jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chathurappan Raja | Vinod Kumar | Chinnakannu Jayakumar
Source: Indian Journal of Pharmaceutical Education and Research (article, 2021-03-19)
Field: Chemistry — Spectroscopy

Investigation of Solubility of Mebendazole Drug using Linear Prediction and Multilayer Feed Forward Neural Network Objectives: A systematic in-vitro study has been achieved to decide the stability of a selection of three presidential hydrotropes to decorate the obvious aqueous solubility of the sparingly water-soluble drug, mebendazole drug. This study is that the ANN model to be prediction the solubility of the mebendazole drug among the chemical substances. Methodology: These experimental data, together with a selection of recounted and estimate physico-chemical consequences of the hydrotropes are after that utilizing insilico to set up a counterfeit neural system (ANN) to engage for desires mebendazole drug solubilization. These trial information, along with a determination of described and gauge physico-chemical outcomes of the hydrotropes are after that using in-silico to set up a counterfeit neural system (ANN) to engage for desires mebendazole tranquilize solubilization. The readied ANN transformed into once determined to exist particularly correct predictions of mebendazole drug solubilization in the presence of hydrotropes and was once for that purpose validated to provide a precious capacity through which hydrotrope sensibility could in like way be screened computationally. The readied ANN transformed into once resolved to exist especially address forecasts of mebendazole tranquilize solubilization within the sight of hydrotropes and was once for that reason approved to give a valuable limit through which hydrotrope sensibility could in like way be screened computationally. The artificial neural network for predicting the solubility properties of the hydrotropic-ester combination was once developed the utilization of MATLAB 2011. For growing the ANN, solubility records that changed into obtained from the experiments had been used. Results: The interest of the hydrotropic will become set as input to the neural network and thus, the precise solubility turn out to be set as target records. The set of rules used to prepare the community modified into the Levenberg Marquardt algorithm. Conclusion: Two hidden layers were expressed. Randomly chosen 80% of the data became used to train the network, It's far is inferred that in-silico screening of drug/hydrotrope structures the utilization of artificial neural networks presents specifically possible to decrease the want for large laboratory testing of those systems and will thus flexibly an economy in expressions of diminished costs and time in tranquilize framework improvement. Mebendazole ; Solubility ; Biological system ; Biochemical engineering ; Chemistry ; Artificial neural network ; Solubilization ; Active ingredient ; Computer science ; Combinatorial chemistry ; Process engineering ; Organic chemistry ; Artificial intelligence ; Pharmacology ; Engineering ; Biochemistry Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods
Authors: Andrew Falkowski | Steven Kauwe | Taylor D. Sparks
Source: ChemRxiv (preprint, 2021-05-27)
Field: Materials Science — Materials Chemistry

Optimizing Fractional Compositions To Achieve Extraordinary Properties Traditional, data-driven materials discovery involves screening chemical systems with machine learning algorithms and selecting candidates that excel in a target property. The number of screening candidates grows infinitely large as the fractional resolution of compositions the number of included elements increases. The computational infeasibility and probability of overlooking a successful candidate grow likewise. Our approach shifts the optimization focus from model parameters to the fractions of each element in a composition. Using a pretrained network, CrabNet, and writing a custom loss function to govern a vector of element fractions, compositions can be optimized such that a predicted property is maximized or minimized. Single and multi-property optimization examples are presented that highlight the capabilities and robustness of this approach to inverse design. Computer science ; Property (philosophy) ; Robustness (evolution) ; Inverse ; Algorithm ; Mathematical optimization ; Element (criminal law) ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Binbin Lin | Nima Emami | David A. Santos | Yuting Luo | Sarbajit Banerjee | Bai‐Xiang Xu
Source: arXiv (Cornell University) (preprint, 2021-09-09)
Field: Materials Science — Surfaces, Coatings and Films

A deep learned nanowire segmentation model using synthetic data augmentation Automatized object identification and feature analysis of experimental image data are indispensable for data-driven material science; deep-learning-based segmentation algorithms have been shown to be a promising technique to achieve this goal. However, acquiring high-resolution experimental images and assigning labels in order to train such algorithms is challenging and costly in terms of both time and labor. In the present work, we apply synthetic images, which resemble the experimental image data in terms of geometrical and visual features, to train state-of-art deep learning-based Mask R-CNN algorithms to segment vanadium pentoxide (V2O5) nanowires, a canonical cathode material, within optical intensity-based images from spectromicroscopy. The performance evaluation demonstrates that even though the deep learning model is trained on pure synthetically generated structures, it can segment real optical intensity-based spectromicroscopy images of complex V2O5 nanowire structures in overlapped particle networks, thus providing reliable statistical information. The model can further be used to segment nanowires in scanning electron microscopy (SEM) images, which are fundamentally different from the training dataset known to the model. The proposed methodology of using a purely synthetic dataset to train the deep learning model can be extended to any optical intensity-based images of variable particle morphology, extent of agglomeration, material class, and beyond. Artificial intelligence ; Computer science ; Deep learning ; Segmentation ; Pentoxide ; Pattern recognition (psychology) ; Image segmentation ; Computer vision ; Materials science ; Vanadium Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: None Available
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A representation-independent electronic charge density database for crystalline materials In addition to being the core quantity in density functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since it contains information about all the electrons in the system. With increasing utilization of machine-learning tools in materials sciences, a data-rich object like the charge density can be utilized in a wide range of applications. The database presented here provides a modern and user-friendly interface for a large and continuously updated collection of charge densities as part of the Materials Project. In addition to the charge density data, we provide the theory and code for changing the representation of the charge density which should enable more advanced machine-learning studies for the broader community. Representation (politics) ; Charge (physics) ; Database ; Electronic database ; Materials science ; Computer science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Jimmy‐Xuan Shen | Jason M. Munro | Matthew K. Horton | Patrick Huck | Shyam Dwaraknath | Kristin A. Persson
Source: arXiv (Cornell University) (preprint, 2021-07-08)
Field: Materials Science — Materials Chemistry

A representation-independent electronic charge density database for crystalline materials In addition to being the core quantity in density functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since it contains information about all the electrons in the system. With increasing utilization of machine-learning tools in materials sciences, a data-rich object like the charge density can be utilized in a wide range of applications. The database presented here provides a modern and user-friendly interface for a large and continuously updated collection of charge densities as part of the Materials Project. In addition to the charge density data, we provide the theory and code for changing the representation of the charge density which should enable more advanced machine-learning studies for the broader community. Charge (physics) ; Charge density ; Representation (politics) ; Density functional theory ; Object (grammar) ; Electron density ; Computer science ; Range (aeronautics) ; Electron ; Interface (matter) ; Database ; Physics ; Materials science ; Chemical physics ; Artificial intelligence ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Piotr Tomaszewski | Shun Yu | Markus Borg | Jerk Rönnols
Source: arXiv (Cornell University) (preprint, 2021-11-16)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Analysis of Small Angle X-ray Scattering Small angle X-ray scattering (SAXS) is extensively used in materials science as a way of examining nanostructures. The analysis of experimental SAXS data involves mapping a rather simple data format to a vast amount of structural models. Despite various scientific computing tools to assist the model selection, the activity heavily relies on the SAXS analysts' experience, which is recognized as an efficiency bottleneck by the community. To cope with this decision-making problem, we develop and evaluate the open-source, Machine Learning-based tool SCAN (SCattering Ai aNalysis) to provide recommendations on model selection. SCAN exploits multiple machine learning algorithms and uses models and a simulation tool implemented in the SasView package for generating a well defined set of datasets. Our evaluation shows that SCAN delivers an overall accuracy of 95%-97%. The XGBoost Classifier has been identified as the most accurate method with a good balance between accuracy and training time. With eleven predefined structural models for common nanostructures and an easy draw-drop function to expand the number and types training models, SCAN can accelerate the SAXS data analysis workflow. Small-angle X-ray scattering ; Bottleneck ; Computer science ; Exploit ; Workflow ; Machine learning ; Scattering ; Classifier (UML) ; Artificial intelligence ; Data mining ; Optics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Dávid Péter Kovács | Cas van der Oord | Jiří Kučera | Alice E. A. Allen | Daniel J. Cole | Christoph Ortner | Gábor Csányi
Source:  (preprint, 2021-07-19)
Field: Materials Science — Materials Chemistry

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditional molecular mechanics force fields, and the large number of free parameters allows sufficient flexibility that it reaches the accuracy typical of recently proposed machine learning based approaches. We test our model on the MD17 and ISO17 data sets and also on a larger, more flexible molecule, and compare to leading machine learning models as well as re- fitted empirical force fields. We show that the linear body ordered ACE model has excellent transferability for properties beyond raw energy and force RMSE, both for molecular dynamics at different temperatures and for configurations very far from the training set including dihedral scans and even bond breaking. Transferability ; Force field (fiction) ; Flexibility (engineering) ; Dihedral angle ; Cluster (spacecraft) ; Molecular dynamics ; Mean squared error ; Statistical physics ; Raw data ; Computer science ; Molecule ; Physics ; Mathematics ; Chemistry ; Computational chemistry ; Artificial intelligence ; Machine learning ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Jürgen Köfinger | Gerhard Hummer
Source:  (preprint, 2021-08-10)
Field: Materials Science — Materials Chemistry

Empirical optimization of molecular simulation force fields by Bayesian inference The demands on the accuracy of force fields for classical molecular dynamics simulations are steadily growing as larger and more complex systems are studied over longer times. One way to meet these growing demands is to hand over the learning of force fields and their parameters to machines in a systematic (semi-)automatic manner. Doing so, we can take full advantage of exascale computing, the increasing availability of experimental data, and advances in quantum-mechanial computations and the calculation of experimental observables from molecular ensembles. Here, we discuss and illustrate the challenges we face in this endeavor and explore a way forward by adapting the Bayesian inference of ensembles (BioEn) method [Hummer and Köfinger, J. Chem. Phys. (2015)] for force field parameterization. In the Bayesian inference of force fields (BioFF) method developed here, the optimization problem is regularized by a simplified prior on the force field parameters and an entropic prior acting on the ensemble. The latter compensates for the unavoidable over-simplifications in the parameter prior. We determine optimal force field parameters using an iterative predictor-corrector approach, in which we run simula- tions, determine the reference ensemble using the weighted histogram analysis method (WHAM), and optimize the BioFF posterior. We illustrate this approach for a simple polymer model, using the distance between two labeled sites as the experimental observable. By systematically resolving force field issues, the BioFF corrections extend to observables not included in ensemble reweighting. We envision future force field optimization as a formalized, systematic, and (semi-)automatic machine learning effort that incorporates a wide range of data from experiment and high-level quantum chemical calculations and takes advantage of exascale computing resources. Force field (fiction) ; Computer science ; Observable ; Inference ; Bayesian inference ; Field (mathematics) ; Approximate Bayesian computation ; Bayesian probability ; Computation ; Statistical physics ; Artificial intelligence ; Algorithm ; Machine learning ; Mathematics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Phạm Quang Thái | Quan Hoang | Tran Thanh Tuan
Source:  (article, 2021-04-15)
Field: Chemistry — Analytical Chemistry

Bottled Water Classification Using Spectroscopy and Deep Belief Network Neural networks and their variations have been widely applied to classification problems. Among them, the classification of mixtures using neural networks has been investigated by experts from both signal processing and chemical fields. However, the extracted features of beverages such as water, soft drinks, and alcohols, are usually highly collinear. Therefore, traditional methods or simple artificial neural networks may not yield the most accurate results. In this article, several steps were introduced to improve the classification accuracy. Firstly, proper system design and measurement methods were used to mitigate errors. Secondly, deep belief network was applied to adapt to the non-linearity and collinear of features. Finally, suitable learning method was selected. As a result, our system could classify samples from various bottled water branches with nearly 100% accuracy. Artificial neural network ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Machine learning ; Deep learning ; Bottled water ; Signal processing ; Backpropagation ; Deep belief network ; Data mining ; Engineering ; Telecommunications Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: M. Ross Kunz | Adam Yonge | Rakesh Batchu | Zongtang Fang | Yixiao Wang | Gregory S. Yablonsky | Andrew J. Medford | Rebecca Fushimi
Source: arXiv (Cornell University) (preprint, 2021-09-27)
Field: Chemistry — Electrochemistry

A Priori Calibration of Transient Kinetics Data via Machine Learning The temporal analysis of products reactor provides a vast amount of transient kinetic information that may be used to describe a variety of chemical features including the residence time distribution, kinetic coefficients, number of active sites, and the reaction mechanism. However, as with any measurement device, the TAP reactor signal is convoluted with noise. To reduce the uncertainty of the kinetic measurement and any derived parameters or mechanisms, proper preprocessing must be performed prior to any advanced analysis. This preprocessing consists of baseline correction, i.e., a shift in the voltage response, and calibration, i.e., a scaling of the flux response based on prior experiments. The current methodology of preprocessing requires significant user discretion and reliance on previous experiments that may drift over time. Herein we use machine learning techniques combined with physical constraints to convert the raw instrument signal to chemical information. As such, the proposed methodology demonstrates clear benefits over the traditional preprocessing in the calibration of the inert and feed mixture products without need of prior calibration experiments or heuristic input from the user. Calibration ; Computer science ; Preprocessor ; Data pre-processing ; A priori and a posteriori ; Transient (computer programming) ; SIGNAL (programming language) ; Noise (video) ; Heuristic ; Biological system ; Artificial intelligence ; Mathematics ; Statistics Electrochemical Analysis and Applications ; Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems
Authors: Franklin J. Wong | Yudong Hao | Wenmei Ming | Petar Žuvela | Peifen Teh | Jingsheng Shi | Jie Li
Source: Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV (article, 2021-02-19)
Field: Materials Science — Surfaces, Coatings and Films

Methods to overcome limited labeled data sets in machine learning-based optical critical dimension metrology With the aggressive scaling of semiconductor devices, the increasing complexity of device structure coupled with tighter metrology error budget has driven up Optical Critical Dimension (OCD) time to solution to a critical point. Machine Learning (ML), thanks to its extremely fast turnaround, has been successfully applied in OCD metrology as an alternative solution to the conventional physical modeling. However, expensive and limited reference data or labeled data set necessary for ML to learn from often leads to under- or overlearning, limiting its wide adoption. In this paper, we explore techniques that utilize process information to supplement reference data and synergizing physical modeling with ML to prevent under- or overlearning. These techniques have been demonstrated to help overcome the constraint of limited reference data with use cases in challenging OCD metrology for advanced semiconductor nodes. Metrology ; Critical dimension ; Computer science ; Dimension (graph theory) ; Constraint (computer-aided design) ; Set (abstract data type) ; Turnaround time ; Data set ; Process (computing) ; Semiconductor device fabrication ; Machine learning ; Computer engineering ; Reliability engineering ; Artificial intelligence ; Engineering ; Mathematics ; Electrical engineering ; Optics Optical Coatings and Gratings ; Photonic and Optical Devices ; Semiconductor materials and devices
Authors: Xu Guo | Yixian Liu | Guanzhuo Wang
Source: Advances in nano research (article, 2021-08-01)
Field: Materials Science — Materials Chemistry

Computer modeling for frequency performance of viscoelastic magneto-electro-elastic annular micro/nanosystem via adaptive tuned deep learning neural network optimization The presented paper is the first attempt to apply deep learning for predicting the frequency characteristics of a magneto-electro-elastic (MEE) annular nano/microdisk (MEEAD). The optimum amount of the factors participating in the mechanism of the fully connected neural network are achieved through the optimizer based on the momentum. The positive side of the mentioned approach employed in this investigation would be due to its high accuracy along with lower epochs required for training the multi-layered network. This scrutinization would be semi-computational research that estimates the vibrational behavior of a MEEAD employing a non-classical continuum model known as the modified couple stress (MCS) model. First-order shear deformation theory (FSDT) and shell model would be provided for presenting their displacement fields. Then, Kelvin-Voight theory has been applied to model the viscoelastic foundation. Its non-classical governing equations, as well as related boundary conditions (BCs) of small-scaled MEEAD, would be achieved by considering the symmetric spinning gradient along with higher-order stress tensors for the strain energy. The provided non-classical theory would be able to capture the small scale in the MEEAD employing just one length scale of a material factor, then, the mathematical modeling of MEEAD according to the classical theory would be able to be recovered from the provided model by eliminating the material length scale factor. Ultimately, the non-classical governing equations would be solved by applying the generalized differential quadrature (GDQ) approach for multifarious BCs. Moreover, parametric research has been conducted to analyze the influences of the viscoelastic foundation, length scale factor, geometry of MEE, radial and circumferential mode number, radius ratio, and BCs on the frequency behavior of the MEEAD by applying MCST. The outcomes reveal that there would be a crucial radius ratio in which the relationship between these elements and crucial inserted voltage alters from direct to indirect relation. Viscoelasticity ; Artificial neural network ; Parametric statistics ; Boundary value problem ; Length scale ; Computer science ; Statistical physics ; Mechanics ; Physics ; Mathematics ; Mathematical analysis ; Artificial intelligence Nonlocal and gradient elasticity in micro/nano structures ; Advanced machining processes and optimization ; Mechanical stress and fatigue analysis