jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leonardo Medrano Sandonas | Dries Van Rompaey | Alessio Fallani | Mathias Hilfiker | David F. Hahn | Laura Pérez‐Benito | Jonas Verhoeven | Gary Tresadern | Jörg K. Wegner | Hugo Ceulemans | Alexandre Tkatchenko
Source: ChemRxiv (preprint, 2024-02-21)
Field: Materials Science — Materials Chemistry

Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean:50.9), and containing up to 54 (mean:28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global (molecular) and local (atom-in-a-molecule) physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and \textit{de novo} generation of large (solvated) molecules with pharmaceutical and biological relevance. Molecule ; Chemical physics ; Conformational isomerism ; Dispersion (optics) ; Quantum ; Solvent ; Computational chemistry ; Chemistry ; Statistical physics ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Meng Zhang | Siyan Deng | Jianghong Zhang | Daxin Li | Dechang Jia | Yu Zhou
Source: Ceramics International (article, 2024-05-20)
Field: Materials Science — Ceramics and Composites

The structural evolution of SiBCNZr amorphous ceramics analyzed by machine learning potential Materials science ; Ceramic ; Amorphous solid ; Engineering physics ; Composite material ; Crystallography ; Engineering Advanced ceramic materials synthesis ; Advanced materials and composites ; Metallic Glasses and Amorphous Alloys
Authors: Santosh Chhetri | Maryam Naghibolhosseini | Mohsen Zayernouri
Source: Computational Materials Science (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Simulation of stochastic discrete dislocation dynamics in ductile Vs brittle materials Brittleness ; Dislocation ; Materials science ; Molecular dynamics ; Dynamics (music) ; Statistical physics ; Stochastic dynamics ; Composite material ; Physics ; Chemistry ; Computational chemistry Microstructure and mechanical properties ; Metallurgy and Material Forming ; Numerical methods in engineering
Authors: Nichenametla Jai Sai | Kumar Sridharan | Ankur Chauhan
Source: Progress in Nuclear Energy (article, 2024-09-09)
Field: Materials Science — Materials Chemistry

Establishing machine-learning approach for predicting outer-diametral strains in ferritic/martensitic (F/M) steel tubes during in-reactor neutron-irradiation creep experiments Materials science ; Creep ; Irradiation ; Martensite ; Nuclear engineering ; Neutron irradiation ; Neutron ; Composite material ; Metallurgy ; Nuclear physics ; Microstructure ; Physics Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Zhihao Ding | Ting Zhang | Yiran Li | J. Y. Shi | Chen Zhang
Source: arXiv (Cornell University) (preprint, 2024-12-12)
Field: Materials Science — Polymers and Plastics

RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction Organic Solar Cells (OSCs) are a promising technology for sustainable energy production. However, the identification of molecules with desired OSC properties typically involves laborious experimental research. To accelerate progress in the field, it is crucial to develop machine learning models capable of accurately predicting the properties of OSC molecules. While graph representation learning has demonstrated success in molecular property prediction, it remains underexplored for OSC-specific tasks. Existing methods fail to capture the unique structural features of OSC molecules, particularly the intricate ring systems that critically influence OSC properties, leading to suboptimal performance. To fill the gap, we present RingFormer, a novel graph transformer framework specially designed to capture both atom and ring level structural patterns in OSC molecules. RingFormer constructs a hierarchical graph that integrates atomic and ring structures and employs a combination of local message passing and global attention mechanisms to generate expressive graph representations for accurate OSC property prediction. We evaluate RingFormer's effectiveness on five curated OSC molecule datasets through extensive experiments. The results demonstrate that RingFormer consistently outperforms existing methods, achieving a 22.77% relative improvement over the nearest competitor on the CEPDB dataset. Transformer ; Graph ; Materials science ; Computer science ; Theoretical computer science ; Electrical engineering ; Engineering ; Voltage Conducting polymers and applications ; Machine Learning in Materials Science ; Organic Electronics and Photovoltaics
Authors: Kenneth López Pérez | Ramón Alain Miranda‐Quintana
Source: ChemRxiv (preprint, 2024-01-17)
Field: Materials Science — Materials Chemistry

Sampling Chemical Space: Activity Cliffs, Extended Similarity, and ML Performance The presence of Activity Cliffs (ACs) has been known to represent a challenge for QSAR modeling. With its data high dependency, Machine Learning QSAR models will be highly influenced by the activity landscape of the data. We propose several extended similarity and extended SALI methods to study the implications of ACs distribution on the training and test sets on the model’s errors. Non-uniform ACs and chemical space distribution tends to lead to worse models than the proposed uniform methods. ML modeling on AC-rich sets needs to be analyzed case-by-case. Proposed methods can be used as a tool to study the dataset, with random and uniform splitting being the better overall data splitting alternatives. Similarity (geometry) ; Chemical space ; Sampling (signal processing) ; Dependency (UML) ; Quantitative structure–activity relationship ; Computer science ; Space (punctuation) ; Data mining ; Distribution (mathematics) ; Machine learning ; Artificial intelligence ; Mathematics ; Chemistry Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy and Chemometric Analyses
Authors: Jianwei Zhang | Jinyu Lei | Pu Feng | Wenduo Chen | Jiajia Zhou | Guangzhao Zhang
Source: Langmuir (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Formation and Dynamics of Imidazole Supramolecular Chains Investigated by Deep Potential Molecular Dynamics Simulation Imidazole-based materials have attracted considerable attention due to their promising potential for facilitating anhydrous proton transport at high temperatures. Herein, a machine learning-based deep potential (DP) model for bulk imidazole with first-principles accuracy is developed. The trained model exhibits remarkable accuracy in predicting energies and forces, with minor errors of 4.71 × 10<sup>-4</sup> eV/atom and 3.23 × 10<sup>-2</sup> eV/Å, respectively. Utilizing DP molecular dynamics simulations, we have systematically investigated the temperature-dependent formation and dynamics of imidazole supramolecular chains through the partial radial distribution function, quantification of hydrogen bond numbers, incoherent intermediate scattering function, and diffusion coefficient. The findings reveal the influence of temperature on the proton transport path following either the "Grotthuss" and "vehicle" mechanism. Molecular dynamics ; Supramolecular chemistry ; Imidazole ; Dynamics (music) ; Chemical physics ; Chemistry ; Nanotechnology ; Materials science ; Computational chemistry ; Stereochemistry ; Crystallography ; Physics ; Crystal structure Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Ali Bahadori manizani | Leila Shafieian
Source: World Journal of Advanced Engineering Technology and Sciences (article, 2024-10-20)
Field: Materials Science — Materials Chemistry

Investigating the impact of nano copper on the electrical conductivity of aluminum: A comprehensive study utilizing artificial neural networks Nanotechnology has emerged as a transformative field in material science, offering unprecedented opportunities to enhance the electrical properties of conventional materials through the incorporation of nano-sized particles. In this extensive study, we explore the effects of varying weight percentages (1%, 2%, and 3%) and lengths (30 nm, 60 nm, 150 nm, and 250 nm) of nano copper on the electrical conductivity of aluminum (Al) across different temperatures (20°C, 50°C, and 100°C). Additionally, we investigate the predictive capabilities of Artificial Neural Networks (ANN) in forecasting the electrical conductivity variations of Al based on these parameters. Through a detailed analysis of experimental results and ANN modeling, Nano- ; Copper ; Aluminium ; Artificial neural network ; Electrical resistivity and conductivity ; Materials science ; Conductivity ; Metallurgy ; Nanotechnology ; Artificial intelligence ; Computer science ; Composite material ; Engineering ; Chemistry ; Electrical engineering Anodic Oxide Films and Nanostructures ; Advanced Machining and Optimization Techniques ; Neural Networks and Applications
Authors: Fang Liu | Likai Du
Source: International Journal of Quantum Chemistry (article, 2024-07-11)
Field: Chemistry — Physical and Theoretical Chemistry

Unveiling the energetic complexity of noncovalent interactions in halogenated dimers Abstract The understanding of noncovalent interactions is crucial in explaining critical phenomena such as self‐assembly, chemical reactivity, and crystallization. This work examines the energetic diversity of conformations and local minima for several halogenated dimers, represented as R‐X (R = H, F, CH 3 , CF 3 ; X = Cl, Br, I). Thousands of configurations are randomly generated and refined through geometric optimizations to yield a diverse set of molecular conformers. Frequency calculations were performed for all optimized conformers to confirm that they are local minima. The noncovalent interactions in optimized dimers of halogen‐containing molecules were analyzed with atom in molecules (AIM) method and symmetry‐adapted perturbation theory (SAPT). Additionally, a protocol for generating machine learning models to recover accurate predictions of the physically meaningful SAPT energy components with minor computational cost is presented. These results deepen our understanding of the intricate energy balance and dedicated equilibrium of different noncovalent interactions in halogenated dimers. Non-covalent interactions ; Conformational isomerism ; Chemistry ; Molecule ; Maxima and minima ; Computational chemistry ; Chemical physics ; Halogen ; Interaction energy ; Hydrogen bond ; Organic chemistry ; Mathematics Crystallography and molecular interactions ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Jules Schleinitz | Alba Carretero‐Cerdán | Anjali Gurajapu | Yonatan Harnik | Gina Lee | Ashok Pandey | Anat Milo | Sarah E. Reisman
Source: ChemRxiv (preprint, 2024-10-15)
Field: Materials Science — Materials Chemistry

Tailoring Datasets for Regioselectivity Predictions on Complex Substrates The development of machine learning models to predict the regioselectivity of C(sp3)–H functionalization reactions is reported. A dataset for dioxirane oxidations was curated from the literature and used to generate a model to predict the regioselectivity of C–H oxidation. The performance of the model for a specific substrate could then be improved by adding additional datapoints that were selected using acquisition functions. A series of different acquisition functions were compared, and acquisition functions based on active learning were found to outperform those based on molecular and site similarity. Furthermore, it was found that smaller machine-designed datasets can give accurate predictions when larger, randomly-designed datasets fail. The use of acquisition functions for dataset enlargement significantly reduced the number of datapoints needed to perform accurate prediction. Finally, the workflow was shown to be applicable to predicting the regioselectivity of arene C–H radical borylation. These studies provide a quantitative alternative to the intuitive extrapolation from “model substrates” that is frequently used to estimate reactivity on complex molecules. Regioselectivity ; Computer science ; Chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Dahai Yang | Rui Huang | Bolin Zou | Ruoxu Wang | Yong Wang | Edison Huixiang Ang | Xiaohui Song
Source: Materials Horizons (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Unraveling nanosprings: morphology control and mechanical characterization Nanosprings demonstrate promising mechanical characteristics, positioning them as pivotal components in a diverse array of potential nanoengineering applications. To unlock the full potential of these nanosprings, ongoing research is concentrated on emulating springs at the nanoscale in terms of both morphology and function. This review underscores recent advancements in the field and provides a comprehensive overview of the diverse methods employed for nanospring preparation. Understanding the general mechanism behind nanospring formation lays the groundwork for the informed design of nanosprings. The synthesis section delineates four prominent methods employed for nanospring fabrication: vapor phase synthesis, templating methods, post-treatment techniques, and molecular engineering. Each method is critically analyzed, highlighting its strengths, limitations, and potential for scalability. Mechanical properties of nanosprings are explored in depth, discussing their response to external stimuli and their potential applications in various fields such as sensing, energy storage, and biomedical engineering. The interplay between nanospring morphology and mechanical behavior is elucidated, providing insights into the design principles for tailored functionality. Additionally, we anticipate that the evolution of state-of-the-art characterization tools, such as <i>in situ</i> transmission electron microscopy, 3D electron tomography, and machine learning, will significantly contribute to both the study of nanospring mechanisms and their applications. Characterization (materials science) ; Materials science ; Morphology (biology) ; Nanotechnology ; Biology ; Zoology Carbon Nanotubes in Composites ; Advanced Materials and Mechanics ; Nanofabrication and Lithography Techniques
Authors: Gil Goldshlager | Nilin Abrahamsen | Lin Lin
Source: arXiv (Cornell University) (preprint, 2024-01-18)
Field: Materials Science — Materials Chemistry

A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizing such wavefunctions prevents their application to larger systems. We propose the Subsampled Projected-Increment Natural Gradient Descent (SPRING) optimizer to reduce this bottleneck. SPRING combines ideas from the recently introduced minimum-step stochastic reconfiguration optimizer (MinSR) and the classical randomized Kaczmarz method for solving linear least-squares problems. We demonstrate that SPRING outperforms both MinSR and the popular Kronecker-Factored Approximate Curvature method (KFAC) across a number of small atoms and molecules, given that the learning rates of all methods are optimally tuned. For example, on the oxygen atom, SPRING attains chemical accuracy after forty thousand training iterations, whereas both MinSR and KFAC fail to do so even after one hundred thousand iterations. Wave function ; Variational Monte Carlo ; Artificial neural network ; Gradient descent ; Bottleneck ; Spring (device) ; Kronecker delta ; Applied mathematics ; Computer science ; Algorithm ; Mathematics ; Mathematical optimization ; Monte Carlo method ; Quantum Monte Carlo ; Physics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Dongfei Liu | Jianzhong Wu | Diannan Lu
Source: The Journal of Chemical Physics (article, 2024-11-19)
Field: Materials Science — Materials Chemistry

Transferable performance of machine learning potentials across graphene–water systems of different sizes: Insights from numerical metrics and physical characteristics Machine learning potentials (MLPs) are promising for various chemical systems, but their complexity and lack of physical interpretability challenge their broad applicability. This study evaluates the transferability of the deep potential (DP) and neural equivariant interatomic potential (NequIP) models for graphene-water systems using numerical metrics and physical characteristics. We found that the data quality from density functional theory calculations significantly influences MLP predictive accuracy. Prediction errors in transferring systems reveal the particularities of quantum chemical calculations on the heterogeneous graphene-water systems. Even for supercells with non-planar graphene carbon atoms, k-point mesh is necessary to obtain accurate results. In contrast, gamma-point calculations are sufficiently accurate for water molecules. In addition, we performed molecular dynamics (MD) simulations using these two models and compared the physical features such as atomic density profiles, radial distribution functions, and self-diffusion coefficients. It was found that although the NequIP model has higher accuracy than the DP model, the differences in the above physical features between them were not significant. Considering the stochasticity and complexity inherent in simulations, as well as the statistical averaging of physical characteristics, this motivates us to explore the meaning of accurately predicting atomic force in aligning the physical characteristics evolved by MD simulations with the actual physical features. Interpretability ; Physical system ; Computer science ; Graphene ; Molecular dynamics ; Statistical physics ; Artificial intelligence ; Machine learning ; Materials science ; Physics ; Nanotechnology ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Elin Dypvik Sødahl | Jesús Carrete | Georg K. H. Madsen | Kristian Berland
Source: The Journal of Physical Chemistry C (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

Dynamical Disorder in the Mesophase Ferroelectric HdabcoClO<sub>4</sub>: A Machine-Learned Force Field Study Hybrid molecular ferroelectrics with orientationally disordered mesophases offer significant promise as lead-free alternatives to traditional inorganic ferroelectrics owing to properties such as room temperature ferroelectricity, low-energy synthesis, malleability, and potential for multiaxial polarization. The ferroelectric molecular salt HdabcoClO<sub>4</sub> is of particular interest due to its ultrafast ferroelectric room-temperature switching. However, so far, there is limited understanding of the nature of dynamical disorder arising in these compounds. Here, we employ the neural network NeuralIL to train a machine-learned force field (MLFF) with training data generated using density functional theory. The resulting MLFF-MD simulations exhibit phase transitions and thermal expansion in line with earlier reported experimental results, for both a low-temperature phase transition coinciding with the orientational disorder of ClO<sub>4</sub> <sup>-</sup> and the onset of rotation of both Hdabco<sup>+</sup> and ClO<sub>4</sub> <sup>-</sup> in a high-temperature phase transition. We also find proton transfer even in the low-temperature phase, which increases with temperature and leads to associated proton disorder as well as the onset of disorder in the direction of the hydrogen-bonded chains. Ferroelectricity ; Mesophase ; Phase transition ; Condensed matter physics ; Materials science ; Force field (fiction) ; Molecular dynamics ; Phase (matter) ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Dielectric ; Optoelectronics ; Liquid crystal ; Organic chemistry ; Quantum mechanics Solid-state spectroscopy and crystallography ; Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: S. G. Menshikova | N. M. Shchelkachev
Source: Журнал Экспериментальной и Теоретической Физики (article, 2024-07-06)
Field: Materials Science — Materials Chemistry

LOKAL'NAYa STRUKTURA I ZATVERDEVANIE STEKLOOBRAZUYuShchEGO RASPLAVA Al86Ni6Co4Gd2Tb2 POD VYSOKIM DAVLENIEM: EKSPERIMENT, MODELIROVANIE, MAShINNOE OBUChENIE High pressure affects melt solidification and its glass-forming ability. Ab initio molecular dynamics calculations show how the local structure of the melt changes with increasing pressure. High pressure promotes the formation of icosahedral clusters in the melt. Rare earth elements: gadolinium, terbium facilitate the formation of icosahedra. At a pressure of 10 GPa and melt temperature of 1800 K, icosahedra atoms form a "percolation cluster". As pressure decreases, the concentration of icosahedra decreases, and at atmospheric pressure, icosahedra are practically absent. Thus, the glass-forming ability of the melt increases with increasing pressure. Using deep machine learning techniques, the dependence of glass transition temperature on high pressure was evaluated: pressure increase from 0 to 10 GPa increases by 1.3 times. The structure of solid alloy samples obtained by cooling its melt from 1800 K at a rate of 1000 degrees/s under 10 GPa pressure was studied. X-ray diffraction and electron microscopy methods showed that the samples are dense and homogeneous, with a fine-dispersed structure. New crystalline phases with cubic (c P 4 / 2) and tetragonal ( tI 26 / 1) structures, stable for long periods under normal conditions, were synthesized in the alloy. Rare earth elements play a major role in the formation of the phase with cubic structur (c P 4 / 2). Studies showed that the average hardness of samples obtained at 10 GPa is almost 2 times higher than that of the initial sample obtained at atmospheric pressure, and is about 2 GPa. Point of delivery ; Mathematics ; Horticulture ; Biology Quasicrystal Structures and Properties ; Metallic Glasses and Amorphous Alloys ; Intermetallics and Advanced Alloy Properties