jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sina Hossein Zadeh | Amir Behbahanian | John Broucek | Mingzhou Fan | Guillermo Vázquez Tovar | Mohammad Noroozi | William Trehern | Xiaoning Qian | İbrahim Karaman | Raymundo Arróyave
Source: arXiv (Cornell University) (preprint, 2023-02-04)
Field: Materials Science — Materials Chemistry

An Interpretable Boosting-based Predictive Model for Transformation Temperatures of Shape Memory Alloys In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the available dataset, leads to the development of a predictive model for transformation temperatures that can be applied to a wide range of shape memory alloys. We develop a gradient boosting ML surrogate model capable of predicting Martensite Start, Martensite Finish, Austenite Start, and Austenite Finish transformation temperatures with an average accuracy of more than 95% by explicitly taking care of potential distribution changes when modeling different alloy systems. We included heat treatment, rolling, extrusion processing parameters, and alloy system categorical features in the model input features to achieve more accurate and realistic results. In addition, using Shapley values, which are calculated based on the average marginal contribution of features to all possible coalitions, this study was able to gain insights into the governing features and their effect on predicted transformation temperatures, providing a unique opportunity to examine the critical parameters and features in martensite transformation temperatures. Categorical variable ; Austenite ; Shape-memory alloy ; Martensite ; Transformation (genetics) ; Diffusionless transformation ; Alloy ; Boosting (machine learning) ; Materials science ; Computer science ; Artificial intelligence ; Machine learning ; Algorithm ; Metallurgy ; Microstructure Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Saswat Mishra | Sharmila Karumuri | Vincent Mika | Collin Scott | C. L. Choy | Kenneth H. Sandhage | Ilias Bilionis | Michael S. Titus | Alejandro Strachan
Source: arXiv (Cornell University) (preprint, 2023-10-23)
Field: Materials Science — Materials Chemistry

Mass uptake during oxidation of metallic alloys: literature data collection, analysis, and FAIR sharing The area-normalized change of mass ($Δ$m/A) with time during the oxidation of metallic alloys is commonly used to assess oxidation resistance. Analyses of such data can also aid in evaluating underlying oxidation mechanisms. We performed an exhaustive literature search and digitized normalized mass change vs. time data for 407 alloys. To maximize the impact of these and future mass uptake data, we developed and published an open, online, computational workflow that fits the data to various models of oxidation kinetics, uses Bayesian statistics for model selection, and makes the raw data and model parameters available via a queryable database. The tool, Refractory Oxidation Database (https://nanohub.org/tools/refoxdb/), uses nanoHUB's Sim2Ls to make the workflow and data (including metadata) findable, accessible, interoperable, and reusable (FAIR). We find that the models selected by the original authors do not match the most likely one according to the Bayesian information criterion (BIC) in 71% of the cases. Further, in 56% of the cases, the published model was not even in the top 3 models according to the BIC. These numbers were obtained assuming an experimental noise of 2.5% of the mass gain range, a smaller noise leads to more discrepancies. The RefOxDB tool is open access and researchers can add their own raw data (those to be included in future publications, as well as negative results) for analysis and to share their work with the community. Such consistent and systematic analysis of open, community generated data can significantly accelerate the development of machine-learning models for oxidation behavior and assist in the understanding and improvement of oxidation resistance. Computer science ; Workflow ; Metadata ; Interoperability ; Raw data ; Data mining ; Database ; Information retrieval ; World Wide Web Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Materials Characterization Techniques
Authors: Xianlan Li | Yue Luan | Dingling Kong | Yanhua Lu | Wei Li | Meng Tang | Qingyou Zhang | Aimin Pang
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Crystal Density Estimation of a Big Data Set Using 1d and 2d Structure Features Set (abstract data type) ; Data set ; Estimation ; Big data ; Crystal structure ; Computer science ; Data structure ; Data mining ; Algorithm ; Crystallography ; Artificial intelligence ; Engineering ; Chemistry Machine Learning in Materials Science
Authors: Jacobo Jiménez-Rodríguez | Oscar Olea-Mejía | Ana Laura Martínez‐Hernández | Carlos Velasco‐Santos
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Optimization of the Optical Response of 2d [[Equation]] Materials Obtained Through Liquid-Phase Exfoliation Using a Comprehensive Multi-Objective Approach Exfoliation joint ; Liquid phase ; Phase (matter) ; Materials science ; Computer science ; Thermodynamics ; Physics ; Nanotechnology ; Graphene ; Quantum mechanics Luminescence and Fluorescent Materials ; Photonic Crystals and Applications ; Nonlinear Optical Materials Studies
Authors: Lily Wang | Simon Boothroyd | Joshua T. Horton | Matthew W. Thompson | Michael R. Shirts | Daniel J. Cole | John D. Chodera | David L. Mobley
Source: Biophysical Journal (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

Fast, conformation-independent charges with graph convolutional networks Graph ; Computer science ; Combinatorics ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Supramolecular Self-Assembly in Materials
Authors: Alain C. Vaucher | Ye Li | Marco Stenta
Source:  (preprint, 2023-03-08)
Field: Materials Science — Materials Chemistry

Perspectives on AI/ML in Chemistry from Academia and Industry The growing importance of machine learning (ML) and artificial intelligence (AI) extends to chemical research. The need for computational access to and reuse of research data for the prediction of chemical products and retrosynthetic routes is increasing rapidly. In this Thieme WebCheminar, chaired by Dr. Guido Koch (Amphilix, Switzerland), our speakers will talk about challenges for researchers in accessing data, tangible benefits for practicing chemists in predicting synthetic routes and the mind shift required for the application of new digital technologies for discovery of new active ingredients in industry. Chemistry ; Engineering ; Engineering ethics ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Big Data and Business Intelligence
Authors: Fatma Aksakal
Source: Acta Medica Nicomedia (article, 2023-02-28)
Field: Chemistry — Organic Chemistry

Electronic and topological investigation of antitubercular tryptanthrin analogues and their interaction with the enoyl-ACP reductase Objective: This study aimed to investigate the structure-antitubercular activity relationships of a series of tryptanthrin analogues and the binding mechanisms of these analogues with InhA, Mycobacterium tuberculosis enoyl-acyl carrier protein (enoyl-ACP) reductase. Methods: Structure-activity relationships were investigated by using Electronic-Topological Method combined with Neural Networks (ETM-NN). Binding energies and non-covalent interactions stabilizing the ligand-InhA complex were obtained from the results of molecular docking. To assess these interactions, frontier molecular orbital analysis of the ligand-InhA complexes were also analysed. Results: The results of the ETM-NN application to the series of compounds in view are pharmacophoric and anti-pharmacophoric molecular fragments, which are characteristic of the class of compounds demonstrating activity against Mycobacterium tuberculosis strain H37Rv. The statistical characteristics of five pharmacophores (Phi) and five anti-pharmacophores (APhi) entering the forecasting system, are 0.90 and 0.86, correspondingly. Analysis of the electron density distribution has shown that the more effective binding with receptor was observed for active compounds. Conclusions: A system of prognosis for the anti-tubercular activity of Mycobacterium tuberculosis H37Rv was developed on the base of the pharmacophores found, docking results, and electronic structure calculations. This prognosis system allows for designing of new potent antitubercular drugs. Keywords: Tryptanthrin analogues, mycobacterium tuberculosis H37Rv, InhA electronic-topological method, molecular docking, density functional theory INHA ; Pharmacophore ; Chemistry ; Docking (animal) ; Stereochemistry ; Fragment molecular orbital ; In silico ; Mycobacterium tuberculosis ; Combinatorial chemistry ; Molecular orbital ; Molecule ; Biochemistry ; Tuberculosis ; Organic chemistry ; Gene ; Medicine Synthesis and biological activity ; Synthesis and Biological Evaluation ; Multicomponent Synthesis of Heterocycles
Authors: Nausheen Yasin | Syed Mumtaz Danish Naqvi | Syed Mamnoon Akhter
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Simultaneous Spectrophotometric Determination of Co (II) and Co (III) in Acidic Medium with Partial Least Squares Regression and Artificial Neural Networks Partial least squares regression ; Artificial neural network ; Regression ; Chemistry ; Regression analysis ; Mathematics ; Statistics ; Chromatography ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: J.X. Zhang | J. Zhao | Junting Chen | Mengyuan Hua
Source: arXiv (Cornell University) (preprint, 2023-09-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Orientation-Dependent Atomic-Scale Mechanism of $β$-$\mathrm{Ga}_{2}\mathrm{O}_{3}$ Thin Film Epitaxial Growth $β$-$\mathrm{Ga}_{2}\mathrm{O}_{3}$ has gained intensive interests of research and application as an ultrawide bandgap semiconductor. Epitaxial growth technique of the $β$-$\mathrm{Ga}_{2}\mathrm{O}_{3}$ thin film possesses a fundamental and vital role in the $\mathrm{Ga}_{2}\mathrm{O}_{3}$-based device fabrication. In this work, epitaxial growth mechanisms of $β$-$\mathrm{Ga}_{2}\mathrm{O}_{3}$ with four low Miller-index facets, namely (100), (010), (001), and ($\overline{2}$01), are systematically explored using large-scale machine-learning molecular dynamics simulations at the atomic scale. The simulations reveal that the migration of the face-centered cubic stacking O sublattice plays a predominant role in rationalizing the different growth mechanisms between (100)/(010)/(001) and ($\overline{2}$01) orientations. The resultant complex combinations of the stacking faults and twin boundaries are carefully identified, and shows a good agreement with the experimental observation and ab initio calculation. Our results provide useful insights into the gas-phase epitaxial growth of the $β$-$\mathrm{Ga}_{2}\mathrm{O}_{3}$ thin films and suggest possible ways to tailor its properties for specific applications. Epitaxy ; Atomic units ; Materials science ; Ab initio ; Orientation (vector space) ; Crystallography ; Condensed matter physics ; Stacking ; Thin film ; Nanotechnology ; Physics ; Chemistry ; Geometry ; Nuclear magnetic resonance ; Mathematics Ga2O3 and related materials ; ZnO doping and properties ; Machine Learning in Materials Science
Authors: Zhengdong Kang
Source:  (article, 2023-10-20)
Field: Chemistry — Organic Chemistry

Hardware acceleration of the SED algorithm for Biomolecular activity predictionBiomolecular activity algorithm (SED) uses FPGA parallel programmability to achieve hardware acceleration The selection of drug targets is a key link in drug design. The correct selection of targets depends on biomolecular activity, which refers to the ability or property of interaction between biomolecules (such as proteins, enzymes, receptors, etc.) and other molecules (such as drugs, compounds). It describes the effects and reactions of biomolecules on other molecules within an organism or in a laboratory environment. Biomolecular activity can include multiple aspects of properties and effects, such as: binding activity, enzymatic activity, activation or inhibition activity, cell action activity. Biomolecular activity refers to the binding activity of molecules in drug design, so the selection of appropriate targets needs to predict the biological activity of molecular proteins (the binding activity of molecules). In this paper, we present an FPGA hardware accelerator for predicting molecular protein activity prediction, which deploies lasso and deep neural network (SED) algorithms for screening extended connectivity fingerprints. In order to speed up the algorithm to process data more efficiently, it used the minimum integer bit width and decimal point width to reduce the hardware processing of data under the condition of reasonable accuracy loss, and used AXI MASTER interface to improve the bandwidth of the algorithm to transfer data from memory. Experimental results show that our FPGA accelerator implemented on Xilinx Zynq UltraScale+ XCZU7EV is 4.3 times faster than the algorithm implemented on Intel i7-7700K [email protected]. sed ; Acceleration ; Field-programmable gate array ; Computer science ; Hardware acceleration ; Computer hardware ; Embedded system ; Parallel computing ; Physics Click Chemistry and Applications ; Computational Drug Discovery Methods ; Protein Degradation and Inhibitors
Authors: Zezhu Zeng | Felix Wodaczek | Keyang Liu | F. Max Stein | Jürg Hutter | Ji Chen | Bingqing Cheng
Source: arXiv (Cornell University) (preprint, 2023-03-13)
Field: Materials Science — Materials Chemistry

Water dissociation on pristine low-index TiO2 surfaces Water absorption and dissociation processes on pristine low-index TiO$_2$ interfaces are important but poorly understood outside the well-studied anatase (101) and rutile (110). To understand these, we construct three sets of machine learning potentials that are simultaneously applicable to various TiO$_2$ surfaces, based on three density-functional-theory approximations. We compute the water dissociation free energies on seven pristine TiO$_2$ surfaces, and predict that anatase (100), anatase (110), rutile (001), and rutile (011) surfaces favor water dissociation, anatase (101) and rutile (100) surfaces have mostly molecular absorption, while the calculated dissociation fraction on rutile (110) surface may depend on the density functional assumed. Moreover, using an automated algorithm, we reveal that these surfaces follow different types of atomistic mechanisms for proton transfer and water dissociation: one-step, two-step, or both. Our finding thus demonstrates that the different pristine TiO$_2$ surfaces react with water in distinct ways, and cannot be represented using just the low-energy anatase (101) and rutile (110) surfaces. Anatase ; Rutile ; Dissociation (chemistry) ; Density functional theory ; Materials science ; Chemical physics ; Chemical engineering ; Nanotechnology ; Chemistry ; Computational chemistry ; Physical chemistry ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Water Quality Monitoring and Analysis
Authors: R. Tang
Source: ChemRxiv (preprint, 2023-06-07)
Field: Chemistry — Analytical Chemistry

Elucidating Functional Group Presence by Analyzing IR Spectra with 1-Dimensional Convolutional Neural Networks This paper presents a novel solution to the problem of IR spectrum interpretation by applying a 1-dimensional convolutional neural network to classify molecular spectra as either containing a functional group or not containing a functional group. 16 mod- els were trained using a single general model architecture, and 11 were highly effective with accuracy, precision, recall, F1, and AUC scores, all greater than 90%. Phenome- nal classification performance was achieved on aldehydes and ketones, where previous attempts have struggled. Furthermore, a tool was developed to generate saliency maps for each model, allowing for the analysis and study of how the model interprets the IR spectra to make classifications. Our results have applications in fields such as the pharmaceutical industry and drug development, as well as the rapid assay screening of environmental pollutants while being generalizable to be a competitive option for any high-volume IR spectroscopy screening process. Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Phenome ; Group (periodic table) ; Computer science ; Process (computing) ; Machine learning ; Data mining ; Chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Tamoghna Das
Source: arXiv (Cornell University) (preprint, 2023-05-22)
Field: Materials Science — Materials Chemistry

Quantifying `local softness' in a simple liquid Mutual information between local stress and local non-affine deformation is proposed as a collective field variable quantifying the {\em local softness} of soft materials. The liquid-solid transition in a simple liquid is considered as a generic example of mechanical transformation through varying correlation between stress and deformation at the microscopic level. Probing through this new measure, a liquid appears as a spatially heterogeneous medium of interacting interconnected regions of varying softness. In contrast, the soft regions shrink to isolated spots in the background of a negligible mean softness in the case of solids. In this view, the thermodynamic transition becomes purely geometric while keeping the essential mechanical information intact. Besides offering a general framework for understanding the mechanics of materials, this new approach can complement recent machine learning efforts by assigning physical meaning to their findings. Further, this collective variable can be used on the fly during material characterization as both of its ingredient variables are experimentally accessible. Deformation (meteorology) ; Statistical physics ; Variable (mathematics) ; Characterization (materials science) ; Affine transformation ; Simple (philosophy) ; Measure (data warehouse) ; Contrast (vision) ; Complement (music) ; Stress (linguistics) ; Transformation (genetics) ; Field (mathematics) ; Biological system ; Computer science ; Mechanics ; Mathematics ; Materials science ; Physics ; Nanotechnology ; Artificial intelligence ; Mathematical analysis ; Geometry ; Chemistry ; Data mining ; Pure mathematics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Material Dynamics and Properties
Authors: Xiangyue Liu | Weiqi Wang | Jesús Pérez‐Ríos
Source: arXiv (Cornell University) (preprint, 2023-07-23)
Field: Materials Science — Materials Chemistry

Molecular dynamics-driven global tetra-atomic potential energy surfaces: Application to the AlF dimer In this work, we present a general machine learning approach for full-dimensional potential energy surfaces for tetra-atomic systems. Our method employs an active learning scheme trained on {\it ab initio} points, which size grows based on the accuracy required. The training points are selected based on molecular dynamics simulations, choosing the most suitable configurations for different collision energy and mapping the most relevant part of the potential energy landscape of the system. The present approach does not require long-range information and is entirely general. As an example, we provide the full-dimensional AlF-AlF potential energy surface, requiring $\lesssim 0.1\%$ of the configurations to be calculated {\it ab initio}. Furthermore, we analyze the general properties of the AlF-AlF system, finding key difference with other reported results on CaF or bi-alkali dimers. Ab initio ; Potential energy ; Tetra ; Molecular dynamics ; Dimer ; Potential energy surface ; Range (aeronautics) ; Energy (signal processing) ; Work (physics) ; Ab initio quantum chemistry methods ; Computer science ; Statistical physics ; Computational chemistry ; Chemistry ; Physics ; Atomic physics ; Materials science ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Surface and Thin Film Phenomena
Authors: Dimitar Georgiev | Simon Vilms Pedersen | Ruoxiao Xie | A. Fernandez-Galiana | Molly M. Stevens | Mauricio Barahona
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-07-07)
Field: Chemistry — Analytical Chemistry

<i>RamanSPy</i> : An open-source Python package for integrative Raman spectroscopy data analysis Abstract Raman spectroscopy is a non-destructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardisation, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy , an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of ready-to-use tools for spectroscopic analysis, which streamlines day-to-day tasks, integrative analyses, as well as novel research and algorithmic development. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis and machine learning in Python. Python (programming language) ; Workflow ; Raman spectroscopy ; Computer science ; Open source ; Modular design ; Data science ; Software ; Software engineering ; Programming language ; Physics ; Database Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: Ri He | Haowei Xu | Peijun Yang | Kai H. Chang | Hua Wang | Zhicheng Zhong
Source: arXiv (Cornell University) (preprint, 2023-10-16)
Field: Materials Science — Materials Chemistry

Ferroelastic twin wall mediated ferro-flexoelectricity and bulk photovoltaic effect in SrTiO$_3$ Ferroelastic twin walls in nonpolar materials can give rise to a spontaneous polarization due to symmetry breaking. Nevertheless, the bi-stable polarity of twin walls and its reversal have not yet been demonstrated. Here, we report that the polarity of SrTiO$_3$ twin walls can be switched by ultra-low strain gradient. Using first-principles-based machine-learning potential, we demonstrate that the twin walls can be deterministically rotated and realigned in specific directions under strain gradient, which breaks the inversion symmetry of a sequence of walls and leads to a macroscopic polarization. The system can maintain polarity even after the strain gradient is removed. As a result, the polarization of twin walls can exhibit ferroelectric-like hysteresis loop upon cyclic bending, namely ferro-flexoelectricity. Finally, we propose a scheme to experimentally detect the polarity of twin wall by measuring the bulk photovoltaic responses. Our findings suggest a twin-wall-mediated ferro-flexoelectricity in SrTiO$_3$, which could be potentially exploited as functional elements in nano-electronic devices design. Flexoelectricity ; Condensed matter physics ; Polarity (international relations) ; Ferroelectricity ; Polarization (electrochemistry) ; Materials science ; Anomalous photovoltaic effect ; Hysteresis ; Anchoring ; Symmetry breaking ; Point reflection ; Liquid crystal ; Chemistry ; Optoelectronics ; Physics ; Structural engineering Nonlocal and gradient elasticity in micro/nano structures ; Advanced Materials and Mechanics ; Advanced MEMS and NEMS Technologies