jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xia Huang | Xinlu Cheng
Source: The Journal of Chemical Physics (article, 2024-05-02)
Field: Chemistry — Spectroscopy

State-to-state dynamics and machine learning predictions of inelastic and reactive O(3P) + CO(1∑+) collisions relevant to hypersonic flows The state-to-state (STS) inelastic energy transfer and O-atom exchange reaction between O and CO(v), as two fundamental processes in non-equilibrium air flow around spacecraft entering Mars' atmosphere, yield the same products and both make significant contributions to the O + CO(v) → O + CO(v') collisions. The inelastic energy transfer competes with the O-atom exchange reaction. The detailed reaction mechanisms of these two elementary processes and their specific contributions to the CO relaxation process are still unclear. To address these concerns, we performed systematic investigations on the 3A' and 3A″ potential energy surfaces (PESs) of CO2 using quasi-classical trajectory (QCT) calculations. Analysis of the collision mechanisms reveals that inelastic collisions have an apparent PES preference (i.e., they tend to occur on the 3A' PES), while reactive collisions do not. Reactive rates decrease significantly when the total collision energy approaches dissociation energy, which differs from the inelastic process. Inelastic rates are generally lower than the reactive rates below ∼10 000 K, except for single quantum jumps, whereas the reverse is observed above ∼10 000 K. In addition, by combining QCT with convolutional neural networks, we have established neural network (NN)-STS1 (inelastic) and NN-STS2 (reactive) models to generate all possible STS cross sections. The NN-based models accurately reproduce the results calculated from QCT calculations. In this study, all calculations have been focused on analyzing collisions at the ground rotational level. Inelastic collision ; Physics ; Dissociation (chemistry) ; Atomic physics ; Ground state ; Inelastic scattering ; Chemistry ; Nuclear physics ; Physical chemistry ; Quantum mechanics ; Scattering Spectroscopy and Laser Applications ; Gas Dynamics and Kinetic Theory ; Atmospheric and Environmental Gas Dynamics
Authors: Abdalaziz Rashid Al-Maeeni | Mikhail Lazarev | N. Kazeev | Kostya S. Novoselov | A. Ustyuzhanin
Source: 2D Materials (article, 2024-05-02)
Field: Materials Science — Materials Chemistry

Review on automated 2D material design Abstract Deep learning (DL) methodologies have led to significant advancements in various domains, facilitating intricate data analysis and enhancing predictive accuracy and data generation quality through complex algorithms. In materials science, the extensive computational demands associated with high-throughput screening techniques such as density functional theory, coupled with limitations in laboratory production, present substantial challenges for material research. DL techniques are poised to alleviate these challenges by reducing the computational costs of simulating material properties and by generating novel materials with desired attributes. This comprehensive review document explores the current state of DL applications in materials design, with a particular emphasis on two-dimensional materials. The article encompasses an in-depth exploration of data-driven approaches in both forward and inverse design within the realm of materials science. Computer science ; Engineering drawing ; Engineering Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; MXene and MAX Phase Materials
Authors: Jingjing Zhao | Siyu Zhang | Xuejiao Zhang | Wenneng Zhou | Qing Zhao | Fengchang Wu | Baoshan Xing
Source: The Science of The Total Environment (article, 2024-05-22)
Field: Materials Science — Materials Chemistry

Machine learning and experimentally exploring the controversial role of nitrogen in CO2 uptake by waste-derived nitrogen-containing porous carbons Nitrogen ; Porosity ; Porous medium ; Environmental science ; Chemical engineering ; Waste management ; Materials science ; Chemistry ; Environmental chemistry ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Catalysis and Oxidation Reactions
Authors: Shihao Zhang | Yan Li | Shuntaro Suzuki | Atsutomo Nakamura | Shigenobu Ogata
Source: npj Computational Materials (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Neural network potential for dislocation plasticity in ceramics Abstract Dislocations in ceramics are increasingly recognized for their promising potential in applications such as toughening intrinsically brittle ceramics and tailoring functional properties. However, the atomistic simulation of dislocation plasticity in ceramics remains challenging due to the complex interatomic interactions characteristic of ceramics, which include a mix of ionic and covalent bonds, and highly distorted and extensive dislocation core structures within complex crystal structures. These complexities exceed the capabilities of empirical interatomic potentials. Therefore, constructing neural network potentials (NNPs) emerges as the optimal solution. Yet, creating a training dataset that includes dislocation structures proves difficult due to the complexity of their core configurations in ceramics and the computational demands of density functional theory for large atomic models containing dislocation cores. In this work, we propose a training dataset from properties that are easier to compute via high-throughput calculation. Using this dataset, we have successfully developed NNPs for dislocation plasticity in ceramics, specifically for three typical functional ceramics: ZnO, GaN, and SrTiO 3 . These NNPs effectively capture the nonstoichiometric and charged core structures and slip barriers of dislocations, as well as the long-range electrostatic interactions between charged dislocations. The effectiveness of this dataset was further validated by measuring the similarity and uncertainty across snapshots derived from large-scale simulations, alongside extensive validation across various properties. Utilizing the constructed NNPs, we examined dislocation plasticity in ceramics through nanopillar compression and nanoindentation, which demonstrated excellent agreement with experimental observations. This study provides an effective framework for constructing NNPs that enable the detailed atomistic modeling of dislocation plasticity, opening new avenues for exploring the plastic behavior of ceramics. Materials science ; Dislocation ; Ceramic ; Plasticity ; Nanoindentation ; Multiscale modeling ; Interatomic potential ; Nanotechnology ; Molecular dynamics ; Composite material ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Advanced materials and composites ; Electronic and Structural Properties of Oxides
Authors: Renzhe Li | Chuan Zhou | Akksay Singh | Yong Pei | Graeme Henkelman | Lei Li
Source: The Journal of Chemical Physics (article, 2024-02-21)
Field: Materials Science — Materials Chemistry

Local-environment-guided selection of atomic structures for the development of machine-learning potentials Machine learning potentials (MLPs) have attracted significant attention in computational chemistry and materials science due to their high accuracy and computational efficiency. The proper selection of atomic structures is crucial for developing reliable MLPs. Insufficient or redundant atomic structures can impede the training process and potentially result in a poor quality MLP. Here, we propose a local-environment-guided screening algorithm for efficient dataset selection in MLP development. The algorithm utilizes a local environment bank to store unique local environments of atoms. The dissimilarity between a particular local environment and those stored in the bank is evaluated using the Euclidean distance. A new structure is selected only if its local environment is significantly different from those already present in the bank. Consequently, the bank is then updated with all the new local environments found in the selected structure. To demonstrate the effectiveness of our algorithm, we applied it to select structures for a Ge system and a Pd13H2 particle system. The algorithm reduced the training data size by around 80% for both without compromising the performance of the MLP models. We verified that the results were independent of the selection and ordering of the initial structures. We also compared the performance of our method with the farthest point sampling algorithm, and the results show that our algorithm is superior in both robustness and computational efficiency. Furthermore, the generated local environment bank can be continuously updated and can potentially serve as a growing database of feature local environments, aiding in efficient dataset maintenance for constructing accurate MLPs. Computer science ; Robustness (evolution) ; Machine learning ; Feature selection ; Process (computing) ; Artificial intelligence ; Local structure ; Local search (optimization) ; Selection (genetic algorithm) ; Euclidean distance ; Data mining ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jinchen Tang | Takao Okabe | Katsuhiko Nishimura | Anna Ściążko | Hang Zhang | Naoki Shikazono
Source: International Journal of Heat and Mass Transfer (article, 2024-09-21)
Field: Materials Science — Surfaces, Coatings and Films

Frost growth on silver iodide (AgI) stripe patterned surface under condensation frosting condition • AgI stripes first used to manage frost, highlighting innovative control. • UV-lithography employed for scalable AgI pattern fabrication, presenting a new surface engineering method. • Quantitative analysis of frost growth is processed through machine learning techniques. • Experimental and theoretical work reveals keys to a frost-free zone. This study investigates frost accumulation on surfaces with silver iodide (AgI) patterns to control initial formation of liquid droplets and subsequent frost growth under condensation frosting condition. AgI is known as one of the most effective nucleation agents, and a novel UV-lithography technique is adopted to fabricate scalable AgI patterns on a silicon substrate. Image processing with the help of machine learning provided quantitative information of frost evolution dynamics. Frost preferentially initiated from the AgI striped patterns, and showed horizontal crystal growth. Eventually, frost-free zones appeared in between the stripes. In addition to experimental observation, mass balance analysis identified the influential factors and parameters that influence the formation of a complete frost-free zone. Present approach using AgI patterns offers a promising approach to control initial frost formation under condensation frosting condition, which may contribute to mitigate negative effects of frost in diverse applications. Materials science ; Frost (temperature) ; Silver iodide ; Condensation ; Iodide ; Composite material ; Chemical engineering ; Thermodynamics ; Inorganic chemistry ; Chemistry ; Layer (electronics) Surface Modification and Superhydrophobicity ; Icing and De-icing Technologies ; Fluid Dynamics and Thin Films
Authors: Yoshito Saito | Kenta Itakura | Norikuni Ohtake | Hideo Hasegawa
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-07-04)
Field: Chemistry — Analytical Chemistry

Classification of soybean chemical characteristics by excitation emission matrix coupled with t-SNE dimensionality reduction Principal component analysis ; Chemistry ; Dimensionality reduction ; Curse of dimensionality ; Biological system ; Matrix (chemical analysis) ; Pattern recognition (psychology) ; Reduction (mathematics) ; Calibration ; Chemical composition ; Artificial intelligence ; Analytical Chemistry (journal) ; Chromatography ; Statistics ; Computer science ; Mathematics Spectroscopy and Chemometric Analyses ; Phytoestrogen effects and research ; Food composition and properties
Authors: Marie Oestreich | Iva Ewert | Matthias Becker
Source: Journal of Cheminformatics (article, 2024-03-05)
Field: Materials Science — Materials Chemistry

Small molecule autoencoders: architecture engineering to optimize latent space utility and sustainability Abstract Autoencoders are frequently used to embed molecules for training of downstream deep learning models. However, evaluation of the chemical information quality in the latent spaces is lacking and the model architectures are often arbitrarily chosen. Unoptimized architectures may not only negatively affect latent space quality but also increase energy consumption during training, making the models unsustainable. We conducted systematic experiments to better understand how the autoencoder architecture affects the reconstruction and latent space quality and how it can be optimized towards the encoding task as well as energy consumption. We can show that optimizing the architecture allows us to maintain the quality of a generic architecture but using 97% less data and reducing energy consumption by around 36%. We additionally observed that representing the molecules as SELFIES reduced the reconstruction performance compared to SMILES and that training with enumerated SMILES drastically improved latent space quality. Scientific Contribution: This work provides the first comprehensive systematic analysis of how choosing the autoencoder architecture affects the reconstruction performance of small molecules, the chemical information content of the latent space as well as the energy required for training. Demonstrated on the MOSES benchmarking dataset it provides first valuable insights into how autoencoders for the embedding of small molecules can be designed to optimize their utility and simultaneously become more sustainable, both in terms of energy consumption as well as the required amount of training data. All code, data and model checkpoints are made available on Zenodo (Oestreich et al. Small molecule autoencoders: architecture engineering to optimize latent space utility and sustainability. Zenodo, 2024). Furthermore, the top models can be found on GitHub with scripts to encode custom molecules: https://github.com/MarieOestreich/small-molecule-autoencoders . Graphical Abstract Autoencoder ; Computer science ; Chemical space ; Energy consumption ; Artificial intelligence ; Benchmarking ; Machine learning ; Quality (philosophy) ; Architecture ; Deep learning ; Space (punctuation) ; Encoding (memory) ; Data science ; Data mining ; Engineering ; Bioinformatics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods
Authors: Dylan A. Folkner | Zekun Chen | Giuseppe Barbalinardo | Florian Knoop | Davide Donadio
Source: Journal of Applied Physics (article, 2024-12-11)
Field: Materials Science — Materials Chemistry

Elastic moduli and thermal conductivity of quantum materials at finite temperature We describe a theoretical and computational approach to calculate the vibrational, elastic, and thermal properties of materials from the low-temperature quantum regime to the high-temperature anharmonic regime. This approach is based on anharmonic lattice dynamics and the Boltzmann transport equation. It relies on second and third-order force constant tensors estimated by fitting temperature-dependent empirical potentials from path-integral quantum simulations with a first-principles machine learning Hamiltonian. The temperature-renormalized harmonic force constants are used to calculate the elastic moduli and the phonon modes of materials. Harmonic and anharmonic force constants are combined to solve the phonon Boltzmann transport equation to compute the lattice thermal conductivity. We demonstrate the effectiveness of this approach on bulk crystalline silicon in the temperature range from 50 to 1200 K, showing substantial improvement in the prediction of the temperature dependence of the target properties compared to experiments. Anharmonicity ; Phonon ; Boltzmann equation ; Thermal conductivity ; Condensed matter physics ; Hamiltonian (control theory) ; Quantum ; Boltzmann constant ; Physics ; Elastic modulus ; Quantum mechanics ; Thermodynamics ; Mathematics Thermal properties of materials ; Quantum, superfluid, helium dynamics ; High-pressure geophysics and materials
Authors: Siamak Attarian | Dane Morgan | Izabela Szlufarska
Source: Journal of Molecular Liquids (article, 2024-03-19)
Field: Materials Science — Materials Chemistry

Studies of Ni-Cr complexation in FLiBe molten salt using machine learning interatomic potentials Dissolution ; Impurity ; Eutectic system ; Materials science ; Molten salt ; Molecular dynamics ; Chemical physics ; Binding energy ; Salt (chemistry) ; Inorganic chemistry ; Chemistry ; Physical chemistry ; Computational chemistry ; Atomic physics ; Alloy ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Gabriela Naibo | Jackson Freitas Brilhante de São José | Gustavo Pesini | Clayton Chemin | Bruno Brito Lisboa | Luciano Kayser | André Dabdab Abichequer | Jean Michel Moura-Bueno | Rafael Ramon | Tales Tiecher
Source: Journal of Food Composition and Analysis (article, 2024-01-22)
Field: Chemistry — Analytical Chemistry

Combining mid-infrared spectroscopy and machine learning to estimate nutrient content in plant tissues of yerba mate (Ilex paraguariensis A. St. Hil.) Partial least squares regression ; Support vector machine ; Multivariate statistics ; Mathematics ; Analytical Chemistry (journal) ; Coefficient of determination ; Data pre-processing ; Spectroscopy ; Nutrient ; Preprocessor ; Chemistry ; Artificial intelligence ; Statistics ; Computer science ; Environmental chemistry ; Physics Heavy Metals in Plants ; Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping
Authors: Akram Khalajiolyaie | Cuiying Jian
Source: Journal of Water Process Engineering (article, 2024-05-27)
Field: Materials Science — Materials Chemistry

Advancing wastewater treatment from cadmium contamination via functionalized graphene nanosheets Graphene ; Contamination ; Wastewater ; Cadmium ; Materials science ; Nanotechnology ; Environmental science ; Waste management ; Environmental chemistry ; Chemistry ; Environmental engineering ; Metallurgy ; Engineering ; Ecology Graphene research and applications ; Membrane Separation Technologies ; Graphene and Nanomaterials Applications
Authors: Tao Sun | Lili Sun | Haihao Teng | Wenhao Liu | Ruiqi Wang | Xuanjie Zhao | Jie Zhou
Source: Materials (article, 2024-05-17)
Field: Materials Science — Materials Chemistry

Constitutive Model and Microstructure Evolution of Ti65 Titanium Alloy The hot deformation behavior and mechanism of Ti65 alloy with a bimodal microstructure were investigated by isothermal compression experiments conducted on the Thermecmastor-Z simulator equipment at temperatures ranging from 950 to 1110 °C and strain rates ranging from 0.01 to 10.0 s<sup>-1</sup>. The Arrhenius constitutive model, based on strain compensation, and Grey Wolf optimization-neural network with back propagation model (GWO-BP), were both established. The differences between the experimental and predicted value of flow stress were compared and analyzed using the two models. The results show that the prediction accuracy of GWO-BP in the two-phase region is higher than that of Arrhenius model. In the single-phase region, both methods demonstrated high prediction accuracy. Compared to the single-phase region, the flow stress of Ti65 alloy shows a higher degree of softening in the two-phase region. During deformation in the two-phase region, the initial lamellar α phase transformed from a kinked and elongated morphology to a globularized topography as the strain rate decreased. Boundary-splitting was the primary mechanism leading to the spheroidization process. The degree of recrystallization increased with the increase in strain rate during the deformation in the single-phase region, while dynamic recovery and strain-induced grain boundary migration were the main deformation mechanisms at a lower strain rate. Discontinuous dynamic recrystallization may be the dominant recrystallization mechanism under a high strain rate of 10 s<sup>-1</sup>. Materials science ; Dynamic recrystallization ; Strain rate ; Flow stress ; Microstructure ; Isothermal process ; Deformation (meteorology) ; Titanium alloy ; Alloy ; Deformation mechanism ; Recrystallization (geology) ; Composite material ; Constitutive equation ; Metallurgy ; Thermodynamics ; Hot working ; Geology Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Microstructure and mechanical properties
Authors: Tong Shang | Jingran Ge | Jing Yang | Maoyuan Li | Jun Liang
Source: Composites Part A Applied Science and Manufacturing (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Spatiotemporal prediction of surface roughness evolution of C/C composites based on recurrent neural network Materials science ; Composite material ; Surface roughness ; Artificial neural network ; Surface finish ; Surface (topology) ; Artificial intelligence ; Geometry ; Computer science ; Mathematics Machine Learning in Materials Science ; Injection Molding Process and Properties ; Advanced machining processes and optimization
Authors: Giovanni Trezza | Eliodoro Chiavazzo
Source: Journal of Chemical Information and Modeling (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Classification-Based Detection and Quantification of Cross-Domain Data Bias in Materials Discovery It stands to reason that the amount and the quality of data are of key importance for setting up accurate artificial intelligence (AI)-driven models. Among others, a fundamental aspect to consider is the bias introduced during sample selection in database generation. This is particularly relevant when a model is trained on a specialized data set to predict a property of interest and then applied to forecast the same property over samples having a completely different genesis. Indeed, the resulting biased model will likely produce unreliable predictions for many of those out-of-the-box samples, i.e., samples out of the training set. Neglecting such an aspect may hinder the AI-based discovery process, even when high-quality, sufficiently large, and highly reputable data sources are available. To address this challenge, we propose a new method that detects and quantifies data bias, reducing its impact on materials discovery. Our approach, aimed at identifying and excluding those out-of-the-box materials for which the predictions of a pretrained model are likely unreliable, leverages a classification strategy and is validated by means of superconductor and thermoelectric materials as two representative case studies. This methodology, designed to be simple, flexible, and easily adaptable to any architecture, including modern graph equivariant neural networks, aims to enhance the reliability of AI models when applied to diverse and previously unseen materials, thereby contributing to more reliable AI-driven materials discovery. Computer science ; Data mining ; Property (philosophy) ; Process (computing) ; Artificial intelligence ; Machine learning ; Set (abstract data type) ; Reliability (semiconductor) ; Key (lock) ; Applicability domain ; Quality (philosophy) ; Data quality ; Engineering Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods