jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuejiao Li | Bahram Vaghefinazari | Tim Würger | Sviatlana V. Lamaka | Mikhail L. Zheludkevich | Christian Feiler
Source: npj Materials Degradation (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques Abstract Selecting effective corrosion inhibitors from the vast chemical space is not a trivial task, as it is essentially infinite. Fortunately, machine learning techniques have shown great potential in generating shortlists of inhibitor candidates prior to large-scale experimental testing. In this work, we used the corrosion responses of 58 small organic molecules on the magnesium alloy AZ91 and utilized molecular descriptors derived from their geometry and density functional theory calculations to encode their molecular information. Statistical methods were applied to select the most relevant features to the target property for support vector regression and kernel ridge regression models, respectively, to predict the behavior of untested compounds. The performance of the two supervised learning approaches were compared and the robustness of the data-driven models were assessed by experimental blind testing. Robustness (evolution) ; Corrosion ; Support vector machine ; Regression ; Property (philosophy) ; Machine learning ; Computer science ; Molecular descriptor ; Density functional theory ; Statistical learning ; Artificial intelligence ; Chemical space ; Kernel (algebra) ; Biological system ; Experimental data ; Regression analysis ; Ridge ; Quantitative structure–activity relationship ; Materials science ; Mathematics ; Chemistry ; Computational chemistry ; Statistics ; Drug discovery ; Metallurgy Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Cheng-Han Li | Daniel P. Tabor
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Generative organic electronic molecular design informed by quantum chemistry Generative molecular design strategies have emerged as promising alternatives to trial-and-error approaches for exploring and optimizing within large chemical spaces. To date, generative models with reinforcement learning approaches have frequently used low-cost methods to evaluate the quality of the generated molecules, enabling many loops through the generative model. However, for functional molecular materials tasks, such low-cost methods are either not available or would require the generation of large amounts of training data to train surrogate machine learning models. In this work, we develop a framework that connects the REINVENT reinforcement learning framework with excited state quantum chemistry calculations to discover molecules with specified molecular excited state energy levels, specifically molecules with excited state landscapes that would serve as promising singlet fission or triplet-triplet annihilation materials. We employ a two-step curriculum strategy to first find a set of diverse promising molecules, then demonstrate the framework's ability to exploit a more focused chemical space with anthracene derivatives. Under this protocol, we show that the framework can find desired molecules and improve Pareto fronts for targeted properties <i>versus</i> synthesizability. Moreover, we are able to find several different design principles used by chemists for the design of singlet fission and triplet-triplet annihilation molecules. Generative grammar ; Quantum chemistry ; Chemistry ; Quantum ; Computational chemistry ; Computer science ; Organic chemistry ; Artificial intelligence ; Molecule ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Molecular Junctions and Nanostructures
Authors: Zhaosheng Zhang
Source: The Journal of Physical Chemistry Letters (article, 2023-01-30)
Field: Chemistry — Inorganic Chemistry

Automated Graph Neural Networks Accelerate the Screening of Optoelectronic Properties of Metal–Organic Frameworks The numerous organic and inorganic components of metal-organic framework (MOF) materials provide intriguing optoelectronic properties. Accurately predicting the electronic structural properties of MOFs has become the main focus. This work establishes two graph neural network models, crystal graph convolutional neural networks and a materials graph network, for predicting the band gaps of more than 10 000 MOF structures and promotes to improve the prediction accuracy through automatic hyperparameter tuning algorithms. Subsequently, for exploring machine learning-assisted screening of MOFs for the broader electronic properties, the screened copper-based MOFs are compared with lead-based MAPbI<sub>3</sub> solar cells with respect to the band gaps, densities of states, and charge density distributions, and the results have demonstrated that the overlap of the wave functions between the initial and final states of MOFs is weakened, which is conducive to the improvement of photoelectric performance. The chlorine doping strategy further enhances the advantage. The tuning of the machine learning model and hyperparameters and the doping strategy of halogen elements furnish empirical rules for the design of MOFs with excellent optoelectronic properties. Hyperparameter ; Convolutional neural network ; Artificial neural network ; Graph ; Materials science ; Computer science ; Photoelectric effect ; Doping ; Artificial intelligence ; Machine learning ; Nanotechnology ; Optoelectronics ; Theoretical computer science Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Navid Rabiee
Source: Clinical and Translational Discovery (article, 2023-06-01)
Field: Chemistry — Inorganic Chemistry

Artificial intelligence assisted with designing metal‐organic frameworks (MOFs) Abstract This article discusses the role of artificial intelligence (AI) in the design and engineering of porous inorganic nanomaterials, with a special focus on metal‐organic frameworks (MOFs). MOFs are highly porous nanomaterials with a large surface area, making them ideal for various applications, including gas storage, catalysis, and drug/gene delivery. Machine learning algorithms can analyze large datasets of MOF structures and properties to identify trends and correlations, and this information can be used to predict the properties of new MOFs. AI can also optimize MOF properties for specific applications, predict the optimal synthesis conditions for a given MOF structure, and design new ligands and metal ions for MOF synthesis. Mathematical models and tools, such as molecular dynamics simulations and density functional theory calculations, can be used in conjunction with AI algorithms to improve the accuracy and efficiency of MOF synthesis. The article also explores whether AI can design a new MOF, highlighting the complex nature of the question and the different perspectives that need to be considered. Metal-organic framework ; Computer science ; Nanotechnology ; Nanomaterials ; Ideal (ethics) ; Artificial intelligence ; Systems engineering ; Biochemical engineering ; Materials science ; Engineering ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Marcel F. Langer | J. Thorben Frank | Florian Knoop
Source: The Journal of Chemical Physics (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Stress and heat flux via automatic differentiation Machine-learning potentials provide computationally efficient and accurate approximations of the Born-Oppenheimer potential energy surface. This potential determines many materials properties and simulation techniques usually require its gradients, in particular forces and stress for molecular dynamics, and heat flux for thermal transport properties. Recently developed potentials feature high body order and can include equivariant semi-local interactions through message-passing mechanisms. Due to their complex functional forms, they rely on automatic differentiation (AD), overcoming the need for manual implementations or finite-difference schemes to evaluate gradients. This study discusses how to use AD to efficiently obtain forces, stress, and heat flux for such potentials, and provides a model-independent implementation. The method is tested on the Lennard-Jones potential, and then applied to predict cohesive properties and thermal conductivity of tin selenide using an equivariant message-passing neural network potential. Stress (linguistics) ; Computer science ; Heat flux ; Thermal ; Message passing ; Thermal conductivity ; Artificial neural network ; Feature (linguistics) ; Flux (metallurgy) ; Statistical physics ; Mechanics ; Physics ; Heat transfer ; Artificial intelligence ; Chemistry ; Distributed computing ; Thermodynamics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Thermal properties of materials
Authors: Linshuang Zhang | Manyi Yang | Shiwei Zhang | Haiyang Niu
Source: Journal of Material Science and Technology (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

Unveiling the crystallization mechanism of cadmium selenide via molecular dynamics simulation with machine-learning-based deep potential Crystallization ; Materials science ; Wurtzite crystal structure ; Chemical physics ; Molecular dynamics ; Cadmium selenide ; Crystallography ; Atom (system on chip) ; Crystal (programming language) ; Nanotechnology ; Quantum dot ; Computational chemistry ; Zinc ; Thermodynamics ; Chemistry ; Physics Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films
Authors: Weijie Xie | Weihua Wang | Yanhui Liu
Source: Materials Genome Engineering Advances (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

On the application of high‐throughput experimentation and data‐driven approaches in metallic glasses Abstract Materials genome engineering (MGE) has been successfully applied in various fields, resulting in a series of novel materials with excellent performance. Significant progress has been made in high‐throughput simulation, experimentation, and data‐driven techniques, enabling the effective prediction, rapid synthesis, and characterization of many classes of materials. In this brief review, we introduce the achievements made in the field of metallic glasses (MGs) using MGE, in particular high‐throughput experimentation and data‐driven approaches. High‐throughput experiments help to efficiently synthesize and characterize many materials in a short period of time, enabling the construction of high‐quality material databases for data‐driven methods. Paired with machine learning, potential alloys of desired properties may be revealed and predicted. Along with the progress in computational power and algorithms of machine learning, the complex composition‐structure‐properties relationship is hopefully established, which in turn help efficient and precise prediction of new MGs. Throughput ; Computer science ; Field (mathematics) ; Characterization (materials science) ; Computer engineering ; Machine learning ; Nanotechnology ; Materials science ; Wireless Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys ; Phase-change materials and chalcogenides
Authors: Mark DelloStritto | Aaron D. Kaplan | John P. Perdew | Michael L. Klein
Source: APL Materials (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Predicting the properties of NiO with density functional theory: Impact of exchange and correlation approximations and validation of the r2SCAN functional Transition metal oxide materials are of great utility, with a diversity of topical applications ranging from catalysis to electronic devices. Because of their widespread importance in materials science, there is increasing interest in developing computational tools capable of reliable prediction of transition metal oxide phase behavior and properties. The workhorse of materials theory is density functional theory (DFT). Accordingly, we have investigated the impact of various correlation and exchange approximations on their ability to predict the properties of NiO using DFT. We have chosen NiO as a particularly challenging representative of transition metal oxides in general. In so doing, we have provided validation for the use of the r2SCAN density functional for predicting the materials properties of oxides. r2SCAN yields accurate structural properties of NiO and a local spin moment that notably persists under pressure, consistent with experiment. The outcome of our study is a pragmatic scheme for providing electronic structure data to enable the parameterization of interatomic potentials using state-of-the-art artificial intelligence (AI) and machine learning (ML) methodologies. The latter is essential to allow large scale molecular dynamics simulations of bulk and surface materials phase behavior and properties with ab initio accuracy. Density functional theory ; Materials science ; Non-blocking I/O ; Oxide ; Transition metal ; Electronic structure ; Statistical physics ; Ab initio ; Chemical physics ; Computational chemistry ; Chemistry ; Catalysis ; Physics ; Quantum mechanics ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Transition Metal Oxide Nanomaterials
Authors: Kamal Choudhary | Ramya Gurunathan | Brian DeCost | Adam J. Biacchi
Source: Journal of Chemical Information and Modeling (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

AtomVision: A Machine Vision Library for Atomistic Images Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose AtomVision library that can be used to generate and curate microscopy image (such as scanning tunneling microscopy and scanning transmission electron microscopy) data sets and apply a variety of machine learning techniques. To demonstrate the applicability of this library, we (1) establish an atomistic image data set of about 10 000 materials with large structural and chemical diversity, (2) develop and compare convolutional and atomistic line graph neural network models to classify the Bravais lattices, (3) demonstrate the application of fully convolutional neural networks using U-Net architecture to pixelwise classify atom versus background, (4) use a generative adversarial network for super resolution, (5) curate an image data set on the basis of natural language processing using an open-access arXiv data set, and (6) integrate the computational framework with experimental microscopy images for Rh, Fe<sub>3</sub>O<sub>4</sub>, and SnS systems. The AtomVision library is available at https://github.com/usnistgov/atomvision. Computer science ; Artificial intelligence ; Computer vision ; Computer graphics (images) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Zeyuan Tang | Stefan T. Bromley | Bjørk Hammer
Source: The Journal of Chemical Physics (article, 2023-06-08)
Field: Materials Science — Materials Chemistry

A machine learning potential for simulating infrared spectra of nanosilicate clusters The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires an efficient method for the generation of training data. Here, we apply an accurate and efficient protocol to collect training data for constructing a neural network-based ML interatomic potential for nanosilicate clusters. Initial training data are taken from normal modes and farthest point sampling. Later on, the set of training data is extended via an active learning strategy in which new data are identified by the disagreement between an ensemble of ML models. The whole process is further accelerated by parallel sampling over structures. We use the ML model to run molecular dynamics simulations of nanosilicate clusters with various sizes, from which infrared spectra with anharmonicity included can be extracted. Such spectroscopic data are needed for understanding the properties of silicate dust grains in the interstellar medium and in circumstellar environments. Anharmonicity ; Computer science ; Artificial neural network ; Sampling (signal processing) ; Interatomic potential ; Infrared ; Set (abstract data type) ; Molecular dynamics ; Data set ; Training set ; Data point ; Spectral line ; Statistical physics ; Artificial intelligence ; Computational physics ; Physics ; Computational chemistry ; Chemistry ; Optics ; Computer vision ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Catalysis and Oxidation Reactions
Authors: Jianzhong Wu | Mengyang Gu
Source: The Journal of Physical Chemistry Letters (review, 2023-11-17)
Field: Materials Science — Materials Chemistry

Perfecting Liquid-State Theories with Machine Intelligence Recent years have seen a significant increase in the use of machine intelligence for predicting the electronic structure, molecular force fields, and physicochemical properties of various condensed systems. However, substantial challenges remain in developing a comprehensive framework capable of handling a wide range of atomic compositions and thermodynamic conditions. This perspective discusses potential future developments in liquid-state theories leveraging recent advancements in functional machine learning. By harnessing the strengths of theoretical analysis and machine learning techniques including surrogate models, dimension reduction, and uncertainty quantification, we envision that liquid-state theories will gain significant improvements in accuracy, scalability, and computational efficiency, enabling their broader applications across diverse materials and chemical systems. Scalability ; Computer science ; Artificial intelligence ; Dimension (graph theory) ; State (computer science) ; Machine learning ; Dimensionality reduction ; Perspective (graphical) ; Range (aeronautics) ; Reduction (mathematics) ; Management science ; Data science ; Nanotechnology ; Engineering ; Materials science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Xiaoqin Yan | Hai‐Long Wu | Bin Wang | Tong Wang | Yao Chen | An‐Qi Chen | Kun Huang | Yue‐Yue Chang | Jian Yang | Ru-Qin Yu
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2023-03-11)
Field: Chemistry — Analytical Chemistry

Front-face excitation-emission matrix fluorescence spectroscopy combined with interpretable deep learning for the rapid identification of the storage year of Ningxia wolfberry Interpretability ; Chemistry ; Convolutional neural network ; Artificial intelligence ; Identification (biology) ; Matrix (chemical analysis) ; Fluorescence spectroscopy ; Pattern recognition (psychology) ; Face (sociological concept) ; Partial least squares regression ; Spectroscopy ; Set (abstract data type) ; Fluorescence ; Biological system ; Machine learning ; Computer science ; Chromatography ; Botany ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Muhammad Muqeet | Hammad Malik | Sallahuddin Panhwar | Imran Ullah Khan | Fida Hussain | Zeeshan Asghar | Zeeshan Khatri | Rasool Bux Mahar
Source: International Journal of Biological Macromolecules (article, 2023-03-27)
Field: Materials Science — Biomaterials

Enhanced cellulose nanofiber mechanical stability through ionic crosslinking and interpretation of adsorption data using machine learning Cellulose ; Cationic polymerization ; Adsorption ; Ionic strength ; Nanofiber ; Materials science ; Artificial neural network ; Chemical engineering ; Machine learning ; Computer science ; Biological system ; Chemistry ; Artificial intelligence ; Composite material ; Polymer chemistry ; Organic chemistry Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Supercapacitor Materials and Fabrication
Authors: John L. A. Gardner | Zoé Faure Beaulieu | Volker L. Deringer
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Synthetic data enable experiments in atomistic machine learning We introduce a large “synthetic” dataset of atomistic structures and energies, generated using a fast machine-learning model, and we demonstrate its usefulness for supervised and unsupervised ML tasks in chemistry. Computer science ; Machine learning ; Artificial intelligence ; Data science Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods
Authors: Claudius Zelenka | Marius Kamp | Kolja Strohm | Akram Kadoura | Jacob Johny | Reinhard Koch | Lorenz Kienle
Source: Ultramicroscopy (article, 2023-01-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Automated classification of nanoparticles with various ultrastructures and sizes via deep learning Nanoparticle ; Artificial intelligence ; Segmentation ; Artificial neural network ; Bimetallic strip ; Computer science ; Pattern recognition (psychology) ; Categorization ; Deep learning ; Contextual image classification ; Image segmentation ; Set (abstract data type) ; Data set ; Materials science ; Biological system ; Image (mathematics) ; Nanotechnology Gold and Silver Nanoparticles Synthesis and Applications ; Electron and X-Ray Spectroscopy Techniques ; Laser-Ablation Synthesis of Nanoparticles
Authors: Rangasayee Kannan | Peeyush Nandwana
Source: Scripta Materialia (article, 2023-02-06)
Field: Materials Science — Metals and Alloys

Accelerated alloy discovery using synthetic data generation and data mining Alloy ; Materials science ; Throughput ; CALPHAD ; Coupling (piping) ; Data mining ; Computer science ; Machine learning ; Metallurgy ; Phase (matter) ; Phase diagram Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Machine Learning in Materials Science
Authors: Stephen Goodlett | Justin M. Turney | Henry F. Schaefer
Source: The Journal of Chemical Physics (article, 2023-07-26)
Field: Materials Science — Materials Chemistry

Comparison of multifidelity machine learning models for potential energy surfaces Multifidelity modeling is a technique for fusing the information from two or more datasets into one model. It is particularly advantageous when one dataset contains few accurate results and the other contains many less accurate results. Within the context of modeling potential energy surfaces, the low-fidelity dataset can be made up of a large number of inexpensive energy computations that provide adequate coverage of the N-dimensional space spanned by the molecular internal coordinates. The high-fidelity dataset can provide fewer but more accurate electronic energies for the molecule in question. Here, we compare the performance of several neural network-based approaches to multifidelity modeling. We show that the four methods (dual, Δ-learning, weight transfer, and Meng–Karniadakis neural networks) outperform a traditional implementation of a neural network, given the same amount of training data. We also show that the Δ-learning approach is the most practical and tends to provide the most accurate model. Computer science ; Artificial neural network ; Fidelity ; Artificial intelligence ; Context (archaeology) ; Machine learning ; Energy (signal processing) ; Transfer of learning ; Computation ; Dual (grammatical number) ; Algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics