jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ramil Nugmanov | Natalia Dyubankova | Andrey Gedich | Jörg K. Wegner
Source: ChemRxiv (preprint, 2022-03-21)
Field: Materials Science — Materials Chemistry

Bidirectional Graphormer for Reactivity Understanding: neural network trained to reaction atom-to-atom mapping task This work introduces GraphormerMapper – a new algorithm for reactions atom-to-atom mapping (AAM) based on a distance-aware BERT neural network. In benchmarking studies with IBM RxnMapper, the best AAM algorithm according to our previous study, we demonstrate that our AAM algorithm is superior on our “Golden” benchmarking dataset. The mapper is implemented in Chython [https://github.com/chython/chython] and Chytorch [https://github.com/chython/chytorch, https://github.com/chython/chytorch-rxnmap] Python packages which are freely available for out-the-box use. Chython is a cheminformatics library with a simple interface for processing reaction and molecular data. The key features of Chython are: chemical functional groups standardization, checking atom valence errors, substructure search, and advanced reaction manipulation, for example, generating products from reactants and reaction atom-to-atom mapping. Chytorch provides a PyTorch-like interface for graph-based neural networks developed specifically for chemical tasks. Computer science ; Benchmarking ; Python (programming language) ; Atom (system on chip) ; Standardization ; Artificial neural network ; Cheminformatics ; Artificial intelligence ; Preprocessor ; Computational science ; Programming language ; Parallel computing ; Chemistry ; Operating system ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Wei Zhu | Andrew Dickson White | Jiebo Luo
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Federated Learning of Molecular Properties With Graph Neural Networks in a Heterogeneous Setting Computer science ; Artificial neural network ; Artificial intelligence ; Graph ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Chunghee Nam
Source: Korean Journal of Metals and Materials (article, 2022-07-20)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Prediction of Superhard Materials Based on Compositional Features In this study, the mechanical properties of materials were predicted using machine learning to search for superhard materials. Based on an AFOW database consisting of DFT quantum calculation values, the mechanical properties of materials were predicted using various machine learning models. For supervised learning, the entire data was divided into training data and test data at a ratio of 8:2. Since the discovery of superhard materials can be predicted based on the bulk modulus and shear modulus, the bulk modulus was primarily predicted using only the chemical compositional ratio (chemical formula), and then the shear modulus was obtained using the predicted bulk moduli. To obtain good prediction performance, cross-validation and hyper-parameter tuning were carried out. Each characteristic was predicted using XGBoost, one of the ensemble algorithms, and its performance was compared to the treebased machine learning of RandomForest and Support Vector Machine regression using the coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) and root-mean-square-error (RMSE) as metrics. For the recently introduced four superhard materials (Mo0.9W1.1BC, ReWC0.8, MoWC2, and ReWB), the results of this study were similar to those of previous studies including the experimental values or the DFT quantum calculations. The shear modulus was underpredicted, which can be understood since structural properties were not considering as a feature in our machine learning models. Materials science ; Shear modulus ; Mean squared error ; Support vector machine ; Machine learning ; Bulk modulus ; Elastic modulus ; Modulus ; Moduli ; Artificial intelligence ; Algorithm ; Computer science ; Mathematics ; Composite material ; Statistics ; Physics Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; X-ray Diffraction in Crystallography
Authors: Takamitsu Ishiyama | Toshifumi Imajo | Takashi Suemasu | Kaoru Toko
Source: Science and Technology of Advanced Materials Methods (article, 2022-05-27)
Field: Materials Science — Materials Chemistry

Machine learning of fake micrographs for automated analysis of crystal growth process Material informatics is being applied to crystal engineering, which is a core technology in electronics. Micrographs particularly provide important insights; however, they have not benefited significantly from material informatics because of the efforts required to acquire huge numbers of data. Herein, we propose a fast and automated analysis technique for micrographs showing the crystallization process of semiconductor thin films. We automatically generated fake micrographs and trained the crystal domain recognition capability on 10 different machine learning models. Experimentally obtained micrographs were analyzed using the developed model, which correctly determined the domain size and nuclei density. The activation energies required for growth and nucleation were determined from the lateral growth velocity and nucleation frequency, the variations of which were smaller than those measured by humans. Therefore, the proposed analysis framework not only reduces the time required to derive the crystal growth properties, but also enables a high accuracy without human subjectivity. Nucleation ; Process (computing) ; Micrograph ; Crystal (programming language) ; Crystallization ; Domain (mathematical analysis) ; Crystal growth ; Artificial intelligence ; Materials science ; Computer science ; Electronics ; Crystallography ; Nanotechnology ; Chemistry ; Mathematics ; Engineering ; Electrical engineering ; Composite material ; Chemical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Industrial Vision Systems and Defect Detection
Authors: Trupti Mohanty | K.S. Ravi Chandran | Taylor D. Sparks
Source: ChemRxiv (preprint, 2022-09-15)
Field: Materials Science — Materials Chemistry

Machine learning guided optimal composition selection of niobium alloys for high temperature applications Nickel and Cobalt based superalloys are commonly used as turbine materials for hightemperature applications. However, their maximum operating temperature is limited to about 1100 o C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure-property data of limited alloys and extrapolates this information into the unexplored compositional space. In the present work, we harness machine learning and provide a design strategy for finding an Nb-based alloy composition with optimized yield strength and ultimate tensile strength at high temperatures. We use a Bayesian optimization algorithm combined with domain knowledgebased material descriptors to find an optimal Nb-based quaternary and quinary alloy composition for the targeted value of mechanical strengths. Furthermore, we extend our study to multi-objective optimization to suggest an optimal alloy candidate by integrating yield strength and ultimate tensile strength into a single composite property. We developed a detailed design flow and python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime. Alloy ; Ultimate tensile strength ; Materials science ; Superalloy ; Bayesian optimization ; Python (programming language) ; Mechanical engineering ; Computer science ; Process engineering ; Composite material ; Machine learning ; Engineering Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Advanced Materials Characterization Techniques
Authors: Yuanbin Liu | Weixiang Hong | Bing Cao
Source: Advanced Theory and Simulations (article, 2022-03-16)
Field: Materials Science — Materials Chemistry

MolNet‐3D: Deep Learning of Molecular Representations and Properties from 3D Topography Abstract A new paradigm combining quantum‐mechanical calculations with machine learning (ML) to rationally design compounds with specific properties from extremely large chemical space is emerging and developing at a rapid pace. In this context, appropriately describing molecules and efficiently extracting patterns from electronic‐structure calculations are the core challenges for the success of the quantum‐mechanics‐based ML approaches. Here, MolNet‐3D is introduced, a strong deep learning architecture capable of mapping from a flexible and universal 3D topography descriptor to quantum‐mechanical observables of molecules of arbitrary shape. The model can learn an invariant representation without the need for the transformation of atom coordinates into interatomic distances, thus preserving the intrinsic 3D topography information of molecules. The capabilities of MolNet‐3D are shown by accurately predicting the various density functional theory calculated properties for molecules, including energetic, electronic, and thermodynamic properties. Compared with the previously proposed ML methods in the MoleculeNet benchmarks, our model generally offers the best performance in those quantum‐mechanical tasks, elucidating the importance of intrinsic topography information in molecular representation learning. This work may provide new insight into the construction of molecular ML models from 3D topography recognition perspectives. Computer science ; Representation (politics) ; Quantum ; Context (archaeology) ; Invariant (physics) ; Observable ; Artificial intelligence ; Density functional theory ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Zigeng Wang | Xiao Xia | Syam P. Nukavarapu | Sangamesh G. Kumbar | Sanguthevar Rajasekaran
Source: EPiC series in computing (article, 2022-03-22)
Field: Materials Science — Materials Chemistry

Machine Learning Techniques in Structure-Property Optimization of Polymeric Scaffolds for Tissue Engineering Biomaterials and biomedical implants have revolutionized the way medicine is practiced. Technologies, such as 3D printing and electrospinning, are currently employed to create novel biomaterials. Most of the synthesis techniques are ad-hoc, time taking, and expensive. These shortcomings can be overcome greatly with the employment of computational techniques. In this paper we consider the problem of bone tissue engineering as an example and show the potentials of machine learning approaches in biomaterial construction, in which different models was built to predict the elastic modulus of the scaffold at given an arbitrary material composition. Likewise, the methodology was extended to cell-material interaction and prediction at an arbitrary process parameter. Biomaterial ; Computer science ; Property (philosophy) ; Tissue engineering ; Scaffold ; Process (computing) ; Mechatronics ; Electrospinning ; Materials science ; Mechanical engineering ; Biomedical engineering ; Nanotechnology ; Engineering ; Artificial intelligence ; Polymer ; Composite material Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: M. V. Jyothirmai | D. Roshini | B. Moses Abraham | Jayant K. Singh
Source: arXiv (Cornell University) (preprint, 2022-11-03)
Field: Materials Science — Materials Chemistry

Accelerating the Discovery of g-C$_3$N$_4$-Supported Single Atom Catalysts for Hydrogen Evolution Reaction: A Combined DFT and Machine Learning Strategy Two-dimensional materials supported by single atom catalysis (SACs) are foreseen to replace platinum for large-scale industrial scalability of sustainable hydrogen generation. Here, a series of metal (Al, Sc, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn) and non-metal (B, C, N, O, F, Si, P, S, Cl) single atoms embedded on various active sites of g-C$_3$N$_4$ are screened by DFT calculations and six machine learning (ML) algorithms (support vector regression, gradient boosting regression, random forest regression, AdaBoost regression, multilayer perceptron regression, ridge regression). Our results based on formation energy, Gibbs free energy and bandgap analysis demonstrate that the single atoms of B, Mn and Co anchored on g-C$_3$N$_4$ can serve as highly efficient active sites for hydrogen production. The ML model based on support vector regression (SVR) exhibits the best performance to accurately and rapidly predict the Gibbs free energy of hydrogen adsorption ($Δ$GH ) for the test set with a lower mean absolute error (MAE) and a high coefficient of determination (R$^2$) of 0.45 and 0.81, respectively. Feature selection based on the SVR model highlights the top five primary features: formation energy, bond length, boiling point, melting point, and valance electron as key descriptors. Overall, the multistep work-flow employed through DFT calculations combined with ML models for efficient screening of potential hydrogen evolution reaction (HER) from g-C$_3$N$_4$-based single atom catalysis can significantly contribute to the catalyst design and fabrication. Gibbs free energy ; Support vector machine ; Catalysis ; Hydrogen production ; Atom (system on chip) ; Chemistry ; Machine learning ; Materials science ; Thermodynamics ; Computer science ; Physics ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Electrocatalysts for Energy Conversion
Authors: Kamal Choudhary | Bobby G. Sumpter
Source: arXiv (Cornell University) (preprint, 2022-05-17)
Field: Materials Science — Materials Chemistry

A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials The presence of point defects such as vacancies plays an important role in material design. Here, we demonstrate that a graph neural network (GNN) model trained only on perfect materials can also be used to predict vacancy formation energies ($E_{vac}$) of defect structures without the need for additional training data. Such GNN-based predictions are considerably faster than density functional theory (DFT) calculations with reasonable accuracy and show the potential that GNNs are able to capture a functional form for energy predictions. To test this strategy, we developed a DFT dataset of 508 $E_{vac}$ consisting of 3D elemental solids, alloys, oxides, nitrides, and 2D monolayer materials. We analyzed and discussed the applicability of such direct and fast predictions. We applied the model to predict 192494 $E_{vac}$ for 55723 materials in the JARVIS-DFT database. Vacancy defect ; Density functional theory ; Materials science ; Point (geometry) ; Computer science ; Artificial neural network ; Algorithm ; Statistical physics ; Artificial intelligence ; Computational chemistry ; Chemistry ; Condensed matter physics ; Physics ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Rong Zhang | Tiantian Hao | Shihui Hu | Kaiyang Wang | Shuhui Ren | Ziwei Tian | Yunfang Jia
Source: Sensors (article, 2022-12-29)
Field: Materials Science — Materials Chemistry

Electrolyte-Gated Graphene Field Effect Transistor-Based Ca2+ Detection Aided by Machine Learning Flexible electrolyte-gated graphene field effect transistors (Eg-GFETs) are widely developed as sensors because of fast response, versatility and low-cost. However, their sensitivities and responding ranges are often altered by different gate voltages. These bias-voltage-induced uncertainties are an obstacle in the development of Eg-GFETs. To shield from this risk, a machine-learning-algorithm-based LgGFETs' data analyzing method is studied in this work by using Ca<sup>2+</sup> detection as a proof-of-concept. For the as-prepared Eg-GFET-Ca<sup>2+</sup> sensors, their transfer and output features are first measured. Then, eight regression models are trained with the use of different machine learning algorithms, including linear regression, support vector machine, decision tree and random forest, etc. Then, the optimized model is obtained with the random-forest-method-treated transfer curves. Finally, the proposed method is applied to determine Ca<sup>2+</sup> concentration in a calibration-free way, and it is found that the relation between the estimated and real Ca<sup>2+</sup> concentrations is close-to y = x. Accordingly, we think the proposed method may not only provide an accurate result but also simplify the traditional calibration step in using Eg-GFET sensors. Calibration ; Support vector machine ; Random forest ; Decision tree ; Machine learning ; Artificial intelligence ; Computer science ; Algorithm ; Voltage ; Linear regression ; Transistor ; Calibration curve ; Field (mathematics) ; Graphene ; Regression ; Electronic engineering ; Materials science ; Nanotechnology ; Mathematics ; Electrical engineering ; Engineering ; Detection limit ; Statistics Graphene research and applications ; Advancements in Battery Materials ; Electrochemical sensors and biosensors
Authors: Deborah A. Penchoff | Charles C. Peterson | Eleigha M. Wrancher | George Bosilca | Robert J. Harrison | Edward F. Valeev | Paul D. Benny
Source: Journal of Radioanalytical and Nuclear Chemistry (article, 2022-12-01)
Field: Chemistry — Inorganic Chemistry

Evaluations of molecular modeling and machine learning for predictive capabilities in binding of lanthanum and actinium with carboxylic acids Benchmark (surveying) ; Lanthanum ; Actinide ; Chemistry ; Computer science ; Computational model ; Computational chemistry ; Artificial intelligence ; Organic chemistry Radioactive element chemistry and processing ; Nuclear Materials and Properties ; Thermal and Kinetic Analysis
Authors: Takeru Miyagawa | Yugo Sakai | Kazuki Mori | Nobuhiko Kato | Akio Yonezu | Keiji Ishibashi
Source: Materials Today Communications (article, 2022-10-21)
Field: Materials Science — Materials Chemistry

Distribution of the mechanical properties of Ti–Cu combinatorial thin film evaluated using nanoindentation experiments and molecular dynamics with a neural network potential Materials science ; Nanoindentation ; Thin film ; Molecular dynamics ; Interatomic potential ; Alloy ; Titanium ; Nanotechnology ; Composite material ; Computational chemistry ; Metallurgy Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics
Authors: Na Gong | Yong Wang | R.D.K. Misra | Hongfei Liu
Source: Materialia (article, 2022-11-17)
Field: Materials Science — Materials Chemistry

Machine learning guided understanding of microstructural entropy decay in bimodal grain-size distributed alloys with combination of high-strength and high-ductility Materials science ; High entropy alloys ; Grain size ; Microstructure ; Ductility (Earth science) ; Grain growth ; Grain boundary ; Alloy ; Metallurgy ; Creep Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and mechanical properties
Authors: Kaifeng Chen | Kunrong Zeng
Source: Mobile Information Systems (article, 2022-09-10)
Field: Materials Science — Materials Chemistry

Performance Optimization Model of Molecular Dynamics Simulation Based on Machine Learning and Data Mining Algorithm Molecular dynamics simulation is an effective method for related research in the microscopic world, mainly related to molecular atomic research, and it has a wide range of applications in physics, medicine, and other disciplines. Meanwhile, with the expansion of data to be studied, the performance of molecular dynamics simulation in terms of calculation speed and other aspects can no longer fully meet the needs of today’s data analysis and calculation. On the basis of the original method, parallel calculation and optimization improvement of the method have become the focus of researchers. This paper will use machine learning (ML) and data mining algorithms to optimize the performance of molecular dynamics simulation and use Restricted Boltzmann Machines (RBM) and K-Nearest Neighbors (KNN) in the molecular dynamics simulation system. Through the parallel optimization experiment of KNN, the effectiveness of KNN optimization is obtained, and finally the molecular dynamics simulation optimization experiment is designed. Through comparative analysis with the simulation system before optimization, it is concluded that when the number of particles is 4096, the efficiency ratio of force calculation running time is the highest of 31.15%. When the number of particles is 512, the running time efficiency ratio of the motion trajectory equation is up to 30.28%. When the number of particles is 256, the efficiency ratio of running time tending to balance judgment is the highest of 36.96%. All the results show that the performance of the optimized simulation system has been improved. The experimental results are in line with expectations. Computer science ; Molecular dynamics ; Range (aeronautics) ; Focus (optics) ; Trajectory ; Dynamics (music) ; k-nearest neighbors algorithm ; Experimental data ; Algorithm ; Artificial intelligence ; Mathematics ; Materials science Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research
Authors: Yixuan Liu | Jie Li | Hongfei Ji | Jie Zhuang
Source: Frontiers in Neuroscience (article, 2022-05-03)
Field: Materials Science — Materials Chemistry

Comparisons of Glutamate in the Brains of Alzheimer’s Disease Mice Under Chemical Exchange Saturation Transfer Imaging Based on Machine Learning Analysis Chemical exchange saturation transfer (CEST) is one of the molecular magnetic resonance imaging (MRI) techniques that indirectly measures low-concentration metabolite or free protein signals that are difficult to detect by conventional MRI techniques. We applied CEST to Alzheimer's disease (AD) and analyzed both region of interest (ROI) and pixel dimensions. Through the analysis of the ROI dimension, we found that the content of glutamate in the brains of AD mice was higher than that of normal mice of the same age. In the pixel-dimensional analysis, we obtained a map of the distribution of glutamate in the mouse brain. According to the experimental data of this study, we designed an algorithm framework based on data migration and used Resnet neural network to classify the glutamate distribution images of AD mice, with an accuracy rate of 75.6%. We evaluate the possibility of glutamate imaging as a biomarker for AD detection for the first time, with important implications for the detection and treatment of AD. Glutamate receptor ; Magnetic resonance imaging ; Neuroscience ; Nuclear magnetic resonance ; Chemistry ; Biology ; Biochemistry ; Medicine ; Physics ; Radiology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Molecular Sensors and Ion Detection
Authors: Anoop Kini | Amit Kumar Choudhary | Dominic Hohs | Andreas Jansche | Hermann Baumgartl | Ricardo Büttner | Timo Bernthaler | D. Goll | Gerhard Schneider
Source: Chemical Physics Letters (article, 2022-11-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-based mass density model for hard magnetic 14:2:1 phases using chemical composition-based features Magnet ; Chemical composition ; Magnetization ; Lattice (music) ; Composition (language) ; Materials science ; Condensed matter physics ; Chemistry ; Thermodynamics ; Physics ; Magnetic field Magnetic Properties of Alloys ; Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials