jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rolf David | Miguel de la Puente | Axel Gomez | Olaia Anton | Guillaume Stirnemann | Damien Laage
Source: Digital Discovery (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials The emergence of artificial intelligence is profoundly impacting computational chemistry, particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional potential energy surface representations, MLIPs overcome the conventional computational scaling limitations by offering an effective combination of accuracy and efficiency for calculating atomic energies and forces to be used in molecular simulations. These MLIPs have significantly enhanced molecular simulations across various applications, including large-scale simulations of materials, interfaces, chemical reactions, and beyond. Despite these advances, the construction of training datasets-a critical component for the accuracy of MLIPs-has not received proportional attention, especially in the context of chemical reactivity, which depends on rare barrier-crossing events that are not easily included in the datasets. Here we address this gap by introducing ArcaNN, a comprehensive framework designed for generating training datasets for reactive MLIPs. ArcaNN employs a concurrent learning approach combined with advanced sampling techniques to ensure an accurate representation of high-energy geometries. The framework integrates automated processes for iterative training, exploration, new configuration selection, and energy and force labeling, all while ensuring reproducibility and documentation. We demonstrate ArcaNN's capabilities through two paradigm reactions: a nucleophilic substitution and a Diels-Alder reaction. These examples showcase its effectiveness, the uniformly low error of the resulting MLIP everywhere along the chemical reaction coordinate, and its potential for broad applications in reactive molecular dynamics. Finally, we provide guidelines for assessing the quality of MLIPs in reactive systems. Training (meteorology) ; Training set ; Computer science ; Sampling (signal processing) ; Artificial intelligence ; Machine learning ; Biological system ; Physics ; Computer vision ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Didac Martí | Rémi Pétuya | Emanuele Bosoni | Anne‐Claude Dublanchet | Stephan Mohr | Fabien Léonforte
Source: ACS Applied Polymer Materials (article, 2024-04-11)
Field: Materials Science — Materials Chemistry

Predicting the Glass Transition Temperature of Biopolymers via High-Throughput Molecular Dynamics Simulations and Machine Learning Nature has only provided us with a limited number of biobased and biodegradable building blocks. Therefore, the fine-tuning of the sustainable polymer properties is expected to be achieved through the control of the composition of biobased copolymers for targeted applications such as cosmetics. Until now, the main approaches to alleviate the experimental efforts and accelerate the discovery of polymers have relied on machine learning models trained on experimental data, which implies enormous and difficult work in the compilation of data from heterogeneous sources. On the other hand, molecular dynamics simulations of polymers have shown that they can accurately capture the experimental trends for a series of properties. However, the combination of different ratios of monomers in copolymers can rapidly lead to a combinatorial explosion, preventing investigation of all possibilities via molecular dynamics simulations. In this work, we show that the combination of machine learning approaches and high-throughput molecular dynamics simulations permits quick and efficient sampling and characterization of the relevant chemical design space for specific applications. Reliable simulation protocols have been implemented to evaluate the glass transition temperature of a series of 58 homopolymers, which exhibit good agreement with experiments, and 488 copolymers. Overall, 2,184 simulations (four replicas per polymer) were performed, for a total simulation time of 143.052 μs. These results, constituting a data set of 546 polymers, have been used to train a machine learning model for the prediction of the MD-calculated glass transition temperature with a mean absolute error of 19.34 K and an R2 score of 0.83. Overall, within its applicability domain, this machine learning model provides an impressive acceleration over molecular dynamics simulations: the glass transition temperature of thousands of polymers can be obtained within seconds, whereas it would have taken node-years to simulate them. This type of approach can be tuned to address different design spaces or different polymer properties and thus has the potential to accelerate the discovery of polymers. Glass transition ; Dynamics (music) ; Molecular dynamics ; Throughput ; Transition (genetics) ; Materials science ; Statistical physics ; Chemical physics ; Computer science ; Chemistry ; Physics ; Polymer ; Computational chemistry ; Composite material Machine Learning in Materials Science ; Material Dynamics and Properties ; Polymer crystallization and properties
Authors: Biaobiao Yang | Valentín Vassilev-Galindo | Javier LLorca
Source: npj Computational Materials (article, 2024-01-30)
Field: Materials Science — Biomaterials

Application of machine learning to assess the influence of microstructure on twin nucleation in Mg alloys Abstract Twin nucleation in textured Mg alloys was studied by means of electron back-scattered diffraction in samples deformed in tension along different orientations in more than 3000 grains. In addition, 28 relevant parameters, categorized in four different groups (loading condition, grain shape, apparent Schmid factors, and grain boundary features) were also recorded for each grain. This information was used to train supervised machine learning classification models to analyze the influence of the microstructural features on the nucleation of extension twins in Mg alloys. It was found twin nucleation is favored in larger grains and in grains with high twinning Schmid factors, but also that twins may form in the grains with very low or even negative Schmid factors for twinning if they have at least one smaller neighboring grain and another one (or the same) that is more rigid. Moreover, twinning of small grains with high twinning Schmid factors is favored if they have low basal slip Schmid factors and have at least one neighboring grain with a high basal slip Schmid factor that will deform easily. These results reveal the role of many-body relationships, such as differences in stiffness and size between a given grain and its neighbors, to assess extension twin nucleation in grains unfavorably oriented for twinning. Nucleation ; Microstructure ; Materials science ; Metallurgy ; Psychology ; Thermodynamics ; Physics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Microstructure and mechanical properties
Authors: Paulina Wiśniewska | Elnaz Movahedifar | Krzysztof Formela | M.Z. Naser | Henri Vahabi | Mohammad Reza Saeb
Source: Composites Science and Technology (article, 2024-02-24)
Field: Materials Science — Polymers and Plastics

The chemistry, properties and performance of flame-retardant rubber composites: Collecting, analyzing, categorizing, machine learning modeling, and visualizing Fire retardant ; Flammability ; Materials science ; Natural rubber ; Composite material ; Computer science Flame retardant materials and properties ; Polymer Nanocomposites and Properties ; Polymer composites and self-healing
Authors: Dawn Sivan | K. Satheesh Kumar | Aziman Abdullah | Veena Raj | Izan Izwan Misnon | Seeram Ramakrishna | Rajan Jose
Source: Journal of Materials Science (review, 2024-02-01)
Field: Materials Science — Materials Chemistry

Advances in materials informatics: a review Materials informatics ; Computer science ; Artificial intelligence ; Informatics ; Machine learning ; Artificial neural network ; Data science ; Deep learning ; Big data ; Computational intelligence ; Health informatics ; Data mining ; Engineering informatics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jiang Du | Arwa Abdulkreem AL‐Huqail | Yan Cao | Hui Yao | Yiding Sun | Mazen Garaleh | Ehab El Sayed Massoud | Elimam Ali | Hamid Assilzadeh | José Escorcia-Gutierrez
Source: Environmental Research (article, 2024-05-25)
Field: Materials Science — Materials Chemistry

RETRACTED: Green synthesis of zinc oxide nanoparticles from Sida acuta leaf extract for antibacterial and antioxidant applications, and catalytic degradation of dye through the use of convolutional neural network Zinc ; Antioxidant ; Human immunodeficiency virus (HIV) ; Chemistry ; Degradation (telecommunications) ; Nanoparticle ; Nuclear chemistry ; Organic chemistry ; Nanotechnology ; Materials science ; Medicine ; Computer science ; Immunology Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Medicinal Plants and Neuroprotection
Authors: Huizhen Ma | Ling Liu | Hua He | Si Sun | Shuyu Yang | You Huang | Yuan Li | Kexin Tan | Haile Liu | Shuhu Liu | Yonghui Li | Changlong Liu | Pengfei Liu | Hao Wang | Xiaodong Zhang
Source: Matter (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

Atomically precise Ag clusters for intelligent NIR-II imaging Photobleaching ; Materials science ; High resolution ; Nanotechnology ; Optics ; Physics ; Fluorescence Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Irea Mosquera‐Lois | Seán R. Kavanagh | Alex M. Ganose | Aron Walsh
Source: npj Computational Materials (article, 2024-06-06)
Field: Materials Science — Materials Chemistry

Machine-learning structural reconstructions for accelerated point defect calculations Abstract Defects dictate the properties of many functional materials. To understand the behaviour of defects and their impact on physical properties, it is necessary to identify the most stable defect geometries. However, global structure searching is computationally challenging for high-throughput defect studies or materials with complex defect landscapes, like alloys or disordered solids. Here, we tackle this limitation by harnessing a machine-learning surrogate model to qualitatively explore the structural landscape of neutral point defects. By learning defect motifs in a family of related metal chalcogenide and mixed anion crystals, the model successfully predicts favourable reconstructions for unseen defects in unseen compositions for 90% of cases, thereby reducing the number of first-principles calculations by 73%. Using CdSe x Te 1− x alloys as an exemplar, we train a model on the end member compositions and apply it to find the stable geometries of all inequivalent vacancies for a range of mixing concentrations, thus enabling more accurate and faster defect studies for configurationally complex systems. Point (geometry) ; Artificial intelligence ; Computer science ; Machine learning ; Geometry ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Longfei Zhu | Qun Luo | Qiaochuan Chen | Yu Zhang | Lijun Zhang | Bin Hu | Yuexing Han | Qian Li
Source: Materials Genome Engineering Advances (article, 2024-02-20)
Field: Materials Science — Metals and Alloys

Prediction of ultimate tensile strength of Al‐Si alloys based on multimodal fusion learning Abstract Exploring the “composition‐microstructure‐property” relationship is a long‐standing theme in materials science. However, complex interactions make this area of research challenging. Based on the image processing and machine learning techniques, this paper proposes a multimodal fusion learning framework that comprehensively considers both composition and microstructure in prediction of the ultimate tensile strength (UTS) of Al‐Si alloys. Firstly, the composition and image information are collected from the literature and supplementary experiments, followed by the image segmentation and quantitative analysis of eutectic Si images. Subsequently, the quantitative analysis results are combined with other features for three‐step feature screening, and 12 key features are obtained. Finally, four machine‐learning models (i.e., decision tree, random forest, adaptive boosting, and extreme gradient boosting [XGBoost]) are used to predict the UTS of Al‐Si alloys. The results show that the quantitative analysis method proposed in this paper is superior to Image‐Pro Plus (IPP) software in some aspects. The XGBoost model has the best prediction performance with R 2 = 0.94. Furthermore, five mixed features and their critical values that significantly affect UTS are identified. Our study provides enlightenment for the prediction of UTS of Al‐Si alloys from composition and microstructure, and would be applicable to other alloys. Ultimate tensile strength ; Microstructure ; Materials science ; Boosting (machine learning) ; Artificial intelligence ; Computer science ; Eutectic system ; Random forest ; Gradient boosting ; Machine learning ; Pattern recognition (psychology) ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Aluminum Alloy Microstructure Properties ; Machine Learning in Materials Science
Authors: Subhankar Saha | Santosh Kumar | Vijay Kumar Mahakur | Swati Sharma | Abir Saha
Source: Materials Today Communications (article, 2024-10-28)
Field: Materials Science — Polymers and Plastics

Development and machine learning based prediction of carbon/hemp/ramie sandwich composite as sustainable and ecofriendly solution for automobile application Ramie ; Materials science ; Composite number ; Composite material ; Carbon fibers ; Automotive industry ; Pulp and paper industry ; Engineering ; Fiber ; Aerospace engineering Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; biodegradable polymer synthesis and properties
Authors: Kaile Dou | Weibo Zhao | Chenyue Wang | Yuanchen Fan | Chunlin He | Lei Zhang | Siping Pang
Source: Chemical Engineering Journal (article, 2024-11-14)
Field: Chemistry — Organic Chemistry

Switch on amine substrate reactivity towards hexaazaisowurtzitane cage: Insights from a tailored machine learning model Cage ; Substrate (aquarium) ; Reactivity (psychology) ; Amine gas treating ; Materials science ; Chemistry ; Chemical engineering ; Engineering ; Structural engineering ; Organic chemistry ; Geology ; Medicine Chemical Reaction Mechanisms ; Chemical Reactions and Mechanisms ; Chemistry and Chemical Engineering
Authors: Justin Marlowe | Siddharth Deshpande | Dionisios G. Vlachos | Mahdi M. Abu‐Omar | Phillip Christopher
Source: Journal of the American Chemical Society (article, 2024-05-13)
Field: Materials Science — Materials Chemistry

Effect of Dynamic and Preferential Decoration of Pt Catalyst Surfaces by WO<sub><i>x</i></sub> on Hydrodeoxygenation Reactions Catalysts containing Pt nanoparticles and reducible transition-metal oxides (WO<sub><i>x</i></sub>, NbO<sub><i>x</i></sub>, TiO<sub><i>x</i></sub>) exhibit remarkable selectivity to aromatic products in hydrodeoxygenation (HDO) reactions for biomass valorization, contrasting the undesired aromatic hydrogenation typically observed for metal catalysts. However, the active site(s) responsible for the high selectivity remains elusive. Here, theoretical and experimental analyses are combined to explain the observed HDO reactivity by interrogating the organization of reduced WO<sub><i>x</i></sub> domains on Pt surfaces at sub-monolayer coverage. The SurfGraph algorithm is used to develop model structures that capture the configurational space (∼1000 configurations) for density functional theory (DFT) calculations of a W<sub>3</sub>O<sub>7</sub> trimer on stepped Pt surfaces. Machine-learning models trained on the DFT calculations identify the preferential occupation of well-coordinated Pt sites (≥8 Pt coordination number) by WO<sub><i>x</i></sub> and structural features governing WO<sub><i>x</i></sub>-Pt stability. WO<sub><i>x</i></sub>/Pt/SiO<sub>2</sub> catalysts are synthesized with varying W loadings to test the theoretical predictions and relate them to HDO reactivity. Spectroscopy- and microscopy-based catalyst characterizations identify the dynamic and preferential decoration of well-coordinated sites on Pt nanoparticles by reduced WO<sub><i>x</i></sub> species, consistent with theoretical predictions. The catalytic consequences of this preferential decoration on the HDO of a lignin model compound, dihydroeugenol, are clarified. The effect of WO<sub><i>x</i></sub> decoration on Pt nanoparticles for HDO involves WO<sub><i>x</i></sub> inhibition of aromatic ring hydrogenation by preferentially blocking well-coordinated Pt sites. The identification of preferential decoration on specific sites of late-transition-metal surfaces by reducible metal oxides provides a new perspective for understanding and controlling metal-support interactions in heterogeneous catalysis. Chemistry ; Hydrodeoxygenation ; Catalysis ; Organic chemistry ; Selectivity Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Transition Metal Oxide Nanomaterials
Authors: Fateme Molajafari | Tianrui Li | Mehrnaz Abbasichaleshtori | Moein Hajian Z. D. | Anthony F. Cozzolino | Daniel R. Fandrick | Joshua D. Howe
Source: CrystEngComm (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Computational screening for prediction of co-crystals: method comparison and experimental validation COSMO-RS and machine learning-based models can reduce the cost of screening and identifying crystal coformers, facilitating discovery of new cocrystals. Computer science ; Materials science ; Biological system ; Biology X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies ; Crystallography and molecular interactions
Authors: W. G. Noid | Ryan J. Szukalo | Katherine M. Kidder | Maria C. Lesniewski
Source: Annual Review of Physical Chemistry (article, 2024-06-28)
Field: Materials Science — Materials Chemistry

Rigorous Progress in Coarse-Graining Low-resolution coarse-grained (CG) models provide remarkable computational and conceptual advantages for simulating soft materials. In principle, bottom-up CG models can reproduce all structural and thermodynamic properties of atomically detailed models that can be observed at the resolution of the CG model. This review discusses recent progress in developing theory and computational methods for achieving this promise. We first briefly review variational approaches for parameterizing interaction potentials and their relationship to machine learning methods. We then discuss recent approaches for simultaneously improving both the transferability and thermodynamic properties of bottom-up models by rigorously addressing the density and temperature dependence of these potentials. We also briefly discuss exciting progress in modeling high-resolution observables with low-resolution CG models. More generally, we highlight the essential role of the bottom-up framework not only for fundamentally understanding the limitations of prior CG models but also for developing robust computational methods that resolve these limitations in practice. Transferability ; Granularity ; Computer science ; Resolution (logic) ; Observable ; Computational model ; Statistical physics ; Algorithm ; Artificial intelligence ; Machine learning ; Physics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Theoretical and Computational Physics
Authors: Yujiao Xie | Lei Xu | J.Y. Zhang | Chenguang Zhang | Yue Hu | Zhouxu Zhang | Guoxin Chen | Shuyan Qi | Xiawei Xu | Jing Wang | Wenzhi Ren | Jie Lin | Aiguo Wu
Source: Materials Horizons (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Precise diagnosis of tumor cells and hemocytes using ultrasensitive, stable, selective cuprous oxide composite SERS bioprobes assisted with high-efficiency separation microfluidic chips Efficient enrichment and accurate diagnosis of cancer cells from biological samples can guide effective treatment strategies. However, the accessibility and accuracy of rapid identification of tumor cells have been hampered due to the overlap of white blood cells (WBCs) and cancer cells in size. Therefore, a diagnosis system for the identification of tumor cells using reliable surface-enhanced Raman spectroscopy (SERS) bioprobes assisted with high-efficiency microfluidic chips for rapid enrichment of cancer cells was developed. According to this, a homogeneous flower-like Cu<sub>2</sub>O@Ag composite with high SERS performance was constructed. It showed a favorable spectral stability of 5.81% and can detect trace alizarin red (10<sup>-9</sup> mol L<sup>-1</sup>). Finite-difference time-domain (FDTD) simulation of Cu<sub>2</sub>O, Ag and Cu<sub>2</sub>O@Ag, decreased the fluorescence lifetime of methylene blue after adsorption on Cu<sub>2</sub>O@Ag, and surface defects of Cu<sub>2</sub>O observed using a spherical aberration-corrected transmission electron microscope (AC-TEM) demonstrated that the combined effects of electromagnetic enhancement and promoted charge transfer endowed the Cu<sub>2</sub>O@Ag with good SERS activity. In addition, the modulation of the absorption properties of flower-like Cu<sub>2</sub>O@Ag composites significantly improved electromagnetic enhancement and charge transfer effects at 532 nm, providing a reliable basis for the label-free SERS detection. After the cancer cells in blood were separated by a spiral inertial microfluidic chip (purity >80%), machine learning-assisted linear discriminant analysis (LDA) successfully distinguished three types of cancer cells and WBCs with high accuracy (>90%). In conclusion, this study provides a profound reference for the rational design of SERS probes and the efficient diagnosis of malignant tumors. Microfluidics ; Materials science ; Substrate (aquarium) ; Microfluidic chip ; Nanotechnology ; Circulating tumor cell ; Composite number ; Cancer cell ; Lab-on-a-chip ; Cancer ; Biology ; Composite material ; Metastasis Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research