jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dilara Ickecan | Yunus Emre Okyayli | E. A. Bleda | Doğan Erbahar
Source: arXiv (Cornell University) (preprint, 2023-12-20)
Field: Materials Science — Materials Chemistry

Emergent Atomic Environments in Twisted Bilayer Graphene and Their Use in the Prediction of the Vibrational Properties While Bernal stacked bilayer graphene bears two distinct atom types in its lattice, there exists no analytical framework addressing the number of atomic environments that emerge in twisted bilayer graphene superlattices. Here, we computationally analyze 140 different twisted bilayer superlattices using descriptor functions to study the emergent local environments. Our study reveals that the number of atoms with unique local environments depend on the superlattice size linearly manifesting itself on two distinct lines in accordance with the respective space groups. We then introduce the use of local environments in the investigation of vibrational properties. Local phonon density of states of the atoms with unique local environments are calculated by molecular dynamics simulations and are used to train a machine learning model. This model is used to predict the phonon density of states of twisted bilayer structures. Performance of the trained model is cross validated and discussed thoroughly via different selection of training and test sets. It is shown that the model proves effective in predicting the vibrational properties of any given twisted bilayer structure. Since the generic method presented reaches far beyond twisted bilayer graphene the possible applications ranging from non-periodic structures to strain induced moiré superlattices are also discussed. Bilayer ; Bilayer graphene ; Superlattice ; Phonon ; Local density of states ; Graphene ; Atom (system on chip) ; Materials science ; Condensed matter physics ; Statistical physics ; Physics ; Chemical physics ; Nanotechnology ; Computer science ; Chemistry ; Membrane Graphene research and applications ; Thermal properties of materials ; Carbon Nanotubes in Composites
Authors: M. Devereux | Eric D. Boittier | Makrus Meuwly
Source: arXiv (Cornell University) (preprint, 2023-10-28)
Field: Materials Science — Materials Chemistry

Systematic Improvement of Empirical Energy Functions in the Era of Machine Learning The impact of targeted replacement of individual terms in empirical force fields is quantitatively assessed for pure water, dichloromethane (DCM), and solvated K$^+$ and Cl$^-$ ions. For the electrostatics, point charges (PCs) and machine learning (ML)based minimally distributed charges (MDCM) fitted to the molecular electrostatic potential are evaluated together with electrostatics based on the Coulomb integral. The impact of explicitly including second-order terms is investigated by adding a fragment molecular orbital (FMO)-derived polarization energy to an existing force field, in this case CHARMM. It is demonstrated that anisotropic electrostatics reduce the RMSE for water (by 1.6 kcal/mol), DCM (by 0.8 kcal/mol) and for solvated Cl$^-$ clusters (by 0.4 kcal/mol). An additional polarization term can be neglected for DCM but notably improves errors in pure water (by 1.1 kcal/mol) and in Cl$^-$ clusters (by 0.4 kcal/mol) and is key to describing solvated K$^+$, reducing the RMSE by 2.3 kcal/mol. A 12-6 Lennard-Jones functional form is found to perform satisfactorily with PC and MDCM electrostatics, but is not appropriate for descriptions that account for the electrostatic penetration energy. The importance of many-body contributions is assessed by comparing a strictly 2-body approach with self-consistent reference data. DCM can be approximated well with a 2-body potential while water and solvated K$^+$ and Cl$^-$ ions require explicit many-body corrections. The present work systematically quantifies which terms improve the performance of an existing force field and what reference data to use for parametrizing these terms in a tractable fashion for ML fitting of pure and heterogeneous systems. Electrostatics ; Force field (fiction) ; Electric potential energy ; Chemistry ; Ion ; Coulomb ; Molecular dynamics ; Computational chemistry ; Chemical physics ; Atomic physics ; Energy (signal processing) ; Physics ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Molecular spectroscopy and chirality
Authors: Ahmed S. Haiba | Ahmed Eliwa
Source: Research Square (preprint, 2023-08-04)
Field: Materials Science — Materials Chemistry

Neural Network Analysis for Classification of Defected Ceramic Insulators Insulator (electricity) ; Ceramic ; Partial discharge ; Wavelet transform ; Artificial neural network ; Classifier (UML) ; Materials science ; Pattern recognition (psychology) ; Fourier transform ; Computer science ; Wavelet ; Artificial intelligence ; Fast Fourier transform ; Discrete wavelet transform ; Electrical engineering ; Optoelectronics ; Engineering ; Composite material ; Algorithm ; Mathematics ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Geophysical Methods and Applications
Authors: Kieran A. Murphy | Dani S. Bassett
Source: arXiv (Cornell University) (preprint, 2023-07-10)
Field: Materials Science — Materials Chemistry

Information decomposition in complex systems via machine learning One of the fundamental steps toward understanding a complex system is identifying variation at the scale of the system's components that is most relevant to behavior on a macroscopic scale. Mutual information provides a natural means of linking variation across scales of a system due to its independence of functional relationship between observables. However, characterizing the manner in which information is distributed across a set of observables is computationally challenging and generally infeasible beyond a handful of measurements. Here we propose a practical and general methodology that uses machine learning to decompose the information contained in a set of measurements by jointly optimizing a lossy compression of each measurement. Guided by the distributed information bottleneck as a learning objective, the information decomposition identifies the variation in the measurements of the system state most relevant to specified macroscale behavior. We focus our analysis on two paradigmatic complex systems: a Boolean circuit and an amorphous material undergoing plastic deformation. In both examples, the large amount of entropy of the system state is decomposed, bit by bit, in terms of what is most related to macroscale behavior. The identification of meaningful variation in data, with the full generality brought by information theory, is made practical for studying the connection between micro- and macroscale structure in complex systems. Information bottleneck method ; Computer science ; Observable ; Variation (astronomy) ; Lossy compression ; Theoretical computer science ; Bottleneck ; Entropy (arrow of time) ; Complex system ; Generality ; Information theory ; Set (abstract data type) ; Mutual information ; Data mining ; Algorithm ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ildus F. Sharafullin | A. G. Nugumanov | U. Valiakhmetov
Source: BULLETIN of the L N Gumilyov Eurasian National University Physics Astronomy Series (article, 2023-03-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Stable skyrmion lattices found in composite multiferroics with neural networks Magnetoelectric nanofilms are of great interest as functional elements of ultra-dense memory cells. In the ground state they may contain various topological magnetic vortex structures of several nanometers in size. The qualitative and quantitative properties of such structures strongly depend on a set of physical parameters. To calculate the ground state configuration with given parameters, we use the steepest descent method; to study a large parametric space, however, significant computational resources are required. To solve this problem, we propose the use of artificial neural networks (ANN), which can help us uncover the relationship between combinations of parameters and the corresponding ground state configurations, using a relatively small number of pre-computed configurations as training data. The application of the ANN allows one to avoid excessive computational costs in the study of the parametric space and narrow down the parametric area in which the existence of stable non-trivial ground state configurations in the form of a stable skyrmion crystal is possible. Skyrmion ; Parametric statistics ; Artificial neural network ; Ground state ; Vortex ; Stability (learning theory) ; Space (punctuation) ; Parameter space ; Topology (electrical circuits) ; State space ; Computer science ; Statistical physics ; Physics ; Mathematics ; Geometry ; Condensed matter physics ; Artificial intelligence ; Quantum mechanics ; Mechanics ; Machine learning ; Combinatorics Multiferroics and related materials ; Magnetic properties of thin films ; Theoretical and Computational Physics
Authors: Ke Chen | Yueru Jiang | Wei Tao | Tianshuang Wang | Liupeng Zhao | Fangmeng Liu | Yanfeng Sun | Geyu Lu | Peng Sun
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Encapsulated Pt Nanoparticle in Mof Derivative Onto Inverse Opal Zno Creating Long-Period Stability for Intellisense Period (music) ; Inverse ; Nanoparticle ; Materials science ; Nanotechnology ; Derivative (finance) ; Stability (learning theory) ; Chemical engineering ; Computer science ; Business ; Geometry ; Mathematics ; Physics ; Engineering Copper-based nanomaterials and applications ; Advanced Memory and Neural Computing ; Advanced Nanomaterials in Catalysis
Authors: Ashish Kumar | Prashant Singh | Manoj K. Harbola
Source: arXiv (Cornell University) (preprint, 2023-05-24)
Field: Materials Science — Materials Chemistry

Density Functional Theory of Material Design$:$ Fundamentals and Applications$-II$ This is the second and the final part of the review on density functional theory (DFT), referred to as DFT-II. In the first review, DFT-I, we have discussed wavefunction-based methods, their complexity, and the basic of density functional theory. In DFT-II, we focus on fundamentals of DFT and their implications for the betterment of the theory. We start our presentation with the exact DFT result followed by the concept of exchange-correlation (xc) or Fermi-Coulomb hole and its relation with xc energy functional. We also provide the exact conditions for the xc-hole, xc-energy and xc-potential along with their physical interpretation. Next, we describe the extension of DFT for non-integer numbers of electrons, the piecewise linearity of total energy and discontinuity of chemical potential at integer particle numbers, and derivative discontinuity of the xc potential, which has consequences on fundamental gap of solids. After that, we present how one obtain more accurate xc energy functionals by going beyond LDA. We discuss the gradient expansion approximation (GEA), generalized gradient approximation (GGA), and hybrid functional approaches to designing better xc energy functionals that give accurate total energies but fail to predict properties like the ionization potential and the band gap. Thus, we describe different methods of modeling these potentials and the results of their application for the calculation of the band gaps of different solids to highlight accuracy of different xc potential. Finally, we conclude with a glimpse on orbital-free density functional theory and the machine learning approach . Density functional theory ; Hybrid functional ; Orbital-free density functional theory ; Physics ; Piecewise ; Coulomb ; Wave function ; Discontinuity (linguistics) ; Thomas–Fermi model ; Local-density approximation ; Statistical physics ; Time-dependent density functional theory ; Quantum mechanics ; Electron ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Shuaizhen Li | Tianwen Xie | Lin Ma | Daheng Liu | Nan Huang | Wei Liu | Bing Li | Zhigang Gai | Xin Jiang | Song Ma | Zhidong Zhang
Source: Research Square (preprint, 2023-10-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advanced bifunctional bionic neural network-like architecture constructed by multi-scale carbon nanotubes nanocomposites Abstract Accompanied with challenging to develop bifunctional electromagnetic wave (EMW) absorbers to enhance the environmental adaptability and survivability of electromagnetic devices in harsh environment, a three-dimensional (3D) hierarchical bionic neural network-like architectures decorated multi-scale Ni 3 Fe@N-doped carbon nanotube composites (Ni 3 Fe@NCNT@CF) were innovatively designed and constructed to realize the integration of microwave absorption and corrosion protection. The optimal Ni 3 Fe@NCNT@CF demonstrates competitive EMW absorption with a minimum reflection loss of −51.5 dB and a maximum effective absorption bandwidth (EAB) of 6.4 GHz at 10 wt%, due to superior interlinked conductive channels (with dominant and adjustable conduction loss) and magnetic-dielectric matching of the 3D hierarchical bionic neural network. A reliable anti-corrosion performance is further exhibited to show ultra-low corrosion current density ~10 -8 A·cm -2 after 30 days of immersion in corrosive medium, owing to the physical shield provided by the impermeable and inert CNT shells. Our findings may provide design inspiration of versatile EMW materials for applications in complicated environments. Carbon nanotube ; Materials science ; Nanocomposite ; Reflection loss ; Corrosion ; Microwave ; Nanotechnology ; Absorption (acoustics) ; Electrical conductor ; Composite material ; Computer science ; Composite number ; Telecommunications Electromagnetic wave absorption materials ; Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies
Authors: Nedjla Khelfa | Salah Belaıdı | Enfel Zerroug | Fatima Soualmia | Samir Chtita
Source: Main Group Chemistry (article, 2023-05-02)
Field: Chemistry — Organic Chemistry

<i>In silico</i>-based identification of new anti-pfdhfr drug candidates via 1,3,5-triazine derivatives Quantitative structure-activity relationship study was used to investigate the relationship between anti-pfdhfr activity and structure of twenty-eight 1,3,5-triazine derivatives. We performed benchmark studies on the molecular geometry, electron properties of 1,3,5-triazine using semi-empirical(PM3), density functional theory and post Hartree-Fock methods. Followed by a QSAR study using multiple linear regression (MLR) and artificial neural networks (ANN). The QSAR models developed allow identify/describe the relationship between the biological activity of the molecules and their molecular descriptors (topological, physicochemical, electronic...). A further external set of compounds was used for validation where a high correlation between experimental and predicted anti-pfdhfr activity values is noticed. This QSAR study provides useful information for developing novel pfdhfr inhibitors. The set’s ADME properties and drug similarities, as well as newly produced compounds and reference ligand, were investigated. These findings would be extremely useful in guiding optimization for the development of new anti-pfdhfr drug candidates. Chemistry ; In silico ; Triazine ; Identification (biology) ; Combinatorial chemistry ; Drug ; Chromatography ; Pharmacology ; Organic chemistry ; Biochemistry Synthesis and Characterization of Heterocyclic Compounds ; Click Chemistry and Applications ; Synthesis and biological activity
Authors: G. Moskal
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Selection of Low and Ultra-Low Thermal Conductivity Materials for Insulating Layers of Tbc Systems in the Context of Computational Materials Science Opportunities Context (archaeology) ; Materials science ; Thermal conductivity ; Selection (genetic algorithm) ; Engineering physics ; Material selection ; Nanotechnology ; Thermal ; Composite material ; Optoelectronics ; Computer science ; Engineering ; Physics ; Artificial intelligence ; Geography Thermal properties of materials
Authors: Yizhen Situ | Xueying Yuan | Wenyuan Yang | Zenan Shi | Yaling Yan | Shuhua Li | Zili Liu | Qingyuan Yang | Yufang Wu | Hong Liang | Zhiwei Qiao
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Big Data Mining for High-Performance Metal-Organic Framework Membranes by Visualization of Gas Polarizability Polarizability ; Visualization ; Big data ; Membrane ; Metal-organic framework ; Data science ; Data mining ; Computer science ; Materials science ; Chemistry ; Physical chemistry ; Organic chemistry ; Molecule Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Machine Learning in Materials Science
Authors: Ethan Zachary G. Castro | Rhea Mae B. Biñar | Syla Y. Naval | Bonifacio T. Doma
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Modeling the Vapor-Liquid Equilibrium of Co2-H2o-[Cnc1im][Ntf2] Blends Using Machine Learning Techniques to Determine Feasible Solvent Conditions for Co2 Absorption in a Gas-Sweetening Unit Sweetening ; Solvent ; Absorption (acoustics) ; Materials science ; Co2 removal ; Chemical engineering ; Process engineering ; Chemistry ; Organic chemistry ; Thermodynamics ; Carbon dioxide ; Engineering ; Sweetening agents ; Physics ; Composite material Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Hasan M. Sayeed | Wade Smallwood | Sterling G. Baird | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-11-14)
Field: Materials Science — Materials Chemistry

Quantifying the distribution of materials data types in scientific literature across text, tables, and figures Materials science research is a multifaceted field, with valuable data scattered across the pages of research papers in various formats. The efficient extraction of data from these papers is of paramount importance for further analysis and research. This study aims to shed light on the distribution of data in materials science papers and their interconnections. In this preliminary analysis, we systematically examined 10 random materials science papers to discern where key data types—composition, processing conditions, characterization, and performance properties—reside within the textual content, tables, and figures. Our findings reveal intriguing patterns in the presentation of data, ranging from conventional text-based descriptions to detailed tabular presentations and visually informative figures. The analysis encompasses diverse materials and highlights cases where data types are isolated or interconnected across different sources. We also address the challenges and limitations faced during the annotation process. This investigation underscores the importance of understanding data distribution within materials science papers, as it has profound implications for data accessibility and integration in the field. Furthermore, these insights pave the way for future research, particularly in the development of advanced NLP models tailored to the unique characteristics of materials science research papers and other machine learning techniques for more efficient data extraction and analysis in materials science research. Data science ; Computer science ; Presentation (obstetrics) ; Field (mathematics) ; Data extraction ; Information retrieval Machine Learning in Materials Science ; Topic Modeling ; Advanced Text Analysis Techniques
Authors: Hasan M. Sayeed | Wade Smallwood | Sterling G. Baird | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-11-16)
Field: Materials Science — Materials Chemistry

Quantifying the distribution of materials data types in scientific literature across text, tables, and figures Materials science research is a multifaceted field, with valuable data scattered across the pages of research papers in various formats. The efficient extraction of data from these papers is of paramount importance for further analysis and research. This study aims to shed light on the distribution of data in materials science papers and their interconnections. In this preliminary analysis, we systematically examined 10 random materials science papers to discern where key data types—composition, processing conditions, characterization, and performance properties—reside within the textual content, tables, and figures. Our findings reveal intriguing patterns in the presentation of data, ranging from conventional text-based descriptions to detailed tabular presentations and visually informative figures. The analysis encompasses diverse materials and highlights cases where data types are isolated or interconnected across different sources. We also address the challenges and limitations faced during the annotation process. This investigation underscores the importance of understanding data distribution within materials science papers, as it has profound implications for data accessibility and integration in the field. Furthermore, these insights pave the way for future research, particularly in the development of advanced NLP models tailored to the unique characteristics of materials science research papers and other machine learning techniques for more efficient data extraction and analysis in materials science research. Data science ; Computer science ; Presentation (obstetrics) ; Field (mathematics) ; Data extraction ; Information retrieval Machine Learning in Materials Science ; Topic Modeling ; Advanced Text Analysis Techniques
Authors: Somalika Pradhan | Biswajit Brahma | Jitendra Pramanik
Source:  (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

Prediction of Thermal Conductivity and Heat Capacity of PAN and PEG Copolymers Composite using Artificial Neural Network In this investigation, the BIOVIA material studio was used to evaluate the PAN/PEG composite. The composite's thermal conductivity and heat capacity were measured, and the root mean square error was predicted using the ANN model in the Synthia module of the BIOVIA application. The composite's thermal conductivity and heat capacity were evaluated throughout a range of polyethylene glycol mass fractions, from 0% to 1%. Compared to the pure material, the composite's thermal conductivity increased by 46%, and its heat capacity increased by 9.4% when PEG made up 1% of the mass fraction. Thermal conductivity and heat capacity data were taken in a dry laboratory and then analyzed using a bi-layer artificial neural network (ANN). The ANN's thermal conductivity and heat capacity estimates held up throughout testing. The implemented ANN modeled Both attributes well, with a Mean Squared Log Error of 1.29 and 14.46, respectively Thermal conductivity ; Materials science ; Heat capacity ; Composite number ; Composite material ; Mass fraction ; Volumetric heat capacity ; Polyethylene glycol ; Mean squared error ; Heat transfer ; Thermodynamics ; Heat transfer coefficient ; Chemical engineering ; Mathematics ; Engineering ; Physics Thermal properties of materials ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators