jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jun Zhou | Zheng Zhu | Jiajia Qian | Zhenzhen Ge | Shuting Wu
Source:  (article, 2021-08-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Terahertz Metasurface Spectrum Prediction Based On Deep Learning Metasurface, a two-dimensional periodic artificial electromagnetic material, usually composed of metal/medium or all medium. It has become a popular research area due to its unique electromagnetic properties. However, due to the complex modeling and electromagnetic simulation, the study of the electromagnetic characteristics of terahertz metasurface is time-consuming. To solve this problem, we introduced deep learning method to explore and optimize the terahertz metasurface design process. We constructed a terahertz metasurface structure, and obtained a small part of spectral data in the whole sample space through software simulation, and then put these data into the neural network for learning. By verifying the trained neural network, the spectral information of terahertz mteasurface with different structural parameters can be predicted. This method can help us to simplify the design process of metasurface. Terahertz radiation ; Electromagnetic spectrum ; Computer science ; Artificial neural network ; Process (computing) ; Terahertz gap ; Electromagnetic radiation ; Software ; Electromagnetic simulation ; Artificial intelligence ; Terahertz metamaterials ; Electronic engineering ; Materials science ; Optics ; Physics ; Optoelectronics ; Engineering Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Millimeter-Wave Propagation and Modeling
Authors: Yaroslava Pushkarova | V.G. Panchenko | Yuriy V. Kholin
Source:  (article, 2021-07-06)
Field: Materials Science — Materials Chemistry

Application an Artificial Neural Network for Prediction of Substances Solubility This paper presents an application of the artificial neural network methodology to prediction of solubilities of 1-1 electrolytes in nonaqueous solvents and solvent mixtures, using experimental data available in the literature. It is demonstrated that that the fundamental expressions proposed previously to describe correlations of solubility with physical-chemical properties of solvents, as well as common regression equations, exhibit large deviations and are not suitable for the description and prediction of solubility for a wide range of individual and mixed solvents. In comparison, the radial basis function artificial neural network algorithm is capable of reproducing the solubilities of such common salts as NaI, CsClO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> , NaCl and NaBr in a variety of nonaqueous solvents and solvent mixtures. Having used a training set to obtain the fitting coefficients, we are able to calculate accurately the solubilities of the 1-1 electrolytes in other mixtures of nonaqueous solvents. The reported results make it possible to predict solubilities of 1-1 electrolytes in mixed solvents without the need for additional experimental measurements. Solubility ; Artificial neural network ; Electrolyte ; Solvent ; Thermodynamics ; Chemistry ; Biological system ; Materials science ; Computer science ; Organic chemistry ; Artificial intelligence ; Physical chemistry ; Physics Crystallization and Solubility Studies ; Chemical and Physical Properties in Aqueous Solutions ; Electrochemical Analysis and Applications
Authors: Carsten G. Staacke | Hendrik H. Heenen | Christoph Scheurer | Gábor Cśanyi | Karsten Reuter | Johannes T. Margraf
Source:  (preprint, 2021-07-26)
Field: Materials Science — Materials Chemistry

On the role of long-range electrostatics in machine-learned interatomic potentials for complex battery materials Modeling complex energy materials such as solid-state electrolytes (SSEs) realistically at the atomistic level strains the capabilities of state-of-the-art theoretical approaches. On one hand, the system sizes and simulation time scales required are prohibitive for first-principles methods like density functional theory (DFT). On the other hand, parameterizations for empirical potentials are often not available and these potentials may ultimately lack the desired predictive accuracy. Fortunately, modern machine learning (ML) potentials are increasingly able to bridge this gap, promising first-principles accuracy at a much reduced computational cost. However, the local nature of these ML potentials typically means that long-range contributions arising, e.g., from electrostatic interactions are neglected. Clearly, such interactions can be large in polar materials like electrolytes, however. Herein, we investigate the effect that the locality assumption of ML potentials has on lithium mobility and defect formation energies in the SSE Li$_7$P$_3$S$_{11}$. We find that neglecting long-range electrostatics is unproblematic for the description of lithium transport in the isotropic bulk. In contrast (field-dependent) defect formation energies are only adequately captured by a hybrid potential combining ML and a physical model of electrostatic interactions. Broader implications for ML based modelling of energy materials are discussed. Electrostatics ; Range (aeronautics) ; Statistical physics ; Computer science ; Isotropy ; Battery (electricity) ; Physics ; Materials science ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science
Authors: Yafei Wang | Bonita Goh | Phalgun Nelaturu | Thien Duong | Najlaa Hassan | Raphaelle David | Michael Moorehead | Santanu Chaudhuri | Adam Creuziger | Jason Hattrick‐Simpers | Dan J. Thoma | Kumar Sridharan | Adrien Couet
Source: arXiv (Cornell University) (preprint, 2021-04-20)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Molten Salt Corrosion-resistant Alloy by High-throughput Experimental and Modeling Methods Coupled to Data Analytics Insufficient availability of molten salt corrosion-resistant alloys severely limits the fruition of a variety of promising molten salt technologies that could otherwise have significant societal impacts. To accelerate alloy development for molten salt applications and develop fundamental understanding of corrosion in these environments, here we present an integrated approach using a set of high-throughput alloy synthesis, corrosion testing, and modeling coupled with automated characterization and machine learning. By using this approach, a broad range of Cr-Fe-Mn-Ni alloys were evaluated for their corrosion resistances in molten salt simultaneously demonstrating that corrosion-resistant alloy development can be accelerated by thousands of times. Based on the obtained results, we unveiled a sacrificial mechanism in the corrosion of Cr-Fe-Mn-Ni alloys in molten salts which can be applied to protect the less unstable elements in the alloy from being depleted, and provided new insights on the design of high-temperature molten salt corrosion-resistant alloys. Corrosion ; Molten salt ; Alloy ; Metallurgy ; Materials science ; Salt (chemistry) ; Analytics ; Characterization (materials science) ; Computer science ; Nanotechnology ; Chemistry ; Database Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Bumjoon Seo | Zih‐Yu Lin | Qiyuan Zhao | Michael Webb | Brett M. Savoie
Source: ChemRxiv (preprint, 2021-05-05)
Field: Materials Science — Materials Chemistry

Topology Automated Force-Field Interactions (TAFFI): A Framework for Developing Transferable Force-Fields &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Force-field development has undergone a revolution in the past decade with the proliferation of quantum chemistry based parameterizations and the introduction of machine learning approximations of the atomistic potential energy surface. Nevertheless, transferable force-fields with broad coverage of organic chemical space remain necessary for applications in materials and chemical discovery where throughput, consistency, and computational cost are paramount. Here we introduce a force-field development framework called Topology Automated Force-Field Interactions (TAFFI) for developing transferable force-fields of varying complexity against an extensible database of quantum chemistry calculations. TAFFI formalizes the concept of atom typing and makes it the basis for generating systematic training data that maintains a one-to-one correspondence with force-field terms. This feature makes TAFFI arbitrarily extensible to new chemistries while maintaining internal consistency and transferability. As a demonstration of TAFFI, we have developed a fixed-charge force-field, TAFFI-gen, from scratch that includes coverage for common organic functional groups that is comparable to established transferable force-fields. The performance of TAFFI-gen was benchmarked against OPLS and GAFF for reproducing several experimental proper- ties of 87 organic liquids. The consistent performance of these force-fields, despite their distinct origins, validates the TAFFI framework while also providing evidence of the representability limitations of fixed-charge force-fields.&lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Force field (fiction) ; Transferability ; Field (mathematics) ; Consistency (knowledge bases) ; Topology (electrical circuits) ; Computer science ; Nanotechnology ; Artificial intelligence ; Machine learning ; Mathematics ; Engineering ; Materials science ; Electrical engineering ; Pure mathematics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Force Microscopy Techniques and Applications
Authors: Kristof T. Schütt | Oliver T. Unke | Michael Gastegger
Source: International Conference on Machine Learning (article, 2021-07-18)
Field: Materials Science — Materials Chemistry

Equivariant message passing for the prediction of tensorial properties and molecular spectra Message passing neural networks have become a method of choice for learning on graphs, in particular the prediction of chemical properties and the acceleration of molecular dynamics studies. While they readily scale to large training data sets, previous approaches have proven to be less data efficient than kernel methods. We identify limitations of invariant representations as a major reason and extend the message passing formulation to rotationally equivariant representations. On this basis, we propose the polarizable atom interaction neural network (PaiNN) and improve on common molecule benchmarks over previous networks, while reducing model size and inference time. We leverage the equivariant atomwise representations obtained by PaiNN for the prediction of tensorial properties. Finally, we apply this to the simulation of molecular spectra, achieving speedups of 4-5 orders of magnitude compared to the electronic structure reference. Message passing ; Equivariant map ; Leverage (statistics) ; Computer science ; Invariant (physics) ; Artificial neural network ; Kernel (algebra) ; Inference ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Mathematics ; Physics ; Quantum mechanics ; Discrete mathematics ; Distributed computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kakaraparthi Kranthiraja | Akinori Saeki
Source: Advanced Functional Materials (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Machine Learning: Experiment‐Oriented Machine Learning of Polymer:Non‐Fullerene Organic Solar Cells (Adv. Funct. Mater. 23/2021) In article number 2011168, Akinori Saeki and Kakaraparthi Kranthiraja demonstrate machine learning of a polymer:non-fullerene small molecule acceptor solar cell and perform virtual screening of 200,932 conjugated polymers generated by the combinatorial coupling of donor and acceptor units. Based on the prediction and feedback from brain thinking, one of the synthesized polymers exhibits a power conversion efficiency of 10.10%, comparable to the machine learning prediction. Materials science ; Fullerene ; Organic solar cell ; Nanotechnology ; Polymer ; Polymer science ; Composite material ; Organic chemistry Machine Learning in Materials Science
Authors: Christopher Yeung | Ju-Ming Tsai | Brian King | Benjamin Pham | David Ho | Julia Liang | Mark W. Knight | Aaswath P. Raman
Source: Nanophotonics (preprint, 2021-01-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multiplexed supercell metasurface design and optimization with tandem residual networks Abstract Complex nanophotonic structures hold the potential to deliver exquisitely tailored optical responses for a range of applications. Metal–insulator–metal (MIM) metasurfaces arranged in supercells, for instance, can be tailored by geometry and material choice to exhibit a variety of absorption properties and resonant wavelengths. With this flexibility, however, comes a vast space of design possibilities that classical design paradigms struggle to effectively navigate. To overcome this challenge, here, we demonstrate a tandem residual network approach to efficiently generate multiplexed supercells through inverse design. By using a training dataset with several thousand full‐wave electromagnetic simulations in a design space of over three trillion possible designs, the deep learning model can accurately generate a wide range of complex supercell designs given a spectral target. Beyond inverse design, the presented approach can also be used to explore the structure–property relationships of broadband absorption and emission in such supercell configurations. Thus, this study demonstrates the feasibility of high‐dimensional supercell inverse design with deep neural networks, which is applicable to complex nanophotonic structures composed of multiple subunit elements that exhibit coupling. Supercell ; Nanophotonics ; Computer science ; Residual ; Tandem ; Metamaterial ; Multiplexing ; Broadband ; Absorption (acoustics) ; Network planning and design ; Materials science ; Physics ; Optics ; Optoelectronics ; Telecommunications ; Algorithm Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: H S Huang | Rong Li | Cuixia Li | Feng Zheng | Giovanni A. Ramirez | William G. Houf | Qiang Zhen | Sajid Bashir | Jingbo Louise Liu
Source: Pure and Applied Chemistry (article, 2021-12-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Perspective on advanced nanomaterials used for energy storage and conversion Abstract To drive the next ‘technical revolution’ towards commercialization, we must develop sustainable energy materials, procedures, and technologies. The demand for electrical energy is unlikely to diminish over the next 50 years, and how different countries engage in these challenges will shape future discourse. This perspective summarizes the technical aspects of nanomaterials’ design, evaluation, and uses. The applications include solid oxide fuel cells (SOFCs), solid oxide electrolysis cells (SOEC), microbial fuel cells (MFC), supercapacitors, and hydrogen evolution catalysts. This paper also described energy carriers such as ammonia which can be produced electrochemically using SOEC under ambient pressure and high temperature. The rise of electric vehicles has necessitated some form of onboard storage of fuel or charge. The fuels can be generated using an electrolyzer to convert water to hydrogen or nitrogen and steam to ammonia. The charge can be stored using a symmetrical supercapacitor composed of tertiary metal oxides with self-regulating properties to provide high energy and power density. A novel metal boride system was constructed to absorb microwave radiation under harsh conditions to enhance communication systems. These resources can lower the demand for petroleum carbon in portable power devices or replace higher fossil carbon in stationary power units. To improve the energy conversion and storage efficiency, we systematically optimized synthesis variables of nanomaterials using artificial neural network approaches. The structural characterization and electrochemical performance of the energy materials and devices provide guidelines to control new structures and related properties. Systemic study on energy materials and technology provides a feasible transition from traditional to sustainable energy platforms. This perspective mainly covers the area of green chemistry, evaluation, and applications of nanomaterials generated in our laboratory with brief literature comparison where appropriate. The conceptual and experimental innovations outlined in this perspective are neither complete nor authoritative but a snapshot of selecting technologies that can generate green power using nanomaterials. Supercapacitor ; Energy storage ; Process engineering ; Electrochemical energy conversion ; Energy transformation ; Hydrogen storage ; Nanotechnology ; Fossil fuel ; Renewable energy ; Energy carrier ; Chemistry ; Hydrogen ; Electrochemistry ; Materials science ; Power (physics) ; Electrical engineering ; Engineering Supercapacitor Materials and Fabrication ; Microbial Fuel Cells and Bioremediation ; Advanced Photocatalysis Techniques
Authors: Meng Liu | Cong Fu | Xuan Zhang | Limei Wang | Yaochen Xie | Hao Yuan | Youzhi Luo | Xu Zhao | Shenglong Xu | Shuiwang Ji
Source: arXiv (Cornell University) (preprint, 2021-06-16)
Field: Materials Science — Materials Chemistry

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks Molecular property prediction is gaining increasing attention due to its diverse applications. One task of particular interests and importance is to predict quantum chemical properties without 3D equilibrium structures. This is practically favorable since obtaining 3D equilibrium structures requires extremely expensive calculations. In this work, we design a deep graph neural network to predict quantum properties by directly learning from 2D molecular graphs. In addition, we propose a 3D graph neural network to learn from low-cost conformer sets, which can be obtained with open-source tools using an affordable budget. We employ our methods to participate in the 2021 KDD Cup on OGB Large-Scale Challenge (OGB-LSC), which aims to predict the HOMO-LUMO energy gap of molecules. Final evaluation results reveal that we are one of the winners with a mean absolute error of 0.1235 on the holdout test set. Our implementation is available as part of the MoleculeX package (https://github.com/divelab/MoleculeX). Computer science ; Quantum ; Graph ; Artificial neural network ; Quantum chemical ; Property (philosophy) ; Set (abstract data type) ; Deep neural networks ; Graph theory ; Theoretical computer science ; Network science ; Algorithm ; Computational science ; Machine learning ; Complex network ; Molecule ; Mathematics ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Michiko Yoshitake
Source: Materials (article, 2021-11-17)
Field: Materials Science — Materials Chemistry

Tool for Designing Breakthrough Discovery in Materials Science A database of material property relationships, which serves as a scientific principles database, and a database search system are proposed and developed. The use of this database can support a broader research perspective, which is increasingly important in the era of automated computer-aided experimentation and machine learning of experimental and calculated data. Examples of the wider use of scientific principles in materials research are presented. The database and its advantages are described. An implementation of the proposed database and search system as a prototype software is reported. The usefulness of the database and search system is demonstrated by an example of a surprising but reasonable discovery. Computer science ; Database design ; Perspective (graphical) ; Property (philosophy) ; Database ; Scientific discovery ; Software ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: José Daniel Sierra Murillo
Source: Chemical Physics (article, 2021-09-03)
Field: Chemistry — Spectroscopy

Vibrational and Rotational Gaussian Binning selections on QCT calculations for the OH + D2 → HOD((vHO’, vHOD’, vOD’), J’) + D reaction A large sample has been employed to obtain total reaction cross sections and product state distributions (translational, vibrational and rotational) for the title reaction. Quasi-Classical Trajectory (QCT) calculations and the Gaussian Binning (GB) methodology have been used on Wu-Schatz-Lendvay-Fang-Harding (WSLFH) and Neural Networks (NN) potential energy surfaces. After a first selection, Vibrational-Gaussian Binning (V-GB), (vHO’, vHOD’, vOD’), and a second selection, Rotational-Gaussian Binning (R-GB), although the preference observed (odd or even J') is not exactly followed by each vibrational excitation of HOD, there is a similar behavior to the physical laser phenomenon observed in another triatomic molecule utilized in the gas laser of CO2. The dynamics of polyatomic chemical reactions (e.g., OH + D2 → HOD* + D and its isotopic variants), of relevance in the excitation/emission processes of current and future chemical lasers, could be studied theoretically from the results obtained in this article. Chemistry ; Gaussian ; Computational chemistry Spectroscopy and Laser Applications ; Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications
Authors: Alexander Winckelmann | Sascha Nowak | Silke Richter | Sebastian Recknagel | Jens Riedel | Jochen Vogl | Ulrich Panne | Carlos Abad
Source: ChemRxiv (preprint, 2021-01-18)
Field: Chemistry — Analytical Chemistry

High-Resolution Atomic Absorption Spectrometry Combined with Machine Learning Data Processing for Isotope Amount Ratio Analysis of Lithium An alternative method for lithium isotope amount ratio analysis based on a combination of high-resolution atomic absorption spectrometry and spectral data analysis by machine learning (ML) is proposed herein. It is based on the well-known isotope shift of approximately 15 pm for the electronic transition 22P←22S at around the wavelength of 670.8 nm, which can be measured by state-of-the-art high-resolution continuum source graphite furnace atomic absorption spectrometry. For isotope amount ratio analysis, a scalable tree boosting ML algorithm (XGBoost) was employed and calibrated using a set of samples with 6Li isotope amount fractions ranging from 0.06 to 0.99 mol mol−1, previously determined by multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). The calibration ML model was validated with two certified reference materials (LSVEC and IRMM-016). The procedure was applied to the isotope amount ratio determination of a set of stock chemicals (Li2CO3, LiNO3, LiCl, and LiOH) and a BAM candidate reference material, that is, LiNi1/3Mn1/3Co1/3O2 (NMC111) cathode material. The results of these determinations were compared with those obtained by MC-ICP-MS and found to be metrologically comparable and compatible. The residual bias was −1.8‰ and the precision obtained ranged from 1.9‰ to 6.2‰. This precision was sufficient to resolve naturally occurring variations, as demonstrated for samples ranging from approximately −3‰ to +15‰. To assess its suitability to technical applications, the NMC111 cathode candidate reference material was analyzed using high-resolution continuum source molecular absorption spectrometry with and without matrix purification. The results obtained were metrologically compatible with each other. Analytical Chemistry (journal) ; Chemistry ; Isotope ; Mass spectrometry ; Graphite furnace atomic absorption ; Calibration ; Chromatography ; Physics Analytical chemistry methods development ; Radioactive element chemistry and processing ; Isotope Analysis in Ecology