jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: O. V. Sokolova | Sergii Vysloukh
Source: Електронний архів наукових та освітніх матеріалів КПІ ім. Ігоря Сікорського (КПІ ім. Ігоря Сікорського) (article, 2020-01-01)
Field: Materials Science — General Materials Science

Modeling of titanium alloys processing parameters The article discusses the modeling of the efficient cutting tool life in the processing of titanium alloy. To realize this aim, an effective method for processing research results has been selected. The features and algorithm of using the TensorFlow platform for the design, creation and study of models in the field of machine learning are presented. Metallurgy ; Titanium ; Materials science ; Computer science Material Properties and Applications ; Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming
Authors: Christopher Yeung | Ju-Ming Tsai | Brian King | Benjamin Pham | Julia Liang | David Ho | Mark W. Knight | Aaswath P. Raman
Source: arXiv (Cornell University) (article, 2020-08-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Designing Multiplexed Supercell Metasurfaces with Tandem Neural Networks Complex nanophotonic structures hold the potential to deliver exquisitely tailored optical responses for a range of device applications. Metal-insulator-metal (MIM) metasurfaces arranged in supercells, for instance, can be tailored by geometry and material choice to exhibit a range of absorption properties and resonant wavelengths. With this flexibility however comes a vast space of design possibilities that classical design paradigms struggle to efficiently navigate. To overcome this challenge, here we demonstrate that a deep tandem neural network approach that can efficiently generate multiplexed supercells through inverse design. By using a training dataset with several thousand full-wave electromagnetic simulations in a design space of over three trillion possible designs, the deep-learning model can accurately generate a wide range of complex supercell designs given a spectral target. Beyond inverse design, we show that our approach can also be used to explore the limits of broadband absorption and emission in such supercell configurations. Our demonstration of high-dimensional supercell inverse design with deep neural networks is generally applicable to any complex nanophotonic structure composed of multiple subunit elements. Nanophotonics ; Supercell ; Computer science ; Tandem ; Metamaterial ; Multiplexing ; Broadband ; Absorption (acoustics) ; Artificial neural network ; Materials science ; Physics ; Nanotechnology ; Optoelectronics ; Optics ; Artificial intelligence ; Telecommunications Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Plasmonic and Surface Plasmon Research
Authors: Tristan Bereau
Source: Modelling and Simulation in Materials Science and Engineering (preprint, 2020-12-03)
Field: Materials Science — Materials Chemistry

Computational compound screening of biomolecules and soft materials by molecular simulations Abstract Decades of hardware, methodological, and algorithmic development have propelled molecular dynamics (MD) simulations to the forefront of materials-modeling techniques, bridging the gap between electronic-structure theory and continuum methods. The physics-based approach makes MD appropriate to study emergent phenomena, but simultaneously incurs significant computational investment. This topical review explores the use of MD outside the scope of individual systems, but rather considering many compounds. Such an in silico screening approach makes MD amenable to establishing coveted structure-property relationships. We specifically focus on biomolecules and soft materials, characterized by the significant role of entropic contributions and heterogeneous systems and scales. An account of the state of the art for the implementation of an MD-based screening paradigm is described, including automated force-field parametrization, system preparation, and efficient sampling across both conformation and composition. Emphasis is placed on machine-learning methods to enable MD-based screening. The resulting framework enables the generation of compound-property databases and the use of advanced statistical modeling to gather insight. The review further summarizes a number of relevant applications. Bridging (networking) ; Computer science ; Scope (computer science) ; In silico ; Nanotechnology ; Biomolecule ; Molecular dynamics ; Parametrization (atmospheric modeling) ; Data science ; Biochemical engineering ; Chemistry ; Engineering ; Materials science ; Physics ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Surface Chemistry and Catalysis
Authors: Jan‐Joris Devogelaer | Hugo Meekes | Paul Tinnemans | Elias Vlieg | R. De Gelder
Source: ChemRxiv (preprint, 2020-06-11)
Field: Materials Science — Materials Chemistry

Cocrystal Prediction by Artificial Neural Networks &lt;div&gt;A significant amount of attention has been given to the design and synthesis of cocrystals by both industry and academia because of its potential to change a molecule’s physicochemical properties. This paper reports on the application of a data-driven cocrystal prediction method, based on two types of artificial neural network models and cocrystal data present in the Cambridge Structural Database. The models accept pairs of coformers and predict whether a cocrystal is likely to form.&lt;/div&gt; Cocrystal ; Artificial neural network ; Computer science ; Artificial intelligence ; Chemistry ; Molecule ; Organic chemistry ; Hydrogen bond Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Machine Learning in Materials Science
Authors: Kihoon Bang | Byung Chul Yeo | Dong-Hun Kim | Sang Soo Han | Hyuck Mo Lee
Source: arXiv (Cornell University) (preprint, 2020-07-21)
Field: Materials Science — Materials Chemistry

Accelerated Mapping of Electronic Density of States Patterns of Metallic Nanoparticles via Machine-Learning Within first-principles density functional theory (DFT) frameworks, accurate but fast prediction of electronic structures of nanoparticles (NPs) remains challenging. Herein, we propose a machine-learning architecture to rapidly but reasonably predict electronic density of states (DOS) patterns of metallic NPs via a combination of principal component analysis (PCA) and the crystal graph convolutional neural network (CGCNN). By applying PCA, one can convert a mathematically high-dimensional DOS image to a low-dimensional vector. The CGCNN plays a key role in reflecting the effects of local atomic structures on the DOS patterns of NPs with only a few of material features (e.g., melting temperature, the number of d electrons, and atomic radius) that are easily obtained from a periodic table. The PCA-CGCNN model is applicable for all pure and bimetallic NPs, in which a handful DOS training sets that are easily obtained with the typical DFT method, such as bulk, slab, and small-sized NPs, are considered. Although there is a small loss of accuracy with the PCA-CGCNN method compared to DFT calculations, the prediction speed is much faster than that of DFT methods and is not nearly as affected by the system sizes of NPs. Our approach not only can be immediately applied to predict electronic structures of actual nanometer scaled NPs to be experimentally synthesized, but also be used to explore correlations between atomic structures and other spectrum image data of the materials (e.g., X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy). Electronic structure ; Materials science ; Density functional theory ; Bimetallic strip ; X-ray photoelectron spectroscopy ; Electronic density ; Principal component analysis ; Nanoparticle ; Artificial neural network ; Convolutional neural network ; Raman spectroscopy ; Spectroscopy ; Computer science ; Computational physics ; Artificial intelligence ; Nanotechnology ; Metal ; Computational chemistry ; Physics ; Chemistry ; Optics ; Quantum mechanics ; Nuclear magnetic resonance Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electrocatalysts for Energy Conversion
Authors: Jun Zhang | Yao-Kun Lei | Yi Yang | Yi Qin Gao
Source: ChemRxiv (preprint, 2020-02-25)
Field: Materials Science — Materials Chemistry

Reinforcement Learning for Multi-Scale Molecular Modeling Molecular simulations are widely applied in the study of chemical and bio-physical systems of interest. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper we propose a machine-learning approach to take advantage of both strategies. In this approach, simulations on different scales are executed simultaneously and benefit mutually from their cross-talks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations; In turn, FG simulations can be boosted by the guidance of CG models. Our method grounds on unsupervised and reinforcement learning, defined by a variational and adaptive training objective, and allows end-to-end training of parametric models. Through multiple experiments, we show that our method is efficient and flexible, and performs well on challenging chemical and bio-molecular systems. Reinforcement learning ; Computer science ; Artificial intelligence ; Machine learning ; Scale (ratio) ; Parametric statistics ; Mathematics ; Physics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Software Engineering Research
Authors: Myong In Oh | Chang In Oh | Mayuri Gupta | Donald F. Weaver
Source: The Journal of Physical Chemistry (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Decoding Interfacial Water Orientation to Predict Surface Charge Density on a Model Sheet Using a Deep Learning Algorithm The anomalous behavior of water at the aqueous interface of a surface has been thoroughly investigated over the past decades. However, indirect surface characterization based on the properties of interfacial water has not been well-studied at the molecular level despite its practical significance in science and medicine. Here, we present the theoretical development of an inference of surface charge density from orientational polarization of interfacial water using an integrated approach of information theory, molecular dynamics, and deep learning. Our findings demonstrate that the “message” about surface charge is encoded in interfacial water orientation, but the orientation angle itself is a weak predictor. The shape of the orientational distribution curve is instead a better predictor and decodable to surface charge with high accuracy using an artificial neural network. This study stimulates the design of a tunable protocol to predict surface properties using information stored in interfacial water particularly when direct surface characterization is impractical. Surface charge ; Charge density ; Chemistry ; Orientation (vector space) ; Surface (topology) ; Characterization (materials science) ; Chemical physics ; Charge (physics) ; Molecular dynamics ; Inference ; Polarization (electrochemistry) ; Nanotechnology ; Biological system ; Artificial intelligence ; Materials science ; Computational chemistry ; Physical chemistry ; Geometry ; Physics ; Computer science Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Sensor Technologies
Authors: MITCH JACOBY
Source: C&EN Global Enterprise (article, 2020-06-22)
Field: Materials Science — Materials Chemistry

System makes quantum dots autonomously By combining artificial intelligence (AI), machine learning, and microfluidics, researchers have designed an autonomous decision-making synthesis system that chooses optimal reagents, synthetic routes, and reaction conditions to make valuable products with targeted properties. Dubbed “Artificial Chemist,” the system uses tiny quantities of precursors and does not require experienced users to monitor and guide experiment choices. The system, which learns from successive experiments, can reduce the time, cost, and quantity of waste associated with traditional R&D synthesis (Adv. Mater. 2020, DOI: 10.1002/adma.202001626). In a proof-of-concept study, Robert W. Epps and Milad Abolhasani of North Carolina State University and coworkers used the system to make a series of uniform colloidal perovskite quantum dots with preselected emission profiles and other electronic properties. These materials are used in solar cells, light-emitting diodes, and electronic displays. Fitted with syringe pumps, micromixers, and built-in spectroscopy probes, Artificial Chemist reacted cesium lead bromide, zinc halides, and organic Quantum dot ; Computer science ; Physics ; Quantum mechanics Quantum Dots Synthesis And Properties
Authors: Raul A. Flores | Christopher Paolucci | Kirsten T. Winther | Ankit Jain | José Antonio Garrido Torres | Muratahan Aykol | Joseph H. Montoya | Jens K. Nørskov | Michal Bajdich | Thomas Bligaard
Source: ChemRxiv (preprint, 2020-05-06)
Field: Materials Science — Materials Chemistry

Active Learning Accelerated Discovery of Stable Iridium-oxide Polymorphs for the Oxygen Evolution Reaction The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material's structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium-oxide polymorphs of IrO2 and IrO3. The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB2 and AB3 prototypes in existing materials databases (more than 38000). Next, using an active learning approach we are able to find 196 IrO2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO3 polymorphs and report a previously unknown FeF3-type structure as the most stable, termed α-IrO3. To test the algorithms performance, we compare to a random search of the candidate space and report at least a twofold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO2 and IrO3 with less than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low energy structures. Subsequent Pourbaix Ir-H2O analysis shows that α-IrO3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO2. Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO3 than on any other considered iridium-oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal-oxide structure with a defined stoichiometry. Chemical space ; Oxygen evolution ; Amorphous solid ; Density functional theory ; Rutile ; Iridium ; Oxide ; Materials science ; Octahedron ; Catalysis ; Chemistry ; Nanotechnology ; Crystal structure ; Electrochemistry ; Crystallography ; Drug discovery ; Computational chemistry ; Physical chemistry Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electrocatalysts for Energy Conversion
Authors: Ignacio Pickering | Adrián E. Roitberg
Source:  (preprint, 2020-04-30)
Field: Materials Science — Materials Chemistry

Predicting low-lying excited state energies of organic molecules with an atomistic deep neural network potential Excited state ; Lying ; Organic molecules ; Artificial neural network ; Molecule ; State (computer science) ; Atomic physics ; Materials science ; Chemical physics ; Physics ; Statistical physics ; Molecular physics ; Computer science ; Artificial intelligence ; Quantum mechanics ; Medicine ; Algorithm Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures
Authors: Yusuf Shaidu | Emine Küçükbenli | Ruggero Lot | Franco Pellegrini | Efthimios Kaxiras | Stefano de Gironcoli
Source: arXiv (Cornell University) (preprint, 2020-11-09)
Field: Materials Science — Materials Chemistry

A Systematic Approach to Generating Accurate Neural Network Potentials:\n the Case of Carbon Availability of affordable and widely applicable interatomic potentials is\nthe key needed to unlock the riches of modern materials modelling. Artificial\nneural network based approaches for generating potentials are promising;\nhowever neural network training requires large amounts of data, sampled\nadequately from an often unknown potential energy surface. Here we propose a\nself-consistent approach that is based on crystal structure prediction\nformalism and is guided by unsupervised data analysis, to construct an\naccurate, inexpensive and transferable artificial neural network potential.\nUsing this approach, we construct an interatomic potential for Carbon and\ndemonstrate its ability to reproduce first principles results on elastic and\nvibrational properties for diamond, graphite and graphene, as well as energy\nordering and structural properties of a wide range of crystalline and amorphous\nphases.\n Artificial neural network ; Interatomic potential ; Computer science ; Diamond ; Construct (python library) ; Formalism (music) ; Graphene ; Artificial intelligence ; Statistical physics ; Materials science ; Molecular dynamics ; Nanotechnology ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Force Microscopy Techniques and Applications
Authors: Fang Liu | Chenru Duan | Heather J. Kulik
Source: ChemRxiv (preprint, 2020-07-24)
Field: Materials Science — Materials Chemistry

Rapid Detection of Strong Correlation with Machine Learning for Transition Metal Complex High-Throughput Screening Despite its widespread use in chemical discovery, approximate density functional theory (DFT) is poorly suited to many materials targets, such as those containing open-shell, 3d transition metals that can be expected to have strong multireference (MR) character. For DFT workflows to be predictive, we must incorporate automated, low cost methods that can distinguish the regions of chemical space where DFT should be applied and where it should not. We curate over 4,800 open shell transition metal complexes up to hundreds of atoms in size from prior high-throughput DFT studies and evaluate affordable, finite-temperature DFT evaluation of fractional occupation number (FON)-based MR diagnostics. We show that intuitive measures of strong correlation (i.e., the HOMO-LUMO gap) are not predictive of MR character as judged by FON-based diagnostics. We train independent machine learning (ML) models to predict HOMO-LUMO gaps and FON-based diagnostics. ML model analysis reveals differences in metal- and ligand-sensitivity of the two quantities, suggesting opportunities to minimize MR character while tailoring the gap. We use our trained ML models to rapidly evaluate MR character over a space of ca. 187,000 theoretical complexes, identifying large-scale trends in spin-state-dependent MR character and discovering small HOMO-LUMO gap complexes with low MR character. Character (mathematics) ; HOMO/LUMO ; Density functional theory ; Correlation ; Chemical space ; Throughput ; Artificial intelligence ; Computer science ; Parameter space ; Chemical shift ; Transition metal ; Machine learning ; Materials science ; Statistical physics ; Computational chemistry ; Physics ; Chemistry ; Mathematics ; Quantum mechanics ; Molecule ; Drug discovery ; Statistics ; Catalysis ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Andrew E. Sifain | Samuel H. Yalkowsky | Betsy M. Rice | Brian M. Barnes
Source: ChemRxiv (preprint, 2020-11-19)
Field: Materials Science — Materials Chemistry

Machine Learning Transition Temperatures from 2D Structure 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. 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 sp2-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. Computer science ; Representation (politics) ; Chemical space ; A priori and a posteriori ; Boosting (machine learning) ; Machine learning ; Property (philosophy) ; Entropy (arrow of time) ; Artificial intelligence ; Data mining ; Chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis