jablonkagroup/rise_ai_scientists huggingface dataset:
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 A priori knowledge of melting and boiling could expedite the discovery of pharmaceutical, energetic, and energy harvesting materials. The tools of data science are becoming increasingly important for exploring chemical datasets and predicting material properties. A fundamental part of data-driven modeling is molecular featurization. Herein, we propose a molecular representation with group-constitutive and geometrical descriptors that map to enthalpy and entropy–two thermodynamic quantities that drive phase transitions. The descriptors are inspired by the linear regression-based quantitative structure-property relationship of Yalkowsky and coworkers known as the Unified Physicochemical Property Estimation Relationships (UPPER). Used as input to nonlinear machine learning (specifically, eXtreme Gradient Boosting or XGBoost), these concise and easy-to-compute descriptors provide an appealing framework for predicting transition enthalpies, entropies, and temperatures in a diverse chemical space. An application to energetic materials shows that UPPER plus XGBoost is predictive, despite a relatively modest energetics reference dataset. Enthalpy ; A priori and a posteriori ; Property (philosophy) ; Phase transition ; Entropy (arrow of time) ; Computer science ; Boosting (machine learning) ; Nonlinear system ; Chemical space ; Machine learning ; Artificial intelligence ; Thermodynamics ; Statistical physics ; Chemistry ; Physics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Franzi Riegger
Source: Research Repository (Delft University of Technology) (article, 2019-01-01)
Field: Materials Science — Metals and Alloys

Image Segmentation of the γ'-Phase in Nickelbase Superalloys utilising Deep Learning Quantitative analysis of material microstructure is a well-known method to derive chemical and physical properties of a sample. This includes the segmentation of e.g. Light Optical Microscopy or Scanning Electron Microscopy images where each pixel is assigned to a material. Since some phases such as the γ-γ’ structure in nickelbased superalloys feature a high complexity and appear in a wide range of different sizes and shapes, segmentation is traditionally done by experts in a manual ap- proach . With Deep Neural Networks, a competitive method to automate this time-consuming, costly and inconsistent analysis, is found. Recently, their special instances of Convolutional Neu- ral Networks outperform any other machine learning algorithms in computer vision tasks by com- pressing the raw input image content to its most meaningful features. Applying these models to image segmentation demands for an adaption of their architecture, in order to maximize both, lo- cation and semantic information. Current research mainly focuses on two possible modifications, namely extending the encoding Convolutional Neural Network by a decoding substrcuture or partly substituting it by a context module. Maintaining the structural subdivision of encoder, context and decoder module allows a flexible, construction-kit like definition of segmentation models. The Ten- sorFlow library offers a framework for implementation. Based hereon, novel architectures are inves- tigated. Moreover, an insight into the impact of the single subelements and particular combinations of these is gained. For this, a chosen subsets of possible models are trained on 206 images. Com- prehensive understanding of internal mechanisms is achieved by visualization, error analysis and generalized consideration of segmentation relevant model characteristics such as the Effective Re- ceptive Field. The model selection is based on the validation results, derived from segmenting fur- ther 84 images. On this data set, the final model predicts the ground truth data with almost human like accuracy. Computer science ; Artificial intelligence ; Segmentation ; Convolutional neural network ; Context (archaeology) ; Pixel ; Encoder ; Deep learning ; Pattern recognition (psychology) ; Feature (linguistics) ; Artificial neural network ; Computer vision Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep ; Welding Techniques and Residual Stresses
Authors: Daniil Koshelyuk | Ardeshir Talaei | Soroush Garivani | Areti Markopoulou | Angelos Chronis | David Andres Leon | Raimund Krenmuller
Source: ACADIA quarterly (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Alive In the context of data-driven culture, built space still maintains low responsiveness and adaptability. Part of this reality lies in the low resolution of live information we have about the behavior and condition of surfaces and materials. This research addresses this issue by exploring the development of a deformation-sensing composite membrane material system following a bottom-up approach and combining various technologies toward solving related technical issuesÂexploring conductivity properties of graphene and maximizing utilization within an architecture-related proof-of-concept scenario and a workflow including design, fabrication, and application methodology. Introduced simulation of intended deformation helps optimize the pattern of graphene nanoplatelets (GNP) to maximize membrane sensitivity to a specific deformation type while minimizing material usage. Research explores various substrate materials and graphene incorporation methods with initial geometric exploration. Finally, research introduces data collection and machine learning techniques to train recognition of certain types of deformation (single point touch) on resistance changes. The final prototype demonstrates stable and symmetric readings of resistance in a static state and, after training, exhibits an 88% prediction accuracy of membrane shape on a labeled sample data-set through a pre-trained neural network. The proposed framework consisting of a simulation based, graphene-capturing fabrication method on stretchable surfaces, and includes initial exploration in neural network training shape detection, which combined, demonstrate an advanced approach to embedding intelligence. Computer science Graphene research and applications ; Graphene and Nanomaterials Applications
Authors: Sebastian Dick | Mariví Fernández-Serra
Source: ChemRxiv (preprint, 2019-08-06)
Field: Materials Science — Materials Chemistry

Learning from the Density to Correct Total Energy and Forces in First Principle Simulations We propose a new molecular simulation framework that combines the transferability, robustness and chemical flexibility of an ab initio method with the accuracy and efficiency of a machine learned force field. The key to achieve this mix is to use a standard density functional theory (DFT) simulation as a pre-processor for the atomic and molecular information, obtaining a good quality electronic density. General, symmetry preserving, atom-centered electronic descriptors are then built from this density to train a neural network to correct the baseline DFT energies and forces. These electronic descriptors encode much more information than local atomic environments, allowing a simple neural network to reach the accuracy required for the problem of study at a negligible cost. The balance between accuracy and efficiency is determined by the baseline simulation. This is shown in results where high level quantum chemical accuracy is obtained for simulations of liquid water at standard DFT cost, or where high level DFT-accuracy is achieved in simulations with a low-level baseline DFT calculation, at a significantly reduced cost. Density functional theory ; Computer science ; Robustness (evolution) ; Electronic density ; Artificial neural network ; Ab initio ; Baseline (sea) ; ENCODE ; Force field (fiction) ; Flexibility (engineering) ; Atom (system on chip) ; Artificial intelligence ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics ; Mathematics ; Statistics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Devina Darmajaya
Source: DR-NTU (Nanyang Technological University) (article, 2019-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Computer vision for advanced electron microscopy analysis Transmission electron microscopy (TEM) is one of the most powerful techniques used to characterize materials. However, characterization of a set of heterogenous sample images still remains as a persistent challenge. This can be a painstaking process in which researches may spend a tremendous amount of time to classify particle’s crystal structures manually. Moreover, this may also lead to sampling bias by reporting the favourable image in the report that is not representative of the whole sample. Therefore, this study aims to provide solution to this problem by utilizing machine learning. The project focuses especially in building an optimized model to recognize synthetic images with convolutional neural network (CNN). Synthetic images were generated with MATLAB due to relevant dataset is not present at the moment. Afterwards, a CNN model was built and optimized by adjusting several parameters including choice of optimizer, loss function and regularization in Python. The model presented was able to attain 98.44% accuracy and 0.034 loss. This preliminary model is hopefully able to accelerate future works in recognizing synthetic images and real TEM images by transfer learning. Microscopy ; Electron microscope ; Computer vision ; Computer science ; Computer graphics (images) ; Artificial intelligence ; Materials science ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques
Authors: Shenghong Ju | Ryo Yoshida | Chang Liu | Kenta Hongo | Terumasa Tadano | Junichiro Shiomi
Source: ChemRxiv (preprint, 2019-09-20)
Field: Materials Science — Materials Chemistry

Exploring Diamond-Like Lattice Thermal Conductivity Crystals via Feature-Based Transfer Learning Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using machine learning can search for materials with outstanding higher-order properties like thermal conductivity. However, the lack of sufficient data to train a model is a serious hurdle. Herein we show that big data can complement small data for accurate predictions when lower-order feature properties available in big data are selected properly and applied to transfer learning. The connection between the crystal information and thermal conductivity is directly built with a neural network by transferring descriptors acquired through a pre-trained model for the feature property. Successful transfer learning shows the ability of extrapolative prediction and reveals descriptors for lattice anharmonicity. Transfer learning is employed to screen over 60000 compounds to identify novel crystals that can serve as alternatives to diamond. Even though most materials in the top list are superhard materials, we reveal that superhard property do not necessarily lead to high lattice thermal conductivity. Large hardness means high elastic constants and group velocity of phonons in the linear dispersion regime, but the lattice thermal conductivity is determined also by other important factor such as the phonon relaxation time. What’s more, the average or maximum dipole polarizability and the van der Waals radius are revealed to be the leading descriptors among those that can also be qualitatively related to anharmonicity. Thermal conductivity ; Anharmonicity ; Phonon ; Diamond ; Polarizability ; Materials science ; Lattice (music) ; Heat transfer ; Condensed matter physics ; Machine learning ; Transfer of learning ; Statistical physics ; Artificial intelligence ; Computer science ; Thermodynamics ; Physics ; Quantum mechanics ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research
Authors: Huanchen Zhai
Source: eScholarship (California Digital Library) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Potential Energy Surface Exploration of Metal Catalytic Clusters Metal sub-nano clusters are important materials for catalysis of chemical reactions such as dehydrogenation of hydrocarbons. However, their potential energy surface (PES), which is responsible for explaining relative stability of different cluster geometries, can be complicated. The complexity can be greatly reduced by describing the PES by low-energy isomers, which can be found by the standard mathematical method called global optimization. We have proposed global optimization acceleration schemes using Force Field (FF) fitting, or deep neural network (DNN) fitting, for gas phase metal clusters. Both these models can be trained to give an approximation to the PES at the density functional theory level. For surface supported clusters, we found that Basin Hopping (BH) global optimization can usually give satisfactory results. To address the difficulty in performing structural search for surface supported clusters at high hydrogen coverage, we proposed a revised BH approach with core-shell separation scheme. The application of this new approach shows that the cluster shape can be very different from that of their adsorbate-free counterparts. After the structure of isomers is found, it is also necessary to define the geometry similarity between two isomers with the same chemical formula. Structure similarity measurement is an important part of any PES exploration techniques. We have investigated existing algorithms and introduced two new algorithms, namely, atomic matching based on depth-first search and bipartite model atomic matching, for gas phase and surface supported clusters, respectively.Additionally, isomers may interconvert across barriers, i.e., exhibit fluxionality, during catalysis. To study the big picture of the fluxional behavior for surface supported clusters, we model such process as isomerization graph using bipartite matching algorithm, harmonic transition state theory (HTST), and paralleled nudged elastic band (NEB) method. Detailed inspection shows that, at temperatures typical for catalysis, the cluster geometry changes frequently within several regions in the graph, while transition across regions is less likely. This local fluxionality picture provides a new perspective on understanding finite-temperature catalytic processes. Matching (statistics) ; Cluster (spacecraft) ; Potential energy surface ; Surface (topology) ; Global optimization ; Artificial neural network ; Similarity (geometry) ; Computer science ; Chemical physics ; Computational chemistry ; Chemistry ; Molecule ; Algorithm ; Physics ; Mathematics ; Geometry ; Artificial intelligence ; Quantum mechanics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Pikee Priya | Chi Zhang | N. R. Aluru
Source: ECS Meeting Abstracts (article, 2019-05-01)
Field: Materials Science — Materials Chemistry

Prediction of Electrochemical Characteristics for Yttrium Doped Barium Zirconate Based Solid Oxide Fuel and Electrolyzer Cells Using a Multiscale Framework Yttrium doped Barium Zirconate (BZY) is one of the best-known proton-conducting solid oxides and understanding its behavior is the key to designing better proton conducting electrolytes for Solid Oxide Fuel/Electrolyzer Cells (SOFC/SOEC). Functioning of an SOFC/SOEC involves a complex interplay of factors like temperature, chemistry, microstructure, etc., at different length and time scales which necessitates the use of multiscale computational techniques. A multiscale framework involving Density Functional Theory at atomic, Transition State Theory at meso and Electrochemical Theory at continuum scales has, therefore, been developed that could accurately predict Current-Voltage Characteristics for direct current and Impedance Spectra for alternating current in BZY based SOFC/SOECs. The effect of operating conditions on charge carriers and, hence, the performance of the cell has been analyzed. Temperature and the environment (reducing/oxidizing) have a significant effect on proton conductivity of the electrolyte. The activation energy for oxidizing atmosphere is found to be lower facilitating use at lower temperatures in these environments. Using Neural Networks as a surrogate model, a multi-dimensional uncertainty quantification and sensitivity analysis has also been performed to point out the important parameters determining the performance of the SOFC/SOEC. Hydration energy, proton diffusivity, and permittivity are found to influence the efficiency of the cell significantly. These parameters serve to delineate design guidelines for the development of new chemistries and compositions of proton conducting solid oxides through a Random Forest model, correlating material property descriptors for these parameters to the proton conductivity of various proton conducting solid oxides studied in the literature. Materials science ; Electrolyte ; Oxide ; Solid oxide fuel cell ; Conductivity ; Oxidizing agent ; Yttrium ; Barium ; Chemical engineering ; Thermodynamics ; Chemistry ; Physical chemistry ; Electrode ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials
Authors: Deepak Kamal | Anand Chandrashekaran | Rampi Ramprasad
Source: Bulletin of the American Physical Society (article, 2019-03-04)
Field: Materials Science — Materials Chemistry

A charge density prediction model for organic molecules using Deep Neural Networks Artificial neural network ; Organic molecules ; Charge density ; Charge (physics) ; Deep neural networks ; Chemical physics ; Materials science ; Molecule ; Statistical physics ; Biological system ; Computer science ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Васильев Александр Николаевич | Кузнецов Евгений Борисович | Леонов Сергей Сергеевич | Тархов Дмитрий Альбертович
Source: Международный научный журнал «Современные информационные технологии и ИТ-образование» (article, 2019-12-23)
Field: Materials Science — Materials Chemistry

Многослойный метод идентификации характеристик ползучести в интервале напряжений для задачи разрушения металлов в условиях ползучести The paper considers the coefficient inverse problem of identifying the creep characteristics for metal structures deformed under high-temperature creep conditions. Creep deformation is widely used in various engineering problems, including the shaping of structural elements and the calculation of long-term strength. The creep phenomenon is exhibited by most known materials, including metals, concrete, and composites. Herein, there is no unified creep theory, which is explained by the complexity of the described process. The constitutive creep equations, as a rule, contain a set of tunable parameters (material constants) necessary for identification. There are many methods for determining material constants, but most of them are suitable only for specific creep models. In previous works, the authors proposed a method of multilayer functional systems, which in combination with neural network technique led to an original approach in the application of deep learning neural networks to the construction of approximate solutions of differential equations. Such approach allows combining the flexibility of artificial neural networks and the performance of traditional numerical methods for solving inverse and Cauchy problems. With regard to the creep models identification problem multilayer method allows naturally obtain a deep network, giving an approximate solution to the problem under consideration with an explicit occurrence of material constants. The identification of material constants is reduced to approximating experimental data, for example, by the least-squares method using the constructed multilayer solution. The advantages of the multilayer method are shown in solving the identification problem of the material constants for stress range. We selected the creep model describing the tension of cylindrical specimens of steel 45 at a constant stress level and temperature as a test problem for our multilayer method. Authors carry out analysis of obtained results and a comparison of the calculated data with the experiment and the results of other authors to confirm the reliability of the method used. Creep ; Artificial neural network ; Inverse problem ; Computer science ; Flexibility (engineering) ; Applied mathematics ; Inverse ; Identification (biology) ; Set (abstract data type) ; Materials science ; Mathematical analysis ; Mathematics ; Artificial intelligence ; Geometry ; Composite material Structural mechanics and materials ; Material Properties and Failure Mechanisms
Authors: Piero Gasparotto | Davide Bochicchio | Michele Ceriotti | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2019-12-23)
Field: Materials Science — Biomaterials

Identifying and Tracking Defects in Dynamic Supramolecular Polymers A central paradigm of self-assembly is to create ordered structures starting from molecularmonomers that spontaneously recognize and interact with each other via noncovalent interactions.In the recent years, great efforts have been directed toward reaching the perfection in thedesign of a variety of supramolecular polymers and materials with different architectures. Theresulting structures are often thought of as ideally perfect, defect-free supramolecular fibers,micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at thelevel of statistical ensembles to assess their average properties. However, molecular simulationsrecently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamicbehavior and properties. The study of these defects poses considerable challenges, as theflexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutesa defect, and to characterize its stability and evolution. Here, we demonstrate the powerof unsupervised machine learning techniques to systematically identify and compare defects insupramolecular polymer variants in different conditions, using as a benchmark 5°A-resolutioncoarse-grained molecular simulations of a family of supramolecular polymers. We shot that thisapproach allows a complete data-driven characterization of the internal structure and dynamicsof these complex assemblies and of the dynamic pathways for defects formation and resorption.This provides a useful, generally applicable approach to unambiguously identify defects inthese dynamic self-assembled materials and to classify them based on their structure, stabilityand dynamics. Supramolecular chemistry ; Supramolecular polymers ; Nanotechnology ; Computer science ; Biological system ; Materials science ; Chemistry ; Molecule Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Polymer composites and self-healing