jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Javier F. Troncoso | Yang Hu | Nicolo M. della Ventura | Amit Sharma | Xavier Maeder | Vladyslav Turlo
Source: arXiv (Cornell University) (preprint, 2023-03-03)
Field: Materials Science — Materials Chemistry

Machine learning of twin/matrix interfaces from local stress field Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic simulations an attractive method for investigating the role of individual twin/matrix interfaces such as twin boundaries and basal-prismatic interfaces in twin growth kinetics. Unfortunately, there is no single framework that allows researchers to differentiate such interfaces automatically, neither in experimental in-situ transmission electron microscopy analysis images nor in atomistic simulations. Moreover, the presence of alloying elements introduces substantial noise to local atomic environments, making it nearly impossible to identify which atoms belong to which interface. Here, with the help of advanced machine learning methods, we provide a proof-of-concept way of using the local stress field distribution as an indicator for the presence of interfaces and for determining their types. We apply such an analysis to the growth of twin embryos in Mg-10 at.% Al alloys under constant stress and constant strain conditions, corresponding to two extremes of high and low strain rates, respectively. We discover that the kinetics of such growth is driven by high-energy basal-prismatic interfaces, in line with our experimental observations for pure Mg. Crystal twinning ; Materials science ; Stress field ; Stress (linguistics) ; Field (mathematics) ; Matrix (chemical analysis) ; Transmission electron microscopy ; Characterization (materials science) ; Deformation (meteorology) ; Nanoscopic scale ; Crystallography ; Computer science ; Nanotechnology ; Chemical physics ; Chemistry ; Physics ; Composite material ; Thermodynamics ; Finite element method ; Mathematics ; Microstructure Microstructure and mechanical properties ; Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties
Authors: Kuray Preeya | McCarthy Tom | Ancharski Caleb | Nakajima Yoji
Source:  (article, 2023-05-01)
Field: Materials Science — Polymers and Plastics

Properties of low dielectric constant buildup materials containing micron sized hollow silica Build-up substrate materials are high-performing specialty materials that consist of inorganic reinforcing particulates evenly dispersed throughout a continuous thermoset polymer matrix. Build-up materials are of critical importance in high density interconnect applications and often serve as the dielectric layer in multilayer circuitry including interposers, substrate packaging, and redistribution layers. Thermoset build-up materials benefit from low coefficients of thermal expansion, high reliability, and are available as thin dielectrics allowing for high density circuitry. Typically, high concentrations of ceramic particulate reinforcement are necessary to yield a low rate of thermal expansion. Solid particles of silica have the disadvantage of silica naturally raising the dielectric constant of a composite material relative to the low loss polymer resin which contributes to a lower dielectric constant. The future of build-up material requires increasingly lower loss for applications such as machine learning, AI, 5/6G, and high-speed switching. Although not as obvious as dissipation factor, there is also an advantage of lowering the dielectric constant. Increasing densification and finer lines and spaces leads to increasingly higher transmission line losses. Lower dielectric constant dielectrics allow a reduction in dielectric thickness while maintaining a given trace width. In order to meet the rapidly growing needs of increasingly higher density interconnect with the lowest losses, lower dielectric constant and lower dissipation factor build-up materials can be achieved by designing the thermoset matrix with lower dissipation factor and replacing solid particulates of silica with micron sized hollow silica, effectively taking advantage of air within the dielectric medium. In this study, we investigate the dielectric, mechanical and thermal properties of thermoset polymer buildup films that have been reinforced with hollow silica particulates. Dielectric ; Materials science ; Constant (computer programming) ; Permittivity ; Composite material ; Optoelectronics ; Engineering physics ; Computer science ; Physics Synthesis and properties of polymers ; Aerogels and thermal insulation ; Silicone and Siloxane Chemistry
Authors: Musaelian, Albert
Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Large-scale atomistic simulation at near-quantum accuracy with equivariant machine learning Since the first digital computers were built, scientists have programmed them to make concrete predictions about real-world systems by numerically solving the relevant physical laws that govern the behavior of those systems. Many of these efforts have focused on simulating the behavior of the atoms that make up materials and chemicals. As computational power has grown, these simulations have shifted from using simple qualitative models of idealized interatomic interactions to sophisticated techniques for solving the quantum physics of realistic systems of electrons and atomic nuclei. These first-principles models offer a remarkable degree of accuracy and predictive power, but their computational cost also imposes severe limitations on the kinds of length- and time-scales that can be simulated. Machine learning models of interatomic interactions---called machine learning interatomic potentials (MLIPs)---can be used as fast approximations of these accurate but costly quantum calculations. In principle, MLIPs can enable much longer and larger simulations to be run at near-quantum accuracy. This thesis presents algorithms and implementations that can realize this promise through "equivariant" machine learning techniques that leverage the symmetries of the underlying physics. The first half discusses the equivariant MLIP NequIP, with its significantly improved data efficiency, accuracy, generalization, and robustness. The second half presents the development of the novel Allegro architecture. Allegro makes it possible to scale up equivariant machine learning: this ability is demonstrated through comprehensive performance experiments that apply a powerful Allegro model trained on a large dataset of more than 1M quantum calculations to biomolecular systems of realistic sizes of up to 44M atoms while taking advantage of up to 5120 GPUs. Leverage (statistics) ; Quantum ; Equivariant map ; Physical system ; Interatomic potential ; Complex system ; Simple (philosophy) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Chayakon Chanlun | Kittikhun Kiattisaksiri | Lapatrada Dangsungnoen | Kannika Wiratchawa | Likkhasit Thabtham | Supree Pinitsoontorn | Thanapong Intharah
Source:  (article, 2023-06-25)
Field: Materials Science — Materials Chemistry

DeepBET: Estimating the Surface Area of Plant Carbon using SEM Image The importance of alternative and clean energy sources increases as the world faces global warming and energy shortages. Renewable energy sources like solar and wind energy require storage devices to store energy without sunlight or wind. Supercapacitors are high-capacity electrical charge storage devices with a higher power density and are safe for users. Carbon from natural sources is an exciting material for producing electrical charge storage because of its good electrical properties and natural resource conservation. However, a high specific surface area, Brunauer-Emmett-Teller (BET) surface area, is necessary for practical electrical charge storage. Standard-specific surface area calculation requires high resources such as time and cost using the conventional BET method. This research presents a machine learning model developed for estimating the BET surface area of carbon from plants using SEM images through a deep learning model, DeepBET. DeepBET predicts the BET surface area value with 76% accuracy, reducing the time and cost of calculating the specific surface area. This research explored the possibility to train a computer vision model through scientific publication databases. BET theory ; Renewable energy ; Supercapacitor ; Environmental science ; Energy storage ; Electric potential energy ; Wind power ; Computer science ; Process engineering ; Remote sensing ; Materials science ; Electrical engineering ; Power (physics) ; Capacitance ; Engineering ; Geography ; Electrode ; Chemistry Machine Learning in Materials Science ; Advanced Neural Network Applications
Authors: Maurycy Krzyżanowski | Grzegorz Matyszczak
Source: Research Square (preprint, 2023-09-20)
Field: Chemistry — Analytical Chemistry

Machine Learning Prediction Of Organic Moieties From The IR Spectra, Enhanced By Additionally Using The Derivative IR Data Abstract Infrared spectroscopy is a crucial analytical tool in organic chemistry, but interpreting IR data can be challenging. This study provides a comprehensive analysis of five machine learning models: Logistic Regression, KNN (k-nearest neighbors), SVM (support vector machine), Random Forest, and MLP (multilayer perceptron), and their effectiveness in interpreting IR spectra. The simple KNN model outperformed the more complex SVM model in execution time and F1 score, proving the potential of simpler models in interpreting the IR data. The combination of original spectra with its corresponding derivatives, improved the performance of all models with a minimal increase in execution time. Denoising of the IR data was investigated but did not significantly improve performance. Although the MLP model showed better performance than the KNN model, its longer execution time is substantial. Ultimately, KNN is recommended for rapid results with minimal performance compromise, while MLP is suggested for projects prioritizing accuracy despite longer execution time. Support vector machine ; Computer science ; Artificial intelligence ; Random forest ; Multilayer perceptron ; Machine learning ; Logistic regression ; Pattern recognition (psychology) ; Perceptron ; Data mining ; Artificial neural network Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Mehdi Kiani | Mahsa Zolfaghari | Jalal Kiani
Source: arXiv (Cornell University) (preprint, 2023-07-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Transfer Learning for Inverse Design of Tunable Graphene-Based Metasurfaces This paper outlines a new approach to designing tunable electromagnetic (EM) graphene-based metasurfaces using convolutional neural networks (CNNs). EM metasurfaces have previously been used to manipulate EM waves by adjusting the local phase of subwavelength elements within the wavelength scale, resulting in a variety of intriguing devices. However, the majority of these devices have only been capable of performing a single function, making it difficult to achieve multiple functionalities in a single design. Graphene, as an active material, offers unique properties, such as tunability, making it an excellent candidate for achieving tunable metasurfaces. The proposed procedure involves using two CNNs to design the passive structure of the graphene metasurfaces and predict the chemical potentials required for tunable responses. The CNNs are trained using transfer learning, which significantly reduced the time required to collect the training dataset. The proposed inverse design methodology demonstrates excellent performance in designing reconfigurable EM metasurfaces, which can be tuned to produce multiple functions, making it highly valuable for various applications. The results indicate that the proposed approach is efficient and accurate and provides a promising method for designing reconfigurable intelligent surfaces for future wireless communication systems. Graphene ; Computer science ; Convolutional neural network ; Wireless ; Inverse ; Electronic engineering ; Materials science ; Computer architecture ; Nanotechnology ; Artificial intelligence ; Telecommunications ; Engineering Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Yuxing Ma | Yang Zhong | Yu Hongyu | Shiyou Chen | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2023-06-13)
Field: Materials Science — Materials Chemistry

Transferable Machine Learning Approach for Predicting Electronic Structures of Charged Defects The study of the electronic properties of charged defects is crucial for our understanding of various electrical properties of materials. However, the high computational cost of density functional theory (DFT) hinders the research on large defect models. In this study, we present an E(3) equivariant graph neural network framework (HamGNN-Q), which can predict the tight-binding Hamiltonian matrices for various defect types with different charges using only one set of network parameters. By incorporating features of background charge into the element representation, HamGNN-Q enables a direct mapping from structure and background charge to the electronic Hamiltonian matrix of charged defect systems without DFT calculation. We demonstrate the model's high precision and transferability through testing on GaAs systems with various charged defect configurations. Our approach provides a practical solution for accelerating charged defect electronic structure calculations and advancing the design of materials with tailored electronic properties. Transferability ; Electronic structure ; Hamiltonian (control theory) ; Computer science ; Density functional theory ; Charge (physics) ; Artificial neural network ; Tight binding ; Representation (politics) ; Materials science ; Statistical physics ; Computational chemistry ; Physics ; Chemistry ; Artificial intelligence ; Machine learning ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Topic Modeling
Authors: Kanna Omae | Takahiro Yamazaki | Chiemi Oka | Junpei Sakurai | Seiichi Hata
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Stress Measurement Based on Magnetic Barkhausen Noise for Thin Films Barkhausen effect ; Materials science ; Stress (linguistics) ; Noise (video) ; Thin film ; Condensed matter physics ; Nuclear magnetic resonance ; Acoustics ; Computer science ; Magnetic field ; Physics ; Nanotechnology ; Magnetization ; Artificial intelligence Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Surface Roughness and Optical Measurements
Authors: Faridun N. Jalolov | Evgeny V. Podryabinkin | Artem R. Oganov | Alexander V. Shapeev | Alexander G. Kvashnin
Source: arXiv (Cornell University) (preprint, 2023-09-26)
Field: Materials Science — Materials Chemistry

Mechanical properties of single and polycrystalline solids from machine learning Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of $ab$ $initio$ methods and low accuracy of empirical potentials. We propose a computational technique towards efficient calculations of mechanical properties of polycrystals, composites, and multi-phase systems from atomistic simulation with high accuracy and reasonable computational cost. It is based on using actively learned machine learning interatomic potentials (MLIPs) trained on a local fragments of the polycrystalline system for which forces, stresses and energies are computed by using $ab$ $initio$ calculations. Developed approach is used for calculation the dependence of elastic moduli of polycrystalline diamond on the grain size. This technique allows one to perform large-scale calculations of mechanical properties of complex solids of various compositions and structures with high accuracy making the transition from ideal (single crystal) systems to more realistic ones. Crystallite ; Computation ; Materials science ; Ab initio ; Diamond ; Interatomic potential ; Ab initio quantum chemistry methods ; Diamond cubic ; Elastic modulus ; Statistical physics ; Computer science ; Computational chemistry ; Algorithm ; Molecular dynamics ; Chemistry ; Composite material ; Physics ; Metallurgy Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials
Authors: Marcin Maździarz
Source: arXiv (Cornell University) (preprint, 2023-08-14)
Field: Materials Science — Materials Chemistry

Transferability of interatomic potentials for germanene (2D germanium) The capacities of various interatomic potentials available for elemental germanium, with the scope to choose the potential suitable for the modeling of germanene (2D germanium) allotropes were investigated. Structural and mechanical properties of the flat (F), low-buckled (LB), trigonal dumbbell (TD) and large honeycomb dumbbell (LHD) single-layer germanium (germanene) phases, were obtained using the density functional theory (DFT) and molecular statics (MS) computations with Tersoff, MEAM, Stillinger-Weber, EDIP, ReaxFF and machine-learning-based (ML-IAP) interatomic potentials. A systematic quantitative comparative study and discussion of the findings are given. Germanene ; Germanium ; Interatomic potential ; Density functional theory ; Materials science ; Dumbbell ; Transferability ; Silicene ; Molecular physics ; Crystallography ; Molecular dynamics ; Computational chemistry ; Chemistry ; Nanotechnology ; Graphene ; Silicon ; Metallurgy ; Computer science Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Jianping Xu | Tao Zu | Yi‐Cheng Hsu | Yi Sun | Dan Wu | Yi Zhang
Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (article, 2023-08-03)
Field: Materials Science — Materials Chemistry

Cross-domain Variational Network for Fast Chemical Exchange Saturation Transfer Imaging Chemical exchange saturation transfer (CEST) is an emerging molecular imaging technique that can detect various biomolecules in vivo. However, the routine clinical application of CEST MRI is hammered by its long scan time due to the multiple saturation frames acquired. Here, a novel deep neural network modified from the variational network (VN) by utilizing cross-domain regularization structures, dubbed CEST-VN, is proposed for accelerated CEST imaging. In conjunction with multi-coil sensitivity encoding, the CEST-VN method demonstrated superior performance to the conventional parallel imaging and the original VN methods in healthy volunteers and glioma patients. Nuclear magnetic resonance ; Magnetic resonance imaging ; Saturation (graph theory) ; Regularization (linguistics) ; Chemistry ; Biological system ; Computer science ; Physics ; Artificial intelligence ; Radiology ; Mathematics ; Medicine Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Changqing He | Haitao Wu | Yi Hua | Ying Tian | Lin He | Hong Sui
Source:  (preprint, 2023-11-03)
Field: Chemistry — Analytical Chemistry

Fast and precise reconstruction of natural polycyclic aromatic hydrocarbons: novel algorithm and case study The polycyclic aromatic hydrocarbons (PAHs) are common constituents and pose significant influences on many separation and conversion processes. Herein, an efficient approach has been developed to fast and precisely reconstruct the molecular models for PAHs. The method involves stochastic reconstruction for a molecular library and artificial neural network (ANN) optimization for model refinement. Stochastic reconstruction, aided by modeling matrices, allows fast arrangement of benzene molecules, simplifying the construction of fused-ring cores. ANN analysis of multiple PAHs models reveals relationships between modeling parameters (MPs) and evaluation parameters (EPs). By optimizing hyper-parameters, a minimal loss function value of 0.085 is achieved, indicating the high precision of modeling. Case study on interfacially active asphaltene (IAA) shows successful reconstruction of its molecular structure, confirmed by experimental results (deviation error &lt; 10%). These findings provide valuable insights into using an universal algorithm to fast and precisely reconstruct intricate natural PAHs and other natural chemical compounds Asphaltene ; Algorithm ; Ring (chemistry) ; Polycyclic aromatic hydrocarbon ; Computer science ; Benzene ; Artificial neural network ; Molecular model ; Biological system ; Biochemical engineering ; Chemistry ; Artificial intelligence ; Organic chemistry ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Karim Zongo | Hao Sun | Claudiane Ouellet‐Plamondon | Laurent Karim Béland
Source: arXiv (Cornell University) (preprint, 2023-11-26)
Field: Materials Science — Materials Chemistry

A unified moment tensor potential for silicon, oxygen, and silica Si and its oxides have been extensively explored in theoretical research due to their technological and industrial importance. Simultaneously describing interatomic interactions within both Si and SiO$_2$ without the use of \textit{ab initio} methods is considered challenging, given the charge transfers involved. Herein, this challenge is overcome by developing a unified machine learning interatomic potentials describing the Si/ SiO$_2$/ O system, based on the moment tensor potential (MTP) framework. This MTP is trained using a comprehensive database generated using density functional theory simulations, encompassing a wide range of crystal structures, point defects, extended defects, and disordered structure. Extensive testing of the MTP is performed, indicating it can describe static and dynamic features of very diverse Si, O, and SiO$_2$ atomic structures with a degree of fidelity approaching that of DFT Interatomic potential ; Moment (physics) ; Tensor (intrinsic definition) ; Silicon ; Ab initio ; Moment tensor ; Density functional theory ; Point (geometry) ; Materials science ; Range (aeronautics) ; Charge (physics) ; Fidelity ; Computer science ; Chemical physics ; Statistical physics ; Physics ; Condensed matter physics ; Molecular dynamics ; Chemistry ; Computational chemistry ; Quantum mechanics ; Mathematics ; Optoelectronics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; High-pressure geophysics and materials