedo-Mena | Sonia Arrasate | María Auxiliadora Dea‐Ayuela | Nuria Sotomayor | Esther Lete
Source:  (article, 2021-01-15)
Field: Chemistry — Organic Chemistry

Synthesis of Pyrrolo[1,2-b]isoquinolines through Carbopalladation Initiated Domino Reactions. Evaluation as New Antileishmanial Agents A series of C-10 substituted pyrrolo[1,2-b]isoquinolines have been synthesized via palladium-catalyzed Heck/Suzuki and Heck/anion capture cascade reactions. These compounds have shown antileishmanial activity against cutaneous (L. amazonensis) leishmaniasis, being even 10-fold more potent and selective than the drug of reference, Miltefosine. A Perturbation Theory Machine Learning (PTML) model has also been developed for the prediction of the probability with which a query compound reaches a desired level for multiple parameters vs. different Leishmania species and target proteins. Miltefosine ; Domino ; Combinatorial chemistry ; Chemistry ; Leishmaniasis ; Palladium ; Heck reaction ; Stereochemistry ; Catalysis ; Organic chemistry ; Immunology ; Biology ; Visceral leishmaniasis Synthesis and Reactivity of Heterocycles ; Synthesis and Characterization of Pyrroles ; Cyclopropane Reaction Mechanisms
Authors: Luigi Bonati
Source: arXiv (Cornell University) (preprint, 2021-01-18)
Field: Materials Science — Materials Chemistry

Training collective variables for enhanced sampling via neural networks based discriminant analysis A popular way to accelerate the sampling of rare events in molecular dynamics simulations is to introduce a potential that increases the fluctuations of selected collective variables. For this strategy to be successful, it is critical to choose appropriate variables. Here we review some recent developments in the data-driven design of collective variables, with a focus on the combination of Fisher's discriminant analysis and neural networks. This approach allows to compress the fluctuations of metastable states into a low-dimensional representation. We illustrate through several examples the effectiveness of this method in accelerating the sampling, while also identifying the physical descriptors that undergo the most significant changes in the process. Linear discriminant analysis ; Sampling (signal processing) ; Artificial neural network ; Computer science ; Representation (politics) ; Artificial intelligence ; Discriminant ; Machine learning ; Process (computing) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum many-body systems
Authors: Qiang Zou | Mingming Fu | Zhiming Wu | Li Li | David Parker | Athena S. Sefat | Zheng Gai
Source: npj Quantum Materials (preprint, 2021-10-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Competitive and cooperative electronic states in Ba(Fe1−xTx)2As2 with T = Co, Ni, Cr Abstract The electronic inhomogeneities in Co, Ni, and Cr doped BaFe 2 As 2 single crystals are compared within three bulk property regions: a pure superconducting (SC) dome region, a coexisting SC and antiferromagnetic (AFM) region, and a non-SC region. Machine learning is utilized to categorize the inhomogeneous electronic states: in-gap, L-shape, and S-shape states. Although the relative percentages of the states vary in the three samples, the total volume fraction of the three electronic states is quite similar. This is coincident with the number of electrons (Ni 0.04 and Co 0.08 ) and holes (Cr 0.04 ) doped into the compounds. The in-gap state is confirmed as a magnetic impurity state from the Co or Ni dopants, the L-shape state is identified as a spin density wave which competes with the SC phase, and the S-shape state is found to be another form of magnetic order which constructively cooperates with the SC phase, rather than competing with it. Condensed matter physics ; Antiferromagnetism ; Materials science ; Local density of states ; Density of states ; Superconductivity ; Scanning tunneling microscope ; Dopant ; Electronic structure ; Vortex state ; Spin density wave ; Phase (matter) ; Doping ; Impurity ; Physics Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Rare-earth and actinide compounds
Authors: Saulo Martiello Mastelini | Daniel R. Cassar | Edesio Alcobaça | Tiago Botari | André C. P. L. F. de Carvalho | Edgar Dutra Zanotto
Source: arXiv (Cornell University) (preprint, 2021-06-14)
Field: Materials Science — Materials Chemistry

Machine learning unveils composition-property relationships in chalcogenide glasses Due to their unique optical and electronic functionalities, chalcogenide glasses are materials of choice for numerous microelectronic and photonic devices. However, to extend the range of compositions and applications, profound knowledge about composition-property relationships is necessary. To this end, we collected a large quantity of composition-property data on chalcogenide glasses from SciGlass database regarding glass transition temperature ($T_g$), Young's modulus ($E$), coefficient of thermal expansion (CTE), and refractive index ($n_D$). With these data, we induced predictive models using three machine learning algorithms: Random Forest, K-nearest Neighbors, and Classification and Regression Trees. Finally, the induced models were interpreted by computing the SHAP (SHapley Additive exPlanations) values of the chemical features, which revealed the key elements that significantly impacted the tested properties and quantified their impact. For instance, Ge and Ga increase $T_g$ and $E$ and decrease CTE (three properties that depend on bond strength), whereas Se has the opposite effect. Te, As, Tl, and Sb increase $n_D$ (which strongly depends on polarizability), whereas S, Ge, and P diminish it. Knowledge about the effect of each element on the glass properties is precious for semi-empirical compositional development trials or simulation-driven formulations. The induced models can be used to design novel chalcogenide glasses with required combinations of properties. Chalcogenide ; Polarizability ; Microelectronics ; Refractive index ; Materials science ; Thermal expansion ; Glass transition ; Mineralogy ; Computer science ; Nanotechnology ; Chemistry ; Composite material ; Optoelectronics ; Molecule ; Organic chemistry Phase-change materials and chalcogenides
Authors: Jingjing Qian | Zijian Zhao | Qinming Zhang | Sunday Abraham | M. Werner | Randy Petty | Meng Lu
Source:  (article, 2021-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Nanorod-cladding sapphire fiber-based thermometer for extreme condition applications This paper reports a new optical fiber thermometer (OFT) for ultra-high temperature measurement under extreme environments, such as in molten steels. This sapphire fiber-based OFT was designed by coating the sapphire fiber core with a nanorod cladding material, which is then housed inside the ceramic tube, the cladded sapphire fiber can collect thermal emissions from ceramic tube and couple the emission to a near-infrared spectrometer with minimal loss. After dipping OFT into molten steel, real-time temperature can be determined by analyzing the emission spectra using a polynomial curve fitting or a machine learning-based classification algorithm. Materials science ; Sapphire ; Thermometer ; Cladding (metalworking) ; Optical fiber ; Ceramic ; Fiber ; Temperature measurement ; Spectrometer ; Optics ; Coating ; Optoelectronics ; Composite material ; Laser Optical Coatings and Gratings ; Advanced Sensor Technologies Research ; Calibration and Measurement Techniques
Authors: Yoojin Chung | Hyunwoo Kim
Source: International Journal of Internet Technology and Secured Transactions (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Automated analysis of blood vessel permeability using deep learning This paper presented a deep learning-based approach using CNN that automatically detects changes of permeability in the blood vessel network after exposing the vessels to chemicals. Because chemicals can affect the permeability of the vessels, it is important to characterise and quantify the changes to blood vessels accurately from time-lapse microscope images. We increased the number of data by applying diverse augmentation methods to 2,800 images. In this paper, we also devised a method of automatically labelling images as to whether there is any change in the blood vessel to obtain learning data for CNN by pre-treating images using diverse image processing methods and separately establishing thresholds for different images. To verify the performance of the developed CNN, we used 4-fold cross validation and blood vessel changes were automatically detected with an accuracy of 92.07% in the experiment. Computer science ; Artificial intelligence ; Blood vessel ; Deep learning ; Computer vision ; Permeability (electromagnetism) ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Brain Tumor Detection and Classification