jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cuiping Zhang | Zhu Jinfeng | Xiaoling Shen | Shu Mingfang | Ren Qing-Yong | Jie Ma
Source: Acta Physica Sinica (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Application of neutron scattering to studying low lattice thermal conductivity of Zintl phase compounds Due to the unique crystal structures and excellent transport properties, the Zintl phase thermoelectric materials have aroused extensive interest in energy storage and conversion. To explore the origins of those excellent performances, a series of experimental and theoretical techniques have been applied, such as neutron scattering, thermal conductivity, and molecular dynamics simulations with machine learning. In this paper, the progress of neutron scattering research on the structure and dynamics of Zintl phase is summarized, for example &lt;i&gt;A&lt;/i&gt;&lt;sub&gt;14&lt;/sub&gt;&lt;i&gt;MPn&lt;/i&gt;&lt;sub&gt;11&lt;/sub&gt; compounds with zero-dimensional (0D) substructures, 1D chains-based compounds, 2D layered &lt;i&gt;A&lt;/i&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;i&gt;BX&lt;/i&gt;&lt;sub&gt;2&lt;/sub&gt; compounds (including the binary Mg&lt;sub&gt;3&lt;/sub&gt;Sb&lt;sub&gt;2&lt;/sub&gt;) and their structural variants, as well as &lt;i&gt;AB&lt;/i&gt;&lt;sub&gt;4&lt;/sub&gt;&lt;i&gt;X&lt;/i&gt;&lt;sub&gt;3&lt;/sub&gt;, and ZrBeSi-type compounds. The underlying mechanisms of intrinsically low lattice thermal conductivity in those Zintl phase are discussed in detail. These compounds generally exhibit the following characteristics: 1) strong anharmonicity, which is characterized by strong atomic vibrations and anharmonic phonon-phonon scattering; 2) weak chemical bonding, which usually leads to low sound velocity and interatomic force constants, and corresponding to low-energy phonon branches; 3) intrinsic vacancy defect, which weakens the bond strengths, softens the lattice, and enhances anharmonic phonon-phonon scattering. Neutron diffraction is applied to studying crystal structures, lattice parameters, atomic occupancies, and atomic displacement parameters. Inelastic neutron scattering measures the lattice dynamics, and density of states, which are related to lattice thermal conductivity. Hence, the physical mechanisms of Zintl compounds are analyzed for optimizing material properties and designing new functional materials. Neutron scattering ; Thermal conductivity ; Materials science ; Zintl phase ; Lattice (music) ; Scattering ; Condensed matter physics ; Small-angle neutron scattering ; Phase (matter) ; Crystallography ; Chemistry ; Crystal structure ; Physics ; Optics ; Composite material ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Expansion and Ionic Conductivity
Authors: Yuxiang Gao | Fenglin Deng | Ri He | Zhicheng Zhong
Source: arXiv (Cornell University) (preprint, 2024-09-03)
Field: Materials Science — Materials Chemistry

Spontaneous curvature in two-dimensional van der Waals heterostructures Two-dimensional (2D) van der Waals (vdW) heterostructures consist of different 2D crystals with diverse properties, constituting the cornerstone of the new generation of 2D electronic devices. Yet interfaces in heterostructures inevitably break bulk symmetry and structural continuity, resulting in delicate atomic rearrangements and novel electronic structures. In this paper, we predict that 2D interfaces undergo spontaneous curvature, which means when two flat 2D layers approach each other, they inevitably experience out-of-plane curvature. Based on deep-learning-assisted large-scale molecular dynamics simulations, we observed significant out-of-plane displacements up to 3.8 angstrom in graphene/BN bilayers induced by curvature, producing a stable hexagonal moire pattern, which agrees well with experimentally observations. Additionally, the out-of-plane flexibility of 2D crystals enables the propagation of curvature throughout the system, thereby influencing the mechanical properties of the heterostructure. These findings offer fundamental insights into the atomic structure in 2D vdW heterostructures and pave the way for their applications in devices. Curvature ; van der Waals force ; Physics ; Condensed matter physics ; Materials science ; Geometry ; Quantum mechanics ; Mathematics ; Molecule Graphene research and applications ; Quantum Electrodynamics and Casimir Effect ; Gas Dynamics and Kinetic Theory
Authors: Vladimir Kopysov | Ruslan D. Yamaletdinov | Oleg V. Boyarkin
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Biomaterials

Oligomers of diphenylalanine examined using cold ion spectroscopy and neural network-based conformational search Diphenylalanine (Phe<sub>2</sub>) is the primary building block of many self-assembling nanostructures that are important in biology and materials science. Understanding the detailed mechanism of their formation requires knowledge of the structural motives that the smallest oligomers attain at the very first steps of the process. Herein, we first employed high-resolution mass spectrometry to assign protonated Phe<sub>2</sub> and its 2-13-unit oligomers formed in the gas phase from solution <i>via</i> electrospray ionization and then used cold ion spectroscopy to record UV and IR spectra for the monomer, dimer and hexamer. UV spectroscopy suggests the likely lack of specific strong proton-π interactions in oligomers larger than octamers, implying their certain structural stabilization. IR spectroscopy and quantum chemical calculations, enhanced by neural network-based conformational search, jointly determined the lowest-energy structures of the Phe<sub>2</sub> monomer and dimer. Spectroscopy ; Ion ; Artificial neural network ; Chemistry ; Crystallography ; Materials science ; Computer science ; Physics ; Organic chemistry ; Artificial intelligence Supramolecular Self-Assembly in Materials ; Molecular Sensors and Ion Detection ; Crystallography and molecular interactions
Authors: Hamzah Setiawan
Source: MULTITEK INDONESIA (article, 2024-08-16)
Field: Chemistry — Analytical Chemistry

ONION CRACKERS SALES FORECASTING USING ARTIFICIAL NEURAL NETWORK METHOD AND HOLT'S DOUBLE EXPONENTIAL SMOOTHING Changes in product demand is a problem that is often faced by the industry as well as one of them is onion crackers. Tapioca flour is the main ingredient used to make onion crackers. Because the demand for crackers is always changing, this company often experiences excess or shortage of raw materials. If there is an excess of raw materials, the company must incur additional costs for the maintenance and storage of raw materials so that raw materials can be properly stored in accordance with existing standards, which of course costs a lot. Therefore, companies must plan to solve this problem by planning raw material requirements by forecasting raw material requirements using the artificial neural network method and double exponential smoothing holt. The results showed that the artificial network method had a mean square error of 0.120 and the mean square error using the double exponential smoothing method yielded a value of 206.19. Based on these two values, it can be concluded that the artificial neural network method is more accurate than the double exponential smoothing holt method. This can be seen by comparing the roat mean square error values of the two methods.. Exponential smoothing ; Artificial neural network ; Sales forecasting ; Computer science ; Exponential function ; Double exponential function ; Artificial intelligence ; Smoothing ; Econometrics ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses
Authors: Zhixiang Liu | Jiafeng Lu | Zhijie Fang | Dongjing Liu | Ping Yang | Shuhong Yang | Yunqing Tang
Source: Materials Today Communications (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Study on mechanical properties of ZnO/Si interface under thermal shock Materials science ; Thermal shock ; Thermal ; Interface (matter) ; Composite material ; Shock (circulatory) ; Engineering physics ; Thermodynamics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Chieh Fu Hsiao | Georg Feyrer | Anthony Stein
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Classifying Early Stage Soybean Fungal Diseases on Hyperspectral Images Using Convolutional Neural Network Hyperspectral imaging ; Convolutional neural network ; Stage (stratigraphy) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Longchao Da | Lehao Yang | Shanyu Han | Hua Wei | Feng An
Source: ChemRxiv (preprint, 2024-02-07)
Field: Chemistry — Organic Chemistry

Prediction of the Potential Energy Surface for polyatomic molecule with the Three-dimensional Special Euclidean Equivariant Transformer Network The process of fitting potential energy surfaces using machine learning methods typically involves manually constructing feature vectors and transforming molecular graphs into the inputs of networks for the polyatomic molecule structure. In this study, we introduce a novel approach using a three-dimensional special Euclidean equivariant transformer network that can directly learn the potential energy of polyatomic molecules and represent the structure of the molecule in a universal and interpretable way. Our method accurately predicts the potential energy of polyatomic molecules, as determined by coupled cluster theory, for various molecular graphs. Moreover, our framework is interpretable about the molecular physics, as one can extract molecular equivariant positional information regarding the global invariant energy surface. To demonstrate the utility of our approach, we present a detailed description of the training process used to fit the potential energy surfaces of polyatomic molecule CH5, as well as the properties of its resulting potential energy surfaces. Polyatomic ion ; Potential energy surface ; Equivariant map ; Transformer ; Molecule ; Surface (topology) ; Physics ; Mathematics ; Quantum mechanics ; Pure mathematics ; Geometry Chemical Thermodynamics and Molecular Structure ; Various Chemistry Research Topics
Authors: Vivin Vinod | Peter Zaspel
Source: arXiv (Cornell University) (preprint, 2024-10-15)
Field: Materials Science — Materials Chemistry

Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies Recent progress in machine learning (ML) has made high-accuracy quantum chemistry (QC) calculations more accessible. Of particular interest are multifidelity machine learning (MFML) methods where training data from differing accuracies or fidelities are used. These methods usually employ a fixed scaling factor, $γ$, to relate the number of training samples across different fidelities, which reflects the cost and assumed sparsity of the data. This study investigates the impact of modifying $γ$ on model efficiency and accuracy for the prediction of vertical excitation energies using the QeMFi benchmark dataset. Further, this work introduces QC compute time informed scaling factors, denoted as $θ$, that vary based on QC compute times at different fidelities. A novel error metric, error contours of MFML, is proposed to provide a comprehensive view of model error contributions from each fidelity. The results indicate that high model accuracy can be achieved with just 2 training samples at the target fidelity when a larger number of samples from lower fidelities are used. This is further illustrated through a novel concept, the $Γ$-curve, which compares model error against the time-cost of generating training samples, demonstrating that multifidelity models can achieve high accuracy while minimizing training data costs. Excitation ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: St. Maurice, Martin
Source: e-Publications@Marquette (Marquette University) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

AI Is a Viable Alternative to High Throughput Screening: A 318-Target Study High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery. Virtual screening ; Throughput ; Drug discovery ; Convolutional neural network ; Artificial neural network ; High-throughput screening Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Source:  (preprint, 2024-11-21)
Field: Materials Science — Materials Chemistry

Machine Learning and AI for the Sciences: toward understanding In recent years, machine learning (ML) and artificial intelligence (AI) methods have begun to play a more and more enabling role in the sciences and in industry. In particular, the advent of large and/or complex data corpora has given rise to new technological challenges and possibilities. In his talk, Müller will touch upon the topic of ML applications in the sciences, in particular in chemistry and physics. He will also discuss possibilities for extracting information from machine learning models to further our understanding by explaining nonlinear ML models. Finally, Müller will briefly discuss perspectives and limitations. Artificial intelligence ; Computer science ; Data science ; Cognitive science ; Psychology Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Ming Li | Lu Guo | Haodong Yu | Menglei Li | Fawei Zheng
Source: arXiv (Cornell University) (preprint, 2024-01-05)
Field: Materials Science — Materials Chemistry

Scaling Laws Governing the Elastic Properties of 3D-Graphenes In this study, we have comprehensively investigated the scaling law for elastic properties of three-dimensional honeycomb-like graphenes (3D-graphenes) using hybrid neural network potential based molecular dynamics simulations and theoretical analyses. The elastic constants as functions of honeycomb hole size, denoted by the graphene wall length $L$, were provided. All five independent elastic constants in the large $L$ limit are proportional to $L^{-1}$. The associated coefficients are combinations of two-dimensional graphene's elastic constants. High-order terms including $L^{-2}$ and $L^{-3}$ emerge for finite $L$ values. They have three origins, the distorted areas close to the joint lines of 3D-graphenes, the variation of solid angles between graphene plates, and the bending distortion of graphene plates. Significantly, the chirality becomes essential with the decreasing of $L$, because the joint line structures are different between the armchair and zigzag type 3D-graphenes. Our findings provide insights into the elastic properties of graphene-based superstructures and can be used for further studies on graphene-based materials. Zigzag ; Graphene ; Materials science ; Scaling ; Honeycomb ; Bending ; Condensed matter physics ; Chirality (physics) ; Distortion (music) ; Nanotechnology ; Physics ; Geometry ; Composite material ; Quantum mechanics ; Mathematics ; Chiral symmetry Graphene research and applications ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Farhad Ghalami | Philipp M. Dohmen | Mila Krämer | Marcus Elstner | Weiwei Xie
Source: ChemRxiv (preprint, 2024-02-23)
Field: Materials Science — Polymers and Plastics

Modeling Exciton Transport in Organic Semi-conductors Using Machine Learned Hamiltonian and its Gradients In this study, we present a multiscale method to simulate the propagation of Frenkel singlet excitons in Organic Semi-conductors(OSCs). The approach uses advanced neural network models to train Frenkel-type Hamiltonian and its gradient, obtained by the Long-Range Correction version of Density Functional Tight-Binding with Self-Consistent Charges (LC-DFTB2). Our models accurately predict site energies, excitonic couplings, and corresponding gradients, essential for the non-adiabatic molecular dynamics simulations. Combined with Fewest Switches Surface Hopping (FSSH) algorithm, the method was applied to four representative OSCs: Anthracene (ANT), Pentacene (PEN), Perylenediimide (PDI), and Diindenoperylene (DIP). The simulated exciton diffusion constants align well with experimental and reported theoretical values, and offer valuable insights into exciton dynamics in OSCs. Hamiltonian (control theory) ; Exciton ; Electrical conductor ; Statistical physics ; Computer science ; Physics ; Condensed matter physics ; Chemical physics ; Quantum mechanics ; Mathematical optimization ; Mathematics Conducting polymers and applications
Authors: Rackan Mansour | Osama Desouky | Marwa AbdelGawad
Source:  (article, 2024-08-04)
Field: Materials Science — Materials Chemistry

Exploring Artificial Intelligence Tools for Materials Science in Engineering: A Work-in-Progress in Undergraduate Classroom Integration where he has pursued his interests in material science, machine learning, and design independently.His work in material science has equipped him with a deep understanding of the properties and applications of various engineering materials.Rackan has also explored machine learning, focusing on its potential to influence and transform data analysis and decision-making processes.In the realm of design, he has actively engaged in product design and computer-aided design projects, including participation in the Shell Eco-Marathon. Computer science ; Science and engineering ; Work (physics) ; Software engineering ; Data science ; Mathematics education ; Engineering ethics ; Engineering ; Mechanical engineering ; Psychology Machine Learning in Materials Science
Authors: Seongmin Park | Suk-Ju Hong | Chang-Hyup Lee | EungChan Kim | Sang-Yeon Kim | Cheorun Jo | Ghiseok Kim
Source: Journal of Animal Science and Technology (article, 2024-11-20)
Field: Chemistry — Analytical Chemistry

Non-destructive evaluation of microbial quality of beef (M. longissimus thoracis muscle) using visible/NIR hyperspectral imaging and machine learning methods Machine learning models were developed to predict the degree of microbial quality of beef by a non-destructive method using a near-infrared hyperspectral imaging system. Beef was stored under aerobic conditions at different temperature scenarios (refrigerated, thawed after freezing, or left at room temperature) for a period of 15 days to induce freshness change and microbial growth. Hyperspectral data cubes were obtained from a data acquisition system in a darkroom environment. The total aerobic bacteria (TAB) experiment was performed in the established meat science manner to provide reference values for the microbial contamination level of the sample. The region of interest designated as the red meat region was selected for spectral extraction. Regression models were developed to predict the TAB value from the extracted data. Partial least squares regression, support vector machine, artificial neural network, and one-dimensional convolutional neural network methods were employed to construct TAB prediction models. Chemical maps were also created for each developed model to visualize the performance of the model. The model development process concluded with the iteration of all previous steps at completely different times and with different beef samples, generating the data for verification and applying it to the developed model to evaluate its versatility. As a result of the development, it was confirmed that the microbial quality of beef can be predicted by models generated from hyperspectral data (Best validation <i>R</i> <sup>2</sup> = 0.8593, RMSE = 0.6947). Accurate quality prediction helps livestock breeders develop and apply better husbandry practices, which ultimately leads to higher quality beef production. Hyperspectral imaging ; Support vector machine ; Partial least squares regression ; Artificial neural network ; Artificial intelligence ; Convolutional neural network ; Computer science ; Mathematics ; Pattern recognition (psychology) ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Florian Pabst | Stefano Baroni
Source: arXiv (Cornell University) (preprint, 2024-08-10)
Field: Materials Science — Materials Chemistry

Glassy Dynamics from First-Principles Simulations The microscopic understanding of the dramatic increase in viscosity of liquids when cooled towards the glass transition is a major unresolved issue in condensed matter physics. Here, we use machine learning methods to accelerate molecular dynamics simulations with first-principles accuracy for the glass-former toluene. We show that the increase in viscosity is intimately linked to the increasing number of dynamically correlated molecules $N^*$. While certain hallmark features of glassy dynamics, like physical aging, are linked to $N^*$ as well, others, like relaxation stretching, are not. Dynamics (music) ; Statistical physics ; Computer science ; Psychology ; Physics Material Dynamics and Properties
Authors: Chao Zhou | Kuo Bao | Zekun Yu | Jinming Zhu | Hongyu Yu | Tian Cui
Source: Physical review. B./Physical review. B (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Insights into the bonding properties and magnetism of the Mn-B system with a physically constrained neural network functional In this study, we explore the manganese borides (Mn-B) system, chiefly on its complex magnetic properties that are of extensive academic interest and of significant potential for applications in magnetism. We employed an approach by using a machine-learning trained, physically constrained neural network functional to reevaluate the Mn-B system comprehensively. This offers insights into several contentious aspects of the system from an enthalpy perspective, including the spin frustration in $\mathrm{M}{\mathrm{n}}_{2}\mathrm{B}$, the ground state of $\mathrm{Mn}{\mathrm{B}}_{2}$, and the ongoing debates about the synthesis and various phases of MnB and $\mathrm{Mn}{\mathrm{B}}_{4}$. Additionally, we establish a nearly linear relationship between the bonding strength and measured hardness in borides, which enhances the understanding of their mechanical properties with our proposed descriptor of bonding strength. Spin dynamics predictions highlight both discrepancies and consistencies related to the itinerant characteristics of magnetic moments, primarily driven by a high density of states of the magnetic atoms at the Fermi level (${E}_{\mathrm{F}}$). This suggests that traditional local-moment models may be inadequate for describing these itinerant magnetic systems. This work provides insight for understanding the bonding properties and magnetism of magnetic intermetallic compounds. Magnetism ; Artificial neural network ; Materials science ; Computer science ; Condensed matter physics ; Psychology ; Physics ; Artificial intelligence Boron and Carbon Nanomaterials Research ; Magnetic Properties of Alloys ; Rare-earth and actinide compounds