jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Norman Osa-uwagboe | Amadi Gabriel Udu | Vadim V. Silberschmidt | Konstantinos P. Baxevanakis | Emrah Demirci
Source: Materials (article, 2024-05-25)
Field: Materials Science — Polymers and Plastics

Effects of Seawater on Mechanical Performance of Composite Sandwich Structures: A Machine Learning Framework Sandwich structures made with fibre-reinforced plastics are commonly used in maritime vessels thanks to their high strength-to-weight ratios, corrosion resistance, and buoyancy. Understanding their mechanical performance after moisture uptake and the implications of moisture uptake for their structural integrity and safety within out-of-plane loading regimes is vital for material optimisation. The use of modern methods such as acoustic emission (AE) and machine learning (ML) could provide effective techniques for the assessment of mechanical behaviour and structural health monitoring. In this study, the AE features obtained from quasi-static indentation tests on sandwich structures made from E-glass fibre face sheets with polyvinyl chloride foam cores were employed. Time- and frequency-domain features were then used to capture the relevant information and patterns within the AE data. A <i>k</i>-means++ algorithm was utilized for clustering analysis, providing insights into the principal damage modes of the studied structures. Three ensemble learning algorithms were employed to develop a damage-prediction model for samples exposed and unexposed to seawater and were loaded with indenters of different geometries. The developed models effectively identified all damage modes for the various indenter geometries under different loading conditions with accuracy scores between 86.4 and 95.9%. This illustrates the significant potential of ML for the prediction of damage evolution in composite structures for marine applications. Composite number ; Seawater ; Materials science ; Composite material ; Geology ; Oceanography Textile materials and evaluations ; Mechanical Behavior of Composites ; Structural Analysis and Optimization
Authors: Zehua Chen | Weitao Yang
Source: The Journal of Chemical Physics (article, 2024-01-05)
Field: Materials Science — Materials Chemistry

Development of a machine learning finite-range nonlocal density functional Kohn-Sham density functional theory has been the most popular method in electronic structure calculations. To fulfill the increasing accuracy requirements, new approximate functionals are needed to address key issues in existing approximations. It is well known that nonlocal components are crucial. Current nonlocal functionals mostly require orbital dependence such as in Hartree-Fock exchange and many-body perturbation correlation energy, which, however, leads to higher computational costs. Deviating from this pathway, we describe functional nonlocality in a new approach. By partitioning the total density to atom-centered local densities, a many-body expansion is proposed. This many-body expansion can be truncated at one-body contributions, if a base functional is used and an energy correction is approximated. The contribution from each atom-centered local density is a single finite-range nonlocal functional that is universal for all atoms. We then use machine learning to develop this universal atom-centered functional. Parameters in this functional are determined by fitting to data that are produced by high-level theories. Extensive tests on several different test sets, which include reaction energies, reaction barrier heights, and non-covalent interaction energies, show that the new functional, with only the density as the basic variable, can produce results comparable to the best-performing double-hybrid functionals, (for example, for the thermochemistry test set selected from the GMTKN55 database, BLYP based machine learning functional gives a weighted total mean absolute deviations of 3.33 kcal/mol, while DSD-BLYP-D3(BJ) gives 3.28 kcal/mol) with a lower computational cost. This opens a new pathway to nonlocal functional development and applications. Range (aeronautics) ; Computer science ; Statistical physics ; Physics ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Krittapong Deshsorn | Luckhana Lawtrakul | Pawin Iamprasertkun
Source: Journal of Power Sources (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

How false data affects machine learning models in electrochemistry? Electrochemistry ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Faridun N. Jalolov | Evgeny V. Podryabinkin | Artem R. Oganov | Alexander V. Shapeev | Alexander G. Kvashnin
Source: Advanced Theory and Simulations (article, 2024-03-24)
Field: Materials Science — Materials Chemistry

Mechanical Properties of Single and Polycrystalline Solids from Machine Learning Abstract Calculating the elastic and mechanical characteristics of non‐crystalline solids can be challenging due to the high computational cost of ab initio methods and the low accuracy of empirical potentials. This paper proposes a computational technique for efficient calculations of mechanical properties of polycrystals, composites, and multi‐phase systems from atomistic simulations with high accuracy and reasonable computational cost. The calculated elastic moduli of polycrystalline diamond and their dependence on grain size are determined using a developed approach based on actively learned machine learning interatomic potentials (MLIPs). These potentials are trained on local fragments of the polycrystalline system, and ab initio calculations are used to compute forces, stresses, and energies. This technique allows researchers to perform extensive calculations of the mechanical properties of complex solids with different compositions and structures, achieving high accuracy and facilitating the transition from ideal (single crystal) systems to more realistic ones. Crystallite ; Materials science ; Composite material ; Engineering physics ; Metallurgy ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Xiaoming Li | Jiaqi Hu | Zhang De | Xiubin Zhang | Zhetao Wang | Yufeng Wang | Qiang Chen | Pei Liang
Source: Talanta (article, 2024-01-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Realization of qualitative to semi-quantitative trace detection via SERS-ICA based on internal standard method Normalization (sociology) ; Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Independent component analysis ; Chemistry ; Pattern recognition (psychology) ; Mean squared error ; Biological system ; Analytical Chemistry (journal) ; Artificial intelligence ; Chromatography ; Computer science ; Raman scattering ; Statistics ; Mathematics ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Corentin Villot | Ka Un Lao
Source: The Journal of Chemical Physics (article, 2024-05-08)
Field: Materials Science — Materials Chemistry

<i>Ab initio</i> dispersion potentials based on physics-based functional forms with machine learning In this study, we introduce SAPT10K, a comprehensive dataset comprising 9982 noncovalent interaction energies and their binding energy components (electrostatics, exchange, induction, and dispersion) for diverse intermolecular complexes of 944 unique dimers. These complexes cover significant portions of the intermolecular potential energy surface and were computed using higher-order symmetry-adapted perturbation theory, SAPT2+(3)(CCD), with a large aug-cc-pVTZ basis set. The dispersion energy values in SAPT10K serve as crucial inputs for refining the ab initio dispersion potentials based on Grimme’s D3 and many-body dispersion (MBD) models. Additionally, Δ machine learning (ML) models based on newly developed intermolecular features, which are derived from intermolecular histograms of distances for element/substructure pairs to simultaneously account for local environments as well as long-range correlations, are also developed to address deficiencies of the D3/MBD models, including the inflexibility of their functional forms, the absence of MBD contributions in D3, and the standard Hirshfeld partitioning scheme used in MBD. The developed dispersion models can be applied to complexes involving a wide range of elements and charged monomers, surpassing other popular ML models, which are limited to systems with only neutral monomers and specific elements. The efficient D3-ML model, with Cartesian coordinates as the sole input, demonstrates promising results on a testing set comprising 6714 dimers, outperforming another popular ML model, component-based machine-learned intermolecular force field (CLIFF), by 1.5 times. These refined D3/MBD-ML models have the capability to replace the time-consuming dispersion components in symmetry-adapted perturbation theory-based calculations and can promptly illustrate the dispersion contribution in noncovalent complexes for supramolecular assembly and chemical reactions. Intermolecular force ; Ab initio ; Statistical physics ; Dispersion (optics) ; Chemical physics ; Physics ; Basis set ; Computational chemistry ; Chemistry ; Molecular physics ; Density functional theory ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Xiong Xu | Min Li | Xuhui Lin | Hui Wang
Source: Physical review. B./Physical review. B (article, 2024-08-22)
Field: Materials Science — Materials Chemistry

Giant reversible electrocaloric effect in monolayer group-IV monochalcogenides Monolayer electrocaloric (EC) materials have emerged as promising candidates to achieve economic and green solid-state refrigeration, especially in micro- or nanoscale chip cooling in the post-Moore law period. In this paper, we trained a machine-learning-based deep potential (DP) model of monolayer group-IV monochalcogenides $\mathit{MX}\mathrm{s}$ ($M$ = Ge, Sn; $X$ = S, Se) with a database from first-principles calculations, which incorporates the accuracy of ab initio molecular dynamics and efficiency of classical method. The DP model was further applied to study the EC effect (ECE) in monolayer $\mathit{MX}\mathrm{s}$ by simulating the temperature-driven phase transitions and electric polarization dynamics under different external electric fields. The results indicate $\mathit{MX}\mathrm{s}$ featuring giant reversible isothermal entropy change (\ensuremath{\Delta}$S$) and adiabatic temperature change (\ensuremath{\Delta}$T$) near their order-disorder phase transition. Particularly, for $E$ = 100 MV/m, |\ensuremath{\Delta}$S$| = 68 J/(kg K) and |\ensuremath{\Delta}$T$| = 75 K are obtained for GeS, which individually rival the state-of-the-art ECE figures of merit, surpassing most other ferroelectric materials, particularly the two-dimensional (2D) CuInPS and strained $\mathrm{SrTi}{\mathrm{O}}_{3}$ films. Furthermore, electric field shifts of the order of 10 MV/m yield huge reversible EC strengths of \ensuremath{\Delta}$S$/\ensuremath{\Delta}$E$ = 5.4 $\mathrm{J}\phantom{\rule{0.16em}{0ex}}\mathrm{m}\phantom{\rule{0.16em}{0ex}}\mathrm{k}{\mathrm{g}}^{\ensuremath{-}1}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{\ensuremath{-}1}\phantom{\rule{0.16em}{0ex}}\mathrm{M}{\mathrm{V}}^{\ensuremath{-}1}$ and \ensuremath{\Delta}$T$/\ensuremath{\Delta}$E$ = 6 $\mathrm{K}\phantom{\rule{0.16em}{0ex}}\mathrm{m}\phantom{\rule{0.16em}{0ex}}\mathrm{M}{\mathrm{V}}^{\ensuremath{-}1}$. The origin of the giant ECE in $\mathit{MX}\mathrm{s}$ was explained through macroscopic thermodynamic ratios, phenomenological Landau theory, and microscopic phonon vibration analysis. Interestingly, the entropy change can be approximated to be proportional to the square of the polarization change ($\mathrm{\ensuremath{\Delta}}{P}^{2}$), and the application of electric field shifts the low-frequency phonons to the higher-frequency range, with the $M$ atom contributing more to the entropy change than the $X$ atom. In this paper, we provide important insights for exploration and design of 2D EC materials in future practical applications. Monolayer ; Electrocaloric effect ; Materials science ; Group (periodic table) ; Chemistry ; Optoelectronics ; Nanotechnology ; Organic chemistry ; Ferroelectricity Ferroelectric and Piezoelectric Materials ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Marjun C. Alvarado | Ma. Cristine Concepcion D. Ignacio | Ma. Camille Acabal | Anniver Ryan P. Lapuz | Kevin F. Yaptenco
Source: Nano Trends (article, 2024-10-02)
Field: Materials Science — Biomaterials

Review on nanocellulose production from agricultural residue through response surface methodology and its applications • Nanocellulose offers broad industrial applications and is extracted from agricultural residues, with acid concentration, reaction time, and temperature being crucial parameters for optimization using RSM. • Although RSM has been effective in optimizing NC extraction, its limitations in handling complex, nonlinear relationships suggest that advanced techniques like ANN could provide improvements. • Relying on literature-based experimental ranges may overlook the optimal conditions for nanocellulose extraction, highlighting a limitation of RSM and the need for exploring broader ranges. Nanocellulose (NC) shows great potential across industries like food, pharmaceuticals, cosmetics, textiles, electronics, and construction. It can be sustainably extracted from agricultural residues using methods such as mechanical processes, acid hydrolysis, and bacterial biosynthesis. This review emphasizes the use of Response Surface Methodology (RSM) in optimizing NC extraction by examining variables like acid concentration, reaction time, and temperature. While RSM is effective, its assumptions of linear and quadratic relationships limit its accuracy in complex systems. Advanced techniques like artificial neural networks (ANN) offer a better alternative, capturing nonlinear relationships more effectively. However, ANN's application in NC extraction is underexplored, calling for future research to improve model precision. Expanding optimization to include response variables like thermal stability and surface charge is also essential for enhancing NC's industrial applications. Nanocellulose ; Residue (chemistry) ; Agriculture ; Production (economics) ; Biochemical engineering ; Environmental science ; Pulp and paper industry ; Chemistry ; Engineering ; Chemical engineering ; Biology ; Cellulose ; Economics ; Ecology Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Lignin and Wood Chemistry
Authors: Kianoosh Sattari | Yuchao Wu | Zhenru Chen | Alireza Mahjoubnia | Changhua Su | Jian Lin
Source: Additive manufacturing (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Physics-constrained multi-objective bayesian optimization to accelerate 3d printing of thermoplastics Bayesian optimization ; Monomer ; Materials science ; Constraint (computer-aided design) ; Glass transition ; Set (abstract data type) ; 3D printing ; Photopolymer ; Toughness ; Bayesian probability ; Ultimate tensile strength ; Mechanical engineering ; Computer science ; Polymer ; Composite material ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Advanced Multi-Objective Optimization Algorithms
Authors: Aaron Döring | Yuqing Qiu | Andrey L. Rogach
Source: ACS Applied Nano Materials (article, 2024-01-05)
Field: Materials Science — Materials Chemistry

Improving the Accuracy of Carbon Dot Temperature Sensing Using Multi-Dimensional Machine Learning Optical sensing methods offer a convenient noncontact approach to monitor different environmental parameters with a high spatial resolution and fast response times. Temperature monitoring can benefit from optical sensing using luminescent nanoprobes, but many of those substances are toxic or expensive. Carbon dots are a class of luminescent colloidal nanoparticles that have recently gained recognition as optical probes, which are easy to produce by environmentally friendly synthesis, nontoxic, and stable. While carbon dots show temperature-dependent optical properties, their broad emission profiles may constitute a challenge for optical sensing. In this study, three types of carbon dots with different emission profiles were tested as optical probes for intensity-, spectral-shift-, intensity-ratio-, bandwidth-, and lifetime-based temperature sensing. Depending on the optical characteristics of the specific probe, either intensity- or lifetime-based sensing was shown to be the most accurate, with accuracies of up to 1.65 and 0.70 K, respectively. Employing Gaussian fits improved accuracies of the intensity-ratio-based sensing to 1.24 K, with the additional benefit of greater stability against excitation fluctuations. Finally, a multiple linear regression model combining steady-state and time-resolved luminescence data of carbon dots has been applied to further increase the sensing accuracies with carbon dots to 0.54 K. Our study demonstrates how multidimensional machine learning methods can greatly improve temperature sensing with optical probes. Luminescence ; Materials science ; Quantum dot ; Carbon fibers ; Intensity (physics) ; Optoelectronics ; Nanotechnology ; Optics ; Physics Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Quantum Dots Synthesis And Properties
Authors: Shihao Zhang | Fan-Shun Meng | Rong Fu | Shigenobu Ogata
Source: Computational Materials Science (article, 2024-02-01)
Field: Materials Science — Metals and Alloys

Highly efficient and transferable interatomic potentials for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si28.svg" display="inline" id="d1e1011"><mml:mi>α</mml:mi></mml:math>-iron and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si28.svg" display="inline" id="d1e1016"><mml:mi>α</mml:mi></mml:math>-iron/hydrogen binary systems using deep neural networks Hydrogen ; Hydrogen embrittlement ; Embrittlement ; Materials science ; Nucleation ; Atomic units ; Metallurgy ; Chemistry ; Physics ; Thermodynamics Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Material Properties and Failure Mechanisms
Authors: Jialin Tang | Jiongzhi Zheng | Xiaohan Song | Lin Cheng | Ruiqiang Guo
Source: Journal of Applied Physics (article, 2024-05-23)
Field: Materials Science — Materials Chemistry

In-plane thermal conductivity of hexagonal boron nitride from 2D to 3D The in-plane thermal conductivity of hexagonal boron nitride (h-BN) with varying thicknesses is a key property that affects the performance of various applications from electronics to optoelectronics. However, the transition of the thermal conductivity from two-dimensional (2D) to three-dimensional (3D) h-BN remains elusive. To answer this question, we have developed a machine learning interatomic potential within the neuroevolution potential (NEP) framework for h-BN, achieving a high accuracy akin to ab initio calculations in predicting its thermal conductivity and phonon transport from monolayer to multilayers and bulk. Utilizing molecular dynamics simulations based on the NEP, we predict the thermal conductivity of h-BN with a thickness up to ∼100 nm, demonstrating that its thermal conductivity quickly decreases from the monolayer and saturates to the bulk value above four layers. The saturation of its thermal conductivity is attributed to the little change in phonon group velocity and lifetime as the thickness increases beyond four layers. In particular, the weak thickness dependence of phonon lifetime in h-BN with a nanoscale thickness results from its extremely high phonon focusing along the in-plane direction. This research bridges the knowledge gap of phonon transport between 2D and 3D h-BN and will benefit the thermal design and performance optimization of relevant applications. Thermal conductivity ; Hexagonal boron nitride ; Materials science ; Boron nitride ; Boron ; Conductivity ; Condensed matter physics ; Hexagonal crystal system ; Plane (geometry) ; Nitride ; Nanotechnology ; Crystallography ; Composite material ; Chemistry ; Physics ; Physical chemistry ; Geometry ; Graphene ; Nuclear physics Thermal properties of materials ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Agus Arip Munawar | Zulfahrizal Zulfahrizal | Daniel Mörlein
Source: Case Studies in Chemical and Environmental Engineering (article, 2024-08-27)
Field: Chemistry — Analytical Chemistry

Prediction accuracy of near infrared spectroscopy coupled with adaptive machine learning methods for simultaneous determination of chlorogenic acid and caffeine on intact coffee beans Due to the inherent complexity and high dimensionality of near infrared spectroscopy (NIRS) data, it is critical to employ robust methods that can accurately interpret the spectroscopic signals to determine the concentration of chlorogenic acid (CGA) and caffeine on intact coffee beans. Therefore, this work focuses on evaluating the accuracy of four advanced machine learning algorithms: support vector machine regression (SVMR), Ridge regression (RGR), partial least squares regression (PLSR) and extreme gradient boosting (XGBR) for the quantification of CGA and caffeine. A total of 152 NIRS spectra: 104 for calibration and 48 for prediction set, from diverse intact coffee beans were analyzed. Each algorithm was tasked with modeling the spectral data against the reference chemical assays for CGA and caffeine contents. The models were rigorously validated using independent prediction dataset, and their performance was assessed primarily based on the coefficient of determination (R 2 ), the root means square error (RMSE), ratio prediction to deviation (RPD) and range to error ratio (RER) indexes. The primary results indicate that all four algorithms successfully quantified both CGA and caffeine from the NIRS data to varying degrees of accuracy with R 2 prediction from 0.95 to 0.99 for CGA, and 0.75 to 0.97 for caffeine prediction. However, XGBR showed superior performance, yielding exceptionally high R 2 values of 0.97 for caffeine and 0.99 for CGA, outperforming the other techniques. PLSR, SVMR, and RGR, despite showing commendable predictive capabilities, trailed behind the predictive accuracy achieved by XGBR algorithm. Chlorogenic acid ; Caffeine ; Green coffee ; Chemistry ; Chromatography ; Spectroscopy ; Infrared ; Near-infrared spectroscopy ; Infrared spectroscopy ; Artificial intelligence ; Biological system ; Computer science ; Food science ; Organic chemistry ; Physics ; Optics ; Medicine ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research