jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aneta Gądek-Moszczak | R. Filipowska
Source:  (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Efficiency and Accuracy of Simulated Microstructure Images Generated by Machine Learning Computer image analysis is a well-known method in material science, mechanical engineering and other branches of science and engineering. Application of the machine learning method in image processing delivers promising results, especially in images with a high level of noise and low contrast. Employing automatic classification, predictive models, simulation models deliver a huge benefit in research. The former model of science-based mainly on experiments slowly passing away. It is still an important part of research but, using advanced computer methods save time and allow significantly reduces the cost of studies. It begins new branch of material science and new research challenges, interdisciplinary filed of materials informatics, incorporating scientists exploring, mathematics, informatics and material science. Computer science ; Informatics ; Materials informatics ; Science and engineering ; Artificial intelligence ; Machine learning ; Contrast (vision) ; Noise (video) ; Image processing ; Imaging science ; Image (mathematics) ; Data science ; Engineering informatics ; Engineering ; Health informatics Machine Learning in Materials Science ; Metallurgy and Material Forming ; Advanced machining processes and optimization
Authors: Jing Wu | Liyi Bai | Jiawei Huang | Liyang Ma | Jian Liu | Shi Liu
Source: Physical review. B./Physical review. B (preprint, 2021-11-17)
Field: Materials Science — Materials Chemistry

Accurate force field of two-dimensional ferroelectrics from deep learning A force field that accurately describes interatomic interactions is the prerequisite for running molecular dynamics, an efficient method for large-scale simulations. Compared to the large number of two-dimensional (2D) materials, only a handful of force fields of 2D materials have been developed. Using a concurrent learning procedure that efficiently updates the training database, the authors develop here a deep neural-network-based force field for monolayer In${}_{2}$Se${}_{3}$. The deep potential enabled the predictions of a range of thermodynamic properties of In${}_{2}$Se${}_{3}$ polymorphs, including the ferroelectric $\ensuremath{\alpha}$ phase. Ferroelectricity ; Polarization (electrochemistry) ; Molecular dynamics ; Condensed matter physics ; Materials science ; Nanoelectronics ; Force field (fiction) ; Monolayer ; Physics ; Statistical physics ; Nanotechnology ; Optoelectronics ; Chemistry ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Alexandra Boltasseva | Vladimir M. Shalaev | Zhaxylyk A. Kudyshev
Source:  (article, 2021-03-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancing photonics with machine learning Discovering unconventional optical designs via machine-learning promises to advance on-chip circuitry, imaging, sensing, energy, and quantum information technology. In this talk, photonic design approaches and emerging material platforms will be discussed showcasting machine-learning-assisted topology optimization for thermophotovoltaic metasurface designs and machine-learning-enabled quantum optical measurements. Photonics ; Thermophotovoltaic ; Computer science ; Electronic engineering ; Engineering ; Materials science ; Optoelectronics Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Plasmonic and Surface Plasmon Research
Authors: Ganesh Sivaraman | Jicheng Guo | Logan Ward | Nathaniel C. Hoyt | Mark Williamson | Ian Foster | Chris J. Benmore | Nicholas Jackson
Source: ChemRxiv (preprint, 2021-03-18)
Field: Materials Science — Materials Chemistry

Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl The in silico modeling of molten salts is of crucial importance to emerging "carbon free" energy applications, but is inhibited by the computational cost of quantum mechanically treating the high polarizabilities characteristic of molten salts. Here, we integrate configurational sampling using classical force-fields with active learning to automate the generation of near-DFT accurate machine learning Gaussian Approximation Potentials (GAP) for molten LiCl using fewer than 600 atomic configurations. Relative to conventional ab initio molecular dynamics, the molten LiCl GAP model exhibits a 19,000x speedup and improved experimental agreement as gauged by calculated R-factors. The accuracy of the GAP parametrization workflow is validated by its ability to reproduce experimental structure factors, densities, self-diffusion coefficients, and ionic conductivities for molten LiCl. This hybrid simulation strategy significantly accelerates the generation of machine learning potentials for molten salts by reducing the expensive ab initio calculations required for parameterization to O(100) evaluations, enabling the facile generation of first-principles quality predictions of structural and dynamical properties of molten salts. Molten salt ; Parametrization (atmospheric modeling) ; Ab initio ; Ionic liquid ; Molecular dynamics ; Chemistry ; Ionic bonding ; Ab initio quantum chemistry methods ; Diffusion ; Chemical physics ; Computational chemistry ; Computer science ; Thermodynamics ; Ion ; Physics ; Molecule ; Inorganic chemistry ; Catalysis ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes
Authors: Roman Boča | Beáta Vranovičová
Source: Research Square (preprint, 2021-04-12)
Field: Chemistry — Physical and Theoretical Chemistry

Quantum-Chemical Study of the Biogenic Amino Acids Abstract Ten amino acids have been subjected to the quantum chemical calculations using the ab initio MO-LCAO-SCF calculations and semiempirical PM3 method. When the geometry optimization started form the X-ray structure confirming the zwitterionic form, the ab initio calculations in vacuo result in the amino acid (canonical) form with the hydrogen atom attached not to the amine but to the carboxylate group. At the optimum geometry a number of properties were evaluated: dipole moment, dipole polarizability, molecular surface, molecular volume, HOMO, LUMO, ionization energy and electron affinity using the ΔSCF approach and their values corrected for electron correlation by the 2 nd –order perturbation theory (MP2). In addition, the molecular electrostatic potential and the charge density have been drawn. These properties have been mutually correlated by employing the statistical multivariate methods: the cluster analysis, the probabilistic neural network classifier, the principal component analysis and the Pearson pair correlation. Polarizability ; Chemistry ; HOMO/LUMO ; Ab initio ; Electronic correlation ; Ionization energy ; Dipole ; Electron affinity (data page) ; Computational chemistry ; Proton affinity ; Physical chemistry ; Molecule ; Ionization ; Organic chemistry ; Protonation Various Chemistry Research Topics ; Free Radicals and Antioxidants ; Advanced Chemical Physics Studies
Authors: Kevin R. Talley | Robert White | Nick Wunder | Matthew Eash | Marcus Schwarting | Dave Evenson | John D. Perkins | William Tumas | Kristin Munch | Caleb Phillips | Andriy Zakutayev
Source: Patterns (preprint, 2021-01-01)
Field: Materials Science — Materials Chemistry

Research Data Infrastructure for High-Throughput Experimental Materials Science The High-Throughput Experimental Materials Database (HTEM-DB, htem.nrel.gov) is a repository of inorganic thin-film materials data collected during combinatorial experiments at the National Renewable Energy Laboratory (NREL). This data asset is enabled by NREL's Research Data Infrastructure (RDI), a set of custom data tools that collect, process, and store experimental data and metadata. Here, we describe the experimental data flow from the RDI to the HTEM-DB to illustrate the strategies and best practices currently used for materials data at NREL. Integration of the data tools with experimental instruments establishes a data communication pipeline between experimental researchers and data scientists. This work motivates the creation of similar workflows at other institutions to aggregate valuable data and increase their usefulness for future machine learning studies. In turn, such data-driven studies can greatly accelerate the pace of discovery and design in the materials science domain. Workflow ; Metadata ; Computer science ; Pipeline (software) ; Throughput ; Experimental data ; Data science ; Data set ; Data management ; Database ; World Wide Web Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Shiyi Qin | Reid C. Van Lehn | Ví­ctor M. Zavala | Tianyi Jin
Source: ChemRxiv (preprint, 2021-04-08)
Field: Materials Science — Materials Chemistry

Predicting Critical Micelle Concentrations for Surfactants using Graph Convolutional Neural Networks Surfactants are amphiphilic molecules that are widely used in consumer products, industrial processes, and biological applications. A critical property of a surfactant is the critical micelle concentration (CMC), which is the concentration at which surfactant molecules undergo cooperative self-assembly in solution. Notably, the primary method to obtain CMCs experimentally—tensiometry—is laborious and expensive. In this work, we show that graph convolutional neural networks (GCNs) can predict CMCs directly from the surfactant molecular structure. Specifically, we developed a GCN architecture that encodes the surfactant structure in the form of a molecular graph and trained it using experimental CMC data. We found that the GCN can predict CMCs with higher accuracy than previously proposed methods and that it can generalize to anionic, cationic, zwitterionic, and nonionic surfactants. Molecular saliency maps revealed how atom types and surfactant molecular substructures contribute to CMCs and found this to be in agreement with physical rules that correlate constitutional and topological information to CMCs. Following such rules, we proposed a small set of new surfactants for which experimental CMCs are not available; for these molecules, CMCs predicted with our GCN exhibited similar trends to those obtained from molecular simulations. These results provide evidence that GCNs can enable high-throughput screening of surfactants with desired self-assembly characteristics. Pulmonary surfactant ; Critical micelle concentration ; Cationic polymerization ; Micelle ; Molecule ; Graph ; Amphiphile ; Chemistry ; Molecular dynamics ; Computer science ; Biological system ; Materials science ; Combinatorial chemistry ; Computational chemistry ; Organic chemistry ; Polymer ; Theoretical computer science ; Copolymer ; Aqueous solution Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Yifei Li | Yifeng Wu | Heshan Yu | Ichiro Takeuchi | Rafael Jaramillo
Source: OSA Advanced Photonics Congress 2021 (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

High-speed analysis of spectroscopic ellipsometry data using deep learning methods We develop deep-learning methods for rapid analysis of spectroscopic ellipsometry data. Our approach speeds analysis by thousand-fold compared to traditional methods. We demonstrate the usefulness of our approach for a high-throughput study of phase-change alloys. Ellipsometry ; Computer science ; Throughput ; Deep learning ; Materials science ; Artificial intelligence ; Nanotechnology ; Thin film ; Telecommunications Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Md Mahmudul Hasan | Pınar Acar
Source: AIAA Scitech 2021 Forum (article, 2021-01-04)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification of the Metallic Microstructures with Analytical and Machine Learning Based Approaches View Video Presentation: https://doi.org/10.2514/6.2021-1360.vid Uncertainty in the microstructures has a significant influence on the material properties. The microstructural uncertainty arises from the fluctuations that occur during thermomechanical processing and can alter the expected material properties and performance by propagating over multiple length-scales. It can even lead to the material failure if the deviations in the critical properties exceed a certain limit. We introduce a linear programming (LP) based method to quantify the effects of the microstructure uncertainty on the desired material properties of the Titanium-7wt%Aluminum (Ti-7Al) alloy, which is a candidate material for aerospace applications. The microstructure is represented using the orientation distribution function (ODF) approach. The LP problem solves for the mean values and covariance of the ODFs that maximize a volume-averaged linear material property. However, the analytical procedure is not applicable for maximizing non-linear material properties where microstructural uncertainties are present. Therefore, an artificial neural network (ANN) based sampling method is developed to estimate the mean values and covariance of the ODFs that satisfy design constraints and maximize the volume-averaged non-linear material properties. A couple of other design problems are also illustrated to clarify the applications of the proposed models for both linear and non-linear properties. Covariance ; Material properties ; Uncertainty quantification ; Titanium alloy ; Microstructure ; Materials science ; Covariance function ; Computer science ; Linear programming ; Mathematical optimization ; Algorithm ; Mathematics ; Alloy ; Machine learning ; Statistics ; Composite material Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep
Authors: Dominik Lemm | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2021-02-04)
Field: Materials Science — Materials Chemistry

Energy-free machine learning predictions of {\em ab initio} structures The computational prediction of atomistic structure is a long-standing problem in physics, chemistry, materials, and biology. Within conventional force-field or {\em ab initio} calculations, structure is determined through energy minimization, which is either approximate or computationally demanding. Alas, the accuracy-cost trade-off prohibits the generation of synthetic big data records with meaningful energy based conformational search and structure relaxation output. Exploiting implicit correlations among relaxed structures, our kernel ridge regression model, dubbed Graph-To-Structure (G2S), generalizes across chemical compound space, enabling direct predictions of relaxed structures for out-of-sample compounds, and effectively bypassing the energy optimization task. After training on constitutional and compositional isomers (no conformers) G2S infers atomic coordinates relying solely on stoichiometry and bond-network information as input (Our numerical evidence includes closed and open shell molecules, transition states, and solids). For all data considered, G2S learning curves reach mean absolute interatomic distance prediction errors of less than 0.2 {\AA} for less than eight thousand training structures -- on par or better than popular empirical methods. Applicability test of G2S include meaningful structures of molecules for which standard methods require manual intervention, improved initial guesses for subsequent conventional {\em ab initio} based relaxation, and input for structural based representations commonly used in quantum machine learning models, (bridging the gap between graph and structure based models). Ab initio ; Energy minimization ; Computer science ; Relaxation (psychology) ; Force field (fiction) ; Statistical physics ; Algorithm ; Artificial intelligence ; Machine learning ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Ely Setiawan | Karto Wijaya | Mudasir Mudasir
Source: AIP conference proceedings (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Generic QSPR study for predicting critical micelle concentration of gemini cationic surfactants using the online chemical modeling environment (OCHEM) In this paper, a generic QSPR (quantitative structure-property relationship) model have been developed to investigate the relation between the critical micelle concentration (cmc) and the molecular structure of 231 gemini cationic surfactants with the various hydrophilic head group. The QSPR modeling were performed using the OCHEM (Online CHEmical Modeling environment), a web-based model development platform. The OCHEM platform provides several molecular descriptors calculation and machine learning methods as a tool to build QSPR models. Eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, ChemAxon, Inductive Descriptor, Mordred, and PyDescriptor are used to calculate molecular parameters of gemini cationic surfactants. Also, eight machine learning methods (MLRA, ASNN, kNN, LibSVM, FSMLR, DNN, RFR, and PLS) are used to develop QSPR models. A total of 64 QSPR models were generated with 6 top-ranked models. Based on the statistical coefficient of QSPR models, the model 5 which is constructed from combination of ASNN method and Mordred descriptors, provided the best QSPR models. The model 5 performed the highest predictive result with R2 = 0.95, q2 = 0.95, RMSE = 0.17, and MAE = 0.11. The model can be access on OCHEM website (https://ochem.eu/model/25147470) and can be used for prediction of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development. Cationic polymerization ; Quantitative structure–activity relationship ; Micelle ; Critical micelle concentration ; Computer science ; Chemistry ; Chemical engineering ; Biological system ; Organic chemistry ; Engineering ; Machine learning Surfactants and Colloidal Systems ; Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses
Authors: Carl Poelking | Felix A. Faber | Bingqing Cheng
Source: arXiv (Cornell University) (preprint, 2021-12-04)
Field: Materials Science — Materials Chemistry

BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guiding principle underlying the benchmarking approach is to evaluate raw descriptor performance by limiting model complexity to simple regression schemes while enforcing best ML practices, allowing for unbiased hyperparameter optimization, and assessing learning progress through learning curves along series of synchronized train-test splits. The resulting models are intended as baselines that can inform future method development, next to indicating how easily a given dataset can be learnt. Through a comparative analysis of the training outcome across a diverse set of physicochemical, topological and geometric representations, we glean insight into the relative merits of these representations as well as their interrelatedness. Benchmarking ; Computer science ; Hyperparameter ; Machine learning ; Set (abstract data type) ; Simple (philosophy) ; Artificial intelligence ; Limiting ; Scale (ratio) ; Regression ; Data science ; Theoretical computer science ; Mathematics ; Programming language ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Erik H. Thiede | Wenda Zhou | Risi Kondor
Source: arXiv (Cornell University) (preprint, 2021-03-02)
Field: Materials Science — Materials Chemistry

Autobahn: Automorphism-based Graph Neural Nets We introduce Automorphism-based graph neural networks (Autobahn), a new family of graph neural networks. In an Autobahn, we decompose the graph into a collection of subgraphs and apply local convolutions that are equivariant to each subgraph's automorphism group. Specific choices of local neighborhoods and subgraphs recover existing architectures such as message passing neural networks. Our formalism also encompasses novel architectures: as an example, we introduce a graph neural network that decomposes the graph into paths and cycles. The resulting convolutions reflect the natural way that parts of the graph can transform, preserving the intuitive meaning of convolution without sacrificing global permutation equivariance. We validate our approach by applying Autobahn to molecular graphs, where it achieves results competitive with state-of-the-art message passing algorithms. Computer science ; Graph factorization ; Graph ; Equivariant map ; Theoretical computer science ; Voltage graph ; Line graph ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Memory and Neural Computing
Authors: Jakub Szlęk | Adam Pacławski | Natalia Czub | Aleksander Mendyk
Source:  (article, 2021-10-15)
Field: Chemistry — Spectroscopy

Computational Intelligence Model of Orally Disintegrating Tablets: An Attempt to Explain Disintegration Process We obtained a curated database based on the database published elsewhere. Chemical descriptors were introduced as characteristics of active pharmaceutical ingredients (APIs). We used H2O AutoML platform in order to develop a Deep Learning model and SHAP method to explain its predictions. Obtained results were satisfactory with NRMSE of 8.1% and R2 of 0.84. Finally, we identified critical parameters affecting the process of disintegration of directly compressed ODTs. Computer science ; Process (computing) ; Artificial intelligence ; Machine learning Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Shengli Jiang | Zhuo Xu | Medhavi Kamran | Stas Zinchik | Sidike Paheding | Armando G. McDonald | Ezra Bar‐Ziv | Ví­ctor M. Zavala
Source: ChemRxiv (preprint, 2021-04-29)
Field: Chemistry — Analytical Chemistry

Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste We present a convolutional neural network (CNN) framework for classifying different types of plastic materials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses experimental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. We show that the approach reaches accuracies of over 87% and that some plastic types can be perfectly classified. Attenuated total reflection ; Fourier transform infrared spectroscopy ; Convolutional neural network ; Plastic waste ; Fourier transform ; Infrared ; Spectroscopy ; Materials science ; Pattern recognition (psychology) ; Infrared spectroscopy ; Computer science ; Artificial intelligence ; Biological system ; Analytical Chemistry (journal) ; Chemistry ; Mathematics ; Physics ; Optics ; Environmental chemistry ; Engineering ; Waste management ; Organic chemistry ; Biology Spectroscopy and Chemometric Analyses