jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pingluo Zhao | Yangwei Wang | Bingyue Jiang | Mingxuan Wei | Hongmei Zhang | Xingwang Cheng
Source: Materials & Design (article, 2023-02-21)
Field: Materials Science — Materials Chemistry

A new method for classifying and segmenting material microstructure based on machine learning The microstructural characteristics of materials determine their service performance. Therefore, the rapid identification of material microstructure and the accurate extraction of feature parameters are significant for the research and application of materials. However, most materials have diverse structure types and complex microstructures. With the gradual maturity of computer vision technology, it is increasingly being applied to studying material images. In this paper, a neural network-based material microstructure recognition and semantic segmentation model is designed to automatically identify and classify titanium alloy structures and then adaptively process images and extract features to overcome the challenges of efficient recognition and extraction of multiple structures of materials. The study completed the recognition of 2275 images of 15 types of titanium alloys through data set preparation, image preprocessing, model building, and parameter tuning, followed by image segmentation of morphologically processed images and labels based on U-net. Finally, connected domain computation successfully extracted the feature covariates in multiple structures of titanium alloys. This work demonstrates the application of data mining technology in metal microstructure image research and the implementation process. It completes the identification and characterization of the complex microstructure of the material. Artificial intelligence ; Microstructure ; Pattern recognition (psychology) ; Materials science ; Preprocessor ; Segmentation ; Identification (biology) ; Feature extraction ; Artificial neural network ; Computer science ; Feature (linguistics) ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: Junchen Xiao | Jose Hobson | Arnab Ghosh | Maciej Harańczyk | De‐Yi Wang
Source: Composites Communications (article, 2023-04-16)
Field: Materials Science — Polymers and Plastics

Flame retardant properties of metal hydroxide-based polymer composites: A machine learning approach With increasing the practical applications of polymeric materials, surging demands for the high-performance flame-retardant polymer composites are obvious. Accurately predicting the performance of flame-retardant materials is critical for expediting technology development. Here, we develop machine learning models based on Random Forest algorithm to create standard paradigms based on a self-generated comprehensive dataset consisting of 219 experimental data. Integrated by plenty of randomly combined decision trees, the Random Forest is well known for its accurate predictability and easy interpretability. We predict the important flame-retardancy-evaluating parameters including time to ignition (TTI), peak heat release rate (pHRR), total heat release (THR) and flame retardancy index (FRI) of different polymer composites containing metal hydroxides as the main fire-retardant additive. Our optimized models achieve high accuracy with R-squared value of 0.81 for regression and over 0.85 for classification; mean absolute error is below 0.3 for all 4 models. The analysis of important features reveals that mass fraction of MH takes always the first place, and the influence of feature groups on target properties is usually multi-dimensional and complicated. Fire retardant ; Materials science ; Interpretability ; Random forest ; Composite material ; Polymer ; Carbonization ; Computer science ; Algorithm ; Machine learning Flame retardant materials and properties ; Thermal and Kinetic Analysis ; Fire dynamics and safety research
Authors: Shaohan Li | Zening Yang | Rasoul Khaledialidusti | Shuai Lin | Jin Yu | Mohammad Khazaei | Jing Zhang | Litao Sun | Xin Li | Weiwei Sun
Source: Acta Materialia (article, 2023-05-12)
Field: Materials Science — Materials Chemistry

High-throughput study and machine learning on MAX and MAB phases: new materials and fingerprints of superior lattice thermal conductivities Materials science ; Lattice (music) ; Throughput ; Thermal ; Artificial intelligence ; Machine learning ; Crystallography ; Thermodynamics ; Computer science MXene and MAX Phase Materials ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Oliver Schilter | Alain C. Vaucher | Philippe Schwaller | Teodoro Laino
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions The need for more efficient catalytic processes is ever-growing, and so are the costs associated with experimentally searching chemical space to find new promising catalysts. Despite the consolidated use of density functional theory (DFT) and other atomistic models for virtually screening molecules based on their simulated performance, data-driven approaches are rising as indispensable tools for designing and improving catalytic processes. Here, we present a deep learning model capable of generating new catalyst-ligand candidates by self-learning meaningful structural features solely from their language representation and computed binding energies. We train a recurrent neural network-based Variational Autoencoder (VAE) to compress the molecular representation of the catalyst into a lower dimensional latent space, in which a feed-forward neural network predicts the corresponding binding energy to be used as the optimization function. The outcome of the optimization in the latent space is then reconstructed back into the original molecular representation. These trained models achieve state-of-the-art predictive performances in catalysts' binding energy prediction and catalysts' design, with a mean absolute error of 2.42 kcal mol<sup>-1</sup> and an ability to generate 84% valid and novel catalysts. Generative grammar ; Artificial neural network ; Coupling (piping) ; Catalysis ; Computer science ; Deep neural networks ; Artificial intelligence ; Computational chemistry ; Machine learning ; Chemistry ; Biological system ; Organic chemistry ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Graph Neural Networks
Authors: Bina Fu | Dong H. Zhang
Source: National Science Review (review, 2023-11-06)
Field: Materials Science — Materials Chemistry

Accurate fundamental invariant-neural network representation of <i>ab initio</i> potential energy surfaces Highly accurate potential energy surfaces are critically important for chemical reaction dynamics. The large number of degrees of freedom and the intricate symmetry adaption pose a big challenge to accurately representing potential energy surfaces (PESs) for polyatomic reactions. Recently, our group has made substantial progress in this direction by developing the fundamental invariant-neural network (FI-NN) approach. Here, we review these advances, demonstrating that the FI-NN approach can represent highly accurate, global, full-dimensional PESs for reactive systems with even more than 10 atoms. These multi-channel reactions typically involve many intermediates, transition states, and products. The complexity and ruggedness of this potential energy landscape present even greater challenges for full-dimensional PES representation. These PESs exhibit a high level of complexity, molecular size, and accuracy of fit. Dynamics simulations based on these PESs have unveiled intriguing and novel reaction mechanisms, providing deep insights into the intricate dynamics involved in combustion, atmospheric, and organic chemistry. Ab initio ; Invariant (physics) ; Representation (politics) ; Artificial neural network ; Physics ; Statistical physics ; Computational chemistry ; Mathematical physics ; Quantum mechanics ; Computer science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Neural Networks and Applications
Authors: Ronaldo Giro | Hsianghan Hsu | Akihiro Kishimoto | Toshiyuki Hama | Rodrigo Neumann Barros Ferreira | Binquan Luan | Seiji Takeda | Lisa Hamada | M. Steiner
Source: npj Computational Materials (article, 2023-07-29)
Field: Materials Science — Materials Chemistry

AI powered, automated discovery of polymer membranes for carbon capture Abstract The generation of molecules with artificial intelligence (AI) or, more specifically, machine learning (ML), is poised to revolutionize materials discovery. Potential applications range from development of potent drugs to efficient carbon capture and separation technologies. However, existing computational discovery frameworks for polymer membranes lack automated training data creation, generative design, and physical performance validation at meso-scale where complex properties of amorphous materials emerge. The methodological gaps are less relevant to the ML design of individual molecules such as the monomers which constitute the building blocks of polymers. Here, we report automated discovery of complex materials through inverse molecular design which is informed by meso-scale target features and process figures-of-merit. We have explored the multi-scale discovery regime by computationally generating and validating hundreds of polymer candidates designed for application in post-combustion carbon dioxide filtration. Specifically, we have validated each discovery step, from training dataset creation, via graph-based generative design of optimized monomer units, to molecular dynamics simulation of gas permeation through the polymer membranes. For the latter, we have devised a representative elementary volume (REV) enabling permeability simulations at about 1000× the volume of an individual, ML-generated monomer, obtaining quantitative agreement. The discovery-to-validation time per polymer candidate is on the order of 100 h using one CPU and one GPU, offering a computational screening alternative prior to lab validation. Computer science ; Polymer ; Generative grammar ; Artificial intelligence ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Membrane Separation and Gas Transport ; Fuel Cells and Related Materials
Authors: Zhipeng Lin | Uttam Dhawa | Xiaoyan Hou | Max Surke | Binbin Yuan | Shu-Wen Li | Yan‐Cheng Liou | Magnus J. Johansson | Li‐Cheng Xu | Chen-Hang Chao | Xin Hong | Lutz Ackermann
Source: Nature Communications (article, 2023-07-15)
Field: Chemistry — Organic Chemistry

Electrocatalyzed direct arene alkenylations without directing groups for selective late-stage drug diversification Electrooxidation has emerged as an increasingly viable platform in molecular syntheses that can avoid stoichiometric chemical redox agents. Despite major progress in electrochemical C-H activations, these arene functionalizations generally require directing groups to enable the C-H activation. The installation and removal of these directing groups call for additional synthesis steps, which jeopardizes the inherent efficacy of the electrochemical C-H activation approach, leading to undesired waste with reduced step and atom economy. In sharp contrast, herein we present palladium-electrochemical C-H olefinations of simple arenes devoid of exogenous directing groups. The robust electrocatalysis protocol proved amenable to a wide range of both electron-rich and electron-deficient arenes under exceedingly mild reaction conditions, avoiding chemical oxidants. This study points to an interesting approach of two electrochemical transformations for the success of outstanding levels of position-selectivities in direct olefinations of electron-rich anisoles. A physical organic parameter-based machine learning model was developed to predict position-selectivity in electrochemical C-H olefinations. Furthermore, late-stage functionalizations set the stage for the direct C-H olefinations of structurally complex pharmaceutically relevant compounds, thereby avoiding protection and directing group manipulations. Electrochemistry ; Combinatorial chemistry ; Chemistry ; Redox ; Selectivity ; Electrocatalyst ; Nanotechnology ; Catalysis ; Materials science ; Organic chemistry ; Electrode Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques
Authors: Johannes T. Margraf
Source: Angewandte Chemie International Edition (review, 2023-03-10)
Field: Materials Science — Materials Chemistry

Science‐Driven Atomistic Machine Learning Machine learning (ML) algorithms are currently emerging as powerful tools in all areas of science. Conventionally, ML is understood as a fundamentally data-driven endeavour. Unfortunately, large well-curated databases are sparse in chemistry. In this contribution, I therefore review science-driven ML approaches which do not rely on "big data", focusing on the atomistic modelling of materials and molecules. In this context, the term science-driven refers to approaches that begin with a scientific question and then ask what training data and model design choices are appropriate. As key features of science-driven ML, the automated and purpose-driven collection of data and the use of chemical and physical priors to achieve high data-efficiency are discussed. Furthermore, the importance of appropriate model evaluation and error estimation is emphasized. Computer science ; Data science ; Context (archaeology) ; Big data ; Key (lock) ; Data-driven ; Artificial intelligence ; Term (time) ; Machine learning ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Ziyue Zou | Eric R. Beyerle | Sun-Ting Tsai | Pratyush Tiwary
Source: Proceedings of the National Academy of Sciences (article, 2023-02-09)
Field: Materials Science — Materials Chemistry

Driving and characterizing nucleation of urea and glycine polymorphs in water Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases, its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights into the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here, we employ the machine learning-augmented molecular dynamics framework "reweighted autoencoded variational Bayes for enhanced sampling (RAVE)." We study two molecular systems-urea and glycine-in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth nucleation events of different polymorphs from homogeneous solution; from these trajectories, we calculate the relative ranking of finite-sized polymorph crystals embedded in solution, in terms of the free-energy difference between the finite-sized crystal polymorph and the original solution state. We further observe that the obtained reaction coordinates and transitions are highly nonclassical. Nucleation ; Molecular dynamics ; Crystal (programming language) ; Statistical physics ; Chemical physics ; Chemistry ; Physics ; Materials science ; Computer science ; Computational chemistry ; Thermodynamics Crystallization and Solubility Studies ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Yipeng Zhou | Yixin Ouyang | Yehui Zhang | Qiang Li | Jinlan Wang
Source: The Journal of Physical Chemistry Letters (review, 2023-02-27)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Simulations of Electrochemical Interfaces: Recent Progress and Challenges The electrochemical interface, where the adsorption of reactants and electrocatalytic reactions take place, has long been a focus of attention. Some of the important processes on it tend to possess relatively slow kinetic characteristics, which are usually beyond the scope of <i>ab initio</i> molecular dynamics. The newly emerging technique, machine learning methods, provides an alternative approach to achieve thousands of atoms and nanosecond time scale while ensuring precision and efficiency. In this Perspective, we summarize in detail the recent progress and achievements made by the introduction of machine learning to simulate electrochemical interfaces, and focus on the limitations of current machine learning models, such as accurate descriptions of long-range electrostatic interactions and the kinetics of the electrochemical reactions occurring at the interface. Finally, we further point out the future directions for machine learning to expand in the field of electrochemical interfaces. Computer science ; Scope (computer science) ; Perspective (graphical) ; Interface (matter) ; Nanotechnology ; Artificial intelligence ; Machine learning ; Materials science Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: William J. Baldwin | Xia Liang | Johan Klarbring | Miloš Dubajić | David Dell’Angelo | Christopher Sutton | Claudia Caddeo | Samuel D. Stranks | Alessandro Mattoni | Aron Walsh | Gábor Csányi
Source: Small (article, 2023-09-21)
Field: Materials Science — Materials Chemistry

Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains Metal halide perovskites are multifunctional semiconductors with tunable structures and properties. They are highly dynamic crystals with complex octahedral tilting patterns and strongly anharmonic atomic behavior. In the higher temperature, higher symmetry phases of these materials, several complex structural features are observed. The local structure can differ greatly from the average structure and there is evidence that dynamic 2D structures of correlated octahedral motion form. An understanding of the underlying complex atomistic dynamics is, however, still lacking. In this work, the local structure of the inorganic perovskite CsPbI<sub>3</sub> is investigated using a new machine learning force field based on the atomic cluster expansion framework. Through analysis of the temporal and spatial correlation observed during large-scale simulations, it is revealed that the low frequency motion of octahedral tilts implies a double-well effective potential landscape, even well into the cubic phase. Moreover, dynamic local regions of lower symmetry are present within both higher symmetry phases. These regions are planar and the length and timescales of the motion are reported. Finally, the spatial arrangement of these features and their interactions are investigated and visualized, providing a comprehensive picture of local structure in the higher symmetry phases. Anharmonicity ; Local symmetry ; Cluster (spacecraft) ; Chemical physics ; Perovskite (structure) ; Octahedron ; Symmetry (geometry) ; Materials science ; Atomic units ; Planar ; Phase (matter) ; Molecular dynamics ; Condensed matter physics ; Molecular physics ; Physics ; Crystallography ; Crystal structure ; Chemistry ; Computational chemistry ; Geometry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Solid-state spectroscopy and crystallography
Authors: Semere Araya A. Asefa | Sang-Min Shim | Myeongsu Seong | Dasol Lee
Source: Applied Sciences (review, 2023-09-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chiral Metasurfaces: A Review of the Fundamentals and Research Advances Chirality, the absence of mirror symmetry, is predominant in nature. The chiral nature of the electromagnetic field behaves differently with chiral matter for left circularly polarized and right circularly polarized light. The chiroptical behavior in the sensing of naturally occurring chiral objects is weak, and improving the chiroptical response enhances the chiral sensing platform. This review covers the fundamental concepts of chiral metasurfaces and various types of single- and multi-layered chiral metasurfaces. In addition, we discuss tunable and deep-learning-based chiral metasurfaces. Tunability is achieved by manipulating the meta-atom’s property in response to external stimuli for applications such as optical modulation, chiral photonics, advanced sensing, and adaptive optics. Deep-learning modeling techniques, such as CNNs and GANs, offer efficient learning of the complex relationships in data, enabling the optimization and accurate prediction of chiral metasurface properties. The challenges in the design and fabrication of chiral metasurface include achieving broadband performance and scalability and addressing material limitations. Chiral metasurface performance is evaluated by optical rotation, circular dichroism enhancement, and tunability, which are quantified through the spectroscopic measurement of circular dichroism and optical rotation. Chiral metasurface progress enables applications, including metaholography, metalenses, and chiral sensing. Chiral sensing improves the detection of pharmaceuticals and biomolecules, increasing the sensitivity and accuracy of analytical diagnostics. Optical rotation ; Circular dichroism ; Chirality (physics) ; Photonics ; Metamaterial ; Optical phenomena ; Circular polarization ; Nanotechnology ; Physics ; Materials science ; Optics ; Symmetry breaking ; Chiral symmetry breaking ; Chemistry Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: Hui‐Wen Gu | Huihui Zhou | Yi Lv | Qiao Wu | Yuan Pan | Zhi-Xin Peng | Xiaohua Zhang | Xiaoli Yin
Source: Journal of Food Composition and Analysis (article, 2023-03-05)
Field: Chemistry — Analytical Chemistry

Geographical origin identification of Chinese red wines using ultraviolet-visible spectroscopy coupled with machine learning techniques Wine ; Principal component analysis ; Artificial intelligence ; Linear discriminant analysis ; Support vector machine ; Terroir ; Pattern recognition (psychology) ; Partial least squares regression ; Mathematics ; Computer science ; Machine learning ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies