jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Takuya Taniguchi | Mayuko Hosokawa | Toru Asahi
Source: ChemRxiv (preprint, 2022-10-20)
Field: Materials Science — Materials Chemistry

Materials Informatics of Molecular Crystals Compared by Molecular and Crystal Representations: The Case of Band Gap Prediction In materials informatics, the representation of material structures is fundamentally important to obtain better prediction results. Molecular crystals can be represented by both molecular and crystal representations, but there has been no examination to determine which representation is the most effective for the materials informatics of molecular crystals. In this work, different representations for molecular crystals were compared in an exemplified task of band gap prediction. We demonstrated that the predictive ability using molecular graph outperformed those of molecular fingerprints and crystal graphs. This result motivated the screening of molecular big data from PubChem, and the inference suggested candidate molecules of organic semiconductors for photovoltaics and luminescence. The novelty of this work relies on the representation comparison of molecular crystals and the finding that molecular graph works better even though the property prediction of crystalline materials. This finding will enable to machine-learning-aided screening and design of functional molecular crystals. Molecular graph ; PubChem ; Crystal (programming language) ; Representation (politics) ; Computer science ; Materials informatics ; Materials science ; Novelty ; Graph ; Artificial intelligence ; Chemistry ; Theoretical computer science ; Health informatics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Thomas Plé | Nastasia Mauger | Olivier Adjoua | Théo Jaffrelot-Inizan | Louis Lagardère | Simon Huppert | Jean‐Philip Piquemal
Source: arXiv (Cornell University) (preprint, 2022-12-06)
Field: Materials Science — Materials Chemistry

Routine Molecular Dynamics Simulations Including Nuclear Quantum Effects: from Force Fields to Machine Learning Potentials We report the implementation of a multi-CPU and multi-GPU massively parallel platform dedicated to the explicit inclusion of nuclear quantum effects (NQEs) in the Tinker-HP molecular dynamics (MD) package. The platform, denoted Quantum-HP, exploits two simulation strategies: the Ring-Polymer Molecular Dynamics (RPMD) that provides exact structural properties at the cost of a MD simulation in an extended space of multiple replicas, and the adaptive Quantum Thermal Bath (adQTB) that imposes the quantum distribution of energy on a classical system via a generalized Langevin thermostat and provides computationally affordable and accurate (though approximate) NQEs. We discuss some implementation details, efficient numerical schemes, parallelization strategies and quickly review the GPU acceleration of our code. Our implementation allows an efficient inclusion of NQEs in MD simulations for very large systems, as demonstrated by scaling tests on water boxes with more than 200,000 atoms (simulated using the AMOEBA polarizable force field). We test the compatibility of the approach with Tinker-HP's recently introduced Deep-HP machine learning potentials module by computing water properties using the DeePMD potential with adQTB thermostating. Finally, we show that the platform is also compatible with the alchemical free energy estimation capabilities of Tinker-HP and fast enough to perform simulations. Therefore, we study how the NQEs affect the hydration free energy of small molecules solvated with the recently developed Q-AMOEBA water force field. Overall, the Quantum-HP platform allows users to perform routine quantum MD simulations of large condensed-phase systems and will participate to shed a new light on the quantum nature of important interactions in biological matter. Molecular dynamics ; Computer science ; Force field (fiction) ; Quantum ; Quantum simulator ; Computational science ; Langevin dynamics ; Statistical physics ; Quantum computer ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Quantum, superfluid, helium dynamics
Authors: Andrea Martini | Chiara Negri | Luca Bugarin | Gabriele Deplano | Reza K. Abasabadi | Kirill A. Lomachenko | Ton V. W. Janssens | Silvia Bordiga | Gloria Berlier | Elisa Borfecchia
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-07-27)
Field: Materials Science — Materials Chemistry

Assessing the Influence of Zeolite Composition on Oxygen-Bridged Diamino Dicopper(II) Complexes in Cu-CHA DeNOx Catalysts by Machine Learning-Assisted X-ray Absorption Spectroscopy Cu-exchanged chabazite is the catalyst of choice for NOx abatement in diesel vehicles aftertreatment systems via ammonia-assisted selective catalytic reduction (NH3− SCR). Herein, we exploit in situ X-ray absorption spectroscopy powered by wavelet transform analysis and machine learning-assisted fitting to assess the impact of the zeolite composition on NH3-mobilized Cu-complexes formed during the reduction and oxidation half-cycles in NH3− SCR at 200 °C. Comparatively analyzing well-characterized Cu-CHA catalysts, we show that the Si/Al ratio of the zeolite host affects the structure of mobile dicopper(II) complexes formed during the oxidation of the [CuI (NH3)2] + complexes by O2. Al-rich zeolites promote a planar coordination motif with longer Cu−Cu interatomic distances, while at higher Si/Al values, a bent motif with shorter internuclear separations is also observed. This is paralleled by a more efficient oxidation at a given volumetric Cu density at lower Si/Al, beneficial for the NOx conversion under NH3−SCR conditions at 200 °C. Catalysis ; Zeolite ; Composition (language) ; Spectroscopy ; Oxygen ; Absorption (acoustics) ; X-ray absorption spectroscopy ; X-ray ; Chemistry ; Materials science ; Absorption spectroscopy ; Nuclear chemistry ; Physical chemistry ; Inorganic chemistry ; Crystallography ; Organic chemistry ; Physics ; Optics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jinming Fan | Chao Qian | Shaodong Zhou
Source: Research Square (preprint, 2022-10-18)
Field: Chemistry — Electrochemistry

Machine Learning Spectroscopy Based on Group Contribution and Molecule Contribution Methods Abstract A group contribution (GC) – molecule contribution (MC) – machine learning (ML) protocol for accurate prediction of absorption spectra is presented. Upon combination of ML with revised GC methods, both the maximum absorption wavelengths and the full spectra of various sorts of dyes are afforded accurately and efficiently – by using only a small data set for training. Further, by employing a MC method designed specifically for revGC and based on MC-interpretated mixing rule by, the spectra for mixtures are obtained with high accuracy. Considering that the revGC-MC-ML protocol combines chemical principles and data-driven tools, most likely, it will be proven efficient to solve molecular-property mattered problems in wider fields. Mixing (physics) ; Group (periodic table) ; Protocol (science) ; Molecule ; Absorption (acoustics) ; Set (abstract data type) ; Absorption spectroscopy ; Computer science ; Spectroscopy ; Spectral line ; Training set ; Property (philosophy) ; Artificial intelligence ; Biological system ; Machine learning ; Data mining ; Chemistry ; Physics ; Optics ; Quantum mechanics ; Organic chemistry Electrochemical Analysis and Applications ; Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science
Authors: Wilton J. M. Kort-Kamp | Magali Ferrandon | Xiaoping Wang | Jae Hyung Park | Rajesh K. Malla | Towfiq Ahmed | Edward F. Holby | Deborah J. Myers | Piotr Zelenay
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Adaptive Learning-Driven High-Throughput Synthesis of Oxygen Reduction Reaction Fe-N-C Electrocatalysts Throughput ; Oxygen reduction reaction ; Reduction (mathematics) ; Oxygen ; Chemistry ; Materials science ; Nanotechnology ; Computer science ; Electrochemistry ; Electrode ; Telecommunications ; Mathematics ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning and ELM
Authors: Xinyao Liu | Kasra Amini | Aurelien Sanchez | Blanca Belsa | Tobias Steinle | Katharina Chirvi | Jens Biegert
Source: Conference on Lasers and Electro-Optics (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Atomic imaging of complex and chiral molecular structure with laser-induced electron diffraction and machine learning We show how laser-induced electron diffraction (LIED) combined with a convolutional neural network (CNN) allows to image the complex chiral molecule Fenchone (C 10 H 16 O). Diffraction ; Convolutional neural network ; Electron diffraction ; Laser ; Electron ; Materials science ; Molecule ; Gas electron diffraction ; Optics ; Computer science ; Physics ; Reflection high-energy electron diffraction ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Laser-Matter Interactions and Applications ; Advanced Chemical Physics Studies
Authors: Yongquan Jiang | Hu Jie | Yan Yang | Siqi Yu | Zuo Houchen
Source: Research Square (preprint, 2022-08-01)
Field: Materials Science — Materials Chemistry

Prediction of superconducting properties of materials based on machine learning models Abstract Superconducting materials have extremely high application value in our life. At present, the discovery of new superconducting materials relies on the experience of experts through a large number of "trial and error" experiments. Obtaining the properties of superconductors also requires a large number of experiments. In this paper, we propose to use the XGBoost model to identify superconductors, reaching 0.986 in accuracy. We apply the deep forest algorithm to predict the critical temperature of superconductors and the coefficient of determination reaches 0.944. We propose to apply the same algorithm to predict the forbidden band width of materials and the coefficient of determination reaches 0.917. A new sub-network structure is constructed to predict the Fermi level of materials and the coefficient of determination reaches 0.984. All of these algorithms have state-of-the-art performance. Finally, the model is tested with a publicly available dataset to identify 50 candidate superconducting materials with a critical temperature greater than 90K. Superconductivity ; Computer science ; Materials science ; Condensed matter physics ; Statistical physics ; Physics Machine Learning in Materials Science ; Superconductivity in MgB2 and Alloys ; Physics of Superconductivity and Magnetism
Authors: Wang Diwei
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-07-02)
Field: Chemistry — Spectroscopy

Winter brown carbon over six China's megacities: Light absorption, molecular characterization, and improved source apportionment revealed by multilayer perceptron neural network <strong>Winter brown carbon over six China’s megacities: Light absorption, molecular characterization, and improved source apportionment revealed by multilayer perceptron neural network</strong> Apportionment ; Multilayer perceptron ; Characterization (materials science) ; Megacity ; Beijing ; Artificial neural network ; China ; Materials science ; Environmental science ; Artificial intelligence ; Analytical Chemistry (journal) ; Computer science ; Geography ; Nanotechnology ; Chemistry ; Chromatography ; Political science ; Economics ; Economy ; Archaeology Molecular spectroscopy and chirality ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Hang Hu | Hilary M. Brown | Daniela Mesa Sanchez | Julia Laskin
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Chemistry — Spectroscopy

Molecular Imaging of Biological Samples in Pharmaceutical Development Using Mass Spectrometry Imaging and Machine Learning Journal Article Molecular Imaging of Biological Samples in Pharmaceutical Development Using Mass Spectrometry Imaging and Machine Learning Get access Hang Hu, Hang Hu Purdue University, West Lafayette, Indiana, United States Search for other works by this author on: Oxford Academic Google Scholar Hilary Brown, Hilary Brown Purdue University, West Lafayette, Indiana, United States Search for other works by this author on: Oxford Academic Google Scholar Daniela Mesa Sanchez, Daniela Mesa Sanchez Purdue University, West Lafayette, Indiana, United States Search for other works by this author on: Oxford Academic Google Scholar Julia Laskin Julia Laskin Purdue University, West Lafayette, Indiana, United States Corresponding author: jlaskin@purdue.edu Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 1430–1431, https://doi.org/10.1017/S1431927622005815 Published: 01 August 2022 Library science ; Art history ; Art ; Computer science Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Microfluidic and Capillary Electrophoresis Applications
Authors: Aziz Moumen | Khalifa Mansouri
Source: 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (article, 2022-03-03)
Field: Materials Science — Polymers and Plastics

Towards the Prediction of the Mechanical Properties of a Green Polymer Bioloaded with Alfa Fibers Using a Deep Neural Network Model The investigation of micromechanical properties of bio composites has emerged as a focus of current scientific inquiry. The use of this type of research is due to the ecological, environmental, and long-term development benefits that these bio composites provide. In addition to their economic benefits in meeting industrial constraints. The matrix used in this intelligent study is green epoxy, which is one of the most commonly used polymers in the current industry due to its low cost, availability, and interesting thermomechanical properties, and which has applications in transportation, infrastructure, packaging, home applications, and the automotive industry, among others. The bio load used is Alpfa fibers, which have been largely overlooked by researchers in the field of bio composites. Bio loading with Alfa fibers enhance the mechanical properties of the green epoxy matrix, according to the findings. When compared to the experimental results, the neural network model predicted the Young modulus of the bio composite with high accuracy. The best results obtained in the test phase demonstrate that NNs are capable of generalizing the complicated link between the input and output set of data learned during the training phase, allowing us to create an efficient predictive model for elasiycity of biocomposite. Automotive industry ; Biocomposite ; Epoxy ; Artificial neural network ; Materials science ; Composite number ; Composite material ; Computer science ; Matrix (chemical analysis) ; Modulus ; Field (mathematics) ; Machine learning ; Engineering ; Mathematics Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Textile materials and evaluations
Authors: Hoon Jeong Lee | Ariana B. Hofelmann | Yida Lin | Susanna M. Thon
Source: 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC) (article, 2022-06-05)
Field: Materials Science — Materials Chemistry

Predicting Materials Parameters in Colloidal Quantum Dot Photovoltaic Devices Using Machine Learning Models Trained On Experimental Data Numerous characterization techniques have been developed over the last century, which have advanced progress on the development of a variety of photovoltaic technologies. However, this multitude of techniques leads to increasing experimental costs and complexity. It would be useful to have an approach that does not require the time commitment or operation costs to directly learn and implement every new measurement technique. Herein, we explore several machine learning (ML) models that output complex materials parameters, such as electronic trap state density, solely using illuminated current-voltage curves. This greatly reduces both the complexity and cost of the characterization process. Current-voltage curves were chosen as the only input to our models because this type of measurement is relatively simple to perform and most photovoltaic research labs already collect this information on all devices. We compare several different ML network architectures, all of which are trained on experimental data from PbS colloidal quantum dot thin film solar cells. We predict values for underlying materials parameters and compare them to experimentally measured results. Photovoltaic system ; Characterization (materials science) ; Computer science ; Voltage ; Process (computing) ; Electronics ; Quantum dot ; Range (aeronautics) ; Materials science ; Electronic engineering ; Machine learning ; Nanotechnology ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films
Authors: Nikhil V. S. Avula | Shivanand Kumar Veesam | Sudarshan Behera | Sundaram Balasubramanian
Source: arXiv (Cornell University) (preprint, 2022-08-23)
Field: Materials Science — Materials Chemistry

Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity Shear viscosity, though being a fundamental property of all liquids, is computationally expensive to estimate from equilibrium molecular dynamics simulations. Recently, Machine Learning (ML) methods have been used to augment molecular simulations in many contexts, thus showing promise to estimate viscosity too in a relatively inexpensive manner. However, ML methods face significant challenges like overfitting when the size of the data set is small, as is the case with viscosity. In this work, we train several ML models to predict the shear viscosity of a Lennard-Jones (LJ) fluid, with particular emphasis on addressing issues arising from a small data set. Specifically, the issues related to model selection, performance estimation and uncertainty quantification were investigated. First, we show that the widely used performance estimation procedure of using a single unseen data set shows a wide variability on small data sets. In this context, the common practice of using Cross validation (CV) to select the hyperparameters (model selection) can be adapted to estimate the generalization error (performance estimation) as well. We compare two simple CV procedures for their ability to do both model selection and performance estimation, and find that k-fold CV based procedure shows a lower variance of error estimates. We discuss the role of performance metrics in training and evaluation. Finally, Gaussian Process Regression (GPR) and ensemble methods were used to estimate the uncertainty on individual predictions. The uncertainty estimates from GPR were also used to construct an applicability domain using which the ML models provided more reliable predictions on another small data set generated in this work. Overall, the procedures prescribed in this work, together, lead to robust ML models for small data sets. Overfitting ; Hyperparameter ; Computer science ; Machine learning ; Kriging ; Model selection ; Context (archaeology) ; Artificial intelligence ; Algorithm ; Data mining ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Xinyao Liu | Kasra Amini | Aurelien Sanchez | Blanca Belsa | Tobias Steinle | Katharina Chirvi | Jens Biegert
Source: Optica High-brightness Sources and Light-driven Interactions Congress 2022 (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Imaging complex chiral molecular structure using laser-induced electron diffraction of machine learning with atomic resolution We establish the laser-induced electron diffraction (LIED) technique combined with a convolutional neural network (CNN) to image the complex chiral molecule-Fenchone (C10H16O) with atomic resolution. Electron diffraction ; Resolution (logic) ; Diffraction ; Convolutional neural network ; Laser ; Electron ; Image resolution ; Molecule ; Gas electron diffraction ; Materials science ; Optics ; Computer science ; Physics ; Reflection high-energy electron diffraction ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies ; Advanced Electron Microscopy Techniques and Applications
Authors: Francesca Venturinia | Michela Sperti | Umberto Michelucci | Arnaud Gucciardi | Vanessa M. Martose | Marco A. Deriu
Source: arXiv (Cornell University) (preprint, 2022-03-14)
Field: Chemistry — Analytical Chemistry

Physico-chemical properties extraction from the fluorescence spectrum with 1D-convolutional neural networks: application to olive oil The olive oil sector produces a substantial impact in the Mediterranean's economy and lifestyle. Many studies exist which try to optimize the different steps in the olive oil's production process. One of the main challenges for olive oil producers is the ability to asses and control the quality during the production cycle. For this purpose, several parameters need to be determined, such as the acidity, the UV absorption or the ethyl esters content. To achieve this, samples must be sent to an approved laboratory for chemical analysis. This approach is expensive and cannot be performed very frequently, making quality control of olive oil a real challenge. This work explores a new approach based on fluorescence spectroscopy and artificial intelligence (namely, 1-D convolutional neural networks) to predict the five chemical quality indicators of olive oil (acidity, peroxide value, UV spectroscopic parameters $K_{270}$ and $K_{232}$, and ethyl esters) from simple fluorescence spectra. Fluorescence spectroscopy is a very attractive optical technique since it does not require sample preparation, is non destructive, and, as shown in this work, can be easily implemented in small and cost-effective sensors. The results indicate that the proposed approach gives exceptional results in the quality determination and would make the continuous quality control of olive oil during and after the production process a reality. Additionally, this novel methodology presents potential applications as a support for quality specifications of olive oil, as defined by the European regulation. Olive oil ; Convolutional neural network ; Process engineering ; Environmental science ; Quality (philosophy) ; Artificial neural network ; Process (computing) ; Computer science ; Biochemical engineering ; Pulp and paper industry ; Extraction (chemistry) ; Fluorescence spectroscopy ; Biological system ; Chemistry ; Artificial intelligence ; Fluorescence ; Engineering ; Chromatography ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Pengfan Chen | Changyu Deng | Ze Liu | Xing Guo | Peng Yang | Yi Wang | Yingwen Liu
Source: International Communications in Heat and Mass Transfer (article, 2022-11-24)
Field: Chemistry — Spectroscopy

Structural optimization on the heating shell of a quartz crystal microbalance space atmospheric density sensor based on the self-directed online machine learning optimization approach Quartz crystal microbalance ; Materials science ; Nonlinear system ; Heat transfer ; Mechanics ; Physics ; Chemistry ; Physical chemistry Spectroscopy and Laser Applications ; Atmospheric aerosols and clouds ; Meteorological Phenomena and Simulations