jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saientan Bag
Source: arXiv (Cornell University) (preprint, 2022-07-22)
Field: Materials Science — Materials Chemistry

Simulation of Noncircular Rigid Bodies: Machine Learning Based Overlap Calculation Technique with System Size Independent Computational Cost Standard molecular dynamics (MD) and Monte Carlo (MC) simulation deals with spherical particles. Extending these standard simulation methodologies to the non-spherical cases is non-trivial. To circumvent this problem, non-spherical bodies are considered as a collection of constituent spherical objects. As the number of these constituent objects becomes large, the computational burden to simulate the system also increases. In this article, we propose an alternative way to simulate non-circular rigid bodies in two dimensions having pairwise repulsive interactions. Our approach is based on a machine learning (ML) based model which predicts the overlap between two non-circular bodies. The machine learning model is easy to train and the computation cost of its implementation remains independent of the number of constituents disks used to represent a non-circular rigid body. When used in MC simulation, our approach provides significant speed up in comparison to the standard implementation where overlap determination between two rigid bodies is done by calculating the distance of their constituent disks. Our proposed ML based MC method provided very similar structural features (in comparison to standard implementation) of the systems. We believe this work is a very first step towards a time-efficient simulation of non-spherical rigid bodies. Pairwise comparison ; Computation ; Rigid body ; Computer science ; Monte Carlo method ; Work (physics) ; Algorithm ; Spherical coordinate system ; Computational science ; Geometry ; Mathematics ; Artificial intelligence ; Physics ; Classical mechanics ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties
Authors: Michelangelo Domina | Matteo Cobelli | Stefano Sanvito
Source: arXiv (Cornell University) (preprint, 2022-02-23)
Field: Materials Science — Materials Chemistry

A spectral-neighbour representation for vector fields: machine-learning potentials including spin We introduce a translational and rotational invariant local representation for vector fields, which can be employed in the construction of machine-learning energy models of solids and molecules. This allows us to describe, on the same footing, the energy fluctuations due to the atomic motion, the longitudinal and transverse excitations of the vector field, and their mutual interplay. The formalism can then be applied to physical systems where the total energy is determined by a vector density, as in the case of magnetism. Our representation is constructed over the power spectrum of the combined angular momentum describing the local atomic positions and the vector field, and can be used in conjunction with different machine-learning schemes and data taken from accurate ab initio electronic structure theories. We demonstrate the descriptive power of our representation for a range of classical spin Hamiltonian and machine-learning algorithms. In particular, we construct energy models based on both linear Ridge regression, as in conventional spectral neighbour analysis potentials, and gaussian approximation. These are both built to represent a Heisenberg-type Hamiltonian including a longitudinal energy term and spin-lattice coupling. Physics ; Hamiltonian (control theory) ; Statistical physics ; Spectral density ; Gaussian ; Quantum mechanics ; Theoretical physics ; Mathematics Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Akhtar Zeb | Teemu Mätäsniemi
Source: Research Square (preprint, 2022-07-08)
Field: Materials Science — Materials Chemistry

Ontological modelling of creep void analysis data to automate machine learning training process Abstract This work focuses on the ontological representation of creep void analysis data to automate the training of the machine learning (ML) model detecting creep voids in scanning electron microscope images. Metallic high-temperature structures are subject to creep phenomenon that can lead to rupture and component failure when prolonged. ML models can be deployed to detect and obtain information about the density and location of creep voids using images as input data. However, due to the irregularities in the size and shape of creep voids and the associated uncertainty in ML models, the material engineer is required to inspect the detections and provide feedback on a regular basis. To automate the retraining of the ML model and to facilitate the close collaboration between the ML experts and the material engineers, domain ontologies providing common vocabularies can be utilized. We aligned the relevant concepts of the EMMO ontology, ML-Schema, and the PROV ontology to document creep void data for smooth information sharing and improving the quality of the ML detection process, hence resulting in better analysis and higher productivity compared to manual characterization. The SPARQL queries are used to gain valuable insights, such as the number and area of creep voids, condition of the metallic structure, and accuracy of the ML predictions. Creep ; Computer science ; Void (composites) ; Ontology ; Materials science ; Artificial intelligence ; Data mining ; Composite material Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Emil Thyge Skanning Kjær | Andy S. Anker | Marcus Nørgaard Weng | Simon J. L. Billinge | Raghavendra Selvan | Kirsten M. Ø. Jensen
Source: ChemRxiv (preprint, 2022-08-16)
Field: Materials Science — Materials Chemistry

DeepStruc: Towards structure solution from pair distribution function data using deep generative models Structure solution of nanostructured materials that have limited long-range remains a bottleneck in materials development. We present a deep learning algorithm, DeepStruc, that can solve a simple nanoparticle structure directly from a Pair Distribution Function obtained from total scattering data by using a conditional variational autoencoder (CVAE). We first apply DeepStruc to PDFs from seven different structure types of monometallic nanoparticles, and show that structures can be solved from both simulated and experimental PDFs, including PDFs from nanoparticles that are not present in the training distribution. We also apply DeepStruc to a system of hcp, fcc and stacking faulted nanoparticles, where DeepStruc recognizes stacking faulted nanoparticles as an interpolation between hcp and fcc nanoparticles and is able to solve stacking faulted structures from PDFs. Our findings suggests that DeepStruc is a step towards a general approach for structure solution of nanomaterials. Stacking ; Autoencoder ; Materials science ; Nanoparticle ; Interpolation (computer graphics) ; Function (biology) ; Distribution (mathematics) ; Pair distribution function ; Simple (philosophy) ; Range (aeronautics) ; Nanomaterials ; Biological system ; Algorithm ; Statistical physics ; Computer science ; Deep learning ; Nanotechnology ; Mathematics ; Artificial intelligence ; Physics ; Image (mathematics) ; Mathematical analysis ; Composite material Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Microstructure and mechanical properties
Authors: Marcel Rodekamp | Christoph Gäntgen
Source: arXiv (Cornell University) (preprint, 2022-11-17)
Field: Materials Science — Materials Chemistry

Mitigating the Hubbard Sign Problem. A Novel Application of Machine Learning Many fascinating systems suffer from a severe (complex action) sign problem preventing us from calculating them with Markov Chain Monte Carlo simulations. One promising method to alleviate the sign problem is the transformation of the integration domain towards Lefschetz Thimbles. Unfortunately, this suffers from poor scaling originating in numerically integrating of flow equations and evaluation of an induced Jacobian. In this proceedings we present a new preliminary Neural Network architecture based on complex-valued affine coupling layers. This network performs such a transformation efficiently, ultimately allowing simulation of systems with a severe sign problem. We test this method within the Hubbard Model at finite chemical potential, modelling strongly correlated electrons on a spatial lattice of ions. Jacobian matrix and determinant ; Hubbard model ; Affine transformation ; Sign (mathematics) ; Computer science ; Statistical physics ; Applied mathematics ; Physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Theoretical and Computational Physics ; Quantum many-body systems
Authors: Ravin Chowdhury | Adam A. S. Green | Cheol S. Park | Joseph E. Maclennan | Noel A. Clark
Source: arXiv (Cornell University) (preprint, 2022-11-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Topological defect coarsening in quenched smectic-C films analyzed using artificial neural networks Mechanically quenching a thin film of smectic-C liquid crystal results in the formation of a dense array of thousands of topological defects in the director field. The subsequent rapid coarsening of the film texture by the mutual annihilation of defects of opposite sign has been captured using high-speed, polarized light video microscopy. The temporal evolution of the texture has been characterized using an object-detection convolutional neural network to determine the defect locations, and a binary classification network customized to evaluate the brush orientation dynamics around the defects in order to determine their topological signs. At early times following the quench, inherent limits on the spatial resolution result in undercounting of the defects and deviations from expected behavior. At intermediate to late times, the observed annihilation dynamics scale in agreement with theoretical predictions and simulations of the $2$D XY model. Quenching (fluorescence) ; Texture (cosmology) ; Topological defect ; Annihilation ; Materials science ; Topology (electrical circuits) ; Convolutional neural network ; Condensed matter physics ; Crystallography ; Artificial intelligence ; Optics ; Physics ; Computer science ; Chemistry ; Mathematics ; Image (mathematics) ; Fluorescence Liquid Crystal Research Advancements ; Photonic Crystals and Applications ; Fluid Dynamics and Thin Films
Authors: Yeonsu Kim | Yoon A. Kim | Seo Hwee Park | YunHo Kim
Source: arXiv (Cornell University) (preprint, 2022-05-30)
Field: Materials Science — Materials Chemistry

Hybrid Numerical Modeling of Ballistic Clay under Low-Speed Impact using Artificial Neural Networks Roma Plastilina No. 1 clay has been widely used as a conservative boundary condition in bulletproof vests, namely to play the role of a human body. Interestingly, the effect of this boundary condition on the ballistic performance of the vests is indiscernible. Moreover, back face deformation should be characterized by measuring the indentation in the deformed clay, which is important for determining the lethality of gunshots. Therefore, several studies have focused on modeling not only bulletproof vests but also the clay backing material. Despite various attempts to develop a suitable numerical model, determining the appropriate physical parameters that can capture the high-strain-rate behavior of clay is still challenging. In this study, we predicted indentation depth in clay using an artificial neural network (ANN) and determined the optimal material parameters required for a finite element method (FEM)-based model using an inverse tracking method. Our ANN-FEM hybrid model successfully optimized high-strain-rate material parameters without the need for any independent mechanical tests. The proposed novel model achieved a high prediction accuracy of over 98% referring impact cases. Finite element method ; Artificial neural network ; Indentation ; Computer science ; Boundary value problem ; Structural engineering ; Boundary (topology) ; Strain rate ; Plasticity ; Materials science ; Engineering ; Composite material ; Artificial intelligence ; Mathematics ; Mathematical analysis High-Velocity Impact and Material Behavior ; Diabetic Foot Ulcer Assessment and Management ; Orthopedic Infections and Treatments
Authors: James Chapman | Tim Hsu | Xiao Dong Chen | Tae Wook Heo | Brandon C. Wood
Source: arXiv (Cornell University) (preprint, 2022-03-18)
Field: Materials Science — Materials Chemistry

Quantifying Disorder One Atom at a Time Using an Interpretable Graph Neural Network Paradigm Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this novel methodology to three prototypical examples with varying levels of disorder: (1) solid-liquid interfaces, (2) polycrystalline microstructures, and (3) grain boundaries. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides an intuitive and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena. Leverage (statistics) ; Metric (unit) ; Computer science ; Grain boundary ; Artificial neural network ; Statistical physics ; Artificial intelligence ; Materials science ; Microstructure ; Physics ; Engineering Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Block Copolymer Self-Assembly
Authors: Zhu, Ziyan
Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Multiscale Models for Incommensurate Layered Two-dimensional Materials Manipulating the twist angle or lattice mismatch in stacks of multilayered two-dimensional (2D) materials, referred to as van der Waals (vdW) heterostructures, introduces a long-wavelength moiré potential that fundamentally alters the physical properties of the constituent materials. Following the discovery of superconductivity and unconventional correlated insulating states in single-twist moiré systems, forays into moiré of moiré vdW heterostructures, such as twisted trilayer graphene with two independent twist angles, have led to observations of novel correlated states. These complex multilayered heterostructures exhibit length scales that are generally orders of magnitude longer than the bilayer moiré length, and they are incommensurate even in the continuum limit. To overcome these computational challenges, we present an efficient and accurate multiscale framework based on first principles. Using this framework, we study the electronic structures, mechanical re- laxation, and phonon properties of complex vdW heterostructures and help interpret experimental observations. We also introduce a machine learning-based approach to solving many-body lattice models, which may serve as a starting point toward solving the effective theory for these complex 2D materials. van der Waals force ; Twist ; Lattice (music) ; Phonon ; Superconductivity ; Heterojunction ; Bilayer ; Point (geometry) 2D Materials and Applications ; Graphene research and applications ; Topological Materials and Phenomena
Authors: Vedad Babic | Itai Panas
Source: arXiv (Cornell University) (preprint, 2022-04-19)
Field: Materials Science — Materials Chemistry

Towards In Silico Mining for Superconductors -- Cutting the Gordian Knot A random forest regression based supervised machine learning method to predict experimental critical temperature of superconductivity from the electronic band structure, as obtained from Density Functional Theory, is demonstrated. This complementarity between experiment and theory draws inspiration from the merging of Kohn-Sham and Bogoliubov-De Gennes equations [W. Kohn, W, EKU Gross, and LN Oliveira, Int. J. of Quant. Chem., 36(23), 611-615 (1989)]. Features in the Kohn-Sham Density Functional Theory band structure away from EF becoming decisive for the superconducting gap demonstrates this divide-and-conquer physical understanding. Not committing to any microscopic mechanism for the SC at this stage, it implies that in different classes of materials, different electronic features are responsible for the superconductivity. However, training on known members of a class, the performance of new members may be predicted. The method is validated for the A15 materials, including both binary A3X and ternary A6XY intermetallics, A=V, Nb, demonstrating that the two do indeed belong to the same class of superconductors. Superconductivity ; Density functional theory ; Ternary operation ; Intermetallic ; Complementarity (molecular biology) ; Electronic structure ; Condensed matter physics ; Electronic band structure ; Binary number ; Physics ; Theoretical physics ; Mathematics ; Statistical physics ; Computer science ; Materials science ; Quantum mechanics ; Arithmetic Machine Learning in Materials Science ; Iron-based superconductors research ; Rare-earth and actinide compounds
Authors: Komati Sathish
Source: International Journal of Engineering Research and Science & Technology (article, 2022-11-14)
Field: Chemistry — Analytical Chemistry

FINDING GLASS TRACES USING SPECTROSCOPIC AND MICROSCOPIC MECHANISMS FOR FORENSIC REPORTS The research focuses on the classification of glass types using machine learning techniques based on their chemical composition. Glass identification is critical in various industries, including construction, automotive, and forensic science, where distinguishing between different types ensures proper application and safety. The dataset used comprises attributes such as Refractive Index (RI), Sodium (Na), Magnesium (Mg), Aluminum (Al), Silicon (Si), Potassium (K), Calcium (Ca), Barium (Ba), Iron (Fe), and a target glass label representing the glass type. The project commenced with data importation and preprocessing. Column names were assigned to the dataset, and the presence of missing values was checked. Initial exploratory data analysis (EDA) was performed to understand glass distribution using count plots, which revealed imbalance among the glass types. To address this, the dataset was resampled using the resample function to create a balanced distribution of 10,000 samples. Further visualizations, including annotated count plots, were created to reflect changes in glass frequency after resampling. The feature matrix (X) and target vector (y) were defined, followed by splitting the dataset into training and testing subsets using an 80-20 ratio. Two supervised machine learning algorithms were employed for classification: Support Vector Classifier (SVC) and Random Forest Classifier (RFC). Model training and evaluation involved precision, recall, F1-score, and accuracy metrics. Visualization through confusion matrices provided insights into classification performance. The SVC model achieved an accuracy of 82%, demonstrating strong performance in classifying glass types with a linear margin-based approach. The RFC, leveraging ensemble learning and decision tree aggregation, achieved 100% accuracy, showcasing its robustness and ability to handle complex, high-dimensional data effectively. Confusion matrix ; Support vector machine ; Decision tree ; Classifier (UML) ; Pattern recognition (psychology) ; Robustness (evolution) ; Random forest ; Confusion Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Remote-Sensing Image Classification
Authors: Pier Paolo Poier | Louis Lagardère | Jean‐Philip Piquemal
Source: arXiv (Cornell University) (preprint, 2022-10-18)
Field: Chemistry — Spectroscopy

Generalized Many-Body Dispersion Correction through Random-phase Approximation for Chemically Accurate Density Functional Theory We extend our recently proposed Deep Learning-aided many-body dispersion (DNN-MBD) model to quadrupole polarizability (Q) terms using a generalized Random Phase Approximation (RPA) formalism, thus enabling the inclusion of van der Waals contributions beyond dipole. The resulting DNN-MBDQ model only relies on ab initio-derived quantities as the introduced quadrupole polarizabilities are recursively retrieved from dipole ones, in turn modelled via the Tkatchenko-Scheffler method. A transferable and efficient deep-neuronal network (DNN) provides atom in molecule volumes, while a single range-separation parameter is used to couple the model to Density Functional Theory (DFT). Since it can be computed at a negligible cost, the DNN-MBDQ approach can be coupled with DFT functionals such as PBE,PBE0 and B86bPBE (dispersionless). The DNN-MBQ-corrected functionals reach chemical accuracy while exhibiting lower errors compared to their dipole-only counterparts. Random phase approximation ; Polarizability ; Density functional theory ; Quadrupole ; Dipole ; van der Waals force ; Discrete dipole approximation ; Statistical physics ; Ab initio ; Physics ; Formalism (music) ; Quantum mechanics ; Molecular physics ; Molecule Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; Solid-state spectroscopy and crystallography
Authors: Fujia Dong | Jie Hao | Ruiming Luo | Zhifeng Zhang | Songlei Wang | Kangning Wu | Mengqi Liu
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Two-Dimensional Correlation Spectroscopy Combined with Deep Learning to Identify the Proximate Geographical Origin of Wolfberries and Optimization from Fusion Data Proximate ; Spectroscopy ; Artificial intelligence ; Correlation ; Pattern recognition (psychology) ; Fusion ; Computer science ; Geography ; Evolutionary biology ; Biology ; Mathematics ; Physics ; Geometry Spectroscopy and Chemometric Analyses ; Isotope Analysis in Ecology ; Advanced Chemical Sensor Technologies
Authors: Hossein Mashhadi Meighani | Taraneh Jafari Behbahani | Amin Dehghani
Source:  (preprint, 2022-12-14)
Field: Chemistry — Analytical Chemistry

A new approach to rheological properties of waxy crude oils using the tree-based machine learning methods In this work, the rheological behavior of three crude oils, with low, middle and high wax content, in the absence/presence of polymeric and aromatic flow improvers was investigated. The rheological data cover the temperature range of 0 to 60 °C to include the wax appearance temperature (WAT). The results indicated that EVA copolymer has remarkable performance in change of flow behavior from non-Newtonian to Newtonian even at temperatures below WAT. Moreover, the addition of small quantities of asphaltene solvents such as toluene can improve viscosity of crude oil with high wax content. In addition to experimental and analytical modeling investigations, this work attempted to model measured shear stress and viscosity of waxy crude oils using tree-based machine learning methods and consider wax content and additives concentration as input parameters of models. Amongst all implemented techniques, Extra Trees model performed as a potential predictor in waxy oils rheology studies. Wax ; Rheology ; Asphaltene ; Viscosity ; Materials science ; Crude oil ; Paraffin wax ; Copolymer ; Flow properties ; Chromatography ; Chemistry ; Organic chemistry ; Composite material ; Petroleum engineering ; Polymer ; Geology ; Mechanics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis