jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zachary L. Glick | Derek P. Metcalf | Alexios Koutsoukas | Steven A. Spronk | Daniel L. Cheney | C. David Sherrill
Source: ChemRxiv (preprint, 2020-05-06)
Field: Materials Science — Materials Chemistry

AP-Net: An Atomic-Pairwise Neural Network for Smooth and Transferable Interaction Potentials &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Intermolecular interactions are critical to many chemical phenomena, but their accurate computation using &lt;i&gt;ab initio&lt;/i&gt; methods is often limited by computational cost. The recent emergence of machine learning (ML) potentials may be a promising alternative. Useful ML models should not only estimate accurate interaction energies, but also predict smooth and asymptotically correct potential energy surfaces. However, existing ML models are not guaranteed to obey these constraints. Indeed, systemic deficiencies are apparent in the predictions of our previous hydrogen-bond model as well as the popular ANI-1X model, which we attribute to the use of an atomic energy partition. As a solution, we propose an alternative atomic-pairwise framework specifically for intermolecular ML potentials, and we introduce AP-Net—a neural network model for interaction energies. The AP-Net model is developed using this physically motivated atomic-pairwise paradigm and also exploits the interpretability of symmetry adapted perturbation theory (SAPT). We show that in contrast to other models, AP-Net produces smooth, physically meaningful intermolecular potentials exhibiting correct asymptotic behavior. Initially trained on only a limited number of mostly hydrogen-bonded dimers, AP-Net makes accurate predictions across the chemically diverse S66x8 dataset, demonstrating significant transferability. On a test set including experimental hydrogen-bonded dimers, AP-Net predicts total interaction energies with a mean absolute error of 0.37 kcal mol−1, reducing errors by a factor of 2-5 across SAPT components from previous neural network potentials. The pairwise interaction energies of the model are physically interpretable, and an investigation of predicted electrostatic energies suggests that the model ‘learns’ the physics of hydrogen-bonded interactions. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Pairwise comparison ; Artificial neural network ; Intermolecular force ; Chemistry ; Transferability ; Ab initio ; Computation ; Computer science ; Hydrogen bond ; Interpretability ; Statistical physics ; Interaction energy ; Biological system ; Algorithm ; Computational chemistry ; Artificial intelligence ; Machine learning ; Molecule ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Chandrakanth Gudavalli | Elizabeth Bose | Bryon S. Donohoe | David A. Sievers
Source: Biomass Conversion and Biorefinery (article, 2020-07-25)
Field: Chemistry — Analytical Chemistry

Real-time biomass feedstock particle quality detection using image analysis and machine vision Abstract A common and costly challenge in the nascent biorefinery industry is the consistent handling and conveyance of biomass feedstock materials, which can vary widely in their chemical, physical, and mechanical properties. Solutions to cope with varying feedstock qualities will be required, including advanced process controls to adjust equipment and reject feedstocks that do not meet a quality standard. In this work, we present and evaluate methods to autonomously assess corn stover feedstock quality in real time and provide data to process controls with low-cost camera hardware. We explore the use of neural networks to classify feedstocks based on actual processing behavior and pixel matrix feature parameterization to further assess particle attributes that may explain the variable processing behavior. We used the pretrained ResNet neural network coupled with a gated recurrent unit (GRU) time-series classifier trained on our image data, resulting in binary classification of feedstock anomalies with favorable performance. The textural aspects of the image data were statistically analyzed to determine if the textural features were predictive of operational disruptions. The significant textural features were angular second moment, prominence, mean height of surface profile, mean resultant vector, shade, skewness, variation of the polar facet orientation, and direction of azimuthal facets. Expansion of these models is recommended across a wider variety of labeled feedstock images of different qualities and species to develop a more robust tool that may be deployed using low-cost cameras within biorefineries. Corn stover ; Raw material ; Artificial intelligence ; Computer science ; Biorefinery ; Pixel ; Artificial neural network ; Raw data ; Process engineering ; Environmental science ; Pattern recognition (psychology) ; Engineering ; Biofuel ; Waste management Spectroscopy and Chemometric Analyses ; Biofuel production and bioconversion ; Mineral Processing and Grinding
Authors: Yuxing Zhou | Han-Yi Zhang | Volker L. Deringer | Wei Zhang
Source: physica status solidi (RRL) - Rapid Research Letters (article, 2020-10-14)
Field: Materials Science — Materials Chemistry

Structure and Dynamics of Supercooled Liquid Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> from Machine‐Learning‐Driven Simulations Studies of supercooled liquid phase‐change materials are important for the development of phase‐change memory and neuromorphic computing devices. Herein, a machine‐learning (ML)‐based interatomic potential for Ge 2 Sb 2 Te 5 (GST) to conduct large‐scale molecular dynamics simulations of liquid and supercooled liquid GST is used. A pronounced effect of the thermostat parameters on the simulation results is demonstrated, and it is shown how using a Langevin thermostat with optimized damping values can lead to excellent agreement with reference ab initio molecular dynamics (AIMD) simulations. Structural and dynamical analyses are presented, including the studies of radial and angular distributions, homopolar bonds, and the temperature‐dependent diffusivity. Herein, the usefulness of ML‐driven molecular dynamics for further studies of supercooled liquid GST, with length and timescales far exceeding those that are accessible to AIMD is demonstrated. Supercooling ; Homopolar motor ; Molecular dynamics ; Thermostat ; Materials science ; Thermal diffusivity ; Phase (matter) ; Chemical physics ; Thermodynamics ; Langevin dynamics ; Statistical physics ; Physics ; Chemistry ; Computational chemistry Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Nonlinear Optical Materials Studies
Authors: Anieza Maltsi | Tore Niermann | Timo Streckenbach | Karsten Tabelow | Thomas Koprucki
Source: Optical and Quantum Electronics (article, 2020-05-01)
Field: Materials Science — Surfaces, Coatings and Films

Numerical simulation of TEM images for In(Ga)As/GaAs quantum dots with various shapes Abstract We present a mathematical model and a tool chain for the numerical simulation of TEM images of semiconductor quantum dots (QDs). This includes elasticity theory to obtain the strain profile coupled with the Darwin–Howie–Whelan equations, describing the propagation of the electron wave through the sample. We perform a simulation study on indium gallium arsenide QDs with different shapes and compare the resulting TEM images to experimental ones. This tool chain can be applied to generate a database of simulated TEM images, which is a key element of a novel concept for model-based geometry reconstruction of semiconductor QDs, involving machine learning techniques. Quantum dot ; Indium arsenide ; Gallium arsenide ; Computer science ; Semiconductor ; Finite element method ; Elasticity (physics) ; Materials science ; Indium gallium nitride ; Optoelectronics ; Physics ; Nanotechnology ; Gallium nitride ; Composite material ; Thermodynamics Electron and X-Ray Spectroscopy Techniques ; Semiconductor Quantum Structures and Devices ; Advanced Electron Microscopy Techniques and Applications
Authors: K. V. L. V. Narayanachari | D. Bruce Buchholz | Elise A. Goldfine | Jill K. Wenderott | Sossina M. Haile | Michael J. Bedzyk
Source: ACS Applied Electronic Materials (article, 2020-10-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Combinatorial Approach for Single-Crystalline TaON Growth: Epitaxial β-TaON (100)/α-Al<sub>2</sub>O<sub>3</sub> (012) The photocatalyst β-TaON is of interest due to promising properties, such as stability, suitable band gap for visible light, and carrier mobility. We implemented a combinatorial, material discovery approach that used pulsed laser deposition (PLD) for thin-film growth, X-ray diffraction (XRD) for phase determination, and machine learning for data reduction. A lateral compositional gradient of TaOxNy was grown across the surface of an α-Al2O3 (012) wafer. After annealing, XRD scattering patterns were collected across the lateral gradient. Unsupervised machine learning separated the XRD data into four clusters (phases); one of which turned out to be the desired monoclinic β-TaON phase. Using high-resolution XRD, we determined that the β-TaON region of the film was a 260 Å thick single-crystal epitaxial with the substrate, having out-of-plane β-TaON (100)//α-Al2O3 (012) and in-plane β-TaON (010)//α-Al2O3 (21̅0). X-ray reflectivity (XRR) analysis of the β-TaON region of the film showed an electron density matching that expected for β-TaON. X-ray photoelectron spectroscopy (XPS) showed a Ta5+ valence state in the β-TaON region of the film. This combinatorial approach, which produces a library of phases on a single wafer, proved to be very efficient for the growth of a material’s phase of interest. Materials science ; X-ray photoelectron spectroscopy ; Epitaxy ; Annealing (glass) ; Wafer ; Thin film ; X-ray reflectivity ; Crystallography ; Optoelectronics ; Optics ; Analytical Chemistry (journal) ; Nanotechnology ; Chemical engineering ; Metallurgy Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; Electronic and Structural Properties of Oxides
Authors: Timothy Wong | Roger Newman
Source: Sensors (article, 2020-05-17)
Field: Materials Science — Materials Chemistry

Nanoporous Gold as a VOC Sensor, Based on Nanoscale Electrical Phenomena and Convolutional Neural Networks Volatile organic compounds (VOCs) are prevalent in daily life, from the lab environment to industrial applications, providing tremendous functionality but also posing significant health risk. Moreover, individual VOCs have individual risks associated with them, making classification and sensing of a broad range of VOCs important. This work details the application of electrochemically dealloyed nanoporous gold (NPG) as a VOC sensor through measurements of the complex electrical frequency response of NPG. By leveraging the effects of adsorption and capillary condensation on the electrical properties of NPG itself, classification and regression is possible. Due to the complex nonlinearities, classification and regression are done through the use of a convolutional neural network. This work also establishes key strategies for improving the performance of NPG, both in sensitivity and selectivity. This is achieved by tuning the electrochemical dealloying process through manipulations of the starting alloy and through functionalization with 1-dodecanethiol. Nanoporous ; Convolutional neural network ; Computer science ; Process engineering ; Materials science ; Process (computing) ; Sensitivity (control systems) ; Nanotechnology ; Environmental science ; Artificial intelligence ; Electronic engineering ; Engineering Nanoporous metals and alloys ; Electrocatalysts for Energy Conversion ; Supercapacitor Materials and Fabrication
Authors: Matthew Swenson | Janelle P. Wharry
Source: JOM (article, 2020-09-17)
Field: Materials Science — Materials Chemistry

Rate Theory Model of Irradiation-Induced Solute Clustering in b.c.c. Fe-Based Alloys Nanoclusters ; Irradiation ; Vacancy defect ; Cluster (spacecraft) ; Materials science ; Cluster analysis ; Particle (ecology) ; Morphology (biology) ; Thermodynamics ; Nanotechnology ; Crystallography ; Chemistry ; Nuclear physics ; Physics ; Computer science ; Artificial intelligence Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear materials and radiation effects
Authors: K Nakamura | Naoya Otani | Tetsuya Koike
Source: Journal of the Ceramic Society of Japan (article, 2020-07-31)
Field: Materials Science — Ceramics and Composites

Search for oxide glass compositions using Bayesian optimization with elemental-property-based descriptors Our study shows that machine learning technique, Bayesian optimization (BO) can efficiently find high-refractive-index glasses from a large number of candidate compositions using data from the INTERGLAD database. The effect of the parameters (i.e., descriptors) input to the BO algorithm on search performance is described. The results show that elemental-property-based (EPB) descriptors, recently applied in materials science, are more effective than the component-amount-based ones traditionally used in the study of glass. The results suggest that BO with EPB descriptors can accelerate the search for glass compositions with desirable properties. Bayesian optimization ; Property (philosophy) ; Materials science ; Refractive index ; Bayesian probability ; Component (thermodynamics) ; Oxide ; Computer science ; Biological system ; Algorithm ; Artificial intelligence ; Thermodynamics ; Physics ; Optoelectronics ; Metallurgy Glass properties and applications ; Phase-change materials and chalcogenides ; Machine Learning in Materials Science
Authors: Jacob Søgaard Larsen | Line Katrine Harder Clemmensen
Source:  (article, 2020-04-09)
Field: Chemistry — Analytical Chemistry

Weight Sharing and Deep Learning for Spectral Data We propose a novel method to co-train deep convolutional neural networks for data sets of differing position specific data. This is an advantage in chemometrics where individual measurements represent exact chemical compounds, e.g. for given wavelengths, and thus signals cannot be translated or resized without disturbing their interpretation. Our approach outperforms transfer learning for three small data sets co-trained with a medium sized data set. Chemometrics ; Computer science ; Convolutional neural network ; Artificial intelligence ; Transfer of learning ; Data set ; Interpretation (philosophy) ; Set (abstract data type) ; Deep learning ; Data modeling ; Artificial neural network ; Pattern recognition (psychology) ; Machine learning ; Data mining ; Database Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Jose Isagani B. Janairo | Kathleen B. Aviso | Michael Angelo B. Promentilla | Raymond R. Tan
Source: AI (article, 2020-06-17)
Field: Materials Science — Biomaterials

Enhanced Hyperbox Classifier Model for Nanomaterial Discovery Machine learning tools can be applied to peptide-mediated biomineralization, which is an emerging biomimetic technique of creating functional nanomaterials. In particular, they can be used for the discovery of biomineralization peptides, which currently relies on combinatorial enumeration approaches. In this work, an enhanced hyperbox classifier is developed which can predict if a given peptide sequence has a strong or weak binding affinity towards a gold surface. A mixed-integer linear program is formulated to generate the rule-based classification model. The classifier is optimized to account for false positives and false negatives, and clearly articulates how the classification decision is made. This feature makes the decision-making process transparent, and the results easy to interpret for decision support. The method developed can help accelerate the discovery of more biomineralization peptide sequences, which may expand the utility of peptide-mediated biomineralization as a means for nanomaterial synthesis. Biomineralization ; Classifier (UML) ; False positive paradox ; Computer science ; False positives and false negatives ; Artificial intelligence ; Machine learning ; Data mining ; Biology Diatoms and Algae Research ; Advanced Proteomics Techniques and Applications ; Chemical Synthesis and Analysis
Authors: Juanjuan Xie | Lei Zhang | Zhangwei Chen | Anqi Hu | Shanshan Liu | Danbo Lu | Yan Xia | Juying Qian | Pengyuan Yang | Huali Shen
Source: Analytica Chimica Acta (article, 2020-04-12)
Field: Chemistry — Spectroscopy

Protein-protein correlations based variable dimension expansion algorithm for high efficient serum biomarker discovery Biomarker ; Biomarker discovery ; Pipeline (software) ; Chemistry ; Computational biology ; Curse of dimensionality ; Pattern recognition (psychology) ; Proteomics ; Artificial intelligence ; Computer science ; Gene ; Biochemistry ; Biology Advanced Proteomics Techniques and Applications ; Gene expression and cancer classification ; Bioinformatics and Genomic Networks
Authors: Arun Sharma | D. Satish | Sushmita Sharma | Dinesh Gupta
Source: Frontiers in Plant Science (article, 2020-02-25)
Field: Chemistry — Analytical Chemistry

iRSVPred: A Web Server for Artificial Intelligence Based Prediction of Major Basmati Paddy Seed Varieties The purity of seeds is the most important factor in agriculture that determines crop yield, price, and quality. Rice is a major staple food consumed in different forms globally. The identification of high yielding and good quality paddy seeds is a challenging job and mainly dependent on expensive molecular techniques. The practical and day-to-day usage of the molecular-laboratory based techniques are very costly and time-consuming, and involves several logistical issues too. Moreover, such techniques are not easily accessible to paddy farmers. Thus, there is an unmet need to develop alternative, easily accessible and rapid methods for correct identification of paddy seed varieties, especially of commercial importance. We have developed iRSVPred, deep learning based on seed images, for the identification and differentiation of ten major varieties of basmati rice namely, Pusa basmati 1121 (1121), Pusa basmati 1509 (1509), Pusa basmati 1637 (1637), salt-tolerant basmati rice variety CSR 30 (CSR-30), Dehradoon basmati Type-3 (DHBT-3), Pusa Basmati-1 (PB-1), Pusa Basmati-6 (PB-6), Basmati -370 (BAS-370), Pusa Basmati 1718 (1718) and Pusa Basmati 1728 (1728). The method has an overall accuracy of 100% and 97% on the training set (total 61,632 images) and internal validation set (total 15,408 images), respectively. Furthermore, accuracies of greater than or equal to 80% have been achieved for all the ten varieties on the external validation dataset (642 images) used in the study. The iRSVPred web-server is freely available at http://14.139.62.220/rice/. Biology ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses
Authors: Satyam Srivastava | Bukke Vani | Shashikant Sadistap
Source: Journal of Food Measurement & Characterization (article, 2020-06-26)
Field: Chemistry — Analytical Chemistry

Machine-vision based handheld embedded system to extract quality parameters of citrus cultivars Android (operating system) ; Mobile device ; Computer science ; Artificial intelligence ; Machine vision ; Computer vision ; Segmentation ; Computer hardware ; Embedded system ; Operating system Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Yunhe Lai | Ximin Cui | Nannan Li | Lei Shao | Wei Zhang | Jianfang Wang | Hai‐Qing Lin
Source: Advanced Optical Materials (article, 2020-12-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Asymmetric Light Scattering on Heterodimers Made of Au Nanorods Vertically Standing on Au Nanodisks Abstract Directional light control at nanoscale shows great potential in applications such as holograms, optical neural networks, and ultracompact photonic circuits. Plasmonic nanostructures with designed geometrical shapes or complex architectures have been employed to manipulate light directionally. The interaction among the electric dipole and multipole resonances in plasmonic nanostructures enables the wavefront reshaping of light, leading to directional light scattering behaviors. However, traditional methods for directional light control suffer from complicated fabrication procedures and large footprints. In this work, heterodimers are constructed out of chemically grown Au nanodisks and Au nanorods, with the nanorod sitting vertically on the nanodisk, and their directional light scattering behaviors are studied. When the nanorod is located off the center of the nanodisk, the incident light is scattered asymmetrically by the heterodimer, producing a crescent‐shaped far‐field scattering pattern. The light scattering directionality of the heterodimers is further proved to be highly dependent on the aspect ratio of the nanorod, the relative position of the two nanocrystals, and the orientation of the nanorod. The study provides a solid and useful foundation for the directional light manipulation in ultracompact nanophotonic systems. Nanorod ; Plasmon ; Materials science ; Light scattering ; Ray ; Scattering ; Optics ; Nanophotonics ; Optoelectronics ; Nanostructure ; Photonics ; Nanotechnology ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Carlos Rı́os | Yifei Zhang | Mikhail Y. Shalaginov | Skylar Deckoff–Jones | Haozhe Wang | Sensong An | Hualiang Zhang | Myungkoo Kang | Kathleen A. Richardson | Christopher Roberts | Jeffrey B. Chou | Vladimir Liberman | Steven A. Vitale | Jing Kong | Tian Gu | Juejun Hu
Source: arXiv (Cornell University) (preprint, 2020-07-15)
Field: Materials Science — Materials Chemistry

Multi-level Electro-thermal Switching of Optical Phase-Change Materials Using Graphene Reconfigurable photonic systems featuring minimal power consumption are crucial for integrated optical devices in real-world technology. Current active devices available in foundries, however, use volatile methods to modulate light, requiring a constant supply of power and significant form factors. Essential aspects to overcoming these issues are the development of nonvolatile optical reconfiguration techniques which are compatible with on-chip integration with different photonic platforms and do not disrupt their optical performances. In this paper, a solution is demonstrated using an optoelectronic framework for nonvolatile tunable photonics that employs undoped-graphene microheaters to thermally and reversibly switch the optical phase-change material Ge$_2$Sb$_2$Se$_4$Te$_1$ (GSST). An in-situ Raman spectroscopy method is utilized to demonstrate, in real-time, reversible switching between four different levels of crystallinity. Moreover, a 3D computational model is developed to precisely interpret the switching characteristics, and to quantify the impact of current saturation on power dissipation, thermal diffusion, and switching speed. This model is used to inform the design of nonvolatile active photonic devices; namely, broadband Si$_3$N$_4$ integrated photonic circuits with small form-factor modulators and reconfigurable metasurfaces displaying 2$π$ phase coverage through neural-network-designed GSST meta-atoms. This framework will enable scalable, low-loss nonvolatile applications across a diverse range of photonics platforms. Photonics ; Materials science ; Optical switch ; Optoelectronics ; Control reconfiguration ; Switching time ; Graphene ; Computer science ; Nanotechnology ; Embedded system Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Neural Networks and Reservoir Computing