jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sunae So | Jungho Mun | Junsuk Rho
Source: arXiv (Cornell University) (preprint, 2019-04-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Simultaneous inverse design of materials and parameters of core-shell nanoparticle via deep-learning: Demonstration of dipole resonance engineering Recent introduction of data-driven approaches based on deep-learning technology has revolutionized the field of nanophotonics by allowing efficient inverse design methods. In this paper, simultaneous inverse design of materials and structure parameters of core-shell nanoparticle is achieved for the first time using deep-learning of a neural network. A neural network to learn correlation between extinction spectra of electric and magnetic dipoles and core-shell nanoparticle designs, which include material information and shell thicknesses, is developed and trained. We demonstrate deep-learning-assisted inverse design of core-shell nanoparticle for 1) spectral tuning electric dipole resonances, 2) finding spectrally isolated pure magnetic dipole resonances, and 3) finding spectrally overlapped electric dipole and magnetic dipole resonances. Our finding paves the way of the rapid development of nanophotonics by allowing a practical utilization of a deep-learning technology for nanophotonic inverse design. Nanophotonics ; Dipole ; Inverse ; Discrete dipole approximation ; Shell (structure) ; Magnetic dipole ; Nanoparticle ; Artificial neural network ; Materials science ; Nanotechnology ; Computer science ; Physics ; Artificial intelligence ; Quantum mechanics ; Mathematics ; Geometry Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: Lihua Chen | Tran Doan Huan | Rohit Batra | Chiho Kim | Rampi Ramprasad
Source: arXiv (Cornell University) (preprint, 2019-06-14)
Field: Materials Science — Materials Chemistry

Machine Learning Models for the Lattice Thermal Conductivity Prediction of Inorganic Materials The lattice thermal conductivity ($κ_{\rm L} $) is a critical property of thermoelectrics, thermal barrier coating materials and semiconductors. While accurate empirical measurements of $κ_{\rm L} $ are extremely challenging, it is usually approximated through computational approaches, such as semi-empirical models, Green-Kubo formalism coupled with molecular dynamics simulations, and first-principles based methods. However, these theoretical methods are not only limited in terms of their accuracy, but sometimes become computationally intractable owing to their cost. Thus, in this work, we build a machine learning (ML)-based model to accurately and instantly predict $κ_{\rm L}$ of inorganic materials, using a benchmark data set of experimentally measured $κ_{\rm L} $ of about 100 inorganic solids. We use advanced and universal feature engineering techniques along with the Gaussian process regression algorithm, and compare the performance of our ML model with past theoretical works. The trained ML model is not only helpful for rational design and screening of novel materials, but we also identify key features governing the thermal transport behavior in non-metals. Thermal conductivity ; Machine learning ; Thermoelectric materials ; Lattice (music) ; Empirical modelling ; Formalism (music) ; Computer science ; Thermal ; Artificial intelligence ; Algorithm ; Materials science ; Statistical physics ; Thermodynamics ; Physics ; Simulation Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Yasuharu Okamoto
Source: The Journal of Chemical Physics (article, 2019-04-03)
Field: Materials Science — Materials Chemistry

Data sampling scheme for reproducing energies along reaction coordinates in high-dimensional neural network potentials We propose a data sampling scheme for high-dimensional neural network potentials that can predict energies along the reaction pathway calculated using the hybrid density functional theory. We observed that a data sampling scheme that combined partial geometry optimization of intermediate structures with random displacement of atoms successfully predicted the energies along the reaction path with respect to five chemical reactions: Claisen rearrangement, Diels-Alder reaction, [1,5]-sigmatropic hydrogen shift, concerted hydrogen transfer in the water hexamer, and Cornforth rearrangement. Density functional theory ; Sampling (signal processing) ; Random hexamer ; Computational chemistry ; Chemistry ; Sigmatropic reaction ; Hydrogen ; Physics ; Molecular physics ; Quantum mechanics ; Stereochemistry ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hassan A. Tahini | Xin Tan | Sean C. Smith
Source: Advanced Theory and Simulations (article, 2019-03-01)
Field: Materials Science — Materials Chemistry

Computational Materials Science: Discovering and Accelerating Future Technologies Abstract Moving the boundaries of knowledge, scientific research and technologies forward through the field of computational materials science requires a combination of fundamental understanding of materials properties, an appreciation of the limitations with our current approaches, and the ability to embrace new and emergent technologies. This short Essay highlights the works of the authors in the special issue “Computational Materials Design” published in Advanced Theory and Simulations . It emphasises the role that computational materials design plays in rationalizing and guiding experimental efforts in in the fields of catalysis, semiconductors, hydrogels, and solid‐state electrolytes. Increases in computational power together with accurate hybrid functionals within density functional theory are enabling more reliable and trustworthy descriptions of solids for various electronic and optoelectronic applications. Additionally, high‐throughput screening and machine learning are rapidly becoming indispensable tools in computational materials science across diverse applications such as engineering and predicting new catalysts. Such advances are setting the pace for our discovery of new and novel materials of the future. Pace ; Computer science ; Field (mathematics) ; Trustworthiness ; Nanotechnology ; Data science ; Computational model ; Management science ; Biochemical engineering ; Artificial intelligence ; Materials science ; Engineering ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Dmitriy D. Matyushin | Anastasia Yu. Sholokhova | Aleksey K. Buryak
Source: Сорбционные и хроматографические процессы (article, 2019-12-05)
Field: Chemistry — Spectroscopy

Gradient boosting for the prediction of gas chromatographic retention indices The estimation of gas chromatographic retention indices based on compounds structures is an importantproblem. Predicted retention indices can be used in a mass spectral library search for the identificationof unknowns. Various machine learning methods are used for this task, but methods based on decisiontrees, in particular gradient boosting, are not used widely. The aim of this work is to examine the usability ofthis method for the retention index prediction. 177 molecular descriptors computed with Chemistry Development Kit are used as the input representation of a molecule. Random subsets of the whole NIST 17 database are used as training, test and validation sets. 8000 trees with 6 leaves each are used. A neural network with one hidden layer (90 hidden nodes) is used for the comparison. The same data sets and the set of descriptors are used for the neural network and gradient boosting. The model based on gradient boosting outperforms the neural network with one hidden layer for subsets of NIST 17 and for the set of essential oils.The performance of this model is comparable or better than performance of other modern retention prediction models. The average relative deviation is ~3.0%, the median relative deviation is ~1.7% for subsets of NIST 17. The median absolute deviation is ~34 retention index units. Only non-polar liquid stationary phases (such as polydimethylsiloxane, 5% phenyl 95% polydimethylsiloxane, squalane) are considered. Errors obtained with different machine learning algorithms and with the same representation of the molecule strongly correlate with each other. Gradient boosting ; NIST ; Boosting (machine learning) ; Artificial intelligence ; Artificial neural network ; Random forest ; Standard deviation ; Kovats retention index ; Pattern recognition (psychology) ; Machine learning ; Computer science ; Polydimethylsiloxane ; Test set ; Mathematics ; Biological system ; Chromatography ; Chemistry ; Statistics ; Gas chromatography Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Di Wang | Fengchun Tian | Zhiqin Zhu | Wenjie Pan
Source: International Journal of Robotics and Automation (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

AUTOMATIC PREDICTION OF LEAVE CHEMICAL COMPOSITIONS BASED ON NIR SPECTROSCOPY WITH MACHINE LEARNING Near-infrared (NIR) spectroscopy is an automatic technique todetect spectra of leaves, and it has been a useful tool to extract thedesired chemical information when combined with other multivariatecalibration techniques. Spectroscopy ; Computer science ; Machine learning ; Artificial intelligence ; Materials science ; Physics Spectroscopy and Chemometric Analyses
Authors: Brendan Sullivan | Richard Archibald | Venu Gopal Vandavasi | Patricia S. Langan | Leighton Coates | V. E. Lynch
Source:  (article, 2019-05-01)
Field: Materials Science — Materials Chemistry

Volumetric Segmentation via Neural Networks Improves Neutron Crystallography Data Analysis Crystallography is the powerhouse technique for molecular structure determination, with applications in fields ranging from energy storage to drug design. Accurate structure determination, however, relies partly on determining the precise locations and integrated intensities of Bragg peaks in the resulting data. Here, we describe a method for Bragg peak integration that is accomplished using neural networks. The network is based on a U-Net and identifies peaks in three-dimensional reciprocal space through segmentation, allowing prediction of the full 3D peak shape from noisy data that is commonly difficult to process. The procedure for generating appropriate training sets is detailed. Trained networks achieve Dice coefficients of 0.82 and mean IoUs of 0.69. Carrying out integration over entire datasets, it is demonstrated that integrating neural network-predicted peaks results in improved intensity statistics. Furthermore, using a second dataset, the possibility of transfer learning between datasets is shown. Given the ubiquity and growing complexity of crystallography, we anticipate integration by machine learning to play an increasingly important role across the physical sciences. These early results demonstrate the applicability of deep learning techniques for integrating crystallography data and suggest a possible role in the next generation of crystallography experiments. Computer science ; Artificial neural network ; Segmentation ; Artificial intelligence ; Pattern recognition (psychology) ; Process (computing) ; Algorithm ; Data mining Machine Learning in Materials Science ; Enzyme Structure and Function ; Crystallography and molecular interactions
Authors: Lars Banko | Yury Lysogorskiy | Dario Grochla | Dennis Naujoks | Ralf Drautz | Alfred Ludwig
Source: arXiv (Cornell University) (preprint, 2019-10-21)
Field: Materials Science — Materials Chemistry

Mastering processing-microstructure complexity through the prediction of thin film structure zone diagrams by generative machine learning models Thin films are ubiquitous in modern technology and highly useful in materials discovery and design. For achieving optimal extrinsic properties their microstructure needs to be controlled in a multi-parameter space, which usually requires a too-high number of experiments to map. We propose to master thin film processing microstructure complexity and to reduce the cost of microstructure design by joining combinatorial experimentation with generative deep learning models to extract synthesis-composition-microstructure relations. A generative machine learning approach comprising a variational autoencoder and a conditional generative adversarial network predicts structure zone diagrams. We demonstrate that generative models provide a so far unseen level of quality of generated structure zone diagrams comprising chemical and processing complexity for the optimization of chemical composition and processing parameters to achieve a desired microstructure. Generative grammar ; Autoencoder ; Microstructure ; Computer science ; Artificial intelligence ; Generative model ; Machine learning ; Deep learning ; Algorithm ; Materials science ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Ravinder Bhattoo | Karthikeya H. Sreedhara | Suresh Bishnoi | Hargun Singh Grover | Mathieu Bauchy | Jayadeva | Hariprasad Kodamana | N. M. Anoop Krishnan
Source: arXiv (Cornell University) (preprint, 2019-12-25)
Field: Materials Science — Ceramics and Composites

Deep Learning Aided Rational Design of Oxide Glasses Despite the extensive usage of oxide glasses for a few millennia, the composition-property relationships in these materials still remain poorly understood. While empirical and physics-based models have been used to predict properties, these remain limited to a few select compositions or a series of glasses. Designing new glasses requires a priori knowledge of how the composition of a glass dictates its properties such as stiffness, density, or processability. Thus, accelerated design of glasses for targeted applications remain impeded due to the lack of universal composition-property models. Herein, using deep learning, we present a methodology for the rational design of oxide glasses. Exploiting a large dataset of glasses comprising of up to 37 oxide components and more than 100,000 glass compositions, we develop high-fidelity deep neural networks for the prediction of eight properties that enable the design of glasses, namely, density, Young's modulus, shear modulus, hardness, glass transition temperature, thermal expansion coefficient, liquidus temperature, and refractive index. These models are by far the most extensive models developed as they cover the entire range of human-made glass compositions. We demonstrate that the models developed here exhibit excellent predictability, ensuring close agreement with experimental observations. Using these models, we develop a series of new design charts, termed as glass selection charts. These charts enable the rational design of functional glasses for targeted applications by identifying unique compositions that satisfy two or more constraints, on both compositions and properties, simultaneously. The generic design approach presented herein could catalyze machine-learning assisted materials design and discovery for a large class of materials including metals, ceramics, and proteins. Materials science ; Glass transition ; Thermal expansion ; Liquidus ; Oxide ; Computer science ; Composite material ; Polymer Glass properties and applications ; Pigment Synthesis and Properties ; Phase-change materials and chalcogenides
Authors: Mads Koerstz | Anders S. Christensen | Kurt V. Mikkelsen | Mogens Brøndsted Nielsen | Jan H. Jensen
Source: ChemRxiv (preprint, 2019-04-24)
Field: Materials Science — Materials Chemistry

High Throughput Virtual Screening of 200 Billion Molecular Solar Heat Battery Candidates The dihydroazulene/vinylheptafulvene (DHA/VHF) thermocouple is a promising candidate for thermal heat batteries that absorb and store solar energy as chemical energy without the need for insulation. However, in order to be viable the energy storage capacity and stability of the high energy form (the free energy barrier to the back reaction) must be increased significantly. We use semiempirical quantum chemical methods, machine learning, genetic algorithms, and density functional theory to virtually screen roughly 200 billion substituted DHA molecules to identify promising candidates for further study. We identify three molecules with predicted energy densities of (0.34-0.36 kJ/g), which is significantly larger than the 0.14 kJ/g computed for the parent system. The free energy barriers to the back reaction are between 6.8 and 7.7 kJ/mol higher than the parent compound, which should correspond to half-lives of days - sufficiently long for many practical applications. Energy density ; Throughput ; Molecule ; Density functional theory ; Solar energy ; Energy storage ; Energy (signal processing) ; Battery (electricity) ; Thermal energy ; Materials science ; Computer science ; Chemistry ; Physics ; Engineering physics ; Thermodynamics ; Telecommunications ; Computational chemistry ; Electrical engineering ; Engineering ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics ; Molecular Junctions and Nanostructures
Authors: Mir Saeid Hesarian | Jafar Tavoosi
Source: SHILAP Revista de lepidopterología (article, 2019-01-01)
Field: Materials Science — Polymers and Plastics

Green technology used in finishing process study of wrinkled cotton fabric by radial basis function (Experimental and modeling analysis) The wrinkling of cotton fabric is an important factor that affects a garment's appearance. This paper evaluated the use of a non-toxic green anti-wrinkle material in the finishing process to address this issue. For this purpose, the chemical structure of the wrinkled cotton sample was evaluated and treated with a scouring and anti-creasing finishing material. Due to the environmental issues created by the toxic material used as finishes, the type of anti-wrinkle material used in this study had the least possible environmental impact. The mechanism of this anti-crease finishing was based on the crosslinking of cellulose molecular chains. This process limited the chain movements made by wrinkling. Accordingly, the effect of the mentioned mechanism and structural parameters such as the thickness, weight, density of the weft yarn, and linear density of the weft yarn (Ne) were evaluated. The wrinkle degree of the samples was analyzed by using a radial basis function neural network (RBFN). This RBFN modeled the relationships between the degree of wrinkling in the fabrics and the mentioned parameters, especially the anti-crease finishing of the samples. The simulation results confirmed the effectiveness of the proposed method. Function (biology) ; Process (computing) ; Basis (linear algebra) ; Radial basis function ; Biological system ; Materials science ; Computer science ; Mathematics ; Artificial intelligence ; Biology ; Geometry Textile materials and evaluations ; Dyeing and Modifying Textile Fibers
Authors: Kevin Maik Jablonka | Daniele Ongari | Seyed Mohamad Moosavi | Berend Smit
Source: NCCR MARVEL (preprint, 2019-12-11)
Field: Materials Science — Materials Chemistry

Using collective knowledge to assign oxidation states Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed several theories to predict the oxidation state on the basis of the chemical formula. These methods are quite successful for simple compounds but often fail to describe the oxidation states of more complex systems, such as metal-organic frameworks. In this work, we present a data-driven approach to automatically assign oxidation states, using a machine learning algorithm trained on the assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database. Our approach only considers the immediate local chemical environment around a metal centre and, in this way, is robust to most of the experimental uncertainties in these structures (like incorrect protonation or unbound solvents). We find such excellent accuracy (>98%) in our predictions that we can use our method to identify a large number of incorrect assignments in the database. The predictions of our model follow chemical intuition, without explicitly having taught the model those heuristics. This work nicely illustrates how powerful the collective knowledge of chemists actually is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists. Heuristics ; Computer science ; Intuition ; Artificial intelligence ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Timothy Cox | Benyamin Motevalli | George Opletal | Amanda S. Barnard
Source: Advanced Theory and Simulations (article, 2019-12-15)
Field: Materials Science — Materials Chemistry

Feature Engineering of Solid‐State Crystalline Lattices for Machine Learning Abstract The problem of feature extraction, in crystalline solid‐state systems with point defects, is considered. Novel methods for creating features for use in machine‐learning‐based predictive modeling of such systems are developed. The methods are illustrated in a case study where machine learning methods are used to predict the onset of amorphization in crystalline systems containing vacancy defects. How the methods developed may be generalized to study other problems in solid‐state materials is also discussed. Solid-state ; Feature (linguistics) ; Point (geometry) ; Computer science ; Feature extraction ; State (computer science) ; Materials science ; Artificial intelligence ; Machine learning ; Algorithm ; Engineering ; Mathematics ; Engineering physics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Pigment Synthesis and Properties