jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenyu Yang | Xiaoyan Fu | Dong Luan | Jianping Xiao
Source: Angewandte Chemie International Edition (article, 2024-12-29)
Field: Materials Science — Materials Chemistry

Towards Rational Design of Confined Catalysis in Carbon Nanotube by Machine Learning and Grand Canonical Monte Carlo Simulations The microenvironment is recognized to be as crucial as active sites in heterogeneous catalysis. It was found that the catalytic activity of a set of chemical reactions can be significantly influenced by the confined space of carbon nanotubes (CNTs), with some reactions showing superior activity, while others experience a negative impact. The rational design of confined catalysis must rely on the accurate insights of confined microenvironment. However, the structural complexity of confined catalysts and the interaction with microenvironment hinders the deciphering of chemical origins behind experiments. In this work, Grand-canonical Monte Carlo (GCMC) simulations are conducted for confined catalysis in CNTs at various reaction atmospheres, accelerated by machine learning potentials. The statistical outcomes of GCMC simulations corroborate a general feature that the electronic interaction (binding energy) inside CNTs is weaker than outside cases. By using Random Forest (RF) model, we ascertain that the shortening of the bond lengths of catalysts within the confined space is the dominant factor, resulting in the weakened binding energy and the downshift of the d-band center. Using the bond length variation as a simplified descriptor, our microkinetic models successfully reproduced the seemingly contradictive experiments, namely, the observed enhancement and suppression for the same reaction. Monte Carlo method ; Carbon nanotube ; Rational design ; Computer science ; Catalysis ; Grand canonical ensemble ; Artificial intelligence ; Nanotechnology ; Machine learning ; Statistical physics ; Chemistry ; Materials science ; Physics ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Ivan Malashin | В С Тынченко | Andrei Gantimurov | Vladimir Nelyub | А. С. Бородулин
Source: Polymers (article, 2024-06-20)
Field: Materials Science — Polymers and Plastics

A Multi-Objective Optimization of Neural Networks for Predicting the Physical Properties of Textile Polymer Composite Materials This paper explores the application of multi-objective optimization techniques, including MOPSO, NSGA II, and SPEA2, to optimize the hyperparameters of artificial neural networks (ANNs) and support vector machines (SVMs) for predicting the physical properties of textile polymer composite materials (TPCMs). The optimization process utilizes data on the physical characteristics of the constituent fibers and fabrics used to manufacture these composites. By employing optimization algorithms, we aim to enhance the predictive accuracy of the ANN and SVM models, thereby facilitating the design and development of high-performance textile polymer composites. The effectiveness of the proposed approach is demonstrated through comparative analyses and validation experiments, highlighting its potential for optimizing complex material systems. Hyperparameter ; Artificial neural network ; Support vector machine ; Textile ; Process (computing) ; Computer science ; Composite number ; Materials science ; Process optimization ; Machine learning ; Artificial intelligence ; Process engineering ; Composite material ; Algorithm ; Engineering Textile materials and evaluations ; Mechanical Behavior of Composites ; Fiber-reinforced polymer composites
Authors: An-Jun Liu | Bryan K. Clark
Source: Physical review. B./Physical review. B (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

Neural network backflow for <i>ab initio</i> quantum chemistry The ground state of second-quantized quantum chemistry Hamiltonians provides access to an important set of chemical properties. Wave functions based on machine-learning architectures have shown promise in approximating these ground states in a variety of physical systems. In this paper, we show how to achieve state-of-the-art energies for molecular Hamiltonians using the the neural network backflow (NNBF) wave function. To accomplish this, we optimize this ansatz with a variant of the deterministic optimization scheme based on selected configuration interaction introduced by Li et al., [J. Chem. Theory Comput. 19, 8156 (2023)], which we find works better than standard Markov chain Monte Carlo sampling. For the molecules we studied, NNBF gives lower energy states than both Coupled Cluster with Single and Double excitations and other neural network quantum states. We systematically explore the role of network size as well as optimization parameters in improving the energy. We find that, while the number of hidden layers and determinants play a minor role in improving the energy, there are significant improvements in the energy from increasing the number of hidden units as well as the batch size used in optimization, with the batch size playing a more important role. Backflow ; Ab initio ; Quantum ; Quantum chemistry ; Ab initio quantum chemistry methods ; Computational chemistry ; Chemistry ; Artificial neural network ; Physics ; Computer science ; Organic chemistry ; Quantum mechanics ; Artificial intelligence ; Reaction mechanism ; Engineering ; Catalysis ; Molecule Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum many-body systems
Authors: Dmytro Tymoshchuk | Oleh Yasniy | Pavlo Maruschak | Volodymyr Iasnii | Iryna Didych
Source: Computers (article, 2024-12-14)
Field: Materials Science — Materials Chemistry

Loading Frequency Classification in Shape Memory Alloys: A Machine Learning Approach This paper investigates the use of machine learning methods to predict the loading frequency of shape memory alloys (SMAs) based on experimental data. SMAs, in particular nickel-titanium (NiTi) alloys, have unique properties that restore the original shape after significant deformation. The frequency of loading significantly affects the functional characteristics of SMAs. Experimental data were obtained from cyclic tensile tests of a 1.5 mm diameter Ni55.8Ti44.2 wire at different loading frequencies (0.1, 0.5, 1.0, and 5.0 Hz). Various machine learning methods were used to predict the loading frequency f (Hz) based on input parameters such as stress σ (MPa), number of cycles N, strain ε (%), and loading–unloading stage: boosted trees, random forest, support vector machines, k-nearest neighbors, and artificial neural networks of the MLP type. Experimental data of 100–140 load–unload cycles for four load frequencies were used for training. The dataset contained 13,365 elements. The results showed that the MLP neural network model demonstrated the highest accuracy in load frequency classification. The boosted trees and random forest models also performed well, although slightly below MLP. The SVM method also performed quite well. The KNN method showed the worst results among all models. Additional testing of the MLP model on cycles that were not included in the training data (200th, 300th, and 1035th cycles) showed that the model retains high efficiency in predicting load frequency, although the accuracy gradually decreases on later cycles due to the accumulation of structural changes in the material. Artificial neural network ; Random forest ; Support vector machine ; Nickel titanium ; Shape-memory alloy ; Computer science ; Artificial intelligence ; Materials science ; Machine learning ; Structural engineering ; Pattern recognition (psychology) ; Engineering Shape Memory Alloy Transformations
Authors: Haoyu Wang | Bin Chen | Hangling Sun | Yuxuan Zhang
Source: Computers in Biology and Medicine (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Carbon-based molecular properties efficiently predicted by deep learning-based quantum chemical simulation with large language models Quantum chemical ; Computer science ; Artificial intelligence ; Natural language processing ; Chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Anastasiia Piven | Darina D. Darmoroz | Ekaterina V. Skorb | Tetiana Orlova
Source: Soft Matter (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning methods for liquid crystal research: phases, textures, defects and physical properties Liquid crystal materials, with their unique properties and diverse applications, have long captured the attention of researchers and industries alike. From liquid crystal displays and electro-optical devices to advanced sensors and emerging technologies, the study and application of liquid crystals continue to be of paramount importance in the fields of materials science, chemistry and physics. With the ever-increasing complexity and diversity of liquid crystal materials, researchers face new challenges in understanding their behaviors, properties, and potential applications. On the other hand, machine learning, a rapidly evolving interdisciplinary field at the intersection of computer science and data analysis, has already become a powerful tool for unraveling implicit correlations and predicting new properties of a wide variety of physical and chemical systems and structures. Here we aim to consider how machine learning methods are suitable for solving fundamental problems in the field of liquid crystals and what are the advantages of this artificial intelligence based approach. Materials science ; Liquid crystal ; Crystallography ; Nanotechnology ; Chemical physics ; Chemistry ; Optoelectronics Liquid Crystal Research Advancements ; Optical Polarization and Ellipsometry ; Material Dynamics and Properties
Authors: Sarah Glaß | Martin A. Schmidt | Petra Merten | Amira Abdul Latif | Kristina Fischer | Agnes Schulze | Pascal Friederich | Volkan Filiz
Source: ACS Applied Materials & Interfaces (article, 2024-04-11)
Field: Materials Science — Materials Chemistry

Design of Modified Polymer Membranes Using Machine Learning Surface modification is an attractive strategy to adjust the properties of polymer membranes. Unfortunately, predictive structure-processing-property relationships between the modification strategies and membrane performance are often unknown. One possibility to tackle this challenge is the application of data-driven methods such as machine learning. In this study, we applied machine learning methods to data sets containing the performance parameters of modified membranes. The resulting machine learning models were used to predict performance parameters, such as the pure water permeability and the zeta potential of membranes modified with new substances. The predictions had low prediction errors, which allowed us to generalize them to similar membrane modifications and processing conditions. Additionally, machine learning methods were able to identify the impact of substance properties and process parameters on the resulting membrane properties. Our results demonstrate that small data sets, as they are common in materials science, can be used as training data for predictive machine learning models. Therefore, machine learning shows great potential as a tool to expedite the development of high-performance membranes while reducing the time and costs associated with the development process at the same time. Materials science ; Membrane ; Polymer ; Nanotechnology ; Chemical engineering ; Polymer science ; Composite material ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Membrane Separation Technologies
Authors: Jingnan Zheng | Shibin Wang | Shengwei Deng | Zihao Yao | Junhua Hu | Jianguo Wang
Source: Energy & environment materials (article, 2024-05-15)
Field: Materials Science — Materials Chemistry

Accelerating the Screening of Modified <scp>MA<sub>2</sub>Z<sub>4</sub></scp> Catalysts for Hydrogen Evolution Reaction by Deep Learning‐Based Local Geometric Analysis Machine learning (ML) integrated with density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of single‐atom catalysts (SACs) by establishing deep structure–activity relationships. The traditional ML models are always difficult to identify the structural differences among the single‐atom systems with different modification methods, leading to the limitation of the potential application range. Aiming to the structural properties of several typical two‐dimensional MA 2 Z 4 ‐based single‐atom systems (bare MA 2 Z 4 and metal single‐atom doped/supported MA 2 Z 4 ), an improved crystal graph convolutional neural network (CGCNN) classification model was employed, instead of the traditional machine learning regression model, to address the challenge of incompatibility in the studied systems. The CGCNN model was optimized using crystal graph representation in which the geometric configuration was divided into active layer, surface layer, and bulk layer (ASB‐GCNN). Through ML and DFT calculations, five potential single‐atom hydrogen evolution reaction (HER) catalysts were screened from chemical space of 600 MA 2 Z 4 ‐based materials, especially V 1 /HfSn 2 N 4 (S) with high stability and activity (Δ G H * is 0.06 eV). Further projected density of states (pDOS) analysis in combination with the wave function analysis of the SAC‐H bond revealed that the SAC‐ dz 2 orbital coincided with the H‐ s orbital around the energy level of −2.50 eV, and orbital analysis confirmed the formation of σ bonds. This study provides an efficient multistep screening design framework of metal single‐atom catalyst for HER systems with similar two‐dimensional supports but different geometric configurations. Catalysis ; Chemistry ; Hydrogen ; Physics ; Biochemistry ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalytic Processes in Materials Science
Authors: Saman Mohammed Abdulrahman | Renas Rajab Asaad | Hawar Bahzad Ahmad | Ahmed Alaa Hani | Subhi R. M. Zeebaree | Amira Bibo Sallow
Source: Journal of Soft Computing and Data Mining (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Machine Learning in Nonlinear Material Physics Researchers and investors can accelerate the development of innovative materials, methods, and procedures by using machine learning technologies. In materials science, one key objective of employing such methods is to make it easier to identify and quantify high features throughout the chain of manipulation, organization, possessions, and efficiency. An overview of effective uses of automated learning and statistics is given in this piece, which addresses specific challenges in continuous materials mechanics. The classification of these applications is based on their nature, categorized as descriptive, predictive, or prescriptive, all aiming to identify, anticipate, or optimize crucial attributes. The selection of the most suitable machine learning technique is influenced by factors such as the unique use case, content type, data characteristics, geographical and temporal scales, formats, targeted knowledge gain, and affordable computing expenses. Various examples are explored, including using various artificially generated share network architectures on an as-needed basis in conjunction with additional data-driven approaches such as basic constituent assessment, decisions shrubs, models, woods, trees, supported matrix, and Gaussian learners. Nonlinear system ; Statistical physics ; Physics ; Theoretical physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science
Authors: Alberto Pacini | Mauro Ferrario | Sophie Loehlé | Maria Clelia Righi
Source: The European Physical Journal Plus (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

Advancing tribological simulations of carbon-based lubricants with active learning and machine learning molecular dynamics Abstract The need to move toward more sustainable lubricant materials has sparked an ever growing interest on the tribological performances of additives based on environmentally friendly molecules, such as carbon-based compounds, and green liquid media as aqueous solutions. The prediction of the solubility of the additives into the liquid and the tribochemistry of decomposition and polymerization of the additive molecules under harsh conditions is essential for understanding the atomistic mechanisms leading to the formation in situ of the carbon-based lubricious tribofilms so effective in reducing friction and wear at solid interfaces. To this extent, the application of tools like ab initio molecular dynamics based on first-principle density functional theory is severely hindered by the size of the systems of interests and the need to simulate their dynamics over relatively long times. To enable tribological simulations with quantum accuracy for a first time, we develop a workflow for smart configuration sampling in active learning, to obtain machine learning interatomic potentials which are shown to be sufficiently robust and accurate also in the characteristic harsh conditions generated by high loads and shear rates. Focusing on glycerol rich lubricants, we apply this active learning strategy to generate a neural network potential to simulate the formation and behavior of nanometer thick molecular tribofilms. The simulations reveal the superior accuracy of the machine learning approach with respect to classical molecular dynamics with reactive force fields, and pave the way for more promising in depth exploration of novel carbon-based lubricants. Molecular dynamics ; Lubricant ; Tribology ; Materials science ; Carbon fibers ; Nanotechnology ; Density functional theory ; Environmentally friendly ; Chemical physics ; Computer science ; Mechanical engineering ; Composite material ; Computational chemistry ; Chemistry ; Engineering Machine Learning in Materials Science ; Lubricants and Their Additives ; Force Microscopy Techniques and Applications
Authors: Shaojun Wang | Yiru Huang | Wenguang Hu | Lei Zhang
Source: npj Computational Materials (article, 2024-05-29)
Field: Materials Science — Materials Chemistry

Data-driven optimization and machine learning analysis of compatible molecules for halide perovskite material Abstract Optoelectronic stability of halide perovskite material in hostile conditions such as water is rather limited, preventing them from further industrial deployment. Here, we optimize and perform machine learning analysis on CH 3 NH 3 PbI 3 materials with additives, solvents and post-treatment molecules using combined experimental and data-driven methods. A champion system consisting of a compatible tertiary molecular combination ‘calcein+PbBr 2 + DMSO’ active at diverse surfaces is identified, delivering a large aqueous photoelectrochemical (PEC) photocurrent of 10 -5 A/cm 2 and an improved aqueous stability of 92.5%. Subsequently, machine interpretation is provided to decouple the multi-molecule contributions with the assistance of genetic programming (GP) and extra-trees (ET) machine learning models, highlighting the intricate molecular features for the target outputs. The post-hoc density functional theory (DFT) calculation suggests the presence of multiple hydrogen bond and anion··π surface interactions to stabilize the interfacial structures. The present ‘PEC + GP + ET + DFT’ approach is suggested to be an effective approach to design and comprehensively evaluate molecule-modified materials. Halide ; Perovskite (structure) ; Molecule ; Computer science ; Materials science ; Chemistry ; Chemical engineering ; Engineering ; Inorganic chemistry ; Organic chemistry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Chen Zhong | Li Li | Yuanzhong Wang
Source: Microchemical Journal (article, 2024-08-15)
Field: Chemistry — Analytical Chemistry

Applications of chemical fingerprints and machine learning in plant ecology: Recent progress and future perspectives With the rapid development of chromatography, spectroscopy and other detection techniques, chemical fingerprinting has become a powerful tool for ecology research. The data generated by these techniques contain a large amount of key information related to the molecular structure, and the optimization and study of these data using chemometrics and machine learning algorithms will have higher precision and accuracy. This paper reviews the analytical techniques used to generate chemical fingerprints in recent years and scrutinises the diversity of applications of chemical fingerprints in the field of plant ecology. Applications in combination with machine learning are emphasized. Prospects for chemical fingerprinting combined with machine learning in plant ecology include the development of fingerprinting databases for accurate species identification, and the integration of advanced techniques to incorporate fingerprinting techniques into ecological modelling to predict plant responses to environmental changes. These innovative avenues hold the promise of improving our understanding of plants and their complex interactions in various ecosystems and integrating them to advance ecological research. Ecology ; Chemical ecology ; Data science ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Species Distribution and Climate Change
Authors: Pan Zhou | Ming Wang | Fei Tang | Ling Liu | Hongfang Yu | Xi Chen
Source: Materials Research Bulletin (article, 2024-06-19)
Field: Materials Science — Materials Chemistry

Machine learning accelerates the screening of efficient metal-oxide catalysts for photocatalytic water splitting Photocatalysis ; Catalysis ; Water splitting ; Oxide ; Metal ; Materials science ; Chemical engineering ; Nanotechnology ; Inorganic chemistry ; Chemistry ; Metallurgy ; Engineering Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Shaojie Li | Zaixing Dong | Jianfeng Jin | Hucheng Pan | Zongqing Hu | Rui Hou | Gaowu Qin
Source: Materials Genome Engineering Advances (article, 2024-05-31)
Field: Materials Science — Biomaterials

Optimal design of high‐performance rare‐earth‐free wrought magnesium alloys using machine learning Abstract In this study, a small dataset of 370 datapoints of Mg alloys are selected for machine learning (ML), in which each datapoint includes five rare‐earth‐free alloying elements (Ca, Zn, Al, Mn and Sn), three extrusion parameters (extrusion speed, temperature and ratio), and three mechanical properties (yield strength [YS], ultimate tensile strength [UTS] and elongation [EL]). The ML algorithms, including support vector machine regression (SVR), artificial neural network, and other three methods, are employed, and the SVR has the best performance in predicting mechanical properties based on the components, and process parameters, with the mean absolute percentage error of YS, UTS, and EL being 6.34%, 4.19%, and 13.64% in the test set, respectively. The SVR model combined with multi‐objective genetic algorithm are successfully used to optimize mechanical properties of four extruded alloys from Mg‐Ca, Mg‐Ca‐Zn, Mg‐Ca‐Mn‐Sn, and Mg‐Ca‐Al‐Zn‐Mn series alloys, respectively, and the corresponding experimental results are in good agreement with the designed ones. Furthermore, new composition schemes are proposed from a wider range of elements and processing features to match the objectives of high‐strength, strength–ductility balanced, and high‐ductility Mg alloys, and the four‐, five‐ and six‐element alloying schemes are provided for the candidates of new‐generation wrought Mg alloys. Rare earth ; Magnesium ; Materials science ; Metallurgy ; Magnesium alloy Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties