jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Austin McDannald | Matthias Frontzek | A. T. Savici | Mathieu Doucet | Efrain E. Rodriguez | Kate Meuse | Jessica Opsahl-Ong | Daniel V. Samarov | Ichiro Takeuchi | William Ratcliff | A. Gilad Kusne
Source: Applied Physics Reviews (preprint, 2022-04-29)
Field: Materials Science — Materials Chemistry

On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning We demonstrate the first live, autonomous control over neutron diffraction experiments by developing and deploying ANDiE: the autonomous neutron diffraction explorer. Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and behavior of materials. However, instruments at neutron scattering facilities in the world is limited, and instruments at such facilities are perennially oversubscribed. We demonstrate a significant reduction in experimental time required for neutron diffraction experiments by implementation of autonomous navigation of measurement parameter space through machine learning. Prior scientific knowledge and Bayesian active learning are used to dynamically steer the sequence of measurements. We show that ANDiE can experimentally determine the magnetic ordering transition of both MnO and Fe 1.09 Te all while providing a fivefold enhancement in measurement efficiency. Furthermore, in a hypothesis testing post-processing step, ANDiE can determine transition behavior from a set of possible physical models. ANDiE's active learning approach is broadly applicable to a variety of neutron-based experiments and can open the door for neutron scattering as a tool of accelerated materials discovery. Neutron scattering ; Neutron diffraction ; Neutron ; Scattering ; Magnetic structure ; Diffraction ; Physics ; Computer science ; Nuclear physics ; Optics ; Magnetic field Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Sebastian Ehlert | Stefan Grimme | Andreas Hansen
Source: ChemRxiv (preprint, 2022-04-11)
Field: Materials Science — Materials Chemistry

Conformational energy benchmark for longer n-alkane chains We present the first benchmark set focusing on the conformational energies of highly flexible, long n-alkane chains, termed ACONFL. Unbranched alkanes are ubiquitous building blocks in nature, so the goal is to be able to calculate their properties most accurately to improve the modeling of, e.g, complex (biological) systems. Very accurate DLPNO-CCSD(T1)/CBS reference values are provided, which allow for a statistical meaningful evaluation of even the best available density functional methods. The performance of established and modern (dispersion corrected) density functionals is comprehensively assessed. The recently introduced r²SCAN-V functional shows excellent performance, similar to efficient composite DFT methods like B97-3c and r²SCAN-3c, which provide an even better cost-accuracy ratio, while almost reaching the accuracy of much more computationally demanding hybrid or double hybrid functionals with large QZ AO basis sets. In addition, we investigated the performance of common wavefunction methods, where MP2/CBS surprisingly performs worse compared to simple D4 dispersion corrected Hartree–Fock. Furthermore, we investigate the performance of several semiempirical and force field methods, which are commonly used for the generation of conformational ensembles in multilevel workflows or in large scale molecular dynamics studies. Outstanding performance is obtained by the recently introduced general force field, GFN-FF, while other commonly applied methods like the universal force field yield large errors. We recommend the ACONFL as a helpful benchmark set for parameterization of new semiempirical or force field methods and machine learning potentials as well as a meaningful validation set for newly developed DFT or dispersion methods. Benchmark (surveying) ; Force field (fiction) ; Statistical physics ; Hybrid functional ; Computer science ; Density functional theory ; Dispersion (optics) ; Set (abstract data type) ; Field (mathematics) ; Wave function ; Alkane ; Scale (ratio) ; Basis set ; Workflow ; Physics ; Algorithm ; Computational chemistry ; Artificial intelligence ; Chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Advanced Chemical Physics Studies
Authors: L Shiva Shankar | Alla Sravani | T Shravan Kumar | S. Rajender | CMAK Zeelan Basha
Source: 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (article, 2022-01-20)
Field: Chemistry — Analytical Chemistry

Convolution Neural Network (CNN) Based Computerized Classification of Adulterated Fruits with SIFT and Bag of words (BOW) Most of the minerals and vitamins can be obtained by humans with the intake of fruits. Fruits are very rich in fibre and health-boosting oxidants can be provided by them. With a daily routine of eating fruits, a human can avoid the risk of diabetes, cancer, heart diseases. But these days fruits are being adultered for the [1] economic and technical benefits. Adulteration is a kind of illegal act of mixing some harmful chemicals into the fruits in order to increase profits. Eating adultered fruits can lead to chronic diseases diarrhoea, cancer, liver disorder, and heart diseases. Adulteration is a serious threat to the health of humans. Even though the adulteration of fruits is banned, many traders are applying adulteration in the thirst of more profits. It is very difficult to identify the fruits which undergone adulteration with naked eyes. Image processing techniques can be applied for detecting the adultered fruits. In this paper, A Convolution Neural Network (CNN) with K-means clustering and Bag of words based technique is proposed to detect the fruits which undergone adulteration. Accuracy of 0.94 is achieved with the proposed system. Convolutional neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Currency Recognition and Detection
Authors: Yanbo Han | Mengyang Li | Xiang Zhao
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Effects of orbital angles on the modeling of conjugated systems with curvature The remaining p orbitals of carbon atoms form some conjugated systems, such as fullerenes. The non-parallel p orbitals of fullerene conjugated systems dominate their stability. In this work, a model is proposed with a good correlation between model outputs and relative energies of fullerenes. The model is built from the extended Hückel molecular orbital theory and geometrical features, especially the orbital axial angle characteristics. The extended charge stability index (XCSI) proposed in this paper offers a good representation of the stability from small cage to at least C<sub>70</sub>. The axial angle feature is no longer necessary in large carbon cages, which is similar to previous models based on fullerene deformation factors. In addition, a deep learning model based on spatial orientation features containing spherical harmonic functions yields surprising transferring results on the stability of charged fullerenes. We hope this work will assist in building more general models dealing with problems related to conjugated systems. Fullerene ; Conjugated system ; Atomic orbital ; Stability (learning theory) ; Molecular orbital ; Work (physics) ; Representation (politics) ; Chemical physics ; Computational chemistry ; Materials science ; Molecular physics ; Curvature ; Carbon fibers ; Orientation (vector space) ; Physics ; Chemistry ; Geometry ; Electron ; Quantum mechanics ; Computer science ; Mathematics ; Molecule ; Composite number ; Nuclear magnetic resonance Fullerene Chemistry and Applications ; Graphene research and applications ; Synthesis and Properties of Aromatic Compounds
Authors: Chunning Song | Jianguo Cao | Jing Xiao | Qiufang Zhao | Shuangtao Sun | Wenhui Xia
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Temperature Constitutive Relationship Involving Phase Transformation for Non-Oriented Electrical Steel Based on PSO-DNN Approach Transformation (genetics) ; Materials science ; Phase (matter) ; Constitutive equation ; Thermodynamics ; Physics ; Chemistry Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Min Li | Zhengyuan Shi | Qiuxia Lai | Sadaf Khan | Cai, Shaowei | Xu, Qiang
Source: arXiv (Cornell University) (preprint, 2022-05-27)
Field: Materials Science — Materials Chemistry

DeepSAT: An EDA-Driven Learning Framework for SAT We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying the knowledge of the well-developed electronic design automation (EDA) field for SAT solving. Specifically, we first resort to logic synthesis algorithms to pre-process SAT instances into optimized and-inverter graphs (AIGs). By doing so, the distribution diversity among various SAT instances can be dramatically reduced, which facilitates improving the generalization capability of the learned model. Next, we regard the distribution of SAT solutions being a product of conditional Bernoulli distributions. Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling. To effectively train the generative model, with the help of logic simulation tools, we obtain the probabilities of nodes in the AIG being logic `1' as rich supervision. We conduct comprehensive experiments on various SAT problems. Our results show that, DeepSAT achieves significant accuracy improvements over state-of-the-art learning-based SAT solutions, especially when generalized to SAT instances that are relatively large or with diverse distributions. Boolean satisfiability problem ; Computer science ; Theoretical computer science ; Generalization ; Satisfiability ; Boolean function ; Artificial intelligence ; Machine learning ; Algorithm ; Mathematics Machine Learning in Materials Science ; Software Engineering Research ; Formal Methods in Verification
Authors: Antti Pihlajamäki | Sami Malola | Tommi Kärkkäinen | Hannu Häkkinen
Source: arXiv (Cornell University) (preprint, 2022-03-18)
Field: Materials Science — Materials Chemistry

Orientation Adaptive Minimal Learning Machine: Application to Thiolate-Protected Gold Nanoclusters and Gold-Thiolate Rings Machine learning (ML) force fields are one of the most common applications of ML methods in the field of physical and chemical science. In the optimal case, they are able to reach accuracy close to the first principles methods with significantly lowered computational cost. However, often the training of the ML methods rely on full atomic structures alongside their potential energies, and applying the force information needs special modifications to standard algorithms. Here we apply distance-based ML methods to predict force norms and estimate the directions of the force vectors of the thiolate-protected gold nanoclusters. The method relies only on local structural information without energy evaluations. We apply the atomic ML forces on the structure optimization of the gold-thiolate rings, $\text{Au}_{25}(\text{SCH}_{3})_{18}$ nanocluster and two known structural isomers of the $\text{Au}_{38}(\text{SCH}_{3})_{24}$ nanocluster. The results demonstrate that the method is well-suited for the structural optimizations of the gold-thiolate systems, where the atomic bonding has a covalent nature in the ligand shell and at the metal-ligand interface. The methodology could be seen as an early attempt to introduce equivariant learning to distance-based ML methods. Nanoclusters ; Force field (fiction) ; Ligand (biochemistry) ; Orientation (vector space) ; Field (mathematics) ; Computer science ; Covalent bond ; Algorithm ; Nanotechnology ; Chemistry ; Materials science ; Computational science ; Chemical physics ; Crystallography ; Physics ; Artificial intelligence ; Geometry ; Mathematics ; Quantum mechanics ; Pure mathematics Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Javier F. Troncoso | Vladyslav Turlo
Source: Modelling and Simulation in Materials Science and Engineering (article, 2022-03-17)
Field: Materials Science — Materials Chemistry

Evaluating the applicability of classical and neural network interatomic potentials for modeling body centered cubic polymorph of magnesium Abstract Magnesium (Mg) is one of the most abundant metallic elements in nature and presents attractive mechanical properties in the industry. Particularly, it has a low density and relatively high strength/weight and stiffness/weight ratios, which make it one of the most attractive lightweight metals. However, the huge potential of Mg is restricted by its low ductility, associated with its hexagonal close packed (hcp) structure. This problem can be solved if Mg adopts the body centered cubic (bcc) structure, which is stable at high pressure or in confinement with stiff bcc metals like Nb. Molecular dynamics method is a magnificent tool to study material’s structure and deformation mechanisms at the atomic level, however, requiring accurate interatomic potentials. The majority of the interatomic potentials available in the literature for Mg have only been fitted to the properties of its stable hcp phase. In the present work, we perform systematic study of applicability of currently available Mg potentials to modeling the properties of metastable bcc polymorph of Mg, taking into account cohesive energy curves, elastic constants, stacking fault energies, and phonon dispersion curves. We conclude that the modified embedded atom method (MEAM) potentials are the most suitable for investigating bcc Mg in Mg/Nb nano-composites, while the properties of high-pressure bcc Mg would be better modeled by neural network interatomic potentials after different local atomic environments corresponding to bcc Mg being included into the fitting database. Materials science ; Interatomic potential ; Stacking-fault energy ; Molecular dynamics ; Metastability ; Atom (system on chip) ; Embedded atom model ; Ductility (Earth science) ; Cubic crystal system ; Deformation (meteorology) ; Stacking fault ; Stiffness ; Phase (matter) ; Work (physics) ; Stacking ; Thermodynamics ; Phonon ; Magnesium ; Crystallography ; Condensed matter physics ; Computational chemistry ; Dislocation ; Metallurgy ; Composite material ; Physics ; Creep ; Chemistry Boron and Carbon Nanomaterials Research ; Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties
Authors: Yin Lu | V. Rakshagan | Shehla Shoukat | Mustafa Z. Mahmoud | Inna Pustokhina | Ahmed Salah Al-Shati | Nader Ibrahim Namazi | Sameer Alshehri | Kareem M. AboRas | Mohammed A. S. Abourehab
Source: Journal of Molecular Liquids (article, 2022-08-25)
Field: Chemistry — Inorganic Chemistry

Molecular separation and computational simulation of contaminant removal from wastewater using zirconium UiO-66-(CO2H)2 metal–organic framework Kriging ; Ground-penetrating radar ; Support vector machine ; Artificial neural network ; Mean squared error ; Zirconium ; Linear regression ; Multilayer perceptron ; Regression ; Computer science ; Chemistry ; Biological system ; Mathematics ; Machine learning ; Statistics ; Inorganic chemistry ; Radar Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal
Authors: A. A. Gvozdenko | А. В. Блинов | Kristina Sergeevna Slyadneva | А. А. Blinova | Alexey Golik | Давид Гурамиевич Маглакелидзе
Source: Russian Journal of General Chemistry (article, 2022-06-01)
Field: Materials Science — Biomaterials

X-Ray Contrast Magnetic Diagnostic Tool Based on a Three-Component Nanosystem Currently, improving the methods of early diagnosis of oncological diseases is an urgent task. With the development of science, there are more and more ways to solve it. The most important at the moment are the methods of magnetic resonance imaging and computed tomography, which allow you to visualize neoplasms less than 1 cm in size. To increase the effectiveness of these methods, X-ray contrast and magnetic resonance contrast agents are used to more accurately visualize and identify various neoplasms, so the development of new X-ray contrast and magnetic resonance contrast agents is a very important direction. The purpose of this work was to develop a X-ray contrast magnetic diagnostic tool based on a three-component Fe3O4–Au–SiO2 nanosystem. With the help of neural network modeling, the synthesis process of the Fe3O4–Au–SiO2 nanosystem was optimized. Optimal conditions for the synthesis of a three-component nanosystem with a diameter of less than 150 nm: V(Fe3O4) = 0.0012–0.0015 mol, V(HAuCl4) = 25–35 cm3, C(Na3C6H5O7∙5.5H2O) = 0.08–0.011 mol/dm3. Based on the results of X-ray phase analysis, it was found that the optimized sample of the three-component nanosystem contains crystalline phases of double iron oxide (Fe3O4), gold (Au) and an amorphous phase of silicon dioxide (SiO2). The results of IR spectroscopy confirmed the formation of a three-component system Fe3O4–Au–SiO2. The results of the X-ray density study showed that different layers of the three-component Fe3O4–Au–SiO2 nanosystem have different X-ray densities: a nucleus consisting of Fe3O4 nanoparticles has an X-ray density from 3000 to 4000 HU depending on the size of the nucleus; the layer formed by Au nanoparticles have an X-ray density of 9 000 to 10 000 HU depending on the size of the layer; the silica layer has an X-ray density of 100 HU to 950 HU. The research of the magnetic properties allowed us to establish a nonlinear decrease in the saturation magnetization from 0.17 to 0.01 kA/m, and the magnetic susceptibility from 0.0026 rel. units to 0.0002 rel. units when the ratio of the components of the Fe3O4–Au–SiO2 nanosystem changes. Contrast (vision) ; Component (thermodynamics) ; Amorphous solid ; Magnetic resonance imaging ; Chemistry ; Oxide ; Spectroscopy ; Nuclear magnetic resonance ; Phase (matter) ; Nanotechnology ; Materials science ; Computer science ; Radiology ; Artificial intelligence ; Crystallography ; Physics Nanoparticle-Based Drug Delivery ; MRI in cancer diagnosis ; Radiomics and Machine Learning in Medical Imaging
Authors: Wensuo Lian | Jinling Wang
Source: Journal of Nanomaterials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Repair Effect of Nanomaterials on Meniscus Injury Induced by Calisthenics Exercise With the development of aerobics, more and more college students participate in this sport, but more and more sports injuries of students. Sports injuries directly or indirectly affect the normal study and life of college students. Therefore, it is very important to study the sports injuries of college students and their causes for improving the sports level of college students and promoting their healthy growth. And it provides proper precautions. This paper presents the investigation of nanomaterials, deep learning, and MRI images of meniscal injuries and concludes that the injury rate of high‐level competitive aerobics athletes is 100%, and the top 4 injured parts are the wrist, ankle, waist, and knee. It can be seen that the meniscus injury occurs more frequently. Calisthenics ; Materials science ; Meniscus ; Nanomaterials ; Composite material ; Forensic engineering ; Biomedical engineering ; Nanotechnology ; Medicine ; Physical therapy ; Engineering ; Optics Diamond and Carbon-based Materials Research ; Tribology and Wear Analysis ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Andrew Rosen | Sudarshan Vijay | Kristin A. Persson
Source: ChemRxiv (preprint, 2022-09-26)
Field: Materials Science — Materials Chemistry

Free-Atom-Like d States Beyond the Dilute Limit of Single-Atom Alloys Through a data-mining and high-throughput density functional theory approach, we identify a diverse range of metallic compounds that are predicted to have transition metals with “free-atom-like” d states that are highly localized in energy space. Design principles that can favor the formation of localized d states are uncovered, among which we note that site isolation is often necessary but that the dilute limit, as in most single-atom alloys, is not a pre-requisite. Additionally, the majority of localized d state transition metals identified from the computational screening study exhibit partial anionic character due to charge transfer from neighboring metal species. Using CO as a representative probe molecule, we show that localized d states for Rh, Ir, Pd, and Pt tend to reduce the binding strength of CO compared to their pure elemental analogues, whereas this does not occur as consistently for the Cu binding sites. These trends are rationalized through the d-band model, which suggests that the significantly reduced d-band width results in an increased orthogonalization energy penalty upon CO chemisorption. With the multitude of inorganic solids that are predicted to have highly localized d states, the results of the screening study may lead to new avenues for heterogeneous catalyst design from an electronic structure perspective. Atom (system on chip) ; Chemisorption ; Transition metal ; Binding energy ; Density functional theory ; Chemical physics ; Density of states ; Band gap ; Chemistry ; Catalysis ; Atomic physics ; Materials science ; Computational chemistry ; Condensed matter physics ; Physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: Mishra Ankit Kumar | V.S Janani KaviPriya | Jose Eldho | Mohan Anjali | Akshay Masetty
Source:  (article, 2022-02-01)
Field: Materials Science — Biomaterials

Review Study on Smart Nanomaterials and their Applications Smart nano materials are becoming increasingly popular in several fields of research. Some of the reasons for a lot of study in this field include the unique properties of these materials to adapt and respond to their environment, as well as increased qualities when combined with other materials. We have highlighted some of the most interesting nanomaterials in this review paper, including carbon-based nanomaterials such as nanodiamonds, metal-based nanoparticles such like gold, silver, and aluminium nanoparticles, and electrospun nanofibers. The characteristics and uses of these chosen materials were described along the review. In addition, some of the most notable characteristics or qualities of each material are addressed, as well as their purposes. This collaborative research will aid in a briefing on the key characteristics of certain frequently utilized nanoparticles as well as their application areas. Nanotechnology ; Nanomaterials ; Materials science ; Computer science Electrospun Nanofibers in Biomedical Applications ; Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials