jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrea Grisafi | Jigyasa Nigam | Michele Ceriotti
Source: Chemical Science (preprint, 2020-12-11)
Field: Materials Science — Materials Chemistry

Multi-scale approach for the prediction of atomic scale properties Electronic nearsightedness is one of the fundamental principles that governs the behavior of condensed matter and supports its description in terms of local entities such as chemical bonds. Locality also underlies the tremendous success of machine-learning schemes that predict quantum mechanical observables - such as the cohesive energy, the electron density, or a variety of response properties - as a sum of atom-centred contributions, based on a short-range representation of atomic environments. One of the main shortcomings of these approaches is their inability to capture physical effects ranging from electrostatic interactions to quantum delocalization, which have a long-range nature. Here we show how to build a multi-scale scheme that combines in the same framework local and non-local information, overcoming such limitations. We show that the simplest version of such features can be put in formal correspondence with a multipole expansion of permanent electrostatics. The data-driven nature of the model construction, however, makes this simple form suitable to tackle also different types of delocalized and collective effects. We present several examples that range from molecular physics to surface science and biophysics, demonstrating the ability of this multi-scale approach to model interactions driven by electrostatics, polarization and dispersion, as well as the cooperative behavior of dielectric response functions. Delocalized electron ; Multipole expansion ; Electrostatics ; Statistical physics ; Observable ; Locality ; Scale (ratio) ; Quantum ; Atomic units ; Physics ; Range (aeronautics) ; Computer science ; Theoretical physics ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Theoretical and Computational Physics
Authors: Maxim Ziatdinov | Chris Nelson | Xiaohang Zhang | Rama K. Vasudevan | Eugene А. Eliseev | Anna N. Morozovska | Ichiro Takeuchi | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2020-02-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Causal analysis of competing atomistic mechanisms in ferroelectric\n materials from high-resolution Scanning Transmission Electron Microscopy data Machine learning has emerged as a powerful tool for the analysis of\nmesoscopic and atomically resolved images and spectroscopy in electron and\nscanning probe microscopy, with the applications ranging from feature\nextraction to information compression and elucidation of relevant order\nparameters to inversion of imaging data to reconstruct structural models.\nHowever, the fundamental limitation of machine learning methods is their\ncorrelative nature, leading to extreme susceptibility to confounding factors.\nHere, we implement the workflow for causal analysis of structural scanning\ntransmission electron microscopy (STEM) data and explore the interplay between\nphysical and chemical effects in ferroelectric perovskite across the\nferroelectric-antiferroelectric phase transitions. The combinatorial library of\nthe Sm-doped BiFeO3 is grown to cover the composition range from pure\nferroelectric BFO to orthorhombic 20% Sm-doped BFO. Atomically resolved STEM\nimages are acquired for selected compositions and are used to create a set of\nlocal compositional, structural, and polarization field descriptors. The\ninformation-geometric causal inference (IGCI) and additive noise model (ANM)\nanalysis are used to establish the pairwise causal directions between the\ndescriptors, ordering the data set in the causal direction. The causal chain\nfor IGCI and ANM across the composition is compared and suggests the presence\nof common causal mechanisms across the composition series. Ultimately, we\nbelieve that the causal analysis of the multimodal data will allow exploring\nthe causal links between multiple competing mechanisms that control the\nemergence of unique functionalities of morphotropic materials and ferroelectric\nrelaxors.\n Ferroelectricity ; Scanning transmission electron microscopy ; Artificial intelligence ; Materials science ; Computer science ; Statistical physics ; Dielectric ; Transmission electron microscopy ; Physics ; Nanotechnology ; Optoelectronics Multiferroics and related materials ; Geochemistry and Geologic Mapping ; Machine Learning in Materials Science
Authors: Sajjad Abdollahramezani | Yashar Kiarashi | Omid Hemmatyar | Mohammadreza Zandehshavar | Ali Adibi
Source: Conference on Lasers and Electro-Optics (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrically programmable phased-array antenna using phase-change materials We present an electrically tunable metasurface as an optical phased array antenna by employing a deep learning approach to intelligently design hybrid phase- change/plasmonic materials-based meta-atoms spatially adding 0 and n phase-shift while minimizing reflectance variation. Phased array ; Phased-array optics ; Antenna (radio) ; Phase (matter) ; Materials science ; Plasmon ; Reflectivity ; Phase change ; Computer science ; Antenna array ; Optoelectronics ; Optics ; Electronic engineering ; Engineering ; Physics ; Telecommunications ; Engineering physics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Jesús Silva | Hugo Hernández Palma | William Niebles Núñez | Alex Ruiz-Lázaro | Noel Varela
Source: Journal of Physics Conference Series (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Neural Networks for Tea Leaf Classification Abstract The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually and at his own discretion, which has a number of associated drawbacks. In this work, a solution is proposed that includes the planting, design, development and testing of a prototype that is able to correctly classify photographs corresponding to samples of raw material arrived at a dryer, using intelligence techniques (IA) type supervised for Classification by Artificial Neural Networks and not supervised with K-means Grouping for class preparation. The prototype performed well and is a reliable tool for classifying the raw material slammed into tea dryers. Artificial neural network ; Raw material ; Computer science ; Artificial intelligence ; Process (computing) ; Product (mathematics) ; Quality (philosophy) ; Class (philosophy) ; Machine learning ; Pattern recognition (psychology) ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Alauddin Ahmed | Donald J. Siegel
Source: ChemRxiv (preprint, 2020-12-08)
Field: Chemistry — Inorganic Chemistry

Predicting Hydrogen Storage in MOFs via Machine Learning The H2 storage capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials under physisorptive conditions. The identified MOFs are predominantly hypothetical compounds having low densities (<0.31 g cm-3) in combination with high surface areas (> 5,300 m2 g-1), void fractions (~0.90), and pore volumes (>3.3 cm3 g-1). In addition, the relative importance of the input features are characterized, and dependencies on the ML algorithm and training set size are quantified. The single most important features for predicting H2 uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available for use via the web, allowing for rapid and accurate predictions of usable hydrogen capacities for MOFs with only minimal structural data as input; for the simplest models only a single input feature is required. USable ; Gravimetric analysis ; Hydrogen storage ; Computer science ; Porosity ; Metal-organic framework ; Data mining ; Chemistry ; Materials science ; Database ; Adsorption ; Hydrogen ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: Jussi Määttä | Viacheslav Bazaliy | Jyri Kimari | Flyura Djurabekova | K. Nordlund | Teemu Roos
Source: Neural Networks (preprint, 2020-11-01)
Field: Materials Science — Materials Chemistry

Gradient-based training and pruning of radial basis function networks with an application in materials physics Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques. We propose a fully gradient-based technique for training radial basis function networks with an efficient and scalable open-source implementation. We derive novel closed-form optimization criteria for pruning the models for continuous as well as binary data which arise in a challenging real-world material physics problem. The pruned models are optimized to provide compact and interpretable versions of larger models based on informed assumptions about the data distribution. Visualizations of the pruned models provide insight into the atomic configurations that determine atom-level migration processes in solid matter; these results may inform future research on designing more suitable descriptors for use with machine learning algorithms. Pruning ; Scalability ; Computer science ; Basis (linear algebra) ; Function (biology) ; Artificial intelligence ; Binary number ; Machine learning ; Radial basis function ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrocarbon exploration and reservoir analysis
Authors: Hayato Obata | Shota Takemura | Kazuyoshi Ogasawara
Source: IOP Conference Series Materials Science and Engineering (article, 2020-04-01)
Field: Chemistry — Inorganic Chemistry

