jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Caetano R. Miranda
Source:  (article, 2019-12-10)
Field: Materials Science — Materials Chemistry

Perceptive immersive experiences on multiscale molecular simulations: applications for energy materials More recently, two new approaches have emerged to tackle materials modeling: i) Data analytics and ii) Perceptive experiences. Several initiatives have explored the use of Big-Data, data mining and machine learning techniques on under-explored applications in materials science by taking advantage of high-throughput molecular simulations. On the other side, a perceptive experience allow us to be immerse and interact in the virtual reality environment and act as an object in that particular world or system in their relative time, size and dynamic scales. In this talk, we will highlight computational materials design studies at IFUSP involving applications of multiscale molecular simulations with data analytics and perceptive materials design. We developed and applied a hierarchical multiscale computational protocol ranging from first principles calculations, molecular dynamics, Lattice Boltzmann and continuum methods. The applications involve nanostructured materials for energy applications ranging from i) Enhanced oil recovery, ii) natural gas separation and iii) ethanol catalysis. We also introduced and integrated virtual reality (VR) and sonification techniques to molecular simulations to provide a perceptive experience of materials at nanoscale and interactive tools for design new materials on the fly. Case studies on these immersive experiences will be presented. Computer science ; Virtual reality ; Human–computer interaction ; Big data ; Analytics ; Data science ; Ranging ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Cell Image Analysis Techniques
Authors: Yuan Qi | Alejandro Santana‐Bonilla | Martijn A. Zwijnenburg | Kim E. Jelfs
Source: ChemRxiv (preprint, 2019-09-30)
Field: Materials Science — Materials Chemistry

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties. Chemical space ; Computer science ; Artificial neural network ; Dipole ; Acceptor ; HOMO/LUMO ; Deep learning ; Artificial intelligence ; Representation (politics) ; Chemistry ; Computational chemistry ; Molecule ; Physics ; Drug discovery ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Michael Sluydts | Michiel Larmuseau | Johan Lauwaert | Stefaan Cottenier
Source: arXiv (Cornell University) (preprint, 2019-03-25)
Field: Materials Science — Materials Chemistry

The road to accuracy: machine-learning-accelerated silicon ab initio simulations Ab initio simulations are capable of providing detailed information of material behavior at the nanoscale. Simulating experimentally relevant situations is, however, often computationally intense. Using hybrid approaches between ab initio methods such as density functional theory (DFT) and machine learning, new models can be constructed which retain quantum accuracy while being computationally faster by several orders of magnitude. Two examples are discussed in this paper. The first is the computational search for low energy substitutional defect complexes in silicon. The second is the construction of deep learning potentials for ab initio-level molecular dynamics simulations. The latter is applied to reproduce the 0 K equation of state and high temperature thermal expansion of Si using the same model. Ab initio ; Silicon ; Statistical physics ; Molecular dynamics ; Computer science ; Density functional theory ; Energy (signal processing) ; Ab initio quantum chemistry methods ; Thermal ; Artificial intelligence ; Computational chemistry ; Materials science ; Computational science ; Physics ; Chemistry ; Quantum mechanics ; Molecule ; Thermodynamics Machine Learning in Materials Science ; Semiconductor materials and devices ; Surface and Thin Film Phenomena
Authors: Lingfei Wei | Xiaojie Xu | Gurudayal Gurudayal | James Bullock | Joel W. Ager
Source: ChemRxiv (preprint, 2019-05-20)
Field: Materials Science — Materials Chemistry

Machine Learning Optimization of P-Type Transparent Conducting Films P-type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, photodetectors, displays, and flexible sensors. Cu-Zn-S (CZS) thin films prepared by chemical bath deposition (CBD) can have both high transparency in the visible range (>80%) as well as excellent hole conductivity (>1000 S cm -1 ). However, the interplay between the deposition parameters in the CBD process (metal and sulfur precursor concentrations, temperature, pH, complexing agents, etc.) creates a multi-dimensional parameter space such that optimization for a specific application is challenging and time consuming. Here, we show that strategic design of experiment (DOE) combined with machine learning (ML) allows for efficient optimization of p-TCM performance. The approach is guided by a figure of merit (FOM) calculated from the film conductivity and optical transmission in the desired spectral range. A specific example is shown using two steps of optimization using a selected subset of 4 experimental CBD factors. The machine learning model is based on support vector regression (SVR) employing a radial basis function (RBF) as the kernel function. 10-fold cross-validation was performed to mitigate overfitting. After the first round of optimization, predicted areas in the parameter space with maximal FOMs were selected for a second round of optimization. Films with optimal FOMs were incorporated into heterojunction solar cells and transparent photodiodes. The optimization approach shown here will be generally applicable to any materials synthesis process with multiple parameters. Computer science ; Type (biology) ; Artificial intelligence ; Nanotechnology ; Materials science ; Biology Machine Learning in Materials Science
Authors: Jakoah Brgoch | Ya Zhuo
Source:  (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

The Pursuit of Novel Phosphors for the Next Generation of LED Lighting The development of new phosphors that are necessary for the next generation of high efficiency LED lighting requires a unique approach for materials discovery. Researchers often rely on chemical substitution or serendipity to identify new materials; however, this inevitably leads to slow, incremental advances in technology development. Our work has recently created a new approach that uses computational chemistry and machine learning to identify new materials guiding our experimental efforts. By predicting the vibrational properties and electronic structure of potential phosphors compounds, high-efficiency materials can be screened a priori ensuring the only best materials are experimentally explored. Following this methodology, our research has found NaBaB9O15:Eu2, which possesses a quantum yield of 95% and excellent thermal stability. Phosphor ; Computer science ; Smart lighting ; LED lamp ; Optoelectronics ; Materials science ; Architectural engineering ; Electrical engineering ; Engineering Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Advanced Chemical Sensor Technologies
Authors: Santosh K. Suram
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

(Invited) Accelerated Materials Design and Discovery: Novel Approaches for Accelerating Synthesis, Characterization, and Screening of Materials High-throughput computations have provided access to multiple properties of several inorganic materials. High-throughput experiments have enabled generation of materials properties under operating conditions. Both these approaches have accelerated discovery of materials. Further acceleration of materials discovery requires integration of these approaches that have different scopes and underlying assumptions. In this talk, I will present novel approaches based on network theory to predict synthesizability and hence reduce the "synthesis gap" between theory and experiments. Another significant gap between theory and experiment is the "characterization gap" which is due to the need for laborious characterization methods to capture an experimentally synthesized material's fingerprint. In this talk, I will present the use of machine learning methods to address this gap to capture structural, chemical, and electronic fingerprints of materials. High-throughput experiments have played a significant role in accessing high-dimensional chemical and screening space for materials discovery. However, practical implementations of high-throughput experimentation focuses on a specific region of the large chemical and screening space due to the exponential increase in number of experiments needed as dictated by the curse of dimensionality. I will present further acceleration of high-throughput experimentation by using machine learning approaches. Particularly, approaches that utilize information obtained from high-throughput computations will be discussed. Throughput ; Computer science ; Characterization (materials science) ; Curse of dimensionality ; Chemical space ; Acceleration ; Computation ; Computer engineering ; Machine learning ; Nanotechnology ; Drug discovery ; Wireless ; Materials science ; Algorithm ; Bioinformatics Machine Learning in Materials Science
Authors: Jeremy Goetz | Yi Zhang | Michael J. Lawler
Source: arXiv (Cornell University) (article, 2019-01-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Detecting Nematic Order in STM/STS Data with Artificial Intelligence Detecting the subtle yet phase defining features in Scanning Tunneling Microscopy and Spectroscopy data remains an important challenge in quantum materials. We meet the challenge of detecting nematic order from local density of states data with supervised machine learning and artificial neural networks for the difficult scenario without sharp features such as visible lattice Bragg peaks or Friedel oscillation signatures in the Fourier transform spectrum. We train the artificial neural networks to classify simulated data of isotropic and anisotropic two-dimensional metals in the presence of disorder. The supervised machine learning succeeds only with at least one hidden layer in the ANN architecture, demonstrating it is a higher level of complexity than nematic order detected from Bragg peaks which requires just two neurons. We apply the finalized ANN to experimental STM data on CaFe2As2, and it predicts nematic symmetry breaking with 99% confidence (probability 0.99), in agreement with previous analysis. Our results suggest ANNs could be a useful tool for the detection of nematic order in STM data and a variety of other forms of symmetry breaking. Liquid crystal ; Artificial neural network ; Scanning tunneling microscope ; Anisotropy ; Isotropy ; Artificial intelligence ; Computer science ; Physics ; Symmetry breaking ; Statistical physics ; Condensed matter physics ; Optics ; Quantum mechanics Iron-based superconductors research ; Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism
Authors: Robert B. Wexler
Source: ScholarlyCommons (University of Pennsylvania) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Toward Realistic Modeling Of Catalytic Surfaces: From First Principles To Machine Learning Computational catalyst design has the potential to revolutionize the energy and chemical industries by alleviating their reliance on fossil fuels, precious metals, and toxic elements. Despite recent advances in understanding catalytic trends, e.g. the chemisorption scaling relations and the d-band model, the description of catalytic surfaces has, for the most part, been far from realistic. It is well known that surfaces can undergo reconstruction where the structure and composition of the surface differs from that of the bulk and the nature of this reconstruction depends on the temperature, pressure, and chemical potentials of the elements in the system. Since catalytic transformations, i.e. bond breaking and formation, occur at the surface, an accurate picture of surface structure and composition is vital. In this thesis, we apply and develop state-of-the-art computational methods for studying the reconstruction of catalytic surfaces and investigate the effect of surface reconstruction on catalysis. First, we show using ab initio thermodynamics that the surfaces of nickel phosphide catalysts for the hydrogen evolution reaction (HER) are P-enriched, which was not previously considered in computational studies. Building on this discovery, we reevaluate the HER mechanism on Ni2P and Ni5P4 and find that P sites are the key to their catalytic activities. While the P sites on Ni2P are highly active toward the HER, they are not stable at conditions suitable for commercial electrolyzers. Under these conditions, the stable surface of Ni2P binds hydrogen too strongly at Ni sites. We demonstrate that these Ni sites can be activated by doping the surface of Ni2P with S, Se, and Te. Additionally, using tree-based machine learning methods, we reveal that nonmetal dopants induce a chemical pressure-like effect on the Ni sites, changing their reactivity through compression and expansion. Finally, we develop a software package for ab initio grand canonical Monte Carlo that automatically predicts surface phase diagrams. The results presented herein provide strong motivation and a methodological foundation for moving toward more realistic modeling of heterogeneous catalysts. Computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Andrew E. Sifain | Samuel H. Yalkowsky | Betsy M. Rice | Brian M. Barnes
Source: ChemRxiv (preprint, 2019-12-19)
Field: Materials Science — Materials Chemistry

Machine Learning Transition Temperatures from 2D Structure &lt;p&gt;A priori knowledge of physicochemical properties such as melting and boiling could expedite materials discovery. However, theoretical modeling from first principles poses a challenge for efficient virtual screening of potential candidates. &lt;/p&gt;&lt;p&gt;As an alternative, the tools of data science are becoming increasingly important for exploring chemical datasets and predicting material properties. Herein, we extend a molecular representation, or set of descriptors, first developed for quantitative structure-property relationship modeling by Yalkowsky and coworkers known as the Unified Physicochemical Property Estimation Relationships (UPPER). This molecular representation has group-constitutive and geometrical descriptors that map to enthalpy and entropy; two thermodynamic quantities that drive thermal phase transitions. We extend the UPPER representation to include additional information about &lt;i&gt;sp&lt;sup&gt;2&lt;/sup&gt;&lt;/i&gt;-bonded fragments. Additionally, instead of using the UPPER descriptors in a series of thermodynamically-inspired calculations, as per Yalkowsky, we use the descriptors to construct a vector representation for use with machine learning techniques. The concise and easy-to-compute representation, combined with a gradient-boosting decision tree model, provides an appealing framework for predicting experimental transition temperatures in a diverse chemical space. An application to energetic materials shows that the method is predictive, despite a relatively modest energetics reference dataset. We also report competitive results on diverse public datasets of melting points (i.e., OCHEM, Enamine, Bradley, and Bergstrom) comprised of over 47k structures. Open source software is available at https://github.com/USArmyResearchLab/ARL-UPPER.&lt;/p&gt; Representation (politics) ; Chemical space ; Computer science ; Boosting (machine learning) ; A priori and a posteriori ; Machine learning ; Artificial intelligence ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Olexandr Isayev
Source: Figshare (article, 2019-09-11)
Field: Materials Science — Materials Chemistry

Artificial Intelligence (AI) Solutions for Computational &amp; Organic Chemistry Olexandr Isayev talks about using neural networks to create fast and accurate molecular potentials trained on high-level QM data. The resulting ANI model (ANAKIN-ME: Accurate NeurAl networK engINefor Molecular Energies) seems to be very promising. The uploaded material contains presentation slides and video. Artificial intelligence ; Computer science ; Chemistry ; Applications of artificial intelligence Machine Learning in Materials Science
Authors: Valentin Vassilev Galindo | Igor Poltavskyi | Alexandre Tkatchenko
Source: Bulletin of the American Physical Society (article, 2019-03-06)
Field: Materials Science — Materials Chemistry

Constructing Accurate Machine Learning Force Fields for Flexible Molecules Force field (fiction) ; Computer science ; Molecule ; Materials science ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Pranav Shetty | Lihua Chen | Rohit Batra | Chao Wu | Zongze Li | Yang Cao | Rampi Ramprasad
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Data-Driven Modeling of Dielectric Breakdown Phenomena in Polymers Polymers are widely used as dielectrics in devices ranging from capacitors to multi-layered IC’s and as insulation for high voltage cables. In these applications, the dielectric breakdown strength is a critical parameter affecting device performance [1]. In this work, we study the dielectric breakdown properties of common polymers using machine learning. The complex nature of the dielectric breakdown process makes it analytically intractable for real systems and thus ideal for application of machine learning. The input to the machine learning model consists of material property values and the model outputs dielectric breakdown. For our training data, dielectric breakdown of polymer thin films is experimentally measured using small area breakdown measurements [2] at multiple temperatures ranging from -100 °C to 100 °C. The input to our model are property data that are known to influence dielectric breakdown. Selecting a subset of our original feature space using variable selection algorithms, we train a machine learning model using gaussian process regression. We test our model on unseen experimental data and find that it predicts dielectric breakdown of unseen polymers accurate to the experimental error of measurement. In addition we find that the underlying physics of the temperature dependence of dielectric breakdown is captured well in the training domain. Our model can be used to systematically search polymer spaces to accelerate discovery of high breakdown strength polymers. References [1] Zulkifli Ahmad (October 3rd 2012). Polymer Dielectric Materials, Dielectric Material, Marius Alexandru Silaghi, IntechOpen, DOI:10.5772/50638 [2] Rytoluoto, I., et al. ”Large-area dielectric breakdown performance of polymer films-part i: measurement method evaluation and statistical considerations on area-dependence.” IEEE Transactions on Dielectrics and Electrical Insulation 22.2 (2015): 689-700. Dielectric ; Dielectric strength ; Materials science ; Capacitor ; Polymer ; Breakdown voltage ; Electronic engineering ; Computer science ; Composite material ; Voltage ; Optoelectronics ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation
Authors: Dorin Cerbu | Sandip Halder | Philippe Leray
Source:  (article, 2019-03-26)
Field: Materials Science — Materials Chemistry

Deep-learning-based SEM image denoiser (Conference Presentation) Deep-learning-based SEM image denoiser Dorin Cerbu1, Sandip Halder1, Philippe Leray1 1IMEC, Kapeldreef 75, B-3001 Leuven, Belgium We report the development of a new method to denoise SEM images with the help of artificial neural networks. Upon using a preprocessing and training scheme tailored for SEM images of structures, most often encountered in semiconductor manufacturing, we can efficiently denoise images affected with varying degrees of noise severity and origin. In the figure below, we show an example of how we can use this filter efficiently to treat noisy images and improve the image quality. This can help in acquisition of more stable and better metrology data. Fig1(a) original image (b) Image which has been denoised using deep-learning based algorithms This development is of utmost importance for the case of post-litho processing step where resist nanostructures when SEM inspected are usually impacted by the electron beam and shrink, hence skewing critical dimension measurements. This is especially true as we push towards sub N-10 nm nodes. Application of our deep-learning processing scheme allows efficient noise reduction on SEM inspection images and helps us discern minor details previously shadowed by noise. This is extremely important as we move towards using EUV in high volume manufacturing. Small details can be crucial to understand the root-cause of stochastic and process defects. In previous work, we have already shown different approaches to understand stochastic defects [1-2]. The goal of this work is to enhance the image quality as much as possible to gain further fundamental understanding on nano-defects. [1] S. Halder et. al., ‘Using machine learning techniques to understand EUV stochastics, SPIE Photomask Technology + Extreme Ultraviolet Lithography, 2018 [2] K. Sah et.al., ‘EUV stochastic defect monitoring with advanced Broadband optical wafer inspection and e-Beam review systems’, SPIE Photomask Technology + Extreme Ultraviolet Lithography, 2018 Computer science ; Presentation (obstetrics) ; Artificial intelligence ; Deep learning ; Image (mathematics) ; Computer vision ; Computer graphics (images) ; Medicine ; Radiology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques