jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: K. Arunteja | Amudha | Vikraman Rakshagan
Source: 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) (article, 2022-11-12)
Field: Chemistry — Analytical Chemistry

Retracted: Identification of Diseased Apple Fruit using 3D and 2D Convolutional Neural Network for Improving Accuracy To identify the diseased apple fruits using 2D and 3D convolution neural network. Materials and Methods: Kaggle dataset is used to characterize the images, dataset contains 100 images. In training 50 images, (Healthy 25 and Diseased 25) and testing 50 images (Healthy 25 and Diseased 25) are used and the algorithm is iterated 25 times (N) for all samples. The images are tested using two machine learning algorithms 2D Con-volutional Neural Network and novel 3D Convolutional Neural Network. The samples are calculated using Gpower with pretest power of 80confidence interval of 95Novel 3D Convolutional Neural Network achieved classification accuracy 84.64 % and 2D Convolutional neural network achieved accuracy of 70.92 % with 2 tailed significance value of 0.001 (P!0.05). Conclusion: Novel 3D Convolution Neural Network appears to be significantly better than the 2D convolutional neural network in terms of accuracy for identifying diseased fruits. Convolutional neural network ; Convolution (computer science) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Artificial neural network ; Identification (biology) ; Iterated function ; Deep learning ; Mathematics ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Ayana Ghosh | Maxim Ziatdinov | Ondrej Dyck | Bobby G. Sumpter | Sergei V. Kalinin
Source: npj Computational Materials (preprint, 2022-04-20)
Field: Materials Science — Materials Chemistry

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline Abstract Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. The pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems. Python (programming language) ; Workflow ; Bridging (networking) ; Computer science ; Molecular dynamics ; Graphene ; Nanotechnology ; Computational science ; Statistical physics ; Artificial intelligence ; Materials science ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Piriyakorn Piriyakulkit | Siripon Anantawaraskul
Source: Chiang Mai Journal of Science (article, 2022-01-31)
Field: Materials Science — Materials Chemistry

Deconvolution of Microstructural Distributions of Ethylene/1- Butene Copolymer Blends using Artifi cial Neural Network Polymer blending is a useful approach to tailor-make microstructural distributions (e.g., molecular weight distribution (MWD), chemical composition distribution (CCD)) and product properties. A technique to help identify polymer components and their weight fractions in the unknown blends is desirable for the product development. In this work, artifi cial neural network (ANN) models were developed to help identify this information from microstructural distributions and validated with simulated datasets of various binary blends of polyolefi n with different characteristics. The proposed models are multilayer perceptron network with 2 hidden layers; the backpropagation algorithm is used for the network training. Three types of input data were compared: (1) MWD, (2) CCD, and (3) MWD+CCD. Optimum topologies for each types of input data were also determined. Artificial neural network ; Biological system ; Materials science ; Deconvolution ; Polymer ; Backpropagation ; Copolymer ; Multilayer perceptron ; Computer science ; Process engineering ; Algorithm ; Artificial intelligence ; Composite material ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Abhijith Gopakumar | Koushik Pal | Chris Wolverton
Source: npj Computational Materials (preprint, 2022-07-07)
Field: Materials Science — Materials Chemistry

Identification of high-dielectric constant compounds from statistical design Abstract The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 &lt; ϵ &lt; 101) and large band gaps (2.9 &lt; E g (eV) &lt; 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu 5 SiCl 6 O 4 and HoClO) and are shown to be thermodynamically stable against common semiconductors via phase diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20 &lt; ϵ &lt; 40) and band gaps (2.3 &lt; E g (eV) &lt; 2.7). While the ANN training-data are obtained from the Materials Project, the search-space consists of materials from the Open Quantum Materials Database (OQMD)—demonstrating a successful implementation of cross-database materials design. Overall, we report the dielectric properties of 17 materials calculated using ab initio calculations, that were selected in our design workflow. The dielectric materials with high-dielectric properties predicted in this work open up further experimental research opportunities. Dielectric ; Materials science ; Workflow ; Band gap ; High-κ dielectric ; Ab initio ; Engineering physics ; Computer science ; Optoelectronics ; Chemistry ; Physics ; Database ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; History and advancements in chemistry
Authors: Surajit Nandi | Jonas Busk | Peter Bjørn Jørgensen | Tejs Vegge | Arghya Bhowmik
Source: ChemRxiv (preprint, 2022-06-15)
Field: Materials Science — Materials Chemistry

Cheap turns superior: A linear regression-based correction method to reaction energy from DFT Workflows to predict chemical reaction networks based on density functional theory (DFT) are prone to systematic errors in reaction energy due to the extensive use of cheap DFT exchange-correlation functionals to limit computational cost. Recently, machine learning-based models are increasingly applied to mitigate this problem. However, machine learning models require systems similar to trained data, and the models often perform poorly for out-of-distribution systems. Here, we present a simple bond-based correction method that improves the accuracy of DFT-derived reaction energies. It is based on linear regression, and the correction terms for each bond are derived from reactions among the QM9 dataset. We demonstrate the effectiveness of this method with three DFT functionals in three different rungs of Jacob's ladder. The simple correction method is effective for all rung but especially so for the cheapest PBE functional. Finally, we applied the correction method to a few reactions with molecules significantly different from those in the QM9 dataset that was used to fit the linear regression model. Once corrected by this method, we found that the DFT reaction energies for such out-of-distribution reactions are within 0.05eV of the G4MP2 method. Density functional theory ; Linear regression ; Simple (philosophy) ; Computer science ; Regression ; Limit (mathematics) ; Distribution (mathematics) ; Algorithm ; Applied mathematics ; Statistical physics ; Computational chemistry ; Mathematics ; Chemistry ; Machine learning ; Physics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Bioinformatics and Genomic Networks
Authors: Gabriel S. Gusmão | Adhika P. Retnanto | Shashwati C. da Cunha | Andrew J. Medford
Source: Catalysis Today (preprint, 2022-04-08)
Field: Materials Science — Materials Chemistry

Kinetics-informed neural networks Ode ; Ordinary differential equation ; Artificial neural network ; Computer science ; Robustness (evolution) ; Inverse problem ; Surrogate model ; Applied mathematics ; Mathematical optimization ; Chemical kinetics ; Biological system ; Differential equation ; Mathematics ; Artificial intelligence ; Machine learning ; Chemistry ; Kinetics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yuxing Zhou | Stephen R. Elliott | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2022-11-09)
Field: Materials Science — Materials Chemistry

Structure and Bonding in Amorphous Red Phosphorus Amorphous red phosphorus (a-P) is one of the remaining puzzling cases in the structural chemistry of the elements. Here, we elucidate the structure, stability, and chemical bond-ing in a-P from first principles, combining machine-learning and density-functional theo-ry (DFT) methods. We show that a-P structures exist with a range of energies slightly higher than those of phosphorus nanorods, to which they are closely related, and that the stability of a-P is linked to the degree of structural relaxation and medium-range order. We thus complete the stability range of phosphorus allotropes [Angew. Chem. Int. Ed. 2014, 53, 11629] by now including the previously poorly understood amorphous phase, and we quantify the covalent and van der Waals interactions in all main phases of phos-phorus. We also study the electronic densities of states, including those of hydrogenated a-P. Beyond the present study, our structural models are expected to enable wider-ranging first-principles investigations - for example, of a-P-based battery materials. van der Waals force ; Amorphous solid ; Phosphorus ; Structural stability ; Covalent bond ; Chemical physics ; Density functional theory ; Materials science ; Relaxation (psychology) ; Crystallography ; Nanotechnology ; Computational chemistry ; Chemistry ; Molecule ; Organic chemistry ; Biology Machine Learning in Materials Science ; 2D Materials and Applications ; Inorganic Chemistry and Materials
Authors: Rishab Nigam | Vedasri Bai Khavala | K. Dash | Nachiketa Mishra
Source: Emerging Materials Research (article, 2022-12-22)
Field: Materials Science — Materials Chemistry

Image-driven deep-learning-enabled automatic microstructural recognition The deep learning (DL) method consisting of a convolutional neural network (CNN) was employed to automate the task of microstructural recognition and classification to identify dendritic characteristics in metallic microstructures. Dendrites are an important feature that decide the mechanical properties of an alloy; further, the dendritic arm spacing is critical in ascertaining the values of strength and ductility. The current work was divided into two tasks – namely, classification of microstructures into dendritic and non-dendritic microstructures (task 1) and further classification of the dendritic microstructures into longitudinal and transverse cross-sectional views (task 2). The data set comprised micrographs from experimental and online sources covering a broad range of alloy compositions, micrograph magnifications and orientations. The tasks were achieved by employing a four-layered CNN to yield an accuracy of 97.17 ± 0.64% for task 1 and 87.86 ± 1.07% for task 2 independently. The employment of the DL model for classification of microstructures circumvents the feature extraction step while ensuring high accuracy. This work reduces dependency on skilled and experienced researchers and expedites the material development cycle. Convolutional neural network ; Microstructure ; Materials science ; Artificial intelligence ; Pattern recognition (psychology) ; Task (project management) ; Feature (linguistics) ; Ductility (Earth science) ; Deep learning ; Feature extraction ; Computer science ; Artificial neural network ; Composite material ; Creep ; Engineering Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Miroslav Kuchta | Sileshi Gizachew Wubshet | Nils Kristian Afseth | Kent‐André Mardal | Kristian Hovde Liland
Source: Journal of Biophotonics (article, 2022-06-03)
Field: Chemistry — Analytical Chemistry

Encoder–decoder neural networks for predicting future <scp>FTIR</scp> spectra – application to enzymatic protein hydrolysis In the process of converting food-processing by-products to value-added ingredients, fine grained control of the raw materials, enzymes and process conditions ensures the best possible yield and economic return. However, when raw material batches lack good characterization and contain high batch variation, online or at-line monitoring of the enzymatic reactions would be beneficial. We investigate the potential of deep neural networks in predicting the future state of enzymatic hydrolysis as described by Fourier-transform infrared spectra of the hydrolysates. Combined with predictions of average molecular weight, this provides a flexible and transparent tool for process monitoring and control, enabling proactive adaption of process parameters. Hydrolysate ; Artificial neural network ; Enzymatic hydrolysis ; Raw material ; Encoder ; Process engineering ; Hydrolysis ; Biological system ; Computer science ; Process (computing) ; Yield (engineering) ; Fourier transform infrared spectroscopy ; Process control ; Fourier transform ; Chemistry ; Materials science ; Chemical engineering ; Artificial intelligence ; Mathematics ; Engineering ; Biochemistry ; Composite material ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: Marta Pasquini | Marco Stenta
Source: ChemRxiv (preprint, 2022-11-17)
Field: Materials Science — Materials Chemistry

LinChemIn: SynGraph. A data model and a toolkit to analyze and compare synthetic routes Background. The increasing amount of chemical reaction data makes traditional ways to navigate its corpus less effective, while the demand for novel approaches and instruments is rising. Recent data science and machine learning techniques support the development of new ways to extract value from the available reaction data. On the one side, Computer-Aided Synthesis Planning tools can predict synthetic routes in a model-driven approach; on the other side, experimental routes can be extracted from the Network of Organic Chemistry, in which reaction data are linked in a network. In this context, the need to combine, compare and analyze synthetic routes generated by different sources arises naturally. Results. Here we present LinChemIn, a python toolkit that allows chemoinformatics operations on synthetic routes and reaction networks. Wrapping some third-party packages for handling graph arithmetic and chemoinformatics and implementing new data models and functionalities, LinChemIn allows the interconversion between data formats and data models and enables route-level analysis and operations, including route comparison and descriptors calculation. Object-Oriented Design principles inspire the software architecture, and the modules are structured to maximize code reusability and support code testing and refactoring. The code structure should facilitate external contributions, thus encouraging open and collaborative software development. Conclusions. The current version of LinChemIn allows users to combine synthetic routes generated from various tools and analyze them, and constitutes an open and extensible framework capable of incorporating contributions from the community and fostering scientific discussion. Our roadmap envisages the development of sophisticated metrics for routes evaluation, a multi-parameter scoring system, and the implementation of an entire "ecosystem" of functionalities operating on synthetic routes. LinChemIn is freely available at https://github.com/syngenta/linchemin. Computer science ; Cheminformatics ; Python (programming language) ; Software engineering ; Software ; Reusability ; Code refactoring ; Extensibility ; Source code ; Data science ; Data mining ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Bioinformatics and Genomic Networks
Authors: Qiang Bai | Yunrui Duan | Jie Lian | Xiaomin Wang
Source: Frontiers in Chemistry (article, 2022-08-26)
Field: Materials Science — Materials Chemistry

Computation-accelerated discovery of the K2NiF4-type oxyhydrides combing density functional theory and machine learning approach The emerging K<sub>2</sub>NiF<sub>4</sub>-type oxyhydrides with unique hydride ions (H<sup>-</sup>) and O<sup>2-</sup> coexisting in the anion sublattice offer superior functionalities for numerous applications. However, the exploration and innovations of the oxyhydrides are challenged by their rarity as a limited number of compounds reported in experiments, owing to the stringent laboratory conditions. Herein, we employed a suite of computations involving ab initio methods, informatics and machine learning to investigate the stability relationship of the K<sub>2</sub>NiF<sub>4</sub>-type oxyhydrides. The comprehensive stability map of the oxyhydrides chemical space was constructed to identify 76 new compounds with good thermodynamic stabilities using the high-throughput computations. Based on the established database, we reveal geometric constraints and electronegativities of cationic elements as significant factors governing the oxyhydrides stabilities via informatics tools. Besides fixed stoichiometry compounds, mixed-cation oxyhydrides can provide promising properties due to the enhancement of compositional tunability. However, the exploration of the mixed compounds is hindered by their huge quantity and the rarity of stable oxyhydrides. Therefore, we propose a two-step machine learning workflow consisting of a simple transfer learning to discover 114 formable oxyhydrides from thousands of unknown mixed compositions. The predicted high H<sup>-</sup> conductivities of the representative oxyhydrides indicate their suitability as energy conversion materials. Our study provides an insight into the oxyhydrides chemistry which is applicable to other mixed-anion systems, and demonstrates an efficient computational paradigm for other materials design applications, which are challenged by the unavailable and highly unbalanced materials database. Combing ; Density functional theory ; Computation ; Computer science ; Chemistry ; Artificial intelligence ; Nanotechnology ; Materials science ; Computational chemistry ; Algorithm Machine Learning in Materials Science ; Inorganic Fluorides and Related Compounds ; X-ray Diffraction in Crystallography
Authors: Prottay Malakar | Md Shajedul Hoque Thakur | Shahriar Muhammad Nahid | Md Mahbubul Islam
Source: arXiv (Cornell University) (preprint, 2022-08-10)
Field: Materials Science — Materials Chemistry

Data-Driven Machine Learning to Predict Mechanical Properties of Monolayer TMDs The understanding of the material properties of the layered transition metal dichalcogenides (TMDs) is critical for their applications in structural composites. The data-driven machine learning (ML) based approaches are being developed in contrast to traditional experimental or computational approach to predict and understand materials properties under varied operating conditions. In this study, we used two ML algorithms such as Long Short-Term Memory (LSTM) and Feed Forward Neural Network (FFNN) combined with molecular dynamics (MD) simulations to predict the mechanical properties of MX2 (M = Mo, W, and X = S, Se) TMDs. The LSTM model is found to be capable of predicting the entire stress-strain response whereas the FFNN is used to predict the material properties such as fracture stress, fracture strain, and Young's modulus. The effects of operating temperature, chiral orientation, and pre-existing crack size on the mechanical properties are thoroughly investigated. We carried out 1440 MD simulations to produce the input dataset for the neural network models. Our results indicate that both LSTM and FFNN are capable of predicting the mechanical response of monolayer TMDs under different conditions with more than 95% accuracy. The FFNN model exhibits lower computational cost than LSTM; however, the capability of LSTM model to predict the entire stress-strain curve is advantageous to assess material properties. The study paves the pathway toward extending this approach to predict other important properties, such as optical, electrical, and magnetic properties of TMDs. Materials science ; Artificial neural network ; Monolayer ; Fracture (geology) ; Stress (linguistics) ; Computer science ; Shape-memory alloy ; Modulus ; Artificial intelligence ; Machine learning ; Biological system ; Composite material ; Nanotechnology Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Joy Q. Li | Tuan Vo‐Dinh
Source:  (article, 2022-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Analysis of SERS spectra of plasmonic nanoprobes for multiplexed biomarker detection using machine learning Surface-enhanced Raman spectroscopy (SERS) has wide applications in chemical and biosensing as well as imaging. Raman spectra obtained from SERS exhibit characteristic narrow peaks that allow higher degrees of multiplexing than possible with fluorescence imaging. The nanorattle is a bimetallic nanoparticle which can be loaded with different dyes to produce SERS for multiplexed mRNA detection assays and in vivo imaging. But as multiplexing degree increases, so does spectral complexity, making analysis difficult. Machine learning has been applied for SERS-based chemical recognition and quantification. However, multiplexed, assays using SERS labels or imaging using SERS-labeled materials rarely utilize machine learning. Since the spectral shapes of each multiplexed label is known, analysis is easy when multiplexing &lt;4 dyes given the computational tradeoff. Here we demonstrate and compare the use of spectral decomposition, support vector regression, and convolutional neural network (CNN) for “spectral unmixing” of SERS spectra obtained from a highly multiplexed mixture of 7 SERS-active nanorattles. Training data was simulated by combining individual nanorattle spectra by linear scaling and addition. We show that CNN performed the best in determining relative contributions of each distinct dye-loaded nanorattle. Plasmon ; Multiplexing ; Biomarker ; Computer science ; Materials science ; Nanotechnology ; Optoelectronics ; Chemistry ; Telecommunications Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection