jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: K P Bharath
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2023-12-13)
Field: Chemistry — Analytical Chemistry

Fruit Verity-Detecting Adulteration in fruits Using Machine Learning. Abstract: Detecting fruit adulteration is a critical challenge with traditional methods often falling short due to their timeconsuming nature and high costs. Chemical tests, while common, have limitations that hinder their efficiency. Fortunately, a beacon of hope emerges in the realm of machine learning, offering a promising alternative for identifying adulteration in fruits. By leveraging the power of data, machine learning algorithms can be finely tuned to recognize intricate patterns that act as red flags for adulteration. These data sources span from detailed laboratory tests to the nuances revealed through sensory analysis and even extend to the analysis of fruit images. Once a machine learning model undergoes meticulous training on this diverse array of datasets, it transforms into a potent tool for swift and precise adulteration detection. This breakthrough not only strengthens food safety measures but also accelerates the identification of compromised products, ultimately safeguarding public health. Consumers are empowered with a robust defense against the persistent problem of adulteration. The revolutionary nature of this approach brings about a paradigm shift, offering a proactive solution that outpaces traditional methods Safeguarding ; Identification (biology) ; Computer science ; Artificial intelligence ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Shengbin Chen | Wenming Zhang | Rui Luo | Yidong Zhao | Yang Yang | Bing Zhang | Qiang Lü | Bin Hu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

System Energy and Band Gap Prediction of Titanium Dioxide Based on Machine Learning Titanium dioxide ; Energy (signal processing) ; Band gap ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Physics ; Optoelectronics ; Metallurgy Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Nuclear Physics and Applications
Authors: L. LEE | Hong Yeo | Zhuoyi Lin | J. Senthilnath | Bangjian Zhou | Ji Wei Yoon
Source:  (preprint, 2023-05-10)
Field: Materials Science — Materials Chemistry

Analysis of error distribution in catalyst-adsorbate binding energies generated by GNN Scalable methods of storing renewable energy to reduce climate change require high- performance catalysts that can be discovered using Artificial Intelligence (AI), prompting data scientists to train models. In the OC20 dataset (IS2RE), the Graphormer model, a type of Graph Neural Network (GNN), performed the best. We hope to find trends in the error distribution of catalyst-adsorbate binding energies to explain and make improvements. GNNs are based on Graph Theory, which makes them optimised for certain data structures. In the context of molecules, a graph can be used to represent the atomic and molecular structure of a compound through its edges and nodes. By using a GNN, it is possible to effectively capture the intricate relationships and dependencies between the atoms and bonds in a molecule. This is important for predicting and understanding the properties and behaviour of the molecule. With Python and Jupyter Notebook, we had many cutting-edge packages and libraries at our disposal; some included are matplotlib, pandas, and Atomic Simulation Environment (ASE). Through the data exploration, wrangling, and analysis, several insights were gleaned. Smaller unit cells tended to have larger errors. Across the period, there are local minima of errors in Group 10. Chlorine, perhaps because it is the only halogen, had an abnormally large error. Adsorbates containing Nitrogen had greater errors, as opposed to ones that are grouped as C1, C2 or O/H only. Python (programming language) ; Scalability ; Computer science ; Molecule ; Halogen ; Catalysis ; Graph ; Chemistry ; Data mining ; Theoretical computer science ; Database Machine Learning in Materials Science ; Advanced Graph Neural Networks
Authors: Junkai Deng | Bo Xu | Zhanpeng Gong | Jingran Liu | Yunfei Hong | Yang Yang | Lou Li | Yilun Liu | Jefferson Zhe Liu
Source: Research Square (preprint, 2023-09-13)
Field: Materials Science — Materials Chemistry

Tunable ferroelectric topological defects on 2D topological surfaces: strain engineering skyrmion-like polar structures in 2D materials Abstract Polar topological structures in ferroelectric materials have attracted significant interest due to their fascinating physical properties and promising applications in high-density, nonvolatile memories. Currently, most polar topological patterns are only observed in the bulky perovskite superlattices. In this work, we report a discovery of tunable ferroelectric polar topological structures designed and achieved using topological strain engineering in two-dimensional PbX (X = S, Se, and Te) materials via integrating first-principles calculations, machine learning molecular dynamics simulations, and continuum modelling. Our first-principles calculations discover the strain-induced reversible ferroelectric phase transition with diverse polarization directions strongly correlated to the straining conditions. Taking advantage of the mechanical flexibility of 2D PbX, using MD simulations, we successfully demonstrate that the complex strain fields of 2D topological surfaces under mechanical indentation can generate unique skyrmion-like polar topological vortex patterns. Our further continuum simulations for experimentally accessible larger-scale 2D topological surfaces uncover multiple skyrmion-like structures ( i.e. , vortex, anti-vortex, and flux-closure) and transition between them by adopting/designing different types of mechanical loadings (such as out-of-plane indention and air blowing). Topological surfaces with various designable reversible polar topological structures can be tailored by complex straining flexible 2D materials, which provides excellent opportunities for next-generation nanoelectronics and sensor devices. Skyrmion ; Ferroelectricity ; Polar ; Materials science ; Topological defect ; Topology (electrical circuits) ; Condensed matter physics ; Physics ; Optoelectronics ; Mathematics ; Quantum mechanics ; Dielectric ; Combinatorics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies
Authors: Mina LIM | Sooyeon LIM | Soohyung PARK | Hong-Kyu KIM
Source: Physics and High Technology (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

Advanced Analysis Using Deep Learning Method X-ray Photoelectron Spectroscopy (XPS) is an important analytical method utilized to determine not only the electronic structure of a material, but also the elemental content of the material. However, it requires a high level of expertise to interpret XPS data, and the reliability of XPS analysis depends on the competence of the expert. To overcome these challenges, this article introduces the process of developing a deep learning model that can automatically interpret XPS data without human intervention. Furthermore, by understanding how a deep learning model can quantify elemental content in a spectrum, we provide insights into XPS analysis methods and the interpretation of the spectrum itself. X-ray photoelectron spectroscopy ; Competence (human resources) ; Elemental analysis ; Computer science ; Process (computing) ; Reliability (semiconductor) ; Materials science ; Analytical Chemistry (journal) ; Artificial intelligence ; Chemistry ; Chemical engineering ; Psychology ; Environmental chemistry ; Engineering ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Mamoru Honda | Kazuyoshi Ogasawara
Source: ECS Meeting Abstracts (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Prediction of 4f-5d Transition Energy of Ce<sup>3+</sup> in Oxides By First-Principles Calculations and Machine Learning A Ce 3+ ion has only one electron in the 4f orbitals and its 4f-5d transition energy is strongly influenced by the width of the crystal field splitting of the 5d orbitals. In other words, the absorption and emission wavelengths of Ce 3+ in crystals can be widely controlled by changing the ligand field environment. As an example of Ce 3+ -doped phosphor, Ce:YAG (Y 3 Al 5 O 12 ) is widely used as an yellow phosphor for standard white LEDs. Therefore, novel phosphors with desired emission colors can be developed by adding Ce 3+ to appropriate host crystals. In this work, we aimed to construct an efficient prediction model for the transition energy of Ce 3+ in oxide crystals. Small clusters consisting of the substituted Ce 3+ ion and the first-neighbor anions were constructed and molecular orbital calculations were performed using the relativistic discrete-variational Xα (DV-Xα) method. Then machine learning based on structural and electronic state parameters was performed to create a predictive model for the 4f-5d transition energy of Ce 3+ in crystals. By creating the correlation diagrams, the influence of each parameter on the transition energy was analyzed in detail. Energy (signal processing) ; Transition (genetics) ; Materials science ; Atomic physics ; Engineering physics ; Nuclear physics ; Chemistry ; Physics ; Quantum mechanics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Kushal Bose | Swagatam Das
Source: arXiv (Cornell University) (preprint, 2023-12-11)
Field: Materials Science — Materials Chemistry

HyPE-GT: where Graph Transformers meet Hyperbolic Positional Encodings Graph Transformers (GTs) facilitate the comprehension of graph-structured data by calculating the self-attention of node pairs without considering node position information. To address this limitation, we introduce an innovative and efficient framework that introduces Positional Encodings (PEs) into the Transformer, generating a set of learnable positional encodings in the hyperbolic space, a non-Euclidean domain. This approach empowers us to explore diverse options for optimal selection of PEs for specific downstream tasks, leveraging hyperbolic neural networks or hyperbolic graph convolutional networks. Additionally, we repurpose these positional encodings to mitigate the impact of over-smoothing in deep Graph Neural Networks (GNNs). Comprehensive experiments on molecular benchmark datasets, co-author, and co-purchase networks substantiate the effectiveness of hyperbolic positional encodings in enhancing the performance of deep GNNs. Computer science ; Transformer ; Graph ; Theoretical computer science ; Artificial intelligence ; Smoothing Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Abhijith S. Parackal | Rhys E. A. Goodall | Felix A. Faber | Rickard Armiento
Source: arXiv (Cornell University) (preprint, 2023-09-28)
Field: Materials Science — Materials Chemistry

Identifying Crystal Structures Beyond Known Prototypes from X-ray Powder Diffraction Spectra The large amount of powder diffraction data for which the corresponding crystal structures have not yet been identified suggests the existence of numerous undiscovered, physically relevant crystal structure prototypes. In this paper, we present a scheme to resolve powder diffraction data into crystal structures with precise atomic coordinates by screening the space of all possible atomic arrangements, i.e., structural prototypes, including those not previously observed, using a pre-trained machine learning (ML) model. This involves (i) enumerating all possible symmetry-confined ways in which a given composition can be accommodated in a given spacegroup, (ii) ranking the element-assigned prototype representations using energies predicted using the Wren ML model [Sci.Adv.8, eabn4117 (2022)], (iii) assigning and perturbing atoms along the degree of freedom allowed by the Wyckoff positions to match the experimental diffraction data (iv) validating the thermodynamic stability of the material using density-functional theory (DFT). An advantage of the presented method is that it does not rely on a database of previously observed prototypes and is, therefore capable of finding crystal structures with entirely new symmetric arrangements of atoms. We demonstrate the workflow on unidentified XRD spectra from the ICDD database and identify a number of stable structures, where a majority turns out to be derivable from known prototypes. However, at least two are found not to be part of our prior structural data sets. Diffraction ; Workflow ; Crystal (programming language) ; Crystal structure ; Crystallography ; Space (punctuation) ; Powder diffraction ; Stability (learning theory) ; Ranking (information retrieval) ; Spectral line ; Physics ; X-ray crystallography ; Density functional theory ; Computer science ; Materials science ; Chemistry ; Database ; Artificial intelligence ; Quantum mechanics ; Machine learning X-ray Diffraction in Crystallography ; Crystallography and molecular interactions ; Machine Learning in Materials Science
Authors: Mario G. Zauchner | Andrew P. Horsfield | Johannes Lischner
Source: arXiv (Cornell University) (preprint, 2023-05-04)
Field: Materials Science — Materials Chemistry

Accelerating GW calculations through machine learned dielectric matrices The GW approach produces highly accurate quasiparticle energies, but its application to large systems is computationally challenging, which can be largely attributed to the difficulty in computing the inverse dielectric matrix. To address this challenge, we develop a machine learning approach to efficiently predict density-density response functions (DDRF) in materials. For this, an atomic decomposition of the DDRF is introduced as well as the neighbourhood density-matrix descriptor both of which transform in the same way under rotations. The resulting DDRFs are then used to evaluate quasiparticle energies via the GW approach. This technique is called the ML-GW approach. To assess the accuracy of this method, we apply it to hydrogenated silicon clusters and find that it reliably reproduces HOMO-LUMO gaps and quasiparticle energy levels. The accuracy of the predictions deteriorates when the approach is applied to larger clusters than those included in the training set. These advances pave the way towards GW calculations of complex systems, such as disordered materials, liquids, interfaces and nanoparticles. Quasiparticle ; Dielectric ; Inverse ; Matrix (chemical analysis) ; GW approximation ; Computer science ; Materials science ; Physics ; Computational physics ; Statistical physics ; Algorithm ; Condensed matter physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials
Authors: Yun-Jiang Wang
Source: International Conference on Computational & Experimental Engineering and Sciences (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

TheStructure-Vibration-DeformationCorrelationinAmorphousMetals It is a textbook knowledge that the mechanical properties of crystalline solids are determined by the performances of their defects under certain circumstances of external stress and temperature. However, such a belief in crystals meets difficulty in amorphous solids, in which the concept of defect is hard to define. In this talk, I would like to talk about the unusual structure-property of amorphous metals from three different perspectives – in terms of their structural, vibrational and deformation characteristics. We try to build a super structure-property relationship in the general amorphous solids by machine learning strategy after we realized that their no direct one-to-one correspondence in amorphous metals’ functions. Then we study and understand the deformation behaviors of amorphous metal starting from phonon, which present abundant of novel phenomena compared to crystals. Finally, the fracture of amorphous metals via either shear banding or necking after characterized by a new fracture criterion, which is constructed by the geometry and bonding nature of amorphous materials. All the new knowledges are obtained from atomistic simulations, which is targeted to provide a quantitative structure-property relationship in the generic amorphous metals. Amorphous solid ; Amorphous metal ; Necking ; Materials science ; Deformation (meteorology) ; Fracture (geology) ; Shear (geology) ; Chemical physics ; Condensed matter physics ; Composite material ; Crystallography ; Chemistry ; Physics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Powder Metallurgy Techniques and Materials
Source: Angewandte Chemie (article, 2023-03-24)
Field: Chemistry — Organic Chemistry

A Team for Accelerating Catalyst and Reaction Development Abstract This invited Team Profile was created by the Gessner and Gooßen research groups at Ruhr‐University Bochum (Germany). They recently published a series of articles in Angewandte Chemie on the application of ylide‐substituted phosphines (YPhos) in palladium‐catalyzed coupling reactions. Owing to their inherently strong sigma‐donating ability and their modular synthesis, YPhos ligands can be tailored to fit the demands of various catalytic transformations. In the most recent article, machine learning methods were applied to identify a YPhos catalyst that enables the previously elusive Hiyama coupling of aryl chlorides. “Computer‐Driven Development of Ylide Functionalized Phosphines for Palladium‐Catalyzed Hiyama Couplings” , J. F. Goebel, J. Löffler, Z. Zeng, J. Handelmann, A. Hermann, I. Rodstein, T. Gensch, V. H. Gessner, L. J. Gooßen, Angew. Chem. Int. Ed . 2023, e202216160 ; Angew. Chem . 2023, e202216160 . Catalysis ; Ylide ; Chemistry ; Aryl ; Palladium ; Coupling reaction ; Combinatorial chemistry ; Medicinal chemistry ; Organic chemistry Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Cyclopropane Reaction Mechanisms
Authors: Kevin Propst | Evan Hall | Cory D. Hirsch | Jason G. Walling
Source:  (preprint, 2023-10-11)
Field: Chemistry — Analytical Chemistry

Predicting alpha amylase content and starch breakdown in pregerminated barley seeds using hyperspectral imaging Malting is the process of controlled germination of cereal grain that is steeped, germinated, and dry kilned to develop flavor compounds, fermentable sugars, the hydrolytic enzymes necessary for the brewing process. To meet the strict standards of the brewing industry, variation in the germination of barley grains need to be minimal but also maintain rapid germination and optimum levels of a and B amylase. Preharvest sprouting poses a significant threat to barley cultivars prior to harvest, resulting in premature endosperm modification, reduced enzyme content, and poor malthouse performance. Pregerminated grain is difficult to detect as there are often no signs of damage, and current methods such as the Hagberg falling/Stirring number, or pearling tests, each of which ultimately leads to the destruction of the seed. Here we used time-series hyperspectral imaging of barley seeds undergoing active and or pregerminated seed and used a deep neural network to predict stirring number and alpha amylase values, which are proxy measures for sprout damage. Prediction models were made for seven genotypes tested individually and with all genotypes combined. Stirring number assays ranged from 0 to 190 and our prediction models had mean average errors of 10.5 to 23.9 depending on the model. To increase the resolution and accuracy of the predictions we transitioned from using bulk conventional molecular assavs to single seed assavs and prediction models. These results demonstrate that hyperspectral imaging and machine learning models can be used to predict germinated grain in bulk and single seed assays. Germination ; Brewing ; Endosperm ; Hyperspectral imaging ; Preharvest ; Falling Number ; Starch ; Amylase ; Horticulture ; Cultivar ; Agronomy ; Biology ; Food science ; Botany ; Artificial intelligence ; Enzyme ; Fermentation ; Computer science ; Postharvest Spectroscopy and Chemometric Analyses ; Food composition and properties ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Lu Xu | Qin Yang
Source: Research Square (preprint, 2023-02-14)
Field: Materials Science — Materials Chemistry

Is theoretical chemistry entering a new era of big data learning and reasoning: deep electron cloud-activity and field-activity relationships Cloud computing ; Big data ; Deep learning ; Field (mathematics) ; Data science ; Computer science ; Chemistry ; Cognitive science ; Epistemology ; Artificial intelligence ; Psychology ; Philosophy ; Mathematics ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Amirhossein D. Naghdi | Franco Pellegrini | Emine Küçükbenli | Dario Massa | Francisco Javier Dominguez Gutierrez | Efthimios Kaxiras | Stefanos Papanikolaou
Source: arXiv (Cornell University) (preprint, 2023-12-18)
Field: Materials Science — Materials Chemistry

Neural Network Interatomic Potentials For Open Surface Nano-mechanics Applications Material characterization in nano-mechanical tests requires precise interatomic potentials for the computation of atomic energies and forces with near-quantum accuracy. For such purposes, we develop a robust neural-network interatomic potential (NNIP), and we provide a test for the example of molecular dynamics (MD) nanoindentation, and the case of body-centered cubic crystalline molybdenum (Mo). We employ a similarity measurement protocol, using standard local environment descriptors, to select ab initio configurations for the training dataset that capture the behavior of the indented sample. We find that it is critical to include generalized stacking fault (GSF) configurations, featuring a dumbbell interstitial on the surface, to capture dislocation cores, and also high-temperature configurations with frozen atom layers for the indenter tip contact. We develop a NNIP with distinct dislocation nucleation mechanisms, realistic generalized stacking fault energy (GSFE) curves, and an informative energy landscape for the atoms on the sample surface during nanoindentation. We compare our NNIP results with nanoindentation simulations, performed with three existing potentials -- an embedded atom method (EAM) potential, a gaussian approximation potential (GAP), and a tabulated GAP (tabGAP) potential -- that predict different dislocation nucleation mechanisms, and display the absence of essential information on the shear stress at the sample surface in the elastic region. We believe that these features render specialized NNIPs essential for simulations of nanoindentation and nano-mechanics with near-quantum accuracy. Nanoindentation ; Stacking-fault energy ; Nucleation ; Dislocation ; Interatomic potential ; Materials science ; Atom (system on chip) ; Stacking fault ; Molecular dynamics ; Nanomechanics ; Embedded atom model ; Molecular physics ; Condensed matter physics ; Nanotechnology ; Physics ; Computational chemistry ; Chemistry ; Thermodynamics ; Composite material ; Computer science Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Advanced Materials Characterization Techniques