jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jéssica de Vasconcelos Cardoso Barros | João Daniel S. Castro | Cesário Francisco das Virgens
Source: Polymer (article, 2023-10-26)
Field: Chemistry — Analytical Chemistry

Machine learning approach for ion imprinted (IIP) and non-imprinted (NIP) polymer discrimination based on pyrolysis kinetic data Ethylene glycol dimethacrylate ; NIP ; Methacrylic acid ; Polymer ; Materials science ; Chemical engineering ; Molecularly imprinted polymer ; Molecular imprinting ; Selectivity ; Chemistry ; Monomer ; Organic chemistry ; Catalysis ; Composite material Analytical chemistry methods development ; Adsorption and biosorption for pollutant removal ; Environmental remediation with nanomaterials
Authors: Yingjin Ma | Zhiying Li | Xin Chen | Bowen Ding | Ning Li | Teng Lu | Baohua Zhang | Bingbing Suo | Zhong Jin
Source: Journal of Computational Chemistry (article, 2023-01-17)
Field: Materials Science — Materials Chemistry

Machine‐learning assisted scheduling optimization and its application in quantum chemical calculations Easy and effective usage of computational resources is crucial for scientific calculations, both from the perspectives of timeliness and economic efficiency. This work proposes a bi-level optimization framework to optimize the computational sequences. Machine-learning (ML) assisted static load-balancing, and different dynamic load-balancing algorithms can be integrated. Consequently, the computational and scheduling engine of the ParaEngine is developed to invoke optimized quantum chemical (QC) calculations. Illustrated benchmark calculations include high-throughput drug suit, solvent model, P38 protein, and SARS-CoV-2 systems. The results show that the usage rate of given computational resources for high throughput and large-scale fragmentation QC calculations can primarily profit, and faster accomplishing computational tasks can be expected when employing high-performance computing (HPC) clusters. Quantum chemical ; Computer science ; Scheduling (production processes) ; Quantum ; Mathematical optimization ; Quantum mechanics ; Physics ; Mathematics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Akira Takahashi | Kei Terayama | Yu Kumagai | Ryo Tamura | Fumiyasu Oba
Source: Science and Technology of Advanced Materials Methods (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

Fully autonomous materials screening methodology combining first-principles calculations, machine learning and high-performance computing system Materials screening by high-throughput first-principles calculations is a powerful tool for exploring novel materials with preferable properties. Machine learning techniques are expected to accelerate materials screening by constructing surrogate models and making fast predictions. Especially, black-box optimization methods such as Bayesian optimization, repeating the construction of a prediction model and the selection of data points, have attracted much attention. In this study, we constructed an autonomous materials screening system using first-principles calculations and machine learning working on high-performance computing systems. The performance of the system was evaluated by applying the system to the exploration of high-k dielectrics using band gaps by hybrid functional calculations and dielectric constants by density functional perturbation theory calculations, respectively. The developed system identified materials with anomalous properties, as well as materials with both wide band gaps and high dielectric constants by utilizing appropriate black-box optimization methods, much faster than random exploration. The code for the developed system is published on an open repository. Bayesian optimization ; Computer science ; Black box ; Dielectric ; Supercomputer ; Throughput ; Code (set theory) ; Machine learning ; Artificial intelligence ; Computational science ; Computer engineering ; Materials science ; Parallel computing Machine Learning in Materials Science ; Semiconductor materials and devices ; Ferroelectric and Negative Capacitance Devices
Authors: Lihao Su | Zhongyu Wang | Ya Wang | Zijun Xiao | Deming Xia | Siyu Zhang | Jingwen Chen
Source: Environmental Science and Pollution Research (article, 2023-09-27)
Field: Materials Science — Materials Chemistry

Predicting adsorption of organic compounds onto graphene and black phosphorus by molecular dynamics and machine learning Adsorption ; Graphene ; Support vector machine ; Random forest ; Molecular dynamics ; Decision tree ; Linear regression ; Molecular descriptor ; Benchmark (surveying) ; Boosting (machine learning) ; Computer science ; Biological system ; Chemistry ; Machine learning ; Materials science ; Quantitative structure–activity relationship ; Nanotechnology ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Graphene research and applications ; Adsorption and biosorption for pollutant removal
Authors: Wei Xu | Bo Yang
Source: Molecular Catalysis (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Microkinetic modeling with machine learning predicted binding energies of reaction intermediates of ethanol steam reforming: The limitations Mean squared error ; Kinetic energy ; Work (physics) ; Energetics ; Chemistry ; Range (aeronautics) ; Reaction rate ; Activation energy ; Thermodynamics ; Propagation of uncertainty ; Biological system ; Catalysis ; Mathematics ; Statistics ; Physical chemistry ; Materials science ; Physics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Md Esharuzzaman Emu
Source: Smart Materials and Structures (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Predictions of electromotive force of magnetic shape memory alloy (MSMA) using constitutive model and generalized regression neural network Abstract Ferromagnetic shape memory alloys (MSMAs), such as Ni-Mn-Ga single crystals, can exhibit the shape memory effect due to an applied magnetic field at room temperature. Under a variable magnetic field and a constant bias stress loading, MSMAs have been used for actuation applications. Under variable stress and a constant bias field, MSMAs can be used in power harvesting or sensing devices, e.g. in structural health monitoring applications. This behavior is primarily a result of the approximately tetragonal unit cell whose magnetic easy axis is approximately aligned with the short axis of the unit cell within the Ni-Mn-Ga single crystals. Under an applied field, the magnetic easy axis tends to align with the external field. Similarly, under an applied compressive force, the short side of the unit cell tends to align with the direction of the force. This work introduced a new feature to the existing macro-scale magneto-mechanical model for Ni-Mn-Ga single crystal. This model includes the fact that the magnetic easy axis in the two variants is not exactly perpendicular as observed by D’silva et al (2020 Shape Mem. Superelasticity 6 67–88). This offset helps explain some of the power harvesting capabilities of MSMAs. Model predictions are compared to experimental data collected on a Ni-Mn-Ga single crystal. The experiments include both stress-controlled loading with constant bias magnetic field load (which mimics power harvesting or sensing) and field-controlled loading with constant bias compressive stress (which mimics actuation). Each type of test was performed at several different load levels, and the applied field was measured without the MSMA specimen present so that demagnetization does not affect the experimentally measured field as suggested by Eberle et al (2019 Smart Mater. Struct. 28 025022). Results show decent agreement between model predictions and experimental data. Although the model predicts experimental results decently, it does not capture all the features of the experimental data. In order to capture all the experimental features, finally, a generalized regression neural network (GRNN) was trained using the experimental data (stress, strain, magnetic field, &amp; emf) so that it can make a reasonably better prediction. Electromotive force ; Magnetic field ; Materials science ; Artificial neural network ; Stress (linguistics) ; Test data ; Shape-memory alloy ; Demagnetizing field ; Constitutive equation ; Mechanics ; Offset (computer science) ; Condensed matter physics ; Structural engineering ; Finite element method ; Computer science ; Physics ; Engineering ; Metallurgy ; Magnetization ; Electrical engineering ; Artificial intelligence Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control
Authors: Apurba Nandi | Péter R. Nagy
Source: Artificial Intelligence Chemistry (article, 2023-12-12)
Field: Materials Science — Materials Chemistry

Combining state-of-the-art quantum chemistry and machine learning make gold standard potential energy surfaces accessible for medium-sized molecules Developing full-dimensional machine-learned potentials with the current “gold-standard” coupled-cluster (CC) level is a challenging already for medium-sized molecules due to the high computational cost. Consequently, researchers are often bound to use lower-level electronic structure methods such as density functional theory or second-order Møller–Plesset perturbation theory (MP2). Here, we demonstrate on a representative example that gold-standard potentials can now be effectively constructed for molecules of 15 atoms using off-the-shelf hardware. This is achieved by accelerating the CCSD(T) computations via the accurate and cost-effective frozen natural orbital (FNO) approach. The Δ-machine learning (Δ-ML) approach is employed with the use of permutationally invariant polynomials to fit a full-dimensional PES of the acetylacetone molecule, but any other effective descriptor and ML approach can similarly benefit from the accelerated data generation proposed here. Our benchmarks for the global minima, H-transfer TS, and many high-lying configurations show the excellent agreement of FNO-CCSD(T) results with conventional CCSD(T) while achieving a significant time advantage of about a factor of 30–40. The obtained Δ-ML PES shows high fidelity from multiple perspectives including energetic, structural, and vibrational properties. We obtain the symmetric double well H-transfer barrier of 3.15 kcal/mol in excellent agreement with the direct FNO-CCSD(T) barrier of 3.11 kcal/mol as well as with the benchmark CCSD(F12*)(T+)/CBS value of 3.21 kcal/mol. Furthermore, the tunneling splitting due to H-atom transfer is calculated using a 1D double-well potential, providing improved estimates over previous ones obtained using an MP2-based PES. The methodology introduced here represents a significant advancement in the efficient and precise construction of potentials at the CCSD(T) level for molecules above the current limit of 15 atoms. Maxima and minima ; Molecule ; Density functional theory ; Perturbation theory (quantum mechanics) ; Atom (system on chip) ; Quantum ; Quantum chemistry ; Physics ; Computational chemistry ; Chemistry ; Atomic physics ; Materials science ; Quantum mechanics ; Computer science ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures
Authors: Nikolay O. Grebennikov | Daniil A. Boiko | Darya O. Prima | Malena Madiyeva | Mikhail E. Minyaev | Valentine P. Ananikov
Source: Journal of Catalysis (article, 2023-12-05)
Field: Chemistry — Organic Chemistry

Boosting the generality of catalytic systems by the synergetic ligand effect in Pd-catalyzed C-N cross-coupling Generality ; Catalysis ; Chemistry ; Ligand (biochemistry) ; Carbene ; Combinatorial chemistry ; Biochemical engineering ; Nanotechnology ; Organic chemistry ; Materials science Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Mesfin Diro Chaka | Yedilfana Setarge Mekonnen | Qin Wu | Chernet Amente Geffe
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Advancing energy storage through solubility prediction: leveraging the potential of deep learning Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure-property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log <i>S</i>) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications. Solubility ; Redox ; Computer science ; Deep learning ; Biological system ; Chemistry ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Advanced battery technologies research ; Electrochemical Analysis and Applications
Authors: Hongwei Tu | Yanqiang Han | Zhilong Wang | An Chen | Kehao Tao | Simin Ye | Shiwei Wang | Zhiyun Wei | Jinjin Li
Source: Small Methods (article, 2023-09-19)
Field: Materials Science — Materials Chemistry

RotNet: A Rotationally Invariant Graph Neural Network for Quantum Mechanical Calculations Deep learning has proven promising in biological and chemical applications, aiding in accurate predictions of properties such as atomic forces, energies, and material band gaps. Traditional methods with rotational invariance, one of the most crucial physical laws for predictions made by machine learning, have relied on Fourier transforms or specialized convolution filters, leading to complex model design and reduced accuracy and efficiency. However, models without rotational invariance exhibit poor generalization ability across datasets. Addressing this contradiction, this work proposes a rotationally invariant graph neural network, named RotNet, for accurate and accelerated quantum mechanical calculations that can overcome the generalization deficiency caused by rotations of molecules. RotNet ensures rotational invariance through an effective transformation and learns distance and angular information from atomic coordinates. Benchmark experiments on three datasets (protein fragments, electronic materials, and QM9) demonstrate that the proposed RotNet framework outperforms popular baselines and generalizes well to spatial data with varying rotations. The high accuracy, efficiency, and fast convergence of RotNet suggest that it has tremendous potential to significantly facilitate studies of protein dynamics simulation and materials engineering while maintaining physical plausibility. Rotational invariance ; Invariant (physics) ; Computer science ; Fourier transform ; Generalization ; Quantum ; Benchmark (surveying) ; Algorithm ; Cartesian coordinate system ; Graph ; Artificial intelligence ; Artificial neural network ; Theoretical computer science ; Mathematics ; Physics ; Quantum mechanics ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Maryam Nurhuda | Yusuf Hafidh | Cansu Dogan | Daniel M. Packwood | Carole C. Perry | Matthew A. Addicoat
Source: Inorganic Chemistry Frontiers (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Machine learning of isomerization in porous molecular frameworks: exploring functional group pair distance distributions All possible isomers of a pore in a functionalized molecular framework material may be represented as a single histogram, showing the distances between functional groups. Isomerization ; Functional group ; Porosity ; Group (periodic table) ; Histogram ; Density functional theory ; Chemistry ; Materials science ; Computational chemistry ; Porous medium ; Chemical physics ; Computer science ; Image (mathematics) ; Organic chemistry ; Artificial intelligence ; Polymer ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Xiliang Lian | Mathieu Salanne
Source: The Journal of Chemical Physics (article, 2023-10-10)
Field: Materials Science — Materials Chemistry

Capturing the interactions in the BaSnF4 ionic conductor: Comparison between a machine-learning potential and a polarizable force field BaSnF4 is a prospective solid state electrolyte for fluoride ion batteries. However, the diffusion mechanism of the fluoride ions remains difficult to study, both in experiments and in simulations. In principle, ab initio molecular dynamics could allow to fill this gap, but this method remains very costly from the computational point of view. Using machine learning potentials is a promising method that can potentially address the accuracy issues of classical empirical potentials while maintaining high efficiency. In this work, we fitted a dipole polarizable ion model and trained machine learning potential for BaSnF4 and made comprehensive comparisons on the ease of training, accuracy and efficiency. We also compared the results with the case of a simpler ionic system (NaF). We show that contrarily to the latter, for BaSnF4 the machine learning potential offers much higher versatility. The current work lays foundations for the investigation of fluoride ion mobility in BaSnF4 and provides insight on the choice of methods for atomistic simulations. Polarizability ; Dipole ; Ion ; Ionic bonding ; Computer science ; Conductor ; Work (physics) ; Ab initio ; Point (geometry) ; Force field (fiction) ; Chemical physics ; Machine learning ; Artificial intelligence ; Chemistry ; Materials science ; Physics ; Thermodynamics ; Mathematics ; Molecule Machine Learning in Materials Science ; Inorganic Fluorides and Related Compounds ; Inorganic Chemistry and Materials
Authors: Qingfu Ye | Yu Zhao | Jun Zhu
Source: Artificial Intelligence Chemistry (article, 2023-06-01)
Field: Chemistry — Organic Chemistry

Probing the origin of higher efficiency of terphenyl phosphine over the biaryl framework in Pd-catalyzed C-N coupling: A combined DFT and machine learning study The Pd-catalyzed Buchwald–Hartwig coupling reaction is important in the construction of the C-N bond due to various applications in organic synthesis. Quantum chemical calculations are widely used in understanding reaction mechanisms whereas the machine learning method is extremely popular in recognizing the relationships of data. Here, we combine density functional theory calculations with the support vector regression method to probe the origin of the higher efficiency of terphenyl phosphine ligand over the biaryl counterpart in the Buchwald–Hartwig C-N coupling reaction. By quantum chemical calculations, the turnover frequency-determining transition states are located and ligand features are calculated with high accuracy. By machine learning, the relationship between the reaction barrier and ligand features has been examined. It is found that the interplay of the charge on the metal center, the cone angle of the ligands, and the Sterimol L parameters of the ligand determines the catalytic performance of the palladium catalysts with different phosphine ligands. Our findings could help experimental chemists to design the ligands for Pd-catalyzed C-N coupling reactions with high efficiency. Phosphine ; Terphenyl ; Ligand (biochemistry) ; Catalysis ; Chemistry ; Density functional theory ; Computational chemistry ; Coupling (piping) ; Quantum chemical ; Coupling reaction ; Palladium ; Combinatorial chemistry ; Molecule ; Materials science ; Organic chemistry Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Khalissa Saada | Salah Amroune | Moussa Zaoui
Source: Frattura ed Integrità Strutturale (article, 2023-09-27)
Field: Materials Science — Polymers and Plastics

Prediction of Mechanical Behavior of Epoxy Polymer Using Artificial Neural Networks (ANN) And response Surface Methodology (RSM) The aim of this study is to analyze the effect of different geometries and sections on the mechanical properties of epoxy specimens. Five tensile tests were carried out on three types of series. The experimental results obtained were 1812.21 MPa, 3.90% and 41.91 MPa for intact specimens, 1450.41 MPa, 2.16% and 21.28 MPa for specimens with hole and 750.77 MPa, 2.77% and 11.89 MPa for specimens with elliptical -notched for Young's modulus, strain and stress respectively. In addition, the experimental results indicated that the mechanical properties of both (Young's modulus value and stress value) were higher in an intact specimen. Afterwards, the nonlinear functional relationship of input parameters between epoxy sample geometries and sections was established using the response surface model (RSM) and the artificial neural network (ANN) to predict the output parameters of mechanical properties (Young's modulus and stress). In addition, the design of experiment was developed by the Analysis of the Application of Variance (ANOVA). The results showed the superiority of the ANN model over the RSM model, where the correlation coefficient values for the model datasets exceed ANN (R2 = 0.984 for Young's modulus and R2 = 0.981 for the constraint). Response surface methodology ; Epoxy ; Materials science ; Ultimate tensile strength ; Modulus ; Composite material ; Artificial neural network ; Young's modulus ; Stress (linguistics) ; Mathematics ; Computer science ; Machine learning ; Statistics Textile materials and evaluations ; Mechanical Behavior of Composites