jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xilan Feng | Xiangrui Gong | Dapeng Liu | Yang Li | Ying Jiang | Yu Zhang
Source: Angewandte Chemie International Edition (article, 2023-10-12)
Field: Materials Science — Materials Chemistry

Bayesian Optimization‐guided Discovery of High‐performance Methane Combustion Catalysts based on Multi‐component PtPd@CeZrOx Core–Shell Nanospheres Formula regulation of multi-component catalysts by manual search is undoubtedly a time-consuming task, which has severely impeded the development efficiency of high-performance catalysts. In this work, PtPd@CeZrO<sub>x</sub> core-shell nanospheres, as a successful case study, is explicitly demonstrated how Bayesian optimization (BO) accelerates the discovery of methane combustion catalysts with the optimal formula ratio (the Pt/Pd mole ratio ranges from 1/2.33-1/9.09, and Ce/Zr from 1/0.22-1/0.35), which directly results in a lower conversion temperature (T<sub>50</sub> approaching to 330 °C) than ones reported hitherto. Consequently, the best sample obtained could be efficiently developed after two rounds of iterations, containing only 18 experiments in all that is far less than the common human workload via the traditional trial-and-error search for optimal compositions. Further, this BO-based machine learning strategy can be straightforward extended to serve the autonomous discovery in multi-component material systems, for other desired properties, showing promising opportunities to practical applications in future. Component (thermodynamics) ; Catalysis ; Methane ; Workload ; Computer science ; Combustion ; Bayesian optimization ; Bayesian probability ; Task (project management) ; Materials science ; Nanotechnology ; Process engineering ; Biological system ; Chemistry ; Machine learning ; Artificial intelligence ; Thermodynamics ; Engineering ; Physical chemistry ; Physics ; Organic chemistry ; Systems engineering Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Dániel Patkó | Qizhi Yang | Yangminghao Liu | Panagiotis G. Falireas | Benoît Briou | Bhausaheb V. Tawade | Timothy George | Tim J. Daniell | Michael P. MacDonald | Vincent Ladmiral | Bruno Améduri | Lionel Dupuy
Source: Plant and Soil (article, 2023-07-26)
Field: Materials Science — Biomaterials

Smart soils track the formation of pH gradients across the rhizosphere Abstract Aims Our understanding of the rhizosphere is limited by the lack of techniques for in situ live microscopy. Current techniques are either destructive or unsuitable for observing chemical changes within the pore space. To address this limitation, we have developed artificial substrates, termed smart soils, that enable the acquisition and 3D reconstruction of chemical sensors attached to soil particles. Methods The transparency of smart soils was achieved using polymer particles with refractive index matching that of water. The surface of the particles was modified both to retain water and act as a local sensor to report on pore space pH via fluorescence emissions. Multispectral signals were acquired from the particles using a light sheet microscope, and machine learning algorithms predicted the changes and spatial distribution in pH at the surface of the smart soil particles. Results The technique was able to predict pH live and in situ within ± 0.5 units of the true pH value. pH distribution could be reconstructed across a volume of several cubic centimetres around plant roots at 10 μm resolution. Using smart soils of different composition, we revealed how root exudation and pore structure create variability in chemical properties. Conclusion Smart soils captured the pH gradients forming around a growing plant root. Future developments of the technology could include the fine tuning of soil physicochemical properties, the addition of chemical sensors and improved data processing. Hence, this technology could play a critical role in advancing our understanding of complex rhizosphere processes. Rhizosphere ; Soil water ; Soil science ; Environmental science ; Temporal resolution ; In situ ; Multispectral image ; Biological system ; Chemistry ; Environmental chemistry ; Materials science ; Remote sensing ; Geology ; Optics Diatoms and Algae Research ; Analytical Chemistry and Sensors ; Biosensors and Analytical Detection
Authors: Cheng Chen | Sheng Zhang | Gia-Wei Chern
Source: Physical review. B./Physical review. B (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Machine learning for phase ordering dynamics of charge density waves We present a machine learning (ML) framework for large-scale dynamical simulations of charge density wave (CDW) states. The charge modulation in a CDW state is often accompanied by a concomitant structural distortion, and the adiabatic evolution of a CDW order is governed by the dynamics of the lattice distortion. Calculation of the electronic contribution to the driving forces for large systems, however, is computationally very expensive. Assuming the principle of locality for electron systems, a neural-network model is developed to accurately and efficiently predict local electronic forces with input from neighborhood configurations. Importantly, the ML model enables a linear complexity algorithm for dynamical simulations of CDWs. As a demonstration, we apply our approach to investigate the phase ordering dynamics of the Holstein model, a canonical system of CDW order. Our large-scale simulations reveal an intriguing growth of CDW domains that deviates significantly from the expected Allen-Cahn law for phase ordering of Ising-type order parameter field. This anomalous domain growth could be attributed to the complex structure of domain walls in this system. Our paper highlights the promising potential of ML-based force-field models for dynamical simulations of functional electronic materials. Dynamics (music) ; Charge (physics) ; Phase (matter) ; Charge density wave ; Physics ; Condensed matter physics ; Statistical physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Chemical Physics Studies
Authors: Bulusu Sreenivas | Shaik Kareem Ahmmad | Y. S. Rammah | P. Hima Bindu
Source: Open Ceramics (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

Radiation shielding capacity and machine learning density prediction of boro-bismuth cadmium zinc glasses Bismuth borate cadmium zinc glasses were prepared using melt quenching technique for shielding applications. The glasses had the composition (80-x)B2O3-xBi2O3-10CdO-10ZnO, where 0<x<20mol%. The densities of the glasses were predicted using machine learning tools and were found to increase with increasing Bi2O3 content. This increase in density was correlated with changes in the infrared spectrum of the glasses. The density of the glass changed from 4.410 g/cm³ to 6.9830 g/cm³ when bismuth oxide (Bi2O3) was added to the glass network. This is because Bi2O3 changes the structure of the glass. The presence of octahedral BiO6 units in the glass is confirmed by the peak at 514 cm-1 in the infrared spectrum. This peak shifts to a lower wavenumber as the amount of Bi2O3 in the glass increases. The shift of the infrared peak from 688 cm-1 to 596 cm-1 indicates that Bi2O3 is actively participating in the glass network, which results in a decrease in the number of BO3 units. The shift of the infrared peak from 1060 cm-1 to 1137 cm-1 is attributed to B-O stretching vibrations in BO4- units, and also confirms that the number of BO4 units is increasing. The density of the glass was predicted using machine learning algorithms that are trained on the chemical composition and experimental density of glasses. Polynomial regression and artificial neural networks (ANNs) were used to predict the density of glass from its chemical composition. Polynomial regression performed best with a 10th-degree polynomial, with an R2 value of 0.8820. ANNs performed best with a tanh activation function, with an R2 value of 0.8923. Random forest regression performed better than other machine learning models at predicting the density of glass, with an R2 value of 0.920. The predicted values were very close to the experimental values. The mass (MAC) and linear (LAC) attenuation coefficients of radiation shielding followed the following trend: (MAC, LAC)0 < (MAC, LAC)5 < (MAC, LAC)10 < (MAC, LAC)15 < (MAC, LAC)20. The half value layer followed the trend: HVL followed as: (HVL)0 > (HVL)5 > (HVL)10 > (HVL)15 > (HVL)20. The Radiation Protection Effectiveness (RPE) reinforces the superiority of BBCZ-20 glasses in shielding against gamma radiation, making them promising candidates for radiation protection applications. Bismuth ; Materials science ; Infrared ; Analytical Chemistry (journal) ; Zinc ; Boron ; Cadmium oxide ; Chemical composition ; Cadmium ; Mineralogy ; Metallurgy ; Chemistry ; Optics ; Physics ; Thermodynamics Radiation Shielding Materials Analysis ; Glass properties and applications ; Advanced X-ray and CT Imaging
Authors: Meng Shi | Chang‐Ping Feng | Youlei Tu | Guangsheng Shi | Peiyao He | Yang Zhang | Jie Zhang | Jiang Li | Shaoyun Guo
Source: ACS Applied Materials & Interfaces (article, 2023-04-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Visualization of Deep Convolutional Neural Networks to Investigate Porous Nanocomposites for Electromagnetic Interference Shielding Constructing porous structures in electromagnetic interference (EMI) shielding materials is a common strategy to decrease the secondary pollution caused by the reflection of electromagnetic waves (EMWs). However, the lack of direct analysis methods makes it difficult to fully understand the effect of porous structures on EMI, hindering EMI composites' development. Furthermore, while deep learning techniques, such as deep convolutional neural networks (DCNNs), have significantly impacted material science, their lack of interpretability limits their applications to property predictions and defect detection tasks. Until recently, advanced visualization techniques provided an approach to reveal the relevant information behind DCNNs' decisions. Inspired by it, a visual approach for porous EMI nanocomposite mechanism studies is proposed. This work combines DCNN visualization with experiments to investigate EMI porous nanocomposites. First, a rapid and straightforward salt-leaked cold-pressing powder sintering method is employed to prepare high-EMI CNTs/PVDF composites with various porosities and filler loadings. Notably, the solid sample with 30 wt % loading maintains an ultrahigh shielding effectiveness of 105 dB. The influence of porosity on the shielding mechanism is discussed macroscopically based on the prepared samples. To determine the shielding mechanism, a modified deep residual network (ResNet) is trained on a dataset of scanning electron microscopy (SEM) images of the samples. The Eigen-CAM visualization of the modified ResNet intuitively shows that the amount and depth of the pores impact the shielding mechanisms and that shallow pore structures contribute less to EMW absorption. This work is instructive for material mechanism studies. Besides, the visualization has the potential as a porous-like structure marking tool. Materials science ; Electromagnetic shielding ; EMI ; Convolutional neural network ; Visualization ; Porosity ; Electromagnetic interference ; Composite material ; Computer science ; Artificial intelligence ; Telecommunications Electromagnetic wave absorption materials ; Cellular and Composite Structures ; Radiation Shielding Materials Analysis
Authors: Wael Z. Tawfik | Samar N. Mohammad | Kamel H. Rahouma | Gerges M. Salama | Emad Tammam
Source: Physica Scripta (article, 2023-12-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning models for capacitance prediction of porous carbon-based supercapacitor electrodes Abstract Energy storage devices and systems with better performance, higher reliability, longer life, and wiser management strategies are needed for daily technology advancement. Among these devices, the supercapacitor is the most preferable due to its high-limit capacitance that esteems more than different capacitors. Today, it is considered a significant challenge to design high-performance materials for supercapacitors by exploring the interaction between characteristics and structural features of materials. Because of this, it is essential to predict capacitance when assessing a material’s potential for use in constructing supercapacitor-electrode applications. Machine learning (ML) can significantly speed up computation, capture complex mechanisms to enhance the accuracy of the prediction and make the best choices based on detailed status data. We aimed to develop a new strategy for the assisted design of high-performance supercapacitor materials by applying ML to analyze the relationship between capacitance and structural features of porous carbon materials (PCMs) using hundreds of experimental data in the literature. In the present study, Linear Regression (LR), Regression Tree (RT), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to estimate the supercapacitor’s capacitance. The effectiveness of the ML models was evaluated in terms of the root mean square error (RMSE), mean absolute error (MAE), and the correlation between expected yield and system-provided yield. The developed ANFIS model, with RMSE, MAE, and R values of 22.8, 39.7647, and 0.90004, respectively, compares favourably regarding prediction performance to other models built for this purpose. Supercapacitor ; Capacitance ; Mean squared error ; Adaptive neuro fuzzy inference system ; Computer science ; Materials science ; Fuzzy logic ; Artificial intelligence ; Fuzzy control system ; Mathematics ; Statistics ; Electrode Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrospun Nanofibers in Biomedical Applications
Authors: H. Gietl | Ben Garrison | David Sprouster | Takaaki Koyanagi
Source: Journal of the European Ceramic Society (article, 2023-01-10)
Field: Materials Science — Materials Chemistry

Heterogeneous microstructure of yttrium hydride and its relation to mechanical properties Materials science ; Nanoindentation ; Yttrium ; Microstructure ; Residual stress ; Electron backscatter diffraction ; Homogeneity (statistics) ; Vickers hardness test ; Composite material ; Metallurgy ; Machine learning Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear Physics and Applications
Authors: Xiao Wei | Yunong Zhang | Xi Liu | Junjie Peng | Shengzhou Li | Renchao Che | Huiran Zhang
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A<sub>2</sub>B<sup>+</sup>B<sup>3+</sup>X<sub>6</sub> Material datasets are high-dimensional and high-noise, which makes most machine learning (ML) methods inefficient. We present a new framework which embeds material domain knowledge into the ML method. By doing so, we illustrate its role and improve the prediction accuracy of 540 perovskite materials. Perovskite (structure) ; Halide ; Domain (mathematical analysis) ; Noise (video) ; Domain knowledge ; Computer science ; Materials science ; Artificial intelligence ; Machine learning ; Chemistry ; Crystallography ; Inorganic chemistry ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Matthew L. Brown | Jonathan M. Skelton | Paul L. A. Popelier
Source: Journal of Chemical Theory and Computation (article, 2023-10-17)
Field: Chemistry — Physical and Theoretical Chemistry

Application of the FFLUX Force Field to Molecular Crystals: A Study of Formamide In this work, we present the first application of the quantum chemical topology force field FFLUX to the solid state. FFLUX utilizes Gaussian process regression machine learning models trained on data from the interacting quantum atom partitioning scheme to predict atomic energies and flexible multipole moments that change with geometry. Here, the ambient (α) and high-pressure (β) polymorphs of formamide are used as test systems and optimized using FFLUX. Optimizing the structures with increasing multipolar ranks indicates that the lattice parameters of the α phase differ by less than 5% to the experimental structure when multipole moments up to the quadrupole are used. These differences are found to be in line with the dispersion-corrected density functional theory. Lattice dynamics calculations are also found to be possible using FFLUX, yielding harmonic phonon spectra comparable to dispersion-corrected DFT while enabling larger supercells to be considered than is typically possible with first-principles calculations. These promising results indicate that FFLUX can be used to accurately determine properties of molecular solids that are difficult to access using DFT, including the structural dynamics, free energies, and properties at finite temperature. Multipole expansion ; Force field (fiction) ; Quadrupole ; Density functional theory ; Phonon ; Molecular dynamics ; Lattice (music) ; Formamide ; Physics ; Molecular physics ; Materials science ; Statistical physics ; Chemistry ; Condensed matter physics ; Atomic physics ; Quantum mechanics Crystallography and molecular interactions ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Mahugnon Ezékiel Houngbo | Lucienne Desfontaines | Jean‐Louis Diman | Gemma Arnau | Christian Mestres | Fabrice Davrieux | Lauriane Rouan | Grégory Beurier | Carine Marie-Magdeleine | Karima Meghar | Emmanuel Oladeji Alamu | Bolanle Otegbayo | Denis Cornet
Source: Journal of the Science of Food and Agriculture (article, 2023-07-03)
Field: Chemistry — Analytical Chemistry

Convolutional neural network allows amylose content prediction in yam (<i>Dioscorea alata</i> L.) flour using near infrared spectroscopy According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD < 3 and R<sup>2</sup> < 0.8) for predicting amylose content from yam flour but the CNN model proved to be reliable and efficient method. With the application of deep learning methods, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, can be predicted accurately using NIRS as a high throughput phenotyping method. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Amylose ; Partial least squares regression ; Mathematics ; Coefficient of determination ; Dioscorea ; Food science ; Starch ; Mean squared error ; Chemistry ; Pattern recognition (psychology) ; Artificial intelligence ; Statistics ; Computer science Spectroscopy and Chemometric Analyses ; Food composition and properties ; Seed and Plant Biochemistry
Authors: Jianbo Zhu | Liangjun Xie | Zhuoyang Ti | Jingyu Li | Muchun Guo | Xuemei Zhang | Pengfei Liu | Lingling Tao | Zihang Liu | Yongsheng Zhang | Jiehe Sui
Source: Applied Physics Reviews (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

Computational understanding and prediction of 8-electron half-Heusler compounds with unusual suppressed phonon conduction The conventional thinking of designing materials with low lattice thermal conductivity κL is usually associated with chemical and structural complexity. Here, we proposed a new strategy for establishing the interaction strength between the nested cation and the anionic framework as a control knob for tuning κL in two orders of magnitude in isostructural half-Heusler compounds. A synthesized cubic and light-weight 8-electron half-Heusler compound, namely, MgCuSb, exhibits glass-like thermal conductivity in both magnitude and temperature dependence that seems to contradict common understanding while common 18-electron counterparts are known for high κL. Our studies reveal that both the native strong anharmonicity induced by the tension effect of atomic filling and a low-energy shearing vibration mode triggered by weak Mg–Cu bonding are responsible for the unusual suppressed phonon conduction in MgCuSb. Finally, an analytic model is constructed by machine learning method to predict phonon conduction of both 8- and 18-electron half-Heusler compounds in a unified way, which demonstrates that the interaction between cations and anions is universal by means of adjusting the thermal conductivity of this material family. Anharmonicity ; Isostructural ; Phonon ; Thermal conduction ; Thermal conductivity ; Condensed matter physics ; Materials science ; Shearing (physics) ; Electron ; Lattice (music) ; Grüneisen parameter ; Atomic mass ; Thermal ; Crystal structure ; Chemistry ; Thermodynamics ; Crystallography ; Physics ; Composite material ; Atomic physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Heusler alloys: electronic and magnetic properties
Authors: Sergei Manzhos | Johann Lüder | Manabu Ihara
Source: The Journal of Chemical Physics (article, 2023-12-19)
Field: Materials Science — Materials Chemistry

Machine learning of kinetic energy densities with target and feature smoothing: Better results with fewer training data Machine learning (ML) of kinetic energy functionals (KEFs), in particular kinetic energy density (KED) functionals, is a promising way to construct KEFs for orbital-free density functional theory (DFT). Neural networks and kernel methods including Gaussian process regression (GPR) have been used to learn Kohn-Sham (KS) KED from density-based descriptors derived from KS DFT calculations. The descriptors are typically expressed as functions of different powers and derivatives of the electron density. This can generate large and extremely unevenly distributed datasets, which complicates effective application of ML techniques. Very uneven data distributions require many training datapoints, can cause overfitting, and can ultimately lower the quality of an ML KED model. We show that one can produce more accurate ML models from fewer data by working with smoothed density-dependent variables and KED. Smoothing palliates the issue of very uneven data distributions and associated difficulties of sampling while retaining enough spatial structure necessary for working within the paradigm of KEDF. We use GPR as a function of smoothed terms of the fourth order gradient expansion and KS effective potential and obtain accurate and stable (with respect to different random choices of training points) kinetic energy models for Al, Mg, and Si simultaneously from as few as 2000 samples (about 0.3% of the total KS DFT data). In particular, accuracies on the order of 1% in a measure of the quality of energy-volume dependence B'=EV0-ΔV-2EV0+E(V0+ΔV)ΔV/V02 (where V0 is the equilibrium volume and ΔV is a deviation from it) are obtained simultaneously for all three materials. Overfitting ; Smoothing ; Kinetic energy ; Computer science ; Statistical physics ; Function (biology) ; Artificial intelligence ; Mathematics ; Artificial neural network ; Physics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions