jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yibo Sun | Xinming Wang | Cong Hou | Jun Ni
Source: The Journal of Physical Chemistry C (article, 2023-11-30)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning to Discover Perovskites with High Spontaneous Polarization Machine learning can accelerate the design of new materials by screening large quantities of materials. We investigated the spontaneous polarization intensity of inorganic perovskite ferroelectrics using a machine learning approach. The machine learning model covers the entire structure type of perovskite ferroelectrics. We make a large number of predictions for perovskite materials based on our model and screen 20 perovskite materials that have high spontaneous polarization intensity. We employ the SHAP (Shapley additive explanations) technique to qualitatively explain the machine learning model’s correctness from a physical point of view. The results show that the larger the average atomic radius and the smaller the electronegativity of the metal atoms of the perovskite, the easier it is to find greater spontaneous polarization intensity. We also screen and verify the reasonableness of descriptors based on the model interpretation to improve the reliability of the model. By utilizing an interpretable machine learning approach, we can predict and optimize the properties of ferroelectrics, which facilitates the evaluation and application of materials. Electronegativity ; Correctness ; Artificial intelligence ; Polarization (electrochemistry) ; Machine learning ; Perovskite (structure) ; Materials science ; Algorithm ; Computer science ; Condensed matter physics ; Physics ; Chemistry ; Quantum mechanics ; Physical chemistry ; Crystallography Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Multiferroics and related materials
Authors: Mengke Li | Hongmei Yuan | Yufeng Luo | Xiaoyi Ma | Huijun Liu | Wentao Jiang | Ying Fang
Source: Advanced Energy Materials (article, 2023-04-09)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Advanced Thermoelectric Materials via Transfer Learning Abstract Thermoelectric (TE) technology can realize direct conversion of widely distributed heat into useful electricity, which provides a promising route to solve the global energy crisis that is increasingly severe. However, it is extremely complex and time‐consuming to discover advanced TE materials via conventional trial‐and‐error approaches. In this work, using a pre‐trained neural network architecture for the electronic bandgap, a transfer learning (TL) strategy that allows ready and accurate prediction on the ZT values of any TE materials at arbitrary temperature is proposed. Compared with direct machine learning algorithms, the TL‐driven model exhibits significantly enhanced predictive power beyond the initial dataset, as characterized by improved Pearson correlation coefficient (reduced mean absolute error) from 23% to 95% (0.35 to 0.07) for the p ‐type systems, and 46% to 94% (0.23 to 0.06) for the n ‐type systems. By screening 6353 possible candidates in the AFLOW repository that having relatively smaller gaps, 925 p ‐type and 788 n ‐type systems are quickly identified to exhibit ZT exceeding 2.0. Equally importantly, the established TL model is highly adaptable to ZT prediction in an even larger search space, where the constituent atoms and/or stoichiometric compositions of the screened systems may be variously tuned to further optimize their TE performance. Materials science ; Artificial neural network ; Thermoelectric effect ; Seebeck coefficient ; Band gap ; Mean absolute error ; Power (physics) ; Machine learning ; Computer science ; Work (physics) ; Type (biology) ; Transfer of learning ; Artificial intelligence ; Optoelectronics ; Mean squared error ; Thermodynamics ; Physics ; Mathematics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Nida Katı | Ferhat Uçar
Source: Fractal and Fractional (article, 2023-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An Intelligent Model for Supercapacitors with a Graphene-Based Fractal Electrode to Investigate the Cyclic Voltammetry The aim of this study was to investigate the performance measurement of supercapacitors using the electrochemical properties of cyclic voltammetry (CV). The use of CV is crucial in evaluating the electrochemical performance of supercapacitors and determining the surface area of the catalyst with regard to the fractal properties of the electrode. The study specifically focused on the CV behavior of a supercapacitor formed by a cobalt-doped ceria/reduced graphene oxide (Co-CeO2/rGO) fractal nanocomposite, and its assessment was conducted using a machine learning (ML) model with the enhanced XGBoost. The model was trained using an experimental open-source dataset. The results showed that the proposed XGBoost model had a superior ability to predict the CV behavior of the supercapacitor, with nearly perfect results for the MAE, RMSE, and R-squared metrics, which are effective at evaluating the performance of regression models. With the successful design of the proposed intelligent prediction model, the study is expected to provide valuable insights into forming novel nanocomposite forms with high accuracy and minimal need for experiments. Supercapacitor ; Cyclic voltammetry ; Graphene ; Materials science ; Nanocomposite ; Cobalt oxide ; Electrode ; Electrochemistry ; Fractal ; Fractal dimension ; Oxide ; Nanotechnology ; Biological system ; Computer science ; Mathematics ; Chemistry ; Metallurgy Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Yaning Cui | Kang Chen | Lingyao Zhang | Haotian Wang | Lei Bai | David Elliston | Wei Ren
Source: The Journal of Physical Chemistry Letters (article, 2023-08-30)
Field: Materials Science — Materials Chemistry

Atomic Positional Embedding-Based Transformer Model for Predicting the Density of States of Crystalline Materials The rapid advancement of machine learning has revolutionized quite a few science fields, leading to a surge in the development of highly efficient and accurate materials discovery methods. Recently, predictions of multiple related properties have received attention, with a particular emphasis on spectral properties, where the electronic density of states (DOS) stands out as the fundamental data with enormous potential to advance our understanding of crystalline materials. Leveraging the power of the Transformer framework, we introduce an Atomic Positional Embedding-Based Transformer (APET), which surpasses existing state-of-the-art models for predicting <i>ab initio</i> DOS. APET utilizes atomic periodical positions as its positional embedding, which incorporates all of the structural information in a crystal, providing a more complete and accurate representation. Furthermore, the interpretability of APET enables us to discover the underlying physical properties of materials with greater precision and accuracy. Interpretability ; Embedding ; Transformer ; Computer science ; Artificial intelligence ; Physics ; Voltage ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Ulderico Di Caprio | Jan Degrève | Peter Hellinckx | Steffen Waldherr | M. Enis Leblebici
Source: Chemical Engineering Journal (article, 2023-09-20)
Field: Materials Science — Materials Chemistry

HybridGamma: A thermodynamically consistent framework for hybrid modelling of activity coefficients Bottleneck ; Predictability ; Computer science ; Embedding ; Process (computing) ; Reduction (mathematics) ; Work (physics) ; Biological system ; Data mining ; Machine learning ; Artificial intelligence ; Algorithm ; Mathematics ; Engineering Machine Learning in Materials Science ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: Chenglong Qin | Jinde Liu | Yushu Yu | Zihan Xu | Jiguang Du | Gang Jiang | Liang Zhao
Source: Ceramics International (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

Prediction of thermodynamic stability of actinide compounds by machine learning model Stability (learning theory) ; Robustness (evolution) ; Random forest ; Machine learning ; Ensemble forecasting ; Materials science ; Ensemble learning ; Artificial neural network ; Artificial intelligence ; Binary number ; Computer science ; Statistical physics ; Mathematics ; Physics ; Chemistry Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering
Authors: H. Mohit | Sanjay Mavinkere Rangappa | Krittirash Yorseng | Suchart Siengchin | Hadi M. Marwani | Anish Khan | Abdullah M. Asiri
Source: Journal of Materials Research and Technology (article, 2023-04-15)
Field: Materials Science — Polymers and Plastics

Discarded water hyacinth/pineapple fibers and carbon/innegra fabrics and TiC nanoparticles reinforced UV resistant polyester composites The widespread utilization of plant cellulose fiber-inorganic fillers-synthetic fabric-reinforced laminates is the current trend in the research field. This investigation studies the effect of chemically treated and untreated water hyacinth/pineapple leaf, titanium carbide nanoparticles, Innegra, and carbon fabric reinforcement on the physical, mechanical, and thermal characteristics of ultra-violet resistant polyester hybrid composites. The chemically treated and untreated water hyacinth/pineapple leaf fibers were examined from Fourier transform infrared (FTIR) spectra, X-ray diffraction (XRD), differential scanning calorimeter (DSC), thermogravimetric analysis (TGA), scanning electron microscope (SEM) and found that chemically treated fibers have an excellent interlocking bond with TiC nanoparticles, Innegra, carbon fabric, and polyester matrix. The maximum tensile, flexural, interlaminar shear, impact strength, and shore hardness also affirmed good interfacial bonding of fillers and textiles within the matrix as examined from SEM micrographs. The present investigation also determines the forecasting of the approaches and performance of artificial neural network (ANN) to model the material characteristics of fabricated polyester hybrid composite. Furthermore, the ANN model was more accurate and a valuable technique to optimize the material characteristics of the developed polyester hybrid composite. Materials science ; Composite material ; Polyester ; Thermogravimetric analysis ; Ultimate tensile strength ; Scanning electron microscope ; Composite number ; Fourier transform infrared spectroscopy ; Differential scanning calorimetry ; Chemical engineering Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging ; Advanced Cellulose Research Studies
Authors: Minghui Sun | Dong Zheng | Liyuan Wu | Haodong Yao | Wenchao Niu | Deting Xu | Ping Chen | Himadri S. Gupta | Yi Zhang | Yuhui Dong | Chunying Chen | Lina Zhao
Source: IUCrJ (article, 2023-03-16)
Field: Materials Science — Materials Chemistry

Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning Structural disclosure of biological materials can help our understanding of design disciplines in nature and inspire research for artificial materials. Synchrotron microfocus X-ray diffraction is one of the main techniques for characterizing hierarchically structured biological materials, especially the 3D orientation distribution of their interpenetrating nanofiber networks. However, extraction of 3D fiber orientation from X-ray patterns is still carried out by iterative parametric fitting, with disadvantages of time consumption and demand for expertise and initial parameter estimates. When faced with high-throughput experiments, existing analysis methods cannot meet the real time analysis challenges. In this work, using the assumption that the X-ray illuminated volume is dominated by two groups of nanofibers in a gradient biological composite, a machine-learning based method is proposed for fast and automatic fiber orientation metrics prediction from synchrotron X-ray micro-focused diffraction data. The simulated data were corrupted in the training procedure to guarantee the prediction ability of the trained machine-learning algorithm in real-world experimental data predictions. Label transformation was used to resolve the jump discontinuity problem when predicting angle parameters. The proposed method shows promise for application in the automatic data-processing pipeline for fast analysis of the vast data generated from multiscale diffraction-based tomography characterization of textured biomaterials. Computer science ; Orientation (vector space) ; Artificial intelligence ; Diffraction ; Parametric statistics ; Pipeline (software) ; Machine learning ; Synchrotron ; Throughput ; Materials science ; Algorithm ; Optics ; Mathematics Machine Learning in Materials Science ; Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials
Authors: Haichao Gong | Qunbo Fan | Wenqiang Xie | Hongmei Zhang | Lin Yang | Shun Xu | Xingwang Cheng
Source: Materials & Design (article, 2023-01-16)
Field: Materials Science — Materials Chemistry

Mining the relationship between the dynamic compression performance and basic mechanical properties of Ti20C based on machine learning methods The dynamic compression performance of titanium alloys is important for material design under shock, but the intrinsic relationship between them and basic mechanical properties is still unclear. In this work, based on the mechanical-property data set of Ti20C sheet (4788 pieces of data), the correlation between them was constructed through data-driven and machine-learning methods. Through the trained random-forest regression models, the Quantitative Maps were constructed, and the dynamic compression strength σD, critical fracture strain εf, as well as impact absorption energy ED, were effectively predicted, with the accuracy rates all over 86.11%. Accordingly, the zone of excellent dynamic performance and corresponding quasi-static tensile properties in Maps can be rapidly screened. Furthermore, combined with microstructure observation, it was found that nano-scale acicular secondary α-phase significantly increased σD, micro-scale secondary α-phase resulted in excellent dynamic strength and plasticity, while the equiaxed, bimodal and mixture microstructure without secondary α-phase corresponded to the high value of εf. Meanwhile, both nano-scale and micro-scale secondary α-phase contributed to the high value of ED. Finally, the generalization capabilities of models were validated. By comparing predicted values with experimental values, it was found the models realized the relatively accurate prediction of dynamic compression performance on other titanium alloys. Materials science ; Microstructure ; Ultimate tensile strength ; Equiaxed crystals ; Compression (physics) ; Scale (ratio) ; Titanium alloy ; Fracture (geology) ; Dynamic range compression ; Shock (circulatory) ; Machine learning ; Composite material ; Computer science ; Alloy Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Microstructure and mechanical properties
Authors: S. Ram Prakash | Rajesh Kumar | Anirban Mitra
Source: Photonics and Nanostructures - Fundamentals and Applications (article, 2023-04-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of dynamically tunable phase-change material based metamaterial absorber induced structural color Gamut ; Metamaterial ; Structural coloration ; Inverse ; Color filter array ; Computer science ; Materials science ; Artificial neural network ; Refractive index ; Optics ; Optoelectronics ; Artificial intelligence ; Color gel ; Nanotechnology ; Physics ; Layer (electronics) ; Photonic crystal ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies ; Liquid Crystal Research Advancements
Authors: Qiang Zhu | Yang Ge | Wei Li | Jing Ma
Source: Journal of Chemical Theory and Computation (review, 2023-01-02)
Field: Materials Science — Materials Chemistry

Treating Polarization Effects in Charged and Polar Bio-Molecules Through Variable Electrostatic Parameters Polarization plays important roles in charged and hydrogen bonding containing systems. Much effort ranging from the construction of physics-based models to quantum mechanism (QM)-based and machine learning (ML)-assisted models have been devoted to incorporating the polarization effect into the conventional force fields at different levels, such as atomic and coarse grained (CG). The application of polarizable force fields or polarization models was limited by two aspects, namely, computational cost and transferability. Different from physics-based models, no predetermining parameters were required in the QM-based approaches. Taking advantage of both the accuracy of QM calculations and efficiency of molecular mechanism (MM) and ML, polarization effects could be treated more efficiently while maintaining the QM accuracy. The computational cost could be reduced with variable electrostatic parameters, such as the charge, dipole, and electronic dielectric constant with the help of linear scaling fragmentation-based QM calculations and ML models. Polarization and entropy effects on the prediction of partition coefficient of druglike molecules are demonstrated by using both explicit or implicit all-atom molecular dynamics simulations and machine learning-assisted models. Directions and challenges for future development are also envisioned. Dipole ; Polarizability ; Polarization (electrochemistry) ; Transferability ; Dielectric ; Electrostatics ; Molecular dynamics ; Computer science ; Statistical physics ; Physics ; Molecule ; Chemical physics ; Chemistry ; Quantum mechanics ; Machine learning ; Physical chemistry Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Protein Structure and Dynamics
Authors: Miller Lacerda | Thiago França | Camila Calvani | Bruno Marangoni | Paulo Eduardo Teodoro | Cid Naudi Silva Campos | Fábio Henrique Rojo Baio | Gileno Brito de Azevedo | Cícero Cena
Source: Results in Chemistry (article, 2023-11-30)
Field: Chemistry — Organic Chemistry

A simple method for Eucalyptus species discrimination: FTIR spectroscopy and machine learning Wood sample classification is crucial in forestry, woodworking, and conservation efforts. It involves categorizing wood species based on physical characteristics like grain patterns, color, texture, and density. However, there are scientific gaps that hinder its effectiveness. One challenge is the difficulty in differentiating wood species with similar characteristics. To address this, we propose using FTIR spectroscopy and machine learning algorithms. In our study, we focused on the accurate classification of Corymbia citriodora (CIT), Eucalyptus grandis (GRA), and the hybrid species GG100 (GG). We evaluated bark and sapwood samples to determine the best for such a purpose. Using FTIR spectroscopy and Quadratic Discriminant Analysis with only seven PCs, we achieved 100% accuracy in bark sample classification. This approach offers advantages because FTIR spectroscopy captures unique chemical fingerprints, providing rapid and non-destructive analysis of wood samples. Machine learning algorithms learn from these spectral patterns for accurate species prediction. Notably, bark samples, easily obtained without tree damage, showed excellent classification results. By incorporating FTIR spectroscopy and machine learning, we can overcome the challenges of inter-species similarities and intra-species variations. This improves reliability and accuracy, supporting and ensuring high-quality wood products. Integrating FTIR spectroscopy and machine learning presents a promising solution for enhancing wood sample classification. Further research can expand species classification and improve wood identification processes. Fourier transform infrared spectroscopy ; Linear discriminant analysis ; Bark (sound) ; Artificial intelligence ; Spectroscopy ; Sample (material) ; Quadratic classifier ; Pattern recognition (psychology) ; Species identification ; Machine learning ; Computer science ; Eucalyptus ; Mathematics ; Materials science ; Engineering ; Chemistry ; Botany ; Support vector machine ; Physics ; Biology ; Geography ; Forestry ; Chromatography Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Wood Treatment and Properties
Authors: Georgi Nikolov Palichev | Dicho Stratiev | Sotir Sotirov | Evdokia Sotirova | Svetoslav Nenov | Ivelina Shishkova | Rosen Dinkov | Krassimir Atanassov | Simeon Ribagin | Danail D. Stratiev | Dimitar Pilev | Dobromir Yordanov
Source: Processes (article, 2023-08-02)
Field: Chemistry — Analytical Chemistry

Prediction of Refractive Index of Petroleum Fluids by Empirical Correlations and ANN The refractive index is an important physical property that is used to estimate the structural characteristics, thermodynamic, and transport properties of petroleum fluids, and to determine the onset of asphaltene flocculation. Unfortunately, the refractive index of opaque petroleum fluids cannot be measured unless special experimental techniques or dilution is used. For that reason, empirical correlations, and metaheuristic models were developed to predict the refractive index of petroleum fluids based on density, boiling point, and SARA fraction composition. The capability of these methods to accurately predict refractive index is discussed in this research with the aim of contrasting the empirical correlations with the artificial neural network modelling approach. Three data sets consisting of specific gravity and boiling point of 254 petroleum fractions, individual hydrocarbons, and hetero-compounds (Set 1); specific gravity and molecular weight of 136 crude oils (Set 2); and specific gravity, molecular weight, and SARA composition data of 102 crude oils (Set 3) were used to test eight empirical correlations available in the literature to predict the refractive index. Additionally, three new empirical correlations and three artificial neural network (ANN) models were developed for the three data sets using computer algebra system Maple, NLPSolve with Modified Newton Iterative Method, and Matlab. For Set 1, the most accurate refractive index prediction was achieved by the ANN model, with %AAD of 0.26% followed by the new developed correlation for Set 1 with %AAD of 0.37%. The best literature empirical correlation found for Set 1 was that of Riazi and Daubert (1987), which had %AAD of 0.40%. For Set 2, the best performers were the models of ANN, and the new developed correlation of Set 2 with %AAD of refractive index prediction was 0.21%, and 0.22%, respectively. For Set 3, the ANN model exhibited %AAD of refractive index prediction of 0.156% followed by the newly developed correlation for Set 3 with %AAD of 0.163%, while the empirical correlations of Fan et al. (2002) and Chamkalani (2012) displayed %AAD of 0.584 and 0.552%, respectively. Refractive index ; Boiling point ; Artificial neural network ; Empirical modelling ; Asphaltene ; Set (abstract data type) ; Test set ; API gravity ; Specific gravity ; Mathematics ; Thermodynamics ; Computer science ; Chemistry ; Petroleum ; Artificial intelligence ; Mineralogy ; Statistics ; Optics ; Simulation ; Organic chemistry ; Physics Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques