jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sangho Lee | Hyunwoo Park | Chihyeon Choi | Wonjoon Kim | Ki Kang Kim | Young‐Kyu Han | Joohoon Kang | Chang‐Jong Kang | Youngdoo Son
Source: Scientific Reports (article, 2023-03-02)
Field: Materials Science — Materials Chemistry

Multi-order graph attention network for water solubility prediction and interpretation The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reducing computational costs. Although machine learning-based methods have made significant advances in predictive performance, the existing methods were still lacking in interpreting the predicted results. Therefore, we propose a novel multi-order graph attention network (MoGAT) for water solubility prediction to improve the predictive performance and interpret the predicted results. We extracted graph embeddings in every node embedding layer to consider the information of diverse neighboring orders and merged them by attention mechanism to generate a final graph embedding. MoGAT can provide the atomic-specific importance scores of a molecule that indicate which atoms significantly influence the prediction so that it can interpret the predicted results chemically. It also improves prediction performance because the graph representations of all neighboring orders, which contain diverse range of information, are employed for the final prediction. Through extensive experiments, we demonstrated that MoGAT showed better performance than the state-of-the-art methods, and the predicted results were consistent with well-known chemical knowledge. Computer science ; Embedding ; Graph ; Solubility ; Machine learning ; Node (physics) ; Artificial intelligence ; Data mining ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Miu Lun Lau | Abraham Burleigh | Jeff Terry | Min Long
Source: Journal of Vacuum Science & Technology A Vacuum Surfaces and Films (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Materials characterization: Can artificial intelligence be used to address reproducibility challenges? Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace. Characterization (materials science) ; Computer science ; Workflow ; Data science ; Artificial intelligence ; Representation (politics) ; Interface (matter) ; Identification (biology) ; Machine learning ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides
Authors: Rui Li | Aleksei Makogon | Tatiana Galochkina | Jean‐François Lemineur | Frédéric Kanoufi | Viacheslav Shkirskiy
Source: Small Methods (article, 2023-06-29)
Field: Materials Science — Materials Chemistry

Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy Abstract Operando wide‐field optical microscopy imaging yields a wealth of information about the reactivity of metal interfaces, yet the data are often unstructured and challenging to process. In this study, the power of unsupervised machine learning (ML) algorithms is harnessed to analyze chemical reactivity images obtained dynamically by reflectivity microscopy in combination with ex situ scanning electron microscopy to identify and cluster the chemical reactivity of particles in Al alloy. The ML analysis uncovers three distinct clusters of reactivity from unlabeled datasets. A detailed examination of representative reactivity patterns confirms the chemical communication of generated OH − fluxes within particles, as supported by statistical analysis of size distribution and finite element modelling (FEM). The ML procedures also reveal statistically significant patterns of reactivity under dynamic conditions, such as pH acidification. The results align well with a numerical model of chemical communication, underscoring the synergy between data‐driven ML and physics‐driven FEM approaches. Reactivity (psychology) ; Cluster (spacecraft) ; Finite element method ; Microscopy ; Biological system ; Materials science ; Optical microscope ; Computer science ; Chemistry ; Scanning electron microscope ; Optics ; Physics ; Biology Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Force Microscopy Techniques and Applications
Authors: Pandu Wisesa | Christopher M. Andolina | Wissam A. Saidi
Source: The Journal of Physical Chemistry Letters (article, 2023-01-09)
Field: Materials Science — Materials Chemistry

Development and Validation of Versatile Deep Atomistic Potentials for Metal Oxides Machine learning interatomic potentials powered by neural networks have been shown to readily model a gradient of compositions in metallic systems. However, their application to date on ionic systems tends to focus on specific compositions and oxidation states owing to their more heterogeneous chemical nature. Herein we show that a deep neural network potential (DNP) can model various properties of metal oxides with different oxidation states without additional charge information. We created and validated DNPs for Ag<sub><i>x</i></sub>O<sub><i>y</i></sub>, Cu<sub><i>x</i></sub>O<sub><i>y</i></sub> Mg<sub><i>x</i></sub>O<sub><i>y</i></sub>, Pt<sub><i>x</i></sub>O<sub><i>y</i></sub>, and Zn<sub><i>x</i></sub>O<sub><i>y</i></sub>, whereby each system was trained without any limitations on oxidation states. We illustrate how the database can be augmented to enhance the DNP transferability for a new polymorph, surface energies, and thermal expansion. In addition, we show that these potentials can correctly interpolate significant pressure and temperature ranges, exhibit stability over long molecular dynamics simulation time scales, and replicate nonharmonic thermal expansion, consistent with experimental results. Transferability ; Ionic bonding ; Metal ; Chemical physics ; Materials science ; Thermal stability ; Thermal oxidation ; Artificial neural network ; Focus (optics) ; Stability (learning theory) ; Thermal expansion ; Interatomic potential ; Thermal ; Molecular dynamics ; Computer science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Computational chemistry ; Thermodynamics ; Machine learning ; Physics ; Ion ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Yujie Qian | Jiang Guo | Zhengkai Tu | Connor W. Coley | Regina Barzilay
Source: Journal of Chemical Information and Modeling (article, 2023-06-27)
Field: Materials Science — Materials Chemistry

RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing Reaction diagram parsing is the task of extracting reaction schemes from a diagram in the chemistry literature. The reaction diagrams can be arbitrarily complex; thus, robustly parsing them into structured data is an open challenge. In this paper, we present RxnScribe, a machine learning model for parsing reaction diagrams of varying styles. We formulate this structured prediction task with a sequence generation approach, which condenses the traditional pipeline into an end-to-end model. We train RxnScribe on a dataset of 1378 diagrams and evaluate it with cross validation, achieving an 80.0% soft match F1 score, with significant improvements over previous models. Our code and data are publicly available at https://github.com/thomas0809/RxnScribe. Parsing ; Computer science ; Pipeline (software) ; Task (project management) ; Diagram ; Sequence (biology) ; Sequence diagram ; Code (set theory) ; Artificial intelligence ; Natural language processing ; Programming language ; Database ; Set (abstract data type) ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Hamed Esmaeili | Reza Rizvi
Source: Computational Materials Science (article, 2023-08-12)
Field: Materials Science — Materials Chemistry

An accelerated strategy to characterize mechanical properties of polymer composites using the ensemble learning approach Artificial neural network ; Mean squared error ; Computer science ; Polycarbonate ; Artificial intelligence ; Machine learning ; Materials science ; Characterization (materials science) ; Property (philosophy) ; Test data ; Polyethylene terephthalate ; Composite material ; Mathematics ; Statistics ; Nanotechnology Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Software Engineering Research
Authors: Francesco Bosia | Peikun Zheng | Alain C. Vaucher | Thomas Weymuth | Pavlo O. Dral | Markus Reiher
Source: The Journal of Chemical Physics (article, 2023-01-16)
Field: Materials Science — Materials Chemistry

Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with <scp>Sparrow</scp> Semi-empirical quantum chemical approaches are known to compromise accuracy for the feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screening, and data-driven machine learning has shifted the emphasis toward calculation runtimes recently. This comes with new constraints for the software implementation as many fast calculations would suffer from a large overhead of the manual setup and other procedures that are comparatively fast when studying a single molecular structure, but which become prohibitively slow for high-throughput demands. In this work, we discuss the effect of various well-established semi-empirical approximations on calculation speed and relate this to data transfer rates from the raw-data source computer to the results of the visualization front end. For the former, we consider desktop computers, local high performance computing, and remote cloud services in order to elucidate the effect on interactive calculations, for web and cloud interfaces in local applications, and in world-wide interactive virtual sessions. The models discussed in this work have been implemented into our open-source software SCINE Sparrow. Computer science ; Cloud computing ; Throughput ; Software ; Computational science ; Overhead (engineering) ; Quantum ; Quantum computer ; Distributed computing ; Computer engineering ; Operating system ; Physics Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Pavel Kim | Nikita Serov | Aleksandra S. Falchevskaya | И. Д. Шабалкин | Andrei Dmitrenko | Daniil V. Kladko | Vladimir V. Vinogradov
Source: Small (article, 2023-08-10)
Field: Materials Science — Materials Chemistry

Quantifying the Efficacy of Magnetic Nanoparticles for MRI and Hyperthermia Applications via Machine Learning Methods Magnetic nanoparticles are a prospective class of materials for use in biomedicine as agents for magnetic resonance imagining (MRI) and hyperthermia treatment. However, synthesis of nanoparticles with high efficacy is resource-intensive experimental work. In turn, the use of machine learning (ML) methods is becoming useful in materials design and serves as a great approach to designing nanomagnets for biomedicine. In this work, for the first time, an ML-based approach is developed for the prediction of main parameters of material efficacy, i.e., specific absorption rate (SAR) for hyperthermia and r<sub>1</sub> /r<sub>2</sub> relaxivities in MRI, with parameters of nanoparticles as well as experimental conditions as descriptors. For that, a unique database with more than 980 magnetic nanoparticles collected from scientific articles is assembled. Using this data, several tree-based ensemble models are trained to predict SAR, r<sub>1</sub> and r<sub>2</sub> relaxivity. After hyperparameter optimization, models reach performances of R<sup>2</sup> = 0.86, R<sup>2</sup> = 0.78, and R<sup>2</sup> = 0.75, respectively. Testing the models on samples unseen during the training shows no performance drops. Finally, DiMag, an open access resource created to guide synthesis of novel nanosized magnets for MRI and hyperthermia treatment with machine learning and boost development of new biomedical agents, is developed. Materials science ; Nanoparticle ; Magnetic nanoparticles ; Machine learning ; Magnetic hyperthermia ; Artificial intelligence ; Computer science ; Nanotechnology ; Magnetic resonance imaging ; Hyperthermia Treatment ; Specific absorption rate ; Hyperparameter ; Biomedicine ; Nanomagnet ; Hyperthermia ; Magnetization ; Bioinformatics ; Magnetic field ; Physics ; Medicine ; Radiology Machine Learning in Materials Science ; Characterization and Applications of Magnetic Nanoparticles ; Nanoparticle-Based Drug Delivery
Authors: Eyüp Bilgi | Ceyda Öksel Karakuş
Source: Journal of Nanoparticle Research (article, 2023-07-18)
Field: Materials Science — Materials Chemistry

Machine learning-assisted prediction of the toxicity of silver nanoparticles: a meta-analysis Nanotoxicology ; Random forest ; Machine learning ; Generalizability theory ; Artificial intelligence ; Silver nanoparticle ; Decision tree ; Computer science ; Artificial neural network ; Materials science ; Naive Bayes classifier ; Zeta potential ; Nanotechnology ; Nanoparticle ; Biochemical engineering ; Support vector machine ; Statistics ; Mathematics Nanoparticles: synthesis and applications
Authors: A. Zuranski | Shivaani S. Gandhi | Abigail G. Doyle
Source: Journal of the American Chemical Society (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach to Model Interaction Effects: Development and Application to Alcohol Deoxyfluorination The application of machine learning (ML) techniques to model high-throughput experimentation (HTE) datasets has seen a recent rise in popularity. Nevertheless, the ability to model the interplay between reaction components, known as interaction effects, with ML remains an outstanding challenge. Using a simulated HTE dataset, we find that the presence of irrelevant features poses a strong obstacle to learning interaction effects with common ML algorithms. To address this problem, we propose a two-part statistical modeling approach for HTE datasets: classical analysis of variance of the experiment to identify systematic effects that impact reaction yield across the experiment followed by regression of individual effects using chemistry-informed features. To illustrate this methodology, we use our previously published alcohol deoxyfluorination dataset comprising 740 reactions to build a compact, interpretable generalized additive model that accounts for each significant effect observed in the dataset. We achieve a sizeable performance boost compared to our previously published random forest model, reducing mean absolute error from 18 to 13% and root-mean-squared error from 22 to 17% on a newly generated validation set. Finally, we demonstrate that this approach can facilitate the generation of new mechanistic hypotheses, which, when probed experimentally, can lead to a deeper understanding of chemical reactivity. Machine learning ; Random forest ; Mean squared error ; Set (abstract data type) ; Artificial intelligence ; Variance (accounting) ; Regression ; Popularity ; Interaction ; Chemistry ; Computer science ; Statistics ; Mathematics ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Artem Fediai | Patrick Reiser | Jorge Enrique Olivares Peña | Pascal Friederich | Wolfgang Wenzel
Source: Scientific Data (article, 2023-09-05)
Field: Materials Science — Materials Chemistry

Accurate GW frontier orbital energies of 134 kilo molecules HOMO and LUMO energies are critical molecular properties that typically require high accuracy computations for practical applicability. Until now, a comprehensive dataset containing sufficiently accurate HOMO and LUMO energies has been unavailable. In this study, we introduce a new dataset of HOMO/LUMO energies for QM9 compounds, calculated using the GW method. The GW method offers adequate HOMO/LUMO prediction accuracy for diverse applications, exhibiting mean unsigned errors of 100 meV in the GW100 benchmark dataset. This database may serve as a benchmark of HOMO/LUMO prediction, delta-learning, and transfer learning, particularly for larger molecules where GW is the most accurate but still numerically feasible method. We anticipate that this dataset will enable the development of more accurate machine learning models for predicting molecular properties. HOMO/LUMO ; Benchmark (surveying) ; Computation ; Molecule ; Computer science ; Physics ; Algorithm ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Elisa Roldán | Neil D. Reeves | Glen Cooper | Kirstie Andrews
Source: Frontiers in Physics (article, 2023-02-07)
Field: Materials Science — Biomaterials

Towards the ideal vascular implant: Use of machine learning and statistical approaches to optimise manufacturing parameters Introduction: Electrospinning is a manufacturing technique that creates a net of nano and microfibres able to mimic the natural extracellular matrix (ECM) of biological tissue. Electrospun scaffolds' morphology and mechanical behaviour can be tailored by modifying the environmental, solution and process parameters. This study aims to produce biomimetic vascular implants optimising the manufacturing set up through two machine learning techniques and statistical approaches. Methods: Polyvinyl alcohol (PVA) based scaffolds were produced by modifying the concentration of the polymer, flow rate, voltage, type of collector, diameter of the needle, distance between needle and collector and revolutions of the mandrel. The scaffolds were morphologically and mechanically characterised using scanning electron microscopy and mechanical testing respectively to inform the morphological model (simultaneously predicting diameter of the fibres and inter-fibre separation) and mechanical model (predicting strain at rupture and ultimate tensile strength). Results: Prediction and traditional techniques led to an optimum set up of: 12% PVA, 1 ml/h flow rate, 20 kV, 8 cm between the needle, 18 G gauge needle, rotational mandrel of 15 cm and 2000 rpm. Optimised PVA scaffolds replicated the mechanical properties and morphology of the vascular tissue with an ultimate tensile strength of 6.17 ± 0.18 MPa, a strain at break of 97.39 ± 5.06, fibre diameters of 126 ± 6.11 nm and inter-fibre separation of 1488 ± 91.99 nm. Discussion: This work revealed for the first time that machine learning Chi-squared Automatic Interaction Detection (CHAID) models are a novel and visual route to elect the optimum manufacturing set up to develop biomimetic vascular implants. Novel two-output Artificial Neural Networks (ANN) and multivariate analysis of variance and covariance (MANOVA, MANCOVA) models presented comparable prediction results (R2=0.91); however, two-output ANN predicted models demonstrated to be the most powerful tool for non-parametric conditions, showing cross-validation mean squared errors (MSE) of 0.0001943. Multi Linear Regression models (MLR) exhibited the lowest accuracy in their predictions (R2=0.6). Machine learning, statistical approaches and traditional characterisation methods were studied to successfully achieve vascular substitutes with analogous biomechanical behaviour and physical structure to the native vascular tissue. Mandrel ; Materials science ; Ultimate tensile strength ; Electrospinning ; Biomedical engineering ; Composite material ; Polyvinyl alcohol ; Universal testing machine ; Polymer ; Engineering Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Cardiac and Coronary Surgery Techniques