jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Petr Grigorev | Alexandra M. Goryaeva | Mihai‐Cosmin Marinica | James R. Kermode | Thomas D. Swinburne
Source: arXiv (Cornell University) (preprint, 2021-11-22)
Field: Materials Science — Materials Chemistry

Synergistic coupling in ab initio-machine learning simulations of\n dislocations Ab initio simulations of dislocations are essential to build quantitative\nmodels of material strength, but the required system sizes are often at or\nbeyond the limit of existing methods. Many important structures are thus\nmissing in the training or validation of interatomic potentials, whilst studies\nof dislocation-defect interactions must mitigate the effect of strong periodic\nimage interactions along the line direction. We show how these restrictions can\nbe lifted through the use of linear machine learning potentials in hybrid\nsimulations, where only a subset of atoms are governed by ab initio forces. The\nlinear form is exploited in a constrained retraining procedure, qualitatively\nexpanding the range of training structures for learning and giving precise\nmatching of dislocation core structures, such that lines can cross the\nquantum/classical boundary. We apply our method to fully three dimensional\nstudies of impurity segregation to edge and screw dislocations in tungsten. Our\nretrained potentials give systematically improved accuracy to QM/ML reference\ndata and the three dimensional geometry allows for long-range relaxations that\nqualitatively change impurity-induced core reconstructions compared to\nsimulations using short periodic supercells. More generally, the ability to\ntreat arbitrary sub-regions of large scale simulations with ab initio accuracy\nopens a vast range of previously inaccessible extended defects to quantitative\ninvestigation.\n Ab initio ; Dislocation ; Statistical physics ; Matching (statistics) ; Ab initio quantum chemistry methods ; Range (aeronautics) ; Physics ; Materials science ; Computer science ; Condensed matter physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Xu Jin | Zhiyun Xiao | Alideertu Dong | Yining Wang | Qixin Nie | Lifang Wang
Source:  (article, 2021-11-19)
Field: Chemistry — Analytical Chemistry

Hyperspectral Inversion Modeling of Fat and Protein Content in Milk Based on XGBoost A hyperspectral inversion modeling method is proposed to predict the fat and protein content in milk by integrating SG, CR and XGBoost to address the drawbacks of traditional chemical methods for detecting fat and protein in milk, which require damage to the emulsion itself, complicated operation and environmental pollution. The method first combines Savitzky-Golay (SG) and Continuum-Removal (CR) to pre-process the hyperspectral data, and then uses Correlation-Analysis (CA) to screen the feature bands. Finally, the screened 10 feature bands are used as inputs to build partial least squares regression (PLSR) and XGBoost models, and the model prediction results are compared. The results show that the SG_CR algorithm is an effective hyperspectral preprocessing method to improve modeling accuracy, and the screening of feature bands can greatly reduce model complexity and improve model efficiency while ensuring model accuracy. Compared with the linear model PLSR, the machine learning integration algorithm XGBoost performed optimally. Its fat and protein models have training set coefficients of determination (R2) of 0.9449 and 0.9980, training set root mean square error (RMSE) of 0.2395 (g/100g) and 0.0409 (g/100g), training set relative analysis error (RPD) of 3.8188 and 22.2079, test set R2 of 0.8653 and 0.9273, test set RMSE were 0.4672 (g/100g), 0.2116 (g/100g), and test set RPD were 2.4625, 3.5998. Hyperspectral imaging ; Mean squared error ; Test set ; Partial least squares regression ; Preprocessor ; Mathematics ; Pattern recognition (psychology) ; Artificial intelligence ; Training set ; Computer science ; Remote sensing ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Ashutosh Arora | Prithvi Singh | CM Arora | Sanjay Bansal | Ambesh Dixit
Source: IOP Conference Series Materials Science and Engineering (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

Thermovision study on Alumina’s Ra v/s Ts for AIDHVACS to control COVID-19 Abstract The current study is the authors’ next work from the thermo vision perspective of real time single sun solar field performance infrared thermography (IRT) on commercial grade Alumina solar absorber surface coatings (SASCs) to recognize surface roughness (R a ) as one of the important production process parameters. In a previous study, it was investigated with IRT, and found that R a &lt;1.8 is favorable and hard anodized Alumina (HAAO) coatings exhibits better surface temperature (T s ) gain as compared to organic dyed non-HAAO coatings on Aluminum substrate, and are more stable in solar field for many years in open air environment without degrading their performance. It may be useful in better optimization of SASCs specifically for personal protective equipments (PPEs) sanitization and artificial intelligence (AI) driven heating ventilation air conditioning (HVAC) systems (AIDHVACS) design to control Covid-19 in current situations .The influence of R a of few microns ∼ &lt;15 µm on Alumina SASCs’ T s gain is examined. The presented study shows that more than 1.05 mm thickness of substrate flat is necessary to develop good quality of alumina coating; R a &lt;1.8 µm is favorably expected to the extent of R a value approaching as close as to nanoscale ∼ 5-500 nm; local surface temperature gained is depending upon local R a profile as well as upon surface morphology in addition to the anodizing process parameters and other environmental factors. It suggests that the optimal surface profile should be designed as an integral to the production line processes. The substrate surface chemical composition may also change while processing due to surface contact with the processing tools, which may also result in altered solar field performance due to substrate altered material composition prior to hard anodizing process, as examined with X-ray fluorescence spectrometry (XRF) and ultraviolet–visible spectrophotometry (UV-VIS). The novelty is that other studies of surface roughness parameter is focused upon convective heat transfer inside tunnel or duct solar heat absorbers e.g. air heaters, whereas the authors have focused upon surface roughness of solar radiation receiving outer surface as an important commercial production process variable having effect upon conductive heat transfer in solar thermal power systems. The AIDHVACS needs machine learning and big data analysis as the need of the hour. Materials science ; Substrate (aquarium) ; Coating ; Anodizing ; HVAC ; Surface roughness ; Surface finish ; Air conditioning ; Aluminium ; Optoelectronics ; Composite material ; Mechanical engineering Anodic Oxide Films and Nanostructures ; Thermal Radiation and Cooling Technologies ; Thermal properties of materials
Authors: Akash Ganguly | Virendra Padhya | Harsh Oza | Bhishm Kumar | P. Nagabhushanam | R.D. Deshpande
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

New Insights into Diffusive Kinetic Fractionation During Liquid Condensation Under Supersaturated Environment: A Novel Approach for Isotope Tagging of Ground Level Water Vapor Supersaturation ; Condensation ; Kinetic energy ; Isotope fractionation ; Fractionation ; Equilibrium fractionation ; Mass-independent fractionation ; Isotope ; Chemical engineering ; Chemical physics ; Materials science ; Chemistry ; Environmental science ; Environmental chemistry ; Thermodynamics ; Chromatography ; Physics ; Organic chemistry ; Engineering ; Nuclear physics Crystallization and Solubility Studies ; nanoparticles nucleation surface interactions ; Nuclear Physics and Applications
Authors: Xianglai Liao | Lili Gui | Chuanshuo Wang | Maoyu Feng | Zhenming Yu | Tian Zhang | Kun Xu
Source:  (article, 2021-11-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Efficient Design of 3D Chiral Plasmonic Metasurfaces Assisted by Intelligent Algorithms Chiral plasmonic metasurfaces, which exhibit distinct spectral responses for left-handed circularly polarized (LCP) and right-handed circularly polarized (RCP) incident light, are promising platforms for sensing chiral molecules, thanks to their great potential of enhancing light-matter interaction. A so-called Born-Kuhn analog for 3D chiral plasmonics, consisting of corner-stacked orthogonally arranged gold nanorods with C<inf>4</inf> rotational symmetry, is a classical and valuable candidate for understanding and tailoring the enhanced interactions with chiral light. In spite of relatively clear physical picture behind, it is still very time consuming and may not be sufficient to achieve the optimum chiroptical responses by simply scanning dimensional parameters. Intelligent algorithms (IA) have emerged as efficient tools for nanophotonic design with remarkable time efficiency and achieved performances. Here we mainly develop an IA, by combining an adaptive genetic algorithm (AGA) and finite-difference time-domain (FDTD) simulation, aiming to achieve the most prominent circular dichroism (CD) at a specific wavelength (customized at 1035 nm, corresponding to our laser wavelength for sensing applications). The CD at 1035 nm reaches &#x2212;0.52 for left-handed (LH) enantiomer and &#x2212;0.46 for right-handed (RH) enantiomer assisted by our intelligent algorithm, 41&#x0025; more remarkable compared with the results obtained by parametric scanning (5-fold more time needed). Besides, deep learning algorithms have recently attracted great attention in nanophotonic inverse design. With the spectra are fed to the input, deep learning networks (DNN) can directly output the structural parameters within short time on the order of milliseconds. Here we show some preliminary results that transfer learning could help to reduce the training time and dataset size when predicting dimensional parameters of the 3D chiral metasurfaces with opposite handedness, after transferring the parameters of an existing model used for training a DNN for left-handed enantiomers. We envision that our developed algorithms could provide time-effective routes for optimizing not only chiral metasurfaces but also other nanophotonic devices. Plasmon ; Nanophotonics ; Metamaterial ; Nanorod ; Finite-difference time-domain method ; Photonics ; Circular dichroism ; Computer science ; Physics ; Optics ; Materials science ; Optoelectronics ; Algorithm ; Nanotechnology ; Chemistry Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Animal Vocal Communication and Behavior
Authors: Shanshan Xu | You Li | Donghan Wang | Chao Fang | Chengwei Luo | Jiankun Deng | Lihong Hu | Hui Li | Hongzhi Li
Source: Journal of Computational Chemistry (article, 2021-11-17)
Field: Materials Science — Materials Chemistry

Efficient prediction for high precision <scp>CO‐N<sub>2</sub></scp> potential energy surface by stacking ensemble <scp>DNN</scp> High-dimensional potential energy surface (PES) for van der Waals systems with spectroscopic accuracy, is of great importance for quantum dynamics and an extremely challenge job. CO-N<sub>2</sub> is a typical van der Waals system and its high-precision PES may help elucidate weak interaction mechanisms. Taking CO-N<sub>2</sub> potential energies calculated by CCSD(T)-F12b/aug-cc-pVQZ as the benchmark, we establish an accurate, robust, and efficient machine learning model by using only four molecular structure descriptors based on 7966 benchmark potential energies. The highest accuracy is obtained by a stacking ensemble DNN (SeDNN). Its evaluation parameters MAE, RMSE, and R<sup>2</sup> reach 0.096, 0.163, 0.9999 cm<sup>-1</sup> , respectively, and the spectroscopic accuracy for vibration spectrum is achieved with predicted PES, which shows SeDNN superior goodness-of-fit and prediction performance. An elaborated PES with the reported global minimum has been predicted with the model, which perfectly reproduces CCSD(T) potential energies and the analytical MLR PES [PCCP, 2018, 20, 2036]. The critical points (global minimum, TSI, TSII, and their barriers), potential curve, and entire PES profile are remarkably consistent with CCSD(T) calculations. To further improve the usability of constructing PESs in practice, the size of the training set (energy points) for the model is reduced to 50%, 30%, and 20% of the database, respectively. The results show that even training with the smallest training set (1593 points), the PES only differs 2.555 cm<sup>-1</sup> with the analytic MLR PES. Therefore, the proposed SeDNN is promisingly an alternative efficient tool to construct subtle PES for van der Waals systems. Stacking ; van der Waals force ; Benchmark (surveying) ; Potential energy surface ; Potential energy ; Statistical physics ; Computer science ; Chemistry ; Physics ; Molecule ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Allen P. Liu | Ankur Moitra
Source: arXiv (Cornell University) (preprint, 2021-06-04)
Field: Materials Science — Surfaces, Coatings and Films

Algorithms from Invariants: Smoothed Analysis of Orbit Recovery over $SO(3)$ In this work we study orbit recovery over $SO(3)$, where the goal is to recover a function on the sphere from noisy, randomly rotated copies of it. We assume that the function is a linear combination of low-degree spherical harmonics. This is a natural abstraction for the problem of recovering the three-dimensional structure of a molecule through cryo-electron tomography. For provably learning the parameters of a generative model, the method of moments is the standard workhorse of theoretical machine learning. It turns out that there is a natural incarnation of the method of moments for orbit recovery based on invariant theory. Bandeira et al. [BBSK+18] used invariant theory to give tight bounds on the sample complexity in terms of the noise level. However many of the key challenges remain: Can we prove bounds on the sample complexity that are polynomial in $n$, the dimension of the signal? The bounds in [BBSK+18] hide constants that have an unspecified dependence on $n$ and only hold in the limit as $σ^2 \rightarrow \infty$ where $σ^2$ is the variance of the noise. Moreover can we give efficient algorithms? We revisit these challenges from the perspective of smoothed analysis, where we assume that the coefficients of the signal, in the basis of spherical harmonics, are subject to small random perturbations. Our main result is a quasi-polynomial time algorithm for orbit recovery over $SO(3)$ in this model. Our approach is based on frequency marching, which sequentially solves linear systems to find higher degree coefficients. Our main technical contribution is to show that these linear systems have unique solutions, are well-conditioned, and that the error can be made to compound over at most a logarithmic number of rounds. We believe that our work takes an important first step towards uncovering the algorithmic implications of invariant theory. Mathematics ; Algorithm ; Orbit (dynamics) ; Invariant (physics) ; Spherical harmonics ; Polynomial ; Applied mathematics ; Mathematical analysis Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Computational Physics and Python Applications
Authors: Kohei Makino | Fusataka Kuniyoshi | Jun Ozawa | Makoto Miwa
Source: Research Square (preprint, 2021-06-23)
Field: Materials Science — Materials Chemistry

Extracting and analyzing inorganic material synthesis procedures in the literature Abstract Analyzing synthesis procedures from a considerable amount of literature is required to collect structural information of material names and synthesis procedures for designing materials computationally. There are databases comprising structural data of material names and material synthesis procedures. However, the types of material in these databases and the material property values included are limited and insufficient. Moreover, they are primarily described in the literature in the natural language of the researcher who proposed the procedure, and thus they cannot be understood universally. It is, therefore, necessary to create a framework that represents textual synthesis procedures in a flow graph that contains crucial information for material development such as the order of operations and the linkage between operations and conditions. This will facilitate obtaining material insights from the literature. However, there are no large-scale studies on the extraction of synthesis procedures in the form of a graph and analysis thereof. In this study, we propose a pipeline system that extracts synthesis procedures from a text in the form of a graph with a clear order of operations and objects of conditioning from the literature. The system consists of preprocessing, entity extraction, which is based on Mat-ELMo and Bi-LSTM-CRF models, rule-based relation extraction, and selection for paragraph-containing procedures. We applied the system to a large body of literature and extracted various synthesis procedures. We performed basic analyses of the extracted procedures to examine their usability. We experimentally confirmed that some extracted procedures were specific to the target material, and some of the obvious procedures were correctly extracted. Computer science ; Process engineering ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Byung Chul Yeo | Hyunji Nam | Hyobin Nam | Min Cheol Kim | Hong Woo Lee | Sung-Chul Kim | Sung Ok Won | Dong-Hun Kim | Kwan‐Young Lee | Seung Yong Lee | Sang Soo Han
Source: npj Computational Materials (preprint, 2021-08-20)
Field: Materials Science — Materials Chemistry

High-throughput computational-experimental screening protocol for the discovery of bimetallic catalysts Abstract To accelerate the discovery of materials through computations and experiments, a well-established protocol closely bridging these methods is required. We introduce a high-throughput screening protocol for the discovery of bimetallic catalysts that replace palladium (Pd), where the similarities in the electronic density of states patterns were employed as a screening descriptor. Using first-principles calculations, we screened 4350 bimetallic alloy structures and proposed eight candidates expected to have catalytic performance comparable to that of Pd. Our experiments demonstrate that four bimetallic catalysts indeed exhibit catalytic properties comparable to those of Pd. Moreover, we discover a bimetallic (Ni-Pt) catalyst that has not yet been reported for H 2 O 2 direct synthesis. In particular, Ni 61 Pt 39 outperforms the prototypical Pd catalyst for the chemical reaction and exhibits a 9.5-fold enhancement in cost-normalized productivity. This protocol provides an opportunity for the catalyst discovery for the replacement or reduction in the use of the platinum-group metals. Bimetallic strip ; Catalysis ; Density functional theory ; Throughput ; Palladium ; Materials science ; Combinatorial chemistry ; Band gap ; Computer science ; Chemistry ; Nanotechnology ; Computational chemistry ; Optoelectronics ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Bonan Zhu | Ziheng Lu | Chris J. Pickard | David O. Scanlon
Source: UCL Discovery (University College London) (preprint, 2021-12-01)
Field: Materials Science — Materials Chemistry

Accelerating Cathode Material Discovery through Ab Initio Random Structure Searching The choice of cathode material in Li-ion batteries underpins their overall performance. Discovering new cathode materials is a slow process, and all major commercial cathode materials are still based on those identified in the 1990s. Discovery of materials using high-throughput calculations has attracted great research interest; however, reliance on databases of existing materials begs the question of whether these approaches are applicable for finding truly novel materials. In this work, we demonstrate that ab initio random structure searching (AIRSS), a first-principles structure prediction method that does not rely on any pre-existing data, can locate low energy structures of complex cathode materials efficiently based only on chemical composition. We use AIRSS to explore three Fe-containing polyanion compounds as low-cost cathodes. Using known quaternary LiFePO4 and quinary LiFeSO4F cathodes as examples, we easily reproduce the known polymorphs, in addition to predicting other, hitherto unknown, low energy polymorphs and even finding a new polymorph of LiFeSO4F that is more stable than the known ones. We then explore the phase space for Fe-containing fluoroxalates, predicting a range of redox-active phases that are yet to be experimentally synthesized, demonstrating the suitability of AIRSS as a tool for accelerating the discovery of novel cathode materials. Cathode ; Quinary ; Ab initio ; Materials science ; Chemical space ; Range (aeronautics) ; Chemical physics ; Nanotechnology ; Computer science ; Chemistry ; Physical chemistry ; Drug discovery ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis