jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dmitriy D. Matyushin | Anastasia Yu. Sholokhova
Source: Сорбционные и хроматографические процессы (article, 2024-12-08)
Field: Chemistry — Spectroscopy

A comparison of published in 2018-2024 general-purpose models for predicting gas chromatographic retention indices Retention indices are widely used in gas chromatography and chromatography-mass spectrometry as an additional factor in tentative identification (along with the mass spectrum). Reference data on retention indices are available only for a limited number of molecules; in other cases, retention indices predicted by mathematical models can be used. Models for predicting retention indices developed prior to 2018 mostly have either very low accuracy or a very narrow domain of applicability. However, in recent years, starting from 2018, the situation has begun to change: the use of deep neural networks and large training sets (mainly different versions of the NIST database) made it possible to build both accurate and general-purpose models for predicting gas chromatographic retention indices, with the accuracy increasing over time. In recent years, at least 7 deep learning-based models for predicting gas chromatographic retention indices have been released in the public domain. The authors always declare that their model is more accurate than previous models, however, in all cases, there are no independent measurements of accuracy. This work aimed to objectively and critically compare retention index prediction models and corresponding software using the same retention data set that was guaranteed not to intersect with the training sets used by the authors of the models. Seven models and corresponding software were considered, including MetExpert (2018), DeepReI (2021), SVEKLA (2021), and AIRI (2024). It was shown that for the non-polar stationary phase (ZB-5MS), the accuracy of the newest models gradually approaches the accuracy of the reference libraries and is quite high. The newer models are indeed more accurate than the older ones. At the same time, for the polar stationary phase (SH-Stabilwax), the accuracy (independent data set) is very low and significantly lower than that stated in the original papers devoted to the predictive models. For users with limited experience, the process of compiling and running software can be challenging, particularly when attempting to do so several years after publication. This is often due to incompatibility issues between model files and newer versions of the frameworks. It is not uncommon for software authors to discontinue any support of the software after an article has been published in a journal. Kovats retention index ; Artificial neural network ; Retention time ; Applicability domain ; Set (abstract data type) ; Computer science ; Data set ; Artificial intelligence ; Data mining ; Machine learning ; Gas chromatography ; Chromatography ; Chemistry ; Quantitative structure–activity relationship Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Tao Zhang | Yongjun Jiao | Zhenhai Liu | Shuo Xing | Haoyu Wang | Kun Zhang | Yuanming Li
Source: Nuclear Engineering and Technology (article, 2024-10-16)
Field: Materials Science — Materials Chemistry

Modeling of zirconium alloy cladding corrosion behavior based on neural ordinary differential equation Current zirconium alloy cladding corrosion models are mainly semi-empirical and show significant dispersion when compared to measured data. This study introduces neural ordinary differential equation (neural ODE) to model corrosion behavior, utilizing data-model fusion approach for network training. Initially, a semi-empirical model for zirconium alloy cladding corrosion is established through differential evolution algorithm, generating a large dataset for pre-training the neural network. The network is then fine-tuned using measured data. These methods effectively address the challenges of sparse cladding corrosion data and data available only at fixed time points, resulting in a more accurate model. The results show that the differential evolution algorithm can identify a set of appropriate parameters for the semi-empirical model, achieving a standard deviation of 0.040. The neural ODE model demonstrates even higher accuracy, reducing the standard deviation to 0.031 and improving accuracy by approximately 25%. Additionally, the model demonstrates excellent generalization capacity on other time points and new power histories. • Modeling zirconium alloy cladding corrosion behavior using neural ordinary differential equation. • Using a data-model fusion driven method to train the network to solve the problem of sparse data. • Achieved a 25% improvement in accuracy compared to classical semi-empirical model. Cladding (metalworking) ; Zirconium alloy ; Zirconium ; Corrosion ; Alloy ; Ordinary differential equation ; Materials science ; Metallurgy ; Differential equation ; Composite material ; Mathematics ; Mathematical analysis Nuclear Materials and Properties ; Nuclear Engineering Thermal-Hydraulics ; Nuclear reactor physics and engineering
Authors: Gaoyang Xiong | Haining Ji | Yongxing Chen | Bin Liu | Yi Wang | Long Peng | Jinfang Zeng | Jundong Tao | Cong Deng
Source: Nanomaterials (article, 2024-07-06)
Field: Materials Science — Polymers and Plastics

Preparation of Thermochromic Vanadium Dioxide Films Assisted by Machine Learning In recent years, smart windows have attracted widespread attention due to their ability to respond to external stimuli such as light, heat, and electricity, thereby intelligently adjusting the ultraviolet, visible, and near-infrared light in solar radiation. VO<sub>2</sub>(M) undergoes a reversible phase transition from an insulating phase (monoclinic, M) to a metallic phase (rutile, R) at a critical temperature of 68 °C, resulting in a significant difference in near-infrared transmittance, which is particularly suitable for use in energy-saving smart windows. However, due to the multiple valence states of vanadium ions and the multiphase characteristics of VO<sub>2</sub>, there are still challenges in preparing pure-phase VO<sub>2</sub>(M). Machine learning (ML) can learn and generate models capable of predicting unknown data from vast datasets, thereby avoiding the wastage of experimental resources and reducing time costs associated with material preparation optimization. Hence, in this paper, four ML algorithms, namely multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), were employed to explore the parameters for the successful preparation of VO<sub>2</sub>(M) films via magnetron sputtering. A comprehensive performance evaluation was conducted on these four models. The results indicated that XGB was the top-performing model, achieving a prediction accuracy of up to 88.52%. A feature importance analysis using the SHAP method revealed that substrate temperature had an essential impact on the preparation of VO<sub>2</sub>(M). Furthermore, characteristic parameters such as sputtering power, substrate temperature, and substrate type were optimized to obtain pure-phase VO<sub>2</sub>(M) films. Finally, it was experimentally verified that VO<sub>2</sub>(M) films can be successfully prepared using optimized parameters. These findings suggest that ML-assisted material preparation is highly feasible, substantially reducing resource wastage resulting from experimental trial and error, thereby promoting research on material preparation optimization. Materials science ; Vanadium dioxide ; Substrate (aquarium) ; Transmittance ; Sputtering ; Computer science ; Optoelectronics ; Sputter deposition ; Machine learning ; Artificial intelligence ; Analytical Chemistry (journal) ; Optics ; Nanotechnology ; Thin film ; Chemistry Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; Ga2O3 and related materials
Authors: Ke Yan | Shaobo Qi | Xingyu Shen | Mengqi Yuan | Hao Wu | Yunxian Yang | Yazhuo Qian
Source: Thin-Walled Structures (article, 2024-12-25)
Field: Materials Science — Polymers and Plastics

Impact resistance and fire resistance of solid waste based interface self-assembled fiber reinforced composite structures • Industrial sludge resource utilization provides silicon source for pore structure preparation. • Metal ion bridging grafting reaction of fiber interface composite structure. • Flame retardant , heat-insulating, and bulletproof multi load protection mechanism. • Optimization of coupled protection threshold based on economic and other predictive factors. Inspired by the resource utilization of solid waste, industrial sludge has been developed as a raw material NH 2 -MCM-41@Cu-AF. The preparation method of fiber self-assembly structure design was explored by combining macroscopic grafting behavior with the evolution of microscopic bridging structure. The basic mechanical properties, impact resistance, flame retardant, and thermal insulation properties were analyzed. By exploring and predicting the transition from single fiber composite materials to multi-protection, experimental results have shown that NH 2 -MCM-41@Cu-AF significantly enhances the protective performance. The composite fabric has increased the pulling force by 3.25 times, and the ballistic limit speed has been increased from 57 m/s to 94 m/s. The strong penetration resistance of composite materials has been demonstrated through multi-layer bulletproof performance. Composite fabrics have stronger flame retardancy and thermal insulation, forming a dense protective layer under high temperatures, ensuring structural integrity to the greatest extent possible. Machine learning prediction methods and threshold analysis processes have been established for the final implementation of NH 2 -MCM-41@Cu-AF. The development of new protective materials and their application in engineering practice provide theoretical support and an experimental basis. Materials science ; Fire resistance ; Composite number ; Composite material ; Resistance (ecology) ; Fiber ; Interface (matter) ; Impact resistance ; Fiber-reinforced composite Natural Fiber Reinforced Composites ; Flame retardant materials and properties ; Innovative concrete reinforcement materials
Authors: Abdulrahman Anwar Alarabi | Imtiaz Ali
Source: Discover Chemical Engineering (article, 2024-11-29)
Field: Materials Science — Materials Chemistry

Degradation of poly (2-ethyl-2-oxazoline) prepared at lower temperature: a study of kinetics using thermal analysis and advanced predictive modeling The thermal degradation kinetics of poly(2-ethyl-2-oxazoline) (PEOX) prepared under different pH conditions were studied. Kinetic assessment was conducted by analyzing the thermal degradation of PEOX. The kinetic parameters were estimated using model-free methods; including Friedman, OFW, KAS and Starink methods and model-fitting method; Combined kinetics (Ck). The investigation revealed that the activation energy of PEOX degradation exhibited variability when prepared under different pH conditions. During the process of conversion, the activation energy for PEOX thermal degradation showed an increase when synthesized under neutral conditions, while it remained consistent when synthesized under alkaline conditions. The synthesis of PEOX in an acidic medium led to a compromised thermal stability, as indicated by a gradual reduction in activation energy during its thermal degradation. By employing artificial neural network (ANN) and advanced classification regression tree (C&RT), the prediction of activation energy's evolution was conducted, taking into account pH, conversion, heating rate, and temperature. The predictive performance of the advanced C&RT model surpassed that of ANN in determining the activation energy of PEOX degradation at different pH levels for different heating rates. Kinetics ; Degradation (telecommunications) ; Oxazoline ; Thermal analysis ; Thermal ; Chemistry ; Chemical engineering ; Materials science ; Polymer chemistry ; Thermodynamics ; Organic chemistry ; Computer science ; Physics ; Catalysis ; Engineering Thermal and Kinetic Analysis ; Polymer Science and PVC ; Polymer crystallization and properties
Authors: Sergei Izvekov | Matthew P. Kroonblawd | James P. Larentzos | John K. Brennan | Betsy M. Rice
Source: The Journal of Physical Chemistry B (article, 2024-03-15)
Field: Materials Science — Materials Chemistry

Maximum Entropy Theory of Multiscale Coarse-Graining via Matching Thermodynamic Forces: Application to a Molecular Crystal (TATB) The MSCG/FM (multiscale coarse-graining via force-matching) approach is an efficient supervised machine learning method to develop microscopically informed coarse-grained (CG) models. We present a theory based on the principle of maximum entropy (PME) enveloping the existing MSCG/FM approaches. This theory views the MSCG/FM method as a special case of matching the thermodynamic forces from the extended ensemble described by the set of thermodynamic (relevant) system coordinates. This set may include CG coordinates, the stress tensor, applied external fields, and so forth, and may be characterized by nonequilibrium conditions. Following the presentation of the theory, we discuss the consistent matching of both bonded and nonbonded interactions. The proposed PME formulation is used as a starting point to extend the MSCG/FM method to the constant strain ensemble, which together with the explicit matching of the bonded forces is better suited for coarse-graining anisotropic media at a submolecular resolution. The theory is demonstrated by performing the fine coarse-graining of crystalline 1,3,5-triamino-2,4,6-trinitrobenzene (TATB), a well-known insensitive molecular energetic material, which exhibits highly anisotropic mechanical properties. Granularity ; TATB ; Statistical physics ; Canonical ensemble ; Non-equilibrium thermodynamics ; Anisotropy ; Entropy (arrow of time) ; Physics ; Classical mechanics ; Thermodynamics ; Computer science ; Chemistry ; Mathematics ; Monte Carlo method ; Quantum mechanics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Energetic Materials and Combustion
Authors: Yogesh Khatri | Arti Kashyap
Source: Journal of Magnetism and Magnetic Materials (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

Hund’s matrix: A structure-based descriptor to predict the magnetic properties Matrix (chemical analysis) ; Materials science ; Condensed matter physics ; Statistical physics ; Physics ; Composite material Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; X-ray Diffraction in Crystallography
Authors: Deepti Rekha Sahoo | Trinath Biswal
Source: SPE Polymers (article, 2024-01-03)
Field: Materials Science — Biomaterials

Study of antibacterial and mechanical properties of poly(<scp>methyl methacrylate‐co‐acrylic acid</scp>)/hydroxyapatite biocomposite by using artificial neural network approach Abstract A novel, promising biocomposite was synthesized through the free radical polymerization method using methyl methacrylate (MMA), acrylic acid (AA), and hydroxyapatite derived from oyster shells (Os‐HA). Fourier transform infrared spectroscopy (FTIR) peaks indicate the conjugation of Os‐HA in the biocomposite. From the TGA analysis, it was observed that a weight loss of nearly 2% is achieved at 210°C, up to 15% at 210 to 420°C, and up to 95% at 420 to 580°C. The SEM image shows the uniform dispersion of the Os‐HA particles and strong interaction with the copolymer matrix. The broad spectrum at 2θ = 14.6° in XRD analysis indicates the amorphous nature of poly(MMA‐co‐AA)/Os‐HA. The higher inhibition zone of the biocomposite is observed at 125 μL against E. coli bacteria. The maximum water absorption is observed at 15 wt% of Os‐HA. The young's modulus gradually increases, while the impact strength decreases up to 20 wt% of Os‐HA. The artificial neural network (ANN) model has been used to assess the optimum values of impact strength, tensile strength, and Young's modulus of the biocomposite material. The correlation factor ( R = 0.998) of the ANN model data indicates greater accuracy of the experimental result. Biocomposite ; Materials science ; Ultimate tensile strength ; Fourier transform infrared spectroscopy ; Acrylic acid ; Methyl methacrylate ; Nuclear chemistry ; Composite material ; Izod impact strength test ; Dynamic mechanical analysis ; Polymer chemistry ; Polymerization ; Chemical engineering ; Copolymer ; Composite number ; Polymer ; Chemistry biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Bone Tissue Engineering Materials
Authors: Rafat Mohammadi | Behrad Karimi | John Kieffer | Daniel Hashemi
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

A molecular dynamics simulation study of thermal conductivity of plumbene We investigate the lattice thermal conductivity of plumbene using molecular dynamics simulations, overcoming existing limitations by optimizing the parameters of Tersoff and Stillinger-Weber potentials <i>via</i> artificial neural networks. Our findings indicate that at room temperature, the thermal conductivity of a 1050 Å × 300 Å plumbene sheet is approximately 8 W m<sup>-1</sup> K<sup>-1</sup>, significantly lower (23%) than that of bulk lead. Our analysis elucidates that thermal conductivity is enhanced by increased sample length, while it is reduced by temperature. Moreover, plumbene samples with zigzag edges display superior thermal conductivity compared to those with armchair edges. In addition, the thermal conductivity of plumbene exhibits an increase at low tensile strains, whereas it decreases as the strains become larger. This investigation provides crucial insights into the thermal conductivity behavior of plumbene under varying conditions. Molecular dynamics ; Thermal conductivity ; Statistical physics ; Dynamics (music) ; Chemical physics ; Thermal ; Materials science ; Physics ; Chemistry ; Thermodynamics ; Computational chemistry thermodynamics and calorimetric analyses ; Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics
Authors: Joeluis Cerutti Torres | Pablo Sánchez-Palencia | J.C. Jiménez-Sáez | P. Wahnón | Pablo Palacios
Source: Crystals (article, 2024-04-29)
Field: Materials Science — Materials Chemistry

Effect of Changing and Combining Trivalent Metals in the Structural and Electronic Properties of Cu-Based Crystal Delafossite Materials Cu-based ternary oxides with delafossite structure have received considerable attention in recent years for their versatility in a wide range of applications, among which is the possibility to use them in heterostructure solar cells as hole transport layers, due to their promising behavior as p-type conducting oxides. Ab initio calculations have been performed with density functional theory to investigate the role of the trivalent metal within the CuMO2 structure and the dependence of structural and electronic properties with the species (M = Al, Ga, In, Fe, Cr, Co, Sc, Y) occupying the site of the metal. Generalized Gradient Approximation also including a Hubbard term and nonlocal Heyd–Scuseria–Enzerhof screened hybrid functional schemes were tested and their results were compared. Excellent agreement with experimental lattice parameters and measured gaps have been found. The use of hybrid functionals in HSE approximation considerably improves the bandgaps when compared with the experimental results but takes considerable time to converge, hence the need to explore less demanding methodologies. Trends in the geometry as well as in the electronic properties are discussed, and the effect of mixing different metals (CuMxN1−xO2, M, N being the aforementioned elements) in the geometry and electronic properties of these delafossite materials is investigated. Due to the high cost of HSE calculations, especially when supercells are needed to model several x concentrations, statistical models and techniques based on machine learning have also been explored to predict HSE bandgap values from GGA and structural information. Delafossite ; Ternary operation ; Density functional theory ; Electronic structure ; Materials science ; Band gap ; Heterojunction ; Ab initio ; Crystal structure ; Hybrid functional ; Lattice (music) ; Metal ; Computational chemistry ; Chemistry ; Oxide ; Crystallography ; Optoelectronics ; Computer science ; Physics ; Metallurgy Copper-based nanomaterials and applications ; ZnO doping and properties ; Electronic and Structural Properties of Oxides
Authors: Justin Baker | Massimiliano Lupo Pasini | Cory D. Hauck
Source:  (article, 2024-03-15)
Field: Materials Science — Materials Chemistry

Invariant Features for Accurate Predictions of Quantum Chemical UV-vis Spectra of Organic Molecules Including invariance of global properties of a phys-ical system as an intrinsic feature in graph neural networks (GNNs) enhances the model's robustness and generalizability and reduces the amount of training data required to obtain a desired accuracy for predictions of these properties. Existing open source GNN libraries construct invariant features only for specific GNN architectures. This precludes the generalization of invariant features to arbitrary message passing neural network (MPNN) layers which, in turn, precludes the use of these libraries for new, user-specified predictive tasks. To address this limitation, we implement invariant MPNNs into the flexible and scalable HydraGNN architecture. HydraGNN enables a seamless switch between various MPNNs in a unified layer sequence and allows for a fair comparison between the predictive performance of different MPNNs. We trained this enhanced HydraGNN archi-tecture on the ultraviolet-visible (UV-vis) spectrum of GDB-9 molecules, a feature that describes the molecule's electronic exci-tation modes, computed with time-dependent density functional tight binding (TD-DFTB) and available open source through the GDB-9-Ex dataset. We assess the robustness (i.e., accuracy and generalizability) of the predictions obtained using different invariant MPNNs with respect to different values of the full width at half maximum (FWHM) for the Gaussian smearing of the theoretical peaks. Our numerical results show that incorporating invariance in the HydraGNN architecture significantly enhances both accuracy and generalizability in predicting UV-vis spectra of organic molecules. Invariant (physics) ; Spectral line ; Molecule ; Quantum chemical ; Quantum ; Organic molecules ; Materials science ; Physics ; Chemistry ; Molecular physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy Techniques in Biomedical and Chemical Research