jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chao Xin | Bingqian Song | Guangyong Jin | Yongli Song | Feng Pan
Source: Advanced Theory and Simulations (review, 2023-08-31)
Field: Materials Science — Materials Chemistry

Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review Abstract 2D intrinsic magnetic materials possess unique physical properties distinct from bulk materials, providing an ideal research platform for the development of low‐dimensional spintronics. The traditional approach to developing new materials involves a “trial‐and‐error” method, which is inherently flawed due to long development cycles and high costs. In recent years, with the rapid improvement in computational power, the high throughput (HTP) first‐principles calculation based on density functional theory (DFT) and machine learning (ML) method have provided a highly effective means for the design of novel intrinsic ferromagnetic materials and the study of their magnetic properties. This article reviews the recent research progress in 2D ferromagnetic materials, with particular emphasis on the significant role played by HTP first‐principles calculations and ML in the exploration and fabrication of two‐dimension ferromagnetic (2DFM) materials. Finally, the future development and challenges of 2DFM materials are discussed. Ferromagnetism ; Spintronics ; Density functional theory ; Nanotechnology ; Computer science ; Dimension (graph theory) ; Throughput ; Ideal (ethics) ; Fabrication ; Materials science ; Condensed matter physics ; Physics ; Mathematics ; Quantum mechanics ; Epistemology ; Telecommunications 2D Materials and Applications ; MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Pei Ouyang | Minhui Yuan | Pengbing Tang | Qian Zhang | Shenghua Liu | Jing Shuai | Xiangguo Li
Source: ACS Applied Materials & Interfaces (article, 2023-07-31)
Field: Materials Science — Materials Chemistry

Atomic Local Ordering and Alloying Effects on the Mg<sub>3</sub>(Sb<sub>1–<i>x</i></sub>Bi<sub><i>x</i></sub>)<sub>2</sub> Thermoelectric Material Mg<sub>3</sub>(Sb<sub>1-<i>x</i></sub>Bi<sub><i>x</i></sub>)<sub>2</sub> alloy has been extensively studied in the last 5 years due to its exceptional thermoelectric (TE) performance. The absence of accurate force field for inorganic alloy compounds presents great challenges for computational studies. Here, we explore the atomic microstructure, thermal, and elastic properties of the Mg<sub>3</sub>(Sb<sub>1-<i>x</i></sub>Bi<sub><i>x</i></sub>)<sub>2</sub> alloy at different solution concentrations through atomic simulations with a highly accurate machine learning interatomic potential (ML-IAP). We find atomic local ordering in the optimized structure with the Bi-Bi pair inclined to join adjacent layers and Sb-Sb pair preferring to stay within the same layer. The thermal conductivity changes with the solution concentrations can be correctly predicted through ML-IAP-based molecular dynamics simulations. Spectral thermal conductance analysis shows that the continuous movement of low-frequency peak to high frequency is responsible for the reduction of the thermal conductivity upon alloying. Elastic calculations reveal that similar to the thermal conductivity, solid solution alloying can reduce the overall elastic properties at both Mg<sub>3</sub>Sb<sub>2</sub> and Mg<sub>3</sub>Bi<sub>2</sub> ends, while anisotropic behavior is clearly observed with linear interpolation relationship upon alloying along the interlayer direction and nonlinearity along the intralayer direction. Although the atomic local ordering shows little effects on the properties of the Mg<sub>3</sub>(Sb<sub>1-<i>x</i></sub>Bi<sub><i>x</i></sub>)<sub>2</sub> alloy with only two alloying elements, it possesses potential important impacts on multiprincipal element inorganic TE alloys. This work provides a recipe for computational studies on the TE alloy systems and thus can accelerate the discovery and optimization of TE materials with high TE performance. Materials science ; Thermoelectric effect ; Thermal conductivity ; Alloy ; Thermoelectric materials ; Interatomic potential ; Microstructure ; Coherent potential approximation ; Work (physics) ; Condensed matter physics ; Thermodynamics ; Molecular dynamics ; Electronic structure ; Metallurgy ; Computational chemistry ; Composite material ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Grégory Fonseca | Igor Poltavsky | Alexandre Tkatchenko
Source: Journal of Chemical Theory and Computation (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope As the sophistication of machine learning force fields (MLFF) increases to match the complexity of extended molecules and materials, so does the need for tools to properly analyze and assess the practical performance of MLFFs. To go beyond average error metrics and into a complete picture of a model's applicability and limitations, we developed FFAST (force field analysis software and tools): a cross-platform software package designed to gain detailed insights into a model's performance and limitations, complete with an easy-to-use graphical user interface. The software allows the user to gauge the performance of any molecular force field,─such as popular state-of-the-art MLFF models, ─ on various popular data set types, providing general prediction error overviews, outlier detection mechanisms, atom-projected errors, and more. It has a 3D visualizer to find and picture problematic configurations, atoms, or clusters in a large data set. In this paper, the example of the MACE and NequIP models is used on two data sets of interest [stachyose and docosahexaenoic acid (DHA)]─to illustrate the use cases of the software. With this, it was found that carbons and oxygens involved in or near glycosidic bonds inside the stachyose molecule present increased prediction errors. In addition, prediction errors on DHA rise as the molecule folds, especially for the carboxylic group at the edge of the molecule. We emphasize the need for a systematic assessment of MLFF models for ensuring their successful application to the study of dynamics of molecules and materials. Computer science ; Software ; Field (mathematics) ; Force field (fiction) ; Atomic force microscopy ; Artificial intelligence ; Nanotechnology ; Materials science ; Mathematics ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Anwen Liu | Tongqi Wen | Jian Han | David J. Srolovitz
Source: npj Computational Materials (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Finite-temperature screw dislocation core structures and dynamics in α-titanium Abstract A multiscale approach based on molecular dynamics (MD) and kinetic Monte Carlo (kMC) methods is developed to simulate the dynamics of an 〈 a 〉 screw dislocation in α -Ti. The free energy barriers for the core dissociation transitions and Peierls barriers for dislocation glide as a function of temperature are extracted from the MD simulations (based on machine learning interatomic potentials and optimization); these form the input to kMC simulations. Dislocation random walk trajectories from kMC agree well with those predicted by MD. On some planes, dislocations move via a locking-unlocking mechanism. Surprisingly, some dislocations glide in directions that are not parallel with the core dissociation direction. The MD/kMC multiscale method proposed is applicable to dislocation motion in simple and complex materials (not only screw dislocations in Ti) as a function of temperature and stress state. Dislocation ; Molecular dynamics ; Materials science ; Kinetic Monte Carlo ; Dissociation (chemistry) ; Peierls stress ; Interatomic potential ; Monte Carlo method ; Statistical physics ; Core (optical fiber) ; Kinetic energy ; Dislocation creep ; Condensed matter physics ; Physics ; Classical mechanics ; Computational chemistry ; Physical chemistry ; Chemistry ; Mathematics Microstructure and mechanical properties ; Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties
Authors: Indranil Saha | Andreas Erlebach | Petr Nachtigall | Christopher J. Heard | Lukáš Grajciar
Source: ChemRxiv (preprint, 2023-07-12)
Field: Chemistry — Inorganic Chemistry

Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU using reactive neural network potential. Acidic zeolites are one of the most important catalysts. In many of their catalytic applications, the mode of interaction with water heavily influences their activity, efficiency, and durability as a catalyst. Despite the recent (first principles) computational efforts to understand the mechanistic underpinning of the water-zeolite interactions, it is still prohibitively expensive to carry out comprehensive studies employing realistic zeolitic models. Therefore, we used a recently developed reactive neural network-based potential for aluminosilicate zeolites in the protonic form including their interaction with the aqueous solution that has a capacity to accelerate simulation by orders of magnitude while retaining the reference level of accuracy. We used it to determine how multiple factors (aluminum content, water loading and temperature) influence the proton solvation and water dynamics in one of the industrially most important acidic zeolites, the faujasite (FAU). We found that Si/Al ratio is a significant determinant of the water diffusivity, water capacity to solvate protons in the nanopores, and the zeolite hydrolytic stability. We expect that many of these findings are readily extendable to other acidic zeolites in interaction with water. Faujasite ; Zeolite ; Solvation ; Catalysis ; Aluminosilicate ; Aqueous solution ; Chemistry ; Thermal diffusivity ; Diffusion ; Chemical engineering ; Materials science ; Inorganic chemistry ; Computational chemistry ; Solvent ; Thermodynamics ; Physical chemistry ; Organic chemistry Zeolite Catalysis and Synthesis ; Neural Networks and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Donggeun Park | Jaemin Lee | Kundo Park | Seunghwa Ryu
Source: Advanced Engineering Materials (article, 2023-09-10)
Field: Materials Science — Materials Chemistry

Hierarchical Generative Network: A Hierarchical Multitask Learning Approach for Accelerated Composite Material Design and Discovery Deep learning (DL) methods combined with computational simulations have shown promise for novel material discovery and establishing structure–response relationships within extensive design spaces. However, obtaining sufficient simulation data for precise predictions on nonlinear responses remains challenging, limiting DL models' predictive capabilities for unexplored composite configurations. To address this, we introduce the hierarchical generative network (HGNet), comprising three customized convolutional neural networks (NNs). HGNet predicts stress fields and crack patterns during mechanical failure events, encompassing linear response regimes, ultimate strength, and complete fracture. By jointly training these networks within a unified DL model, predictive capacity significantly improves. HGNet is applied to a two‐phase composite optimization problem with vast design configurations, demonstrating accurate predictions of complex stress distributions and fracture patterns in unseen configurations. Moreover, it explores uncharted designs with superior stiffness and strength across vast design spaces using genetic algorithms. The methodology provides insight into the model's reasoning through visualizing the inference process. Overall, this approach represents a potent and versatile tool for accelerating material discovery, facilitating a more efficient and cost‐effective approach to finding novel materials, even with limited training data. Inference ; Computer science ; Generative grammar ; Generative model ; Machine learning ; Limiting ; Artificial neural network ; Artificial intelligence ; Fracture (geology) ; Materials science ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Composite Material Mechanics ; Material Selection and Properties
Authors: Kai Gong | Elsa Olivetti
Source: Journal of the American Ceramic Society (article, 2023-02-23)
Field: Materials Science — Ceramics and Composites

Data‐driven prediction of room‐temperature density for multicomponent silicate‐based glasses Abstract Density is one of the most commonly measured or estimated material properties, especially for glasses and melts that are of significant interest to many fields, including metallurgy, geology, materials science, and sustainable cements. Here, three types of machine learning models (i.e., random forest [RF], artificial neural network [ANN], and Gaussian process regression [GPR]) have been developed to predict the room‐temperature density of glasses in the compositional space of CaO–MgO–Al 2 O 3 –SiO 2 –TiO 2 –FeO–Fe 2 O 3 –Na 2 O–K 2 O–MnO (CMASTFNKM), based on ∼2100 data points mined from ∼140 literature studies. The results show that the RF, ANN, and GPR models give accurate predictions of glass density with R 2 values, root mean square error (RMSE), and mean absolute percentage error (MAPE) of ∼0.95–0.97, ∼0.03–0.04 g/cm 3 and ∼0.63%–0.83%, respectively, for the 15% testing set, which are more accurate compared with empirical density models based on ionic packing ratio (with R 2 values, RMSE, and MAPE of ∼0.28–0.91, ∼0.05–0.15 g/cm 3 , and ∼1.40%–4.61%, respectively). Furthermore, glass density is shown to be a reliable reactivity indicator for a range of CaO–Al 2 O 3 –SiO 2 (CAS) and volcanic glasses due to its strong correlation ( R 2 values above ∼0.90) with the average metal–oxygen dissociation energy (a structural descriptor) of these glasses. Analysis of the predicted density–composition relationships from these models (for selected compositional subspaces) suggests that the single‐layer ANN and GPR models exhibit a certain level of transferability (i.e., ability to extrapolate to compositional spaces not [or less] covered in the database) and capture some known features, including the mixed alkaline earth (or alkali) effects. Mean squared error ; Mean absolute percentage error ; Mineralogy ; Silicate ; Analytical Chemistry (journal) ; Materials science ; Kriging ; Mathematics ; Chemistry ; Statistics Glass properties and applications ; Recycling and utilization of industrial and municipal waste in materials production ; Building materials and conservation
Authors: Madhumita Rano | Debashree Ghosh
Source: The Journal of Physical Chemistry A (article, 2023-04-16)
Field: Materials Science — Materials Chemistry

Efficient Machine Learning Configuration Interaction for Bond Breaking Problems Machine learning-assisted configuration interaction (MLCI) has been shown earlier as a promising method in determining the electronic structure of the model and molecular Hamiltonians. In the MLCI approach to molecular Hamiltonians, it has been noticed that prediction is strongly dependent on the connectedness of the training and validation spaces. In this work, we have tested three different models with different output parameters (abs-MLCI, transformed-MLCI, and log-MLCI) to verify the robustness of training these models. We define robustness as the extent of error in prediction even when the spaces (training and validation) are not connected. We notice that the log-MLCI model is best suited to this approach and is therefore a powerful model for accurate one-shot variational energies. This is tested for chemical bond breaking in water, carbon monoxide, nitrogen, and dicarbon molecules. Robustness (evolution) ; Computer science ; Social connectedness ; Notice ; Training set ; Machine learning ; Artificial intelligence ; Algorithm ; Chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Christopher M. Andolina | Wissam A. Saidi
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table Machine learning atomistic potentials (MLPs) trained using density functional theory (DFT) datasets allow for the modeling of complex material properties with near-DFT accuracy while imposing a fraction of its computational cost. Density functional theory ; Computer science ; Table (database) ; Periodic table ; Fraction (chemistry) ; Artificial intelligence ; Machine learning ; Statistical physics ; Data mining ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Saaketh Desai | Ankit Shrivastava | Marta D’Elia | Habib N. Najm | Rémi Dingreville
Source: Acta Materialia (article, 2023-11-08)
Field: Materials Science — Materials Chemistry

Trade-offs in the latent representation of microstructure evolution Characterizing and quantifying microstructure evolution is critical to forming quantitative relationships between material processing conditions, resulting microstructure, and observed properties. Machine-learning methods are increasingly accelerating the development of these relationships by treating microstructure evolution as a pattern recognition problem, discovering relationships explicitly or implicitly. These methods often rely on identifying low-dimensional microstructural fingerprints as latent variables. However, using inappropriate latent variables can lead to challenges in learning meaningful relationships. In this work, we survey and discuss the ability of various linear and nonlinear dimensionality reduction methods including principal component analysis, autoencoders, and diffusion maps to quantify and characterize the learned latent space microstructural representations and their time evolution. We characterize latent spaces by their ability to represent high-dimensional microstructural data in terms of compression achieved as a function of the number of latent dimensions required to represent the data accurately, their accuracy based on their reconstruction performance, and the smoothness of the microstructural trajectories in latent dimension. We quantify these metrics for common microstructure evolution problems in material science including spinodal decomposition of a binary metallic alloy, thin film deposition of a binary metallic alloy, solidification of a single element from a melt pool, and grain growth in a single element polycrystal. This study provides considerations and guidelines for choosing dimensionality reduction methods when considering materials problems that involve high dimensional data and a variety of features over a range of lengths and time scales. Spinodal decomposition ; Materials science ; Curse of dimensionality ; Microstructure ; Dimensionality reduction ; Principal component analysis ; Representation (politics) ; Artificial intelligence ; Statistical physics ; Computer science ; Metallurgy ; Phase (matter) Machine Learning in Materials Science ; Microstructure and mechanical properties ; Advanced Electron Microscopy Techniques and Applications
Authors: Y. Borges | Liam Huber | H. Zapolsky | Renaud Patte | Gilles Demange
Source: Computational Materials Science (article, 2023-11-21)
Field: Materials Science — Materials Chemistry

Insights from symmetry: Improving machine-learned models for grain boundary segregation Grain boundary ; Symmetry (geometry) ; Materials science ; Boundary (topology) ; Statistical physics ; Chemical physics ; Computer science ; Chemistry ; Physics ; Metallurgy ; Microstructure ; Mathematics ; Geometry Microstructure and mechanical properties ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties
Authors: Francesco Biscaglia | Andrea Caroppo | Carmela Tania Prontera | Elisa Sciurti | M.A. Signore | Iren Kuznetsova | Alessandro Leone | Pietro Siciliano | Luca Francioso
Source: Chemosensors (article, 2023-01-13)
Field: Chemistry — Electrochemistry

A Comparison between Different Machine Learning Approaches Combined with Anodic Stripping Voltammetry for Copper Ions and pH Detection in Cell Culture Media Recently, the scientific community has shown a great interest about the Organ-on-Chip (OoC) devices, a special kind of micro-fabricated platforms capable of recapitulating the human physiology implementing the traditional cell culture methods and the concept of in vivo studies. Copper ions represent a cellular micronutrient that must be monitored for its potential hazardous effects. The application of electrochemical analysis for heavy metal ions detection and quantification in commercial cell culture media presents several issues due to electrolyte complexity and interferents. In fact, to the best of our knowledge, there is a lack of applications and OoC devices that implement the Anodic Stripping Voltammetry as an ion dosing technique due to the reasons reported above. In fact, considering just the peak intensity value from the measurement, it turns out to be challenging to quantify ion concentration since other ions or molecules in the media may interfere with the measurement. With the aim to overcome these issues, the present work aims to develop an automated system based on machine learning algorithms and demonstrate the possibility to build a reliable forecasting model for copper ion concentration on three different commercial cell culture media (MEM, DMEM, F12). Effectively, combining electrochemical measurements with a multivariate machine learning algorithm leads to a higher classification accuracy. Two different pH media conditions, i.e., physiological (pH 7.4) and acidic (pH 4), were considered to establish how the electrolyte influences the measurement. The experimental datasets were obtained using square-wave anodic stripping voltammetry (SWASV) and were used to carry out a machine learning trained model. The proposed method led to a significant improvement in Cu2+ concentration detection accuracy (96.6% for the SVM model and 93.1% for the NB model in MEM) as well as being able to monitor the pH solution. Anodic stripping voltammetry ; Electrolyte ; Computer science ; Voltammetry ; Anode ; Ion ; Copper ; Stripping (fiber) ; Electrochemistry ; Nanotechnology ; Supporting electrolyte ; Biological system ; Materials science ; Analytical Chemistry (journal) ; Chemistry ; Artificial intelligence ; Process engineering ; Chromatography ; Electrode ; Engineering Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Neuroscience and Neural Engineering
Authors: Prerna Sharma | V. R. Prasath Kumar
Source: Asian Journal of Civil Engineering (article, 2023-05-07)
Field: Materials Science — Polymers and Plastics

Prediction and analysis of sandwich panel with rice husk and polyurethane foam using machine learning model Husk ; Polyurethane ; Materials science ; Composite material ; Factorial experiment ; Flexural strength ; Artificial neural network ; Compression (physics) ; Mathematics ; Computer science ; Artificial intelligence ; Statistics Natural Fiber Reinforced Composites ; Polymer composites and self-healing ; Cellular and Composite Structures