Prediction of 4f<sup>2</sup>−4f<sup>1</sup>5d<sup>1</sup> transition energy of Pr<sup>3+</sup> in fluorides based on first-principles calculations and machine learning Abstract The 4f 2 −4f 1 5d 1 transition energy of Pr 3+ in fluorides are utilized for various optical materials such as solid-state lasers, phosphors, and scintillators. Therefore, it is important to predict such energies of unknown materials for theoretical design of novel optical materials. In this study, we tried to predict the 4f 2 −4f 1 5d1 transition energies of Pr 3+ in fluorides based on first-principles calculations and machine learning. The first-principles calculations were performed based on the relativistic discrete variational multi-electron (DVME) method using the model clusters composed of the central Pr 3+ and the anions closer than the nearest cation. Although the calculated 4f 2 −4f 1 5d 1 transition energies of Pr 3+ in fluorides showed a relatively good correlation with the experimental ones, the calculated values tend to be overestimated by ca. 2 eV. In order to improve the accuracy of the prediction, we used the calculated transition energies as an attribute for machine learning. As a result, the regression formula to predict the 4f 2 −4f 1 5d 1 transition energy of Pr 3+ in fluorides has been derived by machine learning using the calculated 4f 2 −4f 1 5d 1 transition energy as well as some other electronic and structural parameters as the attributes. The accuracy of the prediction was significantly improved compared to the simple first-principles calculations. Energy (signal processing) ; Atomic physics ; Phosphor ; Chemistry ; Physics ; Machine learning ; Nuclear physics ; Computer science ; Quantum mechanics Inorganic Fluorides and Related Compounds ; Luminescence Properties of Advanced Materials ; Advanced Chemical Physics Studies
Authors: Hamdya Sabrina Mohidin Yahya | Nor Aishah Saidina Amin
Source: IOP Conference Series Materials Science and Engineering (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Process optimization of catalytic steam reforming of toluene to hydrogen using response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA) Abstract Catalytic steam reforming of toluene (SRT) over nickel-cobalt supported on modified activated carbon for hydrogen production has been investigated. The center composite design of experiment in response surface methodology (RSM) was initially applied to optimize the catalytic SRT for hydrogen production before being utilized in the model building of the hybrid artificial neural network-genetic algorithm (ANN-GA). The genetic algorithm was carried out over the ANN model to achieve the maximum target response. The process optimization modeling using the best fitness function gave an insight of the optimal operating condition in SRT over the prepared catalyst. The results conferred that maximum hydrogen yield could be obtained at the optimal conditions of 700 °C temperature, 0.034 ml/min feed flow rate, 0.1 g catalyst loading and S/C ratio of 1 by ANN-GA model, and 762 °C temperature, 0.022 ml/min feed flow rate, 0.3 g catalyst loading and S/C ratio of 5.6 by the RSM model. Predicted results from ANN model were in higher agreement with the experimental data at R 2 =0.95 compared with the RSM model. Response surface methodology ; Catalysis ; Artificial neural network ; Genetic algorithm ; Hydrogen production ; Volumetric flow rate ; Hydrogen ; Materials science ; Central composite design ; Toluene ; Yield (engineering) ; Biological system ; Chemical engineering ; Engineering ; Computer science ; Chemistry ; Mathematics ; Chromatography ; Thermodynamics ; Mathematical optimization ; Metallurgy ; Physics ; Organic chemistry ; Machine learning Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Hualin Zhan | Richard D. Sandberg | Feng, Fan | Qinghua Liang | Ke Xie | Lianhai Zu | Dan Li | Jefferson Zhe Liu
Source: arXiv (Cornell University) (preprint, 2020-10-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-based Machine Learning Discovered Nano-circuitry for Nonlinear Ion Transport in Nanoporous Electrodes Confined ion transport is involved in nanoporous ionic systems. However, it is challenging to mechanistically predict its electrical characteristics for rational system design and performance evaluation using electrical circuit model due to the gap between the circuit theory and the underlying physical chemistry. Here we demonstrate that machine learning can bridge this gap and produce physics-based nano-circuitry, based on equation discovery from the modified Poisson-Nernst-Planck simulation results where an anomalous constructive diffusion-migration interplay of confined ions is unveiled. This bridging technique allows us to gain physical insights of ion dynamics in nanoporous electrodes, such as the non-ideal cyclic voltammetry. Supercapacitor ; Nanoscopic scale ; Ion ; Materials science ; Nanotechnology ; Diffusion ; Nanopore ; Ionic bonding ; Electrolyte ; Chemical physics ; Capacitance ; Chemistry ; Physics ; Thermodynamics ; Electrode ; Physical chemistry Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Nanopore and Nanochannel Transport Studies
Authors: Daniil Bash | Yongqiang Cai | Vijila Chellappan | Swee Liang Wong | Yang Xu | Pawan Kumar | Jin Da Tan | Anas Abutaha | Jayce Jian Wei Cheng | Yee‐Fun Lim | Siyu Tian | Danny Zekun Ren | Flore Mekki-Barrada | Wai Kuan Wong | Jatin Kumar | Saif Khan | Qianxiao Li | Tonio Buonassisi | Kedar Hippalgaonkar
Source: arXiv (Cornell University) (preprint, 2020-11-20)
Field: Materials Science — Materials Chemistry

Machine learning and high-throughput robust design of P3HT-CNT composite thin films for high electrical conductivity Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, combined with multi-labeled datasets, can additionally be used for scientific understanding and hypothesis testing. We introduce an automated flow system with high-throughput drop-casting for thin film preparation, followed by fast characterization of optical and electrical properties, with the capability to complete one cycle of learning of fully labeled ~160 samples in a single day. We combine regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive local optimum emerges when 10% of double-walled carbon nanotubes are added with long single wall carbon nanotubes, where the conductivity is seen to be as high as 700 S/cm, which we subsequently explain with high fidelity optical characterization. Employing dataset resampling strategies and graph-based regressions allows us to account for experimental cost and uncertainty estimation of correlated multi-outputs, and supports the proving of the hypothesis linking charge delocalization to electrical conductivity. We therefore present a robust machine-learning driven high-throughput experimental scheme that can be applied to optimize and understand properties of composites, or hybrid organic-inorganic materials. Carbon nanotube ; Materials science ; Throughput ; Conductivity ; Characterization (materials science) ; Computer science ; High fidelity ; Resampling ; Electrical resistivity and conductivity ; Composite number ; Nanotechnology ; Artificial intelligence ; Machine learning ; Algorithm ; Composite material ; Physics ; Engineering ; Electrical engineering Machine Learning in Materials Science
Authors: Reinis Ignatāns | Maxim Ziatdinov | Rama K. Vasudevan | Mani Valleti | Vasiliki Tileli | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2020-11-24)
Field: Materials Science — Materials Chemistry

Latent mechanisms of polarization switching from in situ electron microscopy observations In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switching and phase transition mechanisms. However, identification of these mechanisms from observational data sets has remained a problem due to a large variety of possible configurations, many of which are degenerate. Here, we introduce an approach based on rotationally invariant variational autoencoder (VAE), which enables learning a latent space representation of the data with multiple real-space rotationally equivalent variants mapped to the same latent space descriptors. By varying the size of training sub-images in the VAE, we tune the degree of complexity in the structural descriptors from simple domain wall detection to the identification of switching pathways. This yields a powerful tool for the exploration of the dynamic data in mesoscopic electron, scanning probe, optical, and chemical imaging. Moreover, this work adds to the growing body of knowledge of incorporating physical constraints into the machine and deep-learning methods to improve learned descriptors of physical phenomena. Mesoscopic physics ; Polarization (electrochemistry) ; Autoencoder ; Biological system ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Physics ; Artificial neural network ; Chemistry Machine Learning in Materials Science ; Geophysical and Geoelectrical Methods ; Non-Destructive Testing Techniques
Authors: Chongze Hu | Yunxing Zuo | Chi Chen | Shyue Ping Ong | Jian Luo
Source: arXiv (Cornell University) (preprint, 2020-02-25)
Field: Materials Science — Materials Chemistry

Genetic Algorithm-Guided Deep Learning of Grain Boundary Diagrams: Addressing the Challenge of Five Degrees of Freedom Grain boundaries (GBs) often control the processing and properties of polycrystalline materials. Here, a potentially transformative research is represented by constructing GB property diagrams as functions of temperature and bulk composition, also called "complexion diagrams," as a general materials science tool on par with phase diagrams. However, a GB has five macroscopic (crystallographic) degrees of freedom (DOFs). It is essentially a "mission impossible" to construct property diagrams for GBs as a function of five DOFs by either experiments or modeling. Herein, we combine isobaric semi-grand-canonical ensemble hybrid Monte Carlo and molecular dynamics (hybrid MC/MD) simulations with a genetic algorithm (GA) and deep neural network (DNN) models to tackle this grand challenge. The DNN prediction is ~108 faster than atomistic simulations, thereby enabling the construction of the property diagrams for millions of distinctly different GBs of five DOFs. Notably, excellent prediction accuracies have been achieved for not only symmetric-tilt and twist GBs, but also asymmetric-tilt and mixed tilt-twist GBs; the latter are more complex and much less understood, but they are ubiquitous and often limit the performance properties of real polycrystals as the weak links. The data-driven prediction of GB properties as function of temperature, bulk composition, and five crystallographic DOFs (i.e., in a 7D space) opens a new paradigm. Degrees of freedom (physics and chemistry) ; Algorithm ; Grain boundary ; Genetic algorithm ; Function (biology) ; Twist ; Statistical physics ; Computer science ; Phase diagram ; Artificial intelligence ; Physics ; Phase (matter) ; Materials science ; Mathematics ; Machine learning ; Geometry ; Microstructure ; Thermodynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Azadeh Alavi | David B. Ascher
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-11-13)
Field: Materials Science — Materials Chemistry

DHS-Crystallize: Deep-Hybrid-Sequence based method for predicting protein Crystallization Abstract The key method for determining the structure of a protein to date is X-ray crystallography, which is a very expensive technique that suffers from high attrition rate. On the contrary, a sequence-based predictor that is capable of accurately determining protein crystallization property, would not only overcome such limitations, but also would reduce the trial-and-error settings required to perform crystallization. In this work, to predict protein crystallizability, we have developed a novel sequence-based hybrid method that employs two separate, yet fully automated, concepts for extracting features from protein sequences. Specifically, we use a deep convolutional neural network on a publicly available dataset to extract descriptive features directly from the sequences, then fuse such feature with structural-and-physio-chemical driven features (such as amino-acid composition or AAIndex-based physicochemical properties). Dimentionality reduction is then performed on the resulting features and the output vectors are applied to train optimized gradient boosting machine (XGBoostt). We evaluate our method through three publicly available test sets, and show that our proposed DHS-Crystallize algorithm outperforms state-of-the-art methods, and achieves higher performance compared to using DCNN-deriven features, or structural-and-physio-chemical driven features alone. Computer science ; Artificial intelligence ; Convolutional neural network ; Sequence (biology) ; Fuse (electrical) ; Pattern recognition (psychology) ; Algorithm ; Boosting (machine learning) ; Gradient boosting ; Data mining ; Machine learning ; Chemistry ; Engineering Enzyme Structure and Function ; Machine Learning in Bioinformatics ; Protein Structure and Dynamics