jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alex C. Li | Boya Li | Felipe González‐Cataldo | Robert E. Rudd | Burkhard Militzer | Eduardo M. Bringa | Marc A. Meyers
Source: Materials Science and Engineering R Reports (article, 2024-10-22)
Field: Materials Science — Materials Chemistry

Diamond under extremes Diamond ; Materials science ; Metallurgy Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Electronic and Structural Properties of Oxides
Authors: Yagyank Srivastava | Ankit Jain
Source: Physical review. B./Physical review. B (article, 2024-10-08)
Field: Materials Science — Materials Chemistry

Accelerating prediction of phonon thermal conductivity by an order of magnitude through machine learning assisted extraction of anharmonic force constants The calculation of material phonon thermal conductivity from density functional theory calculations requires the computationally expensive evaluation of anharmonic interatomic force constants and has remained a computational bottleneck in the high-throughput discovery of materials. In this paper, we present a machine learning assisted approach for the extraction of anharmonic force constants through local learning of the potential energy surface. We demonstrate our approach on a diverse collection of 220 ternary materials for which the total computational time for anharmonic force constant evaluation is reduced by more than an order of magnitude from 480 000 CPU hours to less than 12 000 CPU hours while preserving the thermal conductivity prediction accuracy to within 10%. Our approach removes a major hurdle in computational thermal conductivity evaluation and will pave the way forward for the high-throughput discovery of materials. Anharmonicity ; Phonon ; Thermal conductivity ; Magnitude (astronomy) ; Force constant ; Condensed matter physics ; Materials science ; Extraction (chemistry) ; Thermal ; Constant (computer programming) ; Physics ; Thermodynamics ; Computer science ; Chemistry ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Pengjie Shi | Zhiping Xu
Source: Journal of Physics Condensed Matter (article, 2024-06-26)
Field: Materials Science — Materials Chemistry

Exploring fracture of H-BN and graphene by neural network force fields Extreme mechanical processes such as strong lattice distortion and bond breakage during fracture often lead to catastrophic failure of materials and structures. Understanding the nucleation and growth of cracks is challenged by their multiscale characteristics spanning from atomic-level structures at the crack tip to the structural features where the load is applied. Atomistic simulations offer 'first-principles' tools to resolve the progressive microstructural changes at crack fronts and are widely used to explore the underlying processes of mechanical energy dissipation, crack path selection, and dynamic instabilities (e.g. kinking, branching). Empirical force fields developed based on atomic-level structural descriptors based on atomic positions and the bond orders do not yield satisfying predictions of fracture, especially for the nonlinear, anisotropic stress-strain relations and the energy densities of edges. High-fidelity force fields thus should include the tensorial nature of strain and the energetics of bond-breaking and (re)formation events during fracture, which, unfortunately, have not been taken into account in either the state-of-the-art empirical or machine-learning force fields. Based on data generated by density functional theory calculations, we report a neural network-based force field for fracture (NN-F<sup>3</sup>) constructed by using the end-to-end symmetry preserving framework of deep potential-smooth edition (DeepPot-SE). The workflow combines pre-sampling of the space of strain states and active-learning techniques to explore the transition states at critical bonding distances. The capability of NN-F<sup>3</sup>is demonstrated by studying the rupture of hexagonal boron nitride (h-BN) and twisted bilayer graphene as model problems. The simulation results elucidate the roughening physics of fracture defined by the lattice asymmetry in h-BN, explaining recent experimental findings, and predict the interaction between cross-layer cracks in twisted graphene bilayers, which leads to a toughening effect. Materials science ; Force field (fiction) ; Boron nitride ; Fracture (geology) ; Statistical physics ; Condensed matter physics ; Nanotechnology ; Artificial intelligence ; Computer science ; Physics ; Composite material Machine Learning in Materials Science ; Graphene research and applications ; Thermal properties of materials
Authors: Ngoc Cuong Nguyen | Dionysios Sema
Source: Physical review. B./Physical review. B (article, 2024-08-14)
Field: Materials Science — Materials Chemistry

Environment-adaptive machine learning potentials Machine learning interatomic potentials (MLIP) have shown great promise in predicting material properties when tailored for specific applications in mind. However, they face limitations when expanded to capture a diverse set of local atomic environments. Here, the authors introduce a novel method to construct environment-adaptive machine-learned (EAML) interatomic potentials by adapting to the local atomic environment of each atom. The approach shows a significant improvement in the accuracy of the EAML potential compared to the current state-of-the-art, paving the way for a new class of MLIPs. Computer science ; Atom (system on chip) ; Construct (python library) ; Set (abstract data type) ; Class (philosophy) ; Interatomic potential ; Artificial intelligence ; Machine learning ; Face (sociological concept) ; State (computer science) ; Molecular dynamics ; Algorithm ; Physics ; Embedded system ; Programming language ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Fuel Cells and Related Materials
Authors: Qinghai He | Yihang Guo | Xiaoli Li | Yong He | Zhi Lin | Hui Zeng
Source: Foods (article, 2024-11-29)
Field: Chemistry — Analytical Chemistry

Spectral Fingerprinting of Tencha Processing: Optimising the Detection of Total Free Amino Acid Content in Processing Lines by Hyperspectral Analysis The quality and flavor of tea leaves are significantly influenced by chemical composition, with the content of free amino acids serving as a key indicator for assessing the quality of Tencha. Accurately and quickly measuring free amino acids during tea processing is crucial for monitoring and optimizing production processes. However, traditional chemical analysis methods are often time-consuming and costly, limiting their application in real-time quality control. Hyperspectral imaging (HSI) has shown significant effectiveness as a component detection tool in various agricultural applications. This study employs VNIR-HSI combined with machine learning algorithms to develop a model for visualizing the total free amino acid content in Tencha samples that have undergone different processing steps on the production line. Four pretreating methods were employed to preprocess the spectra, and partial least squares regression (PLSR) and least squares support vector machine regression (LS-SVR) models were established from the perspectives of individual processes and the entire process. Combining competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA) methods for characteristic band selection, specific bands were chosen to predict the amino acid content. By comparing modeling evaluation indicators for each model, the optimal model was identified: the overall model CT+CARS+PLSR, with predictive indicators Rc<sup>2</sup> = 0.9885, Rp<sup>2</sup> = 0.9566, RMSEC = 0.0956, RMSEP = 0.1749, RPD = 4.8021, enabling the visualization of total free amino acid content in processed Tencha leaves. Here, we establish a benchmark for machine learning-based HSI, integrating this technology into the tea processing workflow to provide a real-time decision support tool for quality control, offering a novel method for the rapid and accurate prediction of free amino acids during tea processing. This achievement not only provides a scientific basis for the tea processing sector but also opens new avenues for the application of hyperspectral imaging technology in food science. Hyperspectral imaging ; Partial least squares regression ; VNIR ; Artificial intelligence ; Computer science ; Feature selection ; Support vector machine ; Machine learning ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Phytochemicals and Antioxidant Activities
Authors: Pan Zhang | Fang Lyu | Dan Jin | Zhenhua Zhang | Shiwei Chen | Yong Liu | Ziyu Wang | Zhihong Lu | Shiheng Liang | Rui Xiong
Source: Nanoscale (article, 2024-12-27)
Field: Materials Science — Materials Chemistry

Phonon thermal transport in Bi<sub>2</sub>Te<sub>3</sub>/Sb<sub>2</sub>Te<sub>3</sub> monolayer superlattices: a neural network potential study Superlattices are significant means to reduce the lattice thermal conductivity of thermoelectric materials and optimize their performance. In this work, using high-precision first-principles based neural network potentials combined with non-equilibrium molecular dynamics simulations and the phonon Boltzmann transport equation, the lattice thermal conductivities of Bi<sub>2</sub>Te<sub>3</sub> monolayer and lateral Bi<sub>2</sub>Te<sub>3</sub>/Sb<sub>2</sub>Te<sub>3</sub> monolayer superlattices are thoroughly investigated. As the period length increases, the thermal conductivity shows a trend of an initial decrease followed by an increase, which aligns with conventional observations. However, it is found that the decrease of thermal conductivity in coherent short-period superlattices is attributed to the variation of phonon scattering behaviors, including the increased phonon anharmonicity due to enhanced structural complexity and symmetry breaking, as well as intensified scattering between acoustic and optical phonons caused by the downward shift of low-frequency optical branches. This is contrary to expectations that the phonon velocity is the primary driver of reduced cross-plane lattice thermal conductivity in coherent short-period superlattices. In addition, it is found that the four-phonon (4ph) scattering process plays a significant role in the thermal conductivities of the Bi<sub>2</sub>Te<sub>3</sub> monolayer and 1-1 and 3-3 Bi<sub>2</sub>Te<sub>3</sub>/Sb<sub>2</sub>Te<sub>3</sub> monolayer superlattices. After the 4ph scattering process is considered, the <i>a</i>-axis thermal conductivities of these three structures decrease by 27.8%, 26.4%, and 31.1%, respectively. This study not only advances our knowledge of phonon thermal transport behavior in superlattices, but also serves as a good research case for investigating the thermal conductivity of complex superlattice structures using accurate and efficient neural network potentials. Superlattice ; Phonon ; Monolayer ; Materials science ; Condensed matter physics ; Thermal ; Artificial neural network ; Chemical physics ; Nanotechnology ; Optoelectronics ; Physics ; Computer science ; Thermodynamics ; Artificial intelligence Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermography and Photoacoustic Techniques
Authors: Omar Bouaaliouat | Brahim Ydir | Amine Ajdour | Mouad Soumane | Radouane Leghrib | Houda Lahlou
Source: Fibers and Polymers (article, 2024-02-13)
Field: Materials Science — Biomaterials

Inspecting Process-Diameter Relationships of Forcespun PVP Ultrafine Fibers via RSM and ANN-PCA Approaches Spinning ; Principal component analysis ; Fiber ; Relative humidity ; Body orifice ; Biological system ; Materials science ; Mean squared error ; Artificial neural network ; Rotational speed ; Correlation coefficient ; Mathematics ; Statistics ; Analytical Chemistry (journal) ; Composite material ; Computer science ; Artificial intelligence ; Chemistry ; Chromatography ; Mechanical engineering ; Physics ; Thermodynamics ; Engineering Electrospun Nanofibers in Biomedical Applications ; Natural Fiber Reinforced Composites ; Textile materials and evaluations
Authors: Yicheng Lu | Zhi Sun | Khalil I. Elkhodary | H. Xiao | Shan Tang | Xu Guo
Source: Composite Structures (article, 2024-12-16)
Field: Materials Science — Polymers and Plastics

Introducing memory decay network for microstructured viscoelastic composites Viscoelasticity ; Composite material ; Materials science Polymer composites and self-healing ; Epoxy Resin Curing Processes ; Mechanical Behavior of Composites
Authors: Shubham Kumar Pandey | Kunal Roy
Source: Materials Today Communications (article, 2024-09-16)
Field: Materials Science — Materials Chemistry

Predictive cheminformatics modeling of reorganization energy (RE) for p-type organic semiconductors: Integration of quantitative read-across structure-property relationship (q-RASPR) and stacking regression analysis Materials science ; Stacking ; Cheminformatics ; Property (philosophy) ; Organic semiconductor ; Energy (signal processing) ; Biological system ; Quantitative structure–activity relationship ; Type (biology) ; Semiconductor ; Regression analysis ; Chemical physics ; Computational chemistry ; Statistics ; Machine learning ; Optoelectronics ; Organic chemistry ; Computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics
Authors: Priyanka Pandey | Mrinal Arandhara | Paul L. Houston | Chen Qu | Riccardo Conte | Joel M. Bowman | Sai G. Ramesh
Source: The Journal of Physical Chemistry A (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H<sub>3</sub>O<sub>2</sub><sup>–</sup> The singly hydrated hydroxide anion OH<sup>-</sup>(H<sub>2</sub>O) is of central importance to a detailed molecular understanding of water; therefore, there is strong motivation to develop a highly accurate potential to describe this anion. While this is a small molecule, it is necessary to have an extensive data set of energies and, if possible, forces to span several important stationary points. Here, we assess two machine-learned potentials, one using the symmetric gradient domain machine learning (sGDML) method and one based on permutationally invariant polynomials (PIPs). These are successors to a PIP potential energy surface (PES) reported in 2004. We describe the details of both fitting methods and then compare the two PESs with respect to precision, properties, and speed of evaluation. While the precision of the potentials is similar, the PIP PES is much faster to evaluate for energies and energies plus gradient than the sGDML one. Diffusion Monte Carlo calculations of the ground vibrational state, using both potentials, produce similar large anharmonic downshift of the zero-point energy compared to the harmonic approximation of the PIP and sGDML potentials. The computational time for these calculations using the sGDML PES is roughly 300 times greater than using the PIP one. Anharmonicity ; Potential energy ; Invariant (physics) ; Polynomial ; Physics ; Potential energy surface ; Domain (mathematical analysis) ; Monte Carlo method ; Statistical physics ; Computational physics ; Atomic physics ; Ab initio ; Quantum mechanics ; Mathematical analysis ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Suraj Garg | Akanksha Sharma | Vishal Sharma
Source: Forensic Science International Reports (article, 2024-06-18)
Field: Chemistry — Organic Chemistry

Geographical profiling of wood samples via ATR-FTIR spectroscopy and machine learning algorithms: Application in wood forensics Illegal activities associated with deforestation for the lumber and furniture industries pose significant threats to plant and animal biodiversity, as well as natural resources. Accurate identification of wood sources is vital, yet traditional laboratory techniques often fall short in precisely determining the chemical composition of samples for classification. This study aims to leverage ATR-FTIR spectroscopy alongside machine learning algorithms to construct a robust model for discerning the geographical origins of wood samples from India. By systematically comparing various machine learning classifiers, we address the limitations of subjective visual interpretation and evaluate their accuracy using wood spectral data. Logistic regression emerges as the most effective classifier for distinguishing Eucalyptus (75 % accuracy), Dalbergia (68 % accuracy), and Populus (81.5 % accuracy) species. Through a methodology encompassing data pre-processing, classifier selection, and performance evaluation, this research offers promising tools for combating challenges posed by illegal wood trafficking and transportation. The outcomes hold significant potential for enhancing wildlife crime prevention efforts by facilitating the tracing illicit timber sources, apprehension of perpetrators, and implementation of preventive measures. Profiling (computer programming) ; Fourier transform infrared spectroscopy ; Algorithm ; Chemistry ; Computer science ; Engineering ; Chemical engineering ; Operating system Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Wood Treatment and Properties
Authors: Kun Fu | Meiqian Chen | Qinghai Li
Source: Energy (article, 2024-06-29)
Field: Materials Science — Materials Chemistry

Decontamination performance of metallic radionuclides in irradiated graphite via a fluidized bed reactor Human decontamination ; Graphite ; Fluidized bed ; Waste management ; Radionuclide ; Materials science ; Irradiation ; Nuclear engineering ; Environmental science ; Radiochemistry ; Metallurgy ; Chemistry ; Engineering ; Nuclear physics ; Physics Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Recycling and Waste Management Techniques
Authors: D. Nageswara Rao | Mamta Dahiya | Amit Kumar Arora | Paras Jandwani | Gunjan Verma
Source: International Journal of Information Technology (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Exploring the predictive potential of artificial neural networks in enhancing mechanical properties of derivatives of graphene nanocomposites: a data-driven approach Graphene ; Computer science ; Scope (computer science) ; Artificial neural network ; Artificial intelligence ; Nanocomposite ; Realm ; Matrix (chemical analysis) ; Reinforcement learning ; Materials science ; Machine learning ; Nanotechnology ; Composite material Graphene research and applications ; Carbon Nanotubes in Composites ; Graphene and Nanomaterials Applications
Authors: Oumaima Afif | Leonardo Franceschelli | Eleonora Iaccheri | Simone Trovarello | Alessandra Di Florio Di Renzo | Luigi Ragni | Alessandra Costanzo | Marco Tartagni
Source: Sensors (article, 2024-09-25)
Field: Chemistry — Analytical Chemistry

A Versatile, Machine-Learning-Enhanced RF Spectral Sensor for Developing a Trunk Hydration Monitoring System in Smart Agriculture This paper comprehensively explores the development of a standalone and compact microwave sensing system tailored for automated radio frequency (RF) scattered parameter acquisitions. Coupled with an emitting RF device (antenna, resonator, open waveguide), the system could be used for non-invasive monitoring of external matter or latent environmental variables. Central to this design is the integration of a NanoVNA and a Raspberry Pi Zero W platform, allowing easy recording of S-parameters (scattering parameters) in the range of the 50 kHz-4.4 GHz frequency band. Noteworthy features include dual recording modes, manual for on-demand acquisitions and automatic for scheduled data collection, powered seamlessly by a single battery source. Thanks to the flexibility of the system's architecture, which embeds a Linux operating system, we can easily embed machine learning (ML) algorithms and predictive models for information detection. As a case study, the potential application of the integrated sensor system with an RF patch antenna is explored in the context of greenwood hydration detection within the field of smart agriculture. This innovative system enables non-invasive monitoring of wood hydration levels by analyzing scattering parameters (S-parameters). These S-parameters are then processed using ML techniques to automate the monitoring process, enabling real-time and predictive analysis of moisture levels. Radio frequency ; Context (archaeology) ; Computer science ; Flexibility (engineering) ; Antenna (radio) ; Embedded system ; Telecommunications Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Microwave and Dielectric Measurement Techniques
Authors: Xiaoyu Wu | Xianyu Song | Linjiang Chen | Chunyi Yu | Lin Zhao | Mingrui Zuo | Chenrui Li | Heechae Choi | Jianwen Jiang | Lifeng Ding
Source: Separation and Purification Technology (article, 2024-12-04)
Field: Chemistry — Inorganic Chemistry

Accurate and interpretable machine learning with pore+ descriptors for iodide capture in metal–organic frameworks Iodide ; Metal-organic framework ; Artificial intelligence ; Computer science ; Chemistry ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Jill K. Wenderott | Tadesse Billo | Dillon D. Fong
Source: APL Materials (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Epitaxial oxide ionotronics: Interfaces and oxygen vacancies Oxide ionotronics is an interdisciplinary field in which systems and devices rely on the migration of ions/ionic defects to alter or drive functionality. In this perspective, we focus on epitaxial oxide heterostructures and the contributing roles of oxygen vacancies and interfaces in ionotronics. We begin with a description of oxygen vacancy behavior, with a focus on vacancy ordering and the effects of interfaces and electric fields on particular epitaxial oxide systems. We then emphasize the use of synchrotron x-ray techniques for investigating system structure and dynamics in situ at interfaces and surfaces. Finally, an outlook on the future of epitaxial oxide ionotronics is provided, and several key areas for research are identified, such as freestanding heterostructures, combinatorial synthesis and machine learning, and next-generation synchrotron x-ray characterization. Epitaxy ; Heterojunction ; Materials science ; Oxide ; Ionic bonding ; Synchrotron ; Vacancy defect ; Characterization (materials science) ; Oxygen ; Nanotechnology ; Chemical physics ; Engineering physics ; Ion ; Optoelectronics ; Crystallography ; Chemistry ; Optics ; Physics ; Metallurgy Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing ; Semiconductor materials and devices