jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yongcheng Liang | Ping Qin | Haitao Jiang | Lizhen Zhang | Jing Zhang | Chun Tang
Source: AIP Advances (article, 2018-04-01)
Field: Materials Science — Materials Chemistry

Designing superhard metals: The case of low borides The search for new superhard materials has usually focused on strong covalent solids. It is, however, a huge challenge to design superhard metals because of the low resistance of metallic bonds against the formation and movement of dislocations. Here, we report a microscopic mechanism of enhancing hardness by identifying highly stable thermodynamic phases and strengthening weak slip planes. Using the well-known transition-metal borides as prototypes, we demonstrate that several low borides possess unexpectedly high hardness whereas high borides exhibit an anomalous hardness reduction. Such an unusual phenomenon originates from the peculiar bonding mechanisms in these compounds. Furthermore, the low borides have close compositions, similar structures, and degenerate formation energies. This enables facile synthesis of a multiphase material that includes a large number of interfaces among different borides, and these interfaces form nanoscale interlocks that strongly suppress the glide dislocations within the metal bilayers, thereby drastically enhancing extrinsic hardness and achieving true superhard metals. Therefore, this study not only elucidates the unique mechanism responsible for the anomalous hardening in this class of borides but also offers a valid alchemy to design novel superhard metals with multiple functionalities. Materials science ; Hardening (computing) ; Transition metal ; Chemical physics ; Metallurgy ; Nanotechnology Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Diamond and Carbon-based Materials Research
Authors: Junwen Bai | Yexiang Xue | Johan Björck | Ronan Le Bras | Brendan Rappazzo | Richard Bernstein | Santosh K. Suram | R. B. van Dover | John M. Gregoire | Carla P. Gomes
Source: AI Magazine (article, 2018-03-01)
Field: Materials Science — Materials Chemistry

Phase‐Mapper: Accelerating Materials Discovery with AI From the stone age to the bronze, iron, and modern silicon ages, the discovery and characterization of new materials has always been instrumental to humanity's development and progress. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high‐throughput materials discovery, which includes rapid synthesis and characterization via X‐ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase‐Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase‐Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive nonnegative matrix factorization (NMF). AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy's Joint Center for Artificial Photosynthesis, including the Nb‐Mn‐V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI‐enabled high‐throughput materials discovery. Characterization (materials science) ; Identification (biology) ; Computer science ; Nanotechnology ; Materials science ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Wei Wu | Qiang Sun
Source: Zhongguo kexue. Wulixue Lixue Tianwenxue (article, 2018-07-05)
Field: Materials Science — Materials Chemistry

Applying machine learning to accelerate new materials development <p indent="0mm">Materials are not only the foundation of the national economy, but also the carrier of high technology. It has become a research hotspot in the world to overcome the conventional methods and apply new methods to accelerate the development of new materials. Propelled by the great success in other fields, data-driven informatics methods begin to emerge as a new technique in material science. Machine learning, as a representative of data-driven methods, has received extensive attention in various fields. Machine learning is an interdisciplinary science that combines computer science, statistics, computational mathematics and engineering. In the field of materials science, machine learning methods show faster calculation speed and higher prediction accuracy compared with conventional theoretical computational simulations based on solving physical or chemical fundamental equations. Machine learning is an effective addition to the existing theoretical calculation methods and significantly increases the efficiency of materials computational simulation work. Furthermore, it also works for some systems or problems that the traditional theoretical calculation methods fail to solve. This approach could also enable targeted material design and development. This review would provide a brief overview on the fundamentals of machine learning, several typical algorithms in machine learning and the applications in materials science, and discuss the future challenges in this field. Computer science Machine Learning in Materials Science
Authors: Yong Jie Wong | Senthil Kumar Arumugasamy | Jegalakshimi Jewaratnam
Source: Clean Technologies and Environmental Policy (article, 2018-07-09)
Field: Materials Science — Biomaterials

Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization Backpropagation ; Mean absolute percentage error ; Algorithm ; Rprop ; Artificial neural network ; Feedforward neural network ; Conjugate gradient method ; Mean squared error ; Feed forward ; Polycaprolactone ; Computer science ; Machine learning ; Artificial intelligence ; Mathematics ; Engineering ; Control engineering ; Time delay neural network ; Statistics ; Materials science biodegradable polymer synthesis and properties ; Injection Molding Process and Properties ; Electrospun Nanofibers in Biomedical Applications
Authors: Zak E. Hughes | Joseph C. R. Thacker | Alex L. Wilson | Paul L. A. Popelier
Source: Journal of Chemical Theory and Computation (article, 2018-12-03)
Field: Materials Science — Materials Chemistry

Description of Potential Energy Surfaces of Molecules Using FFLUX Machine Learning Models A new type of model, FFLUX, to describe the interaction between atoms has been developed as an alternative to traditional force fields. FFLUX models are constructed by applying the kriging machine learning method to the topological energy partitioning method, interacting quantum atoms (IQA). The effect of varying parameters in the construction of the FFLUX models is analyzed, with the most dominant effects found to be the structure of the molecule and the number of conformations used to build the model. Using these models, the optimization of a variety of small organic molecules is performed, with sub kJ mol<sup>-1</sup> accuracy in the energy of the optimized molecules. The FFLUX models are also evaluated in terms of their performance in describing the potential energy surfaces (PESs) associated with specific degrees of freedoms within molecules. While the accurate description of PESs presents greater challenges than individual minima, FFLUX models are able to achieve errors of <2.5 kJ mol<sup>-1</sup> across the full C-C-C-C dihedral PES of n-butane, indicating the future possibilities of the technique. Maxima and minima ; Dihedral angle ; Molecule ; Computer science ; Quantum ; Potential energy ; Energy (signal processing) ; Statistical physics ; Biological system ; Energy minimization ; Potential energy surface ; Variety (cybernetics) ; Kriging ; Chemical physics ; Computational chemistry ; Physics ; Chemistry ; Machine learning ; Mathematics ; Artificial intelligence ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: George Opletal | Timothy C. Petersen | Salvy P. Russo | Amanda S. Barnard
Source: Journal of Physics Materials (article, 2018-09-18)
Field: Materials Science — Materials Chemistry

PorosityPlus: characterisation of defective, nanoporous and amorphous materials As both porous and amorphous semiconductors have different advantages the challenge becomes knowing how to select one over the other, and knowing how to anticipate the degree of crystallinity or the fraction of voids as a function of a controllable feature such as the density. These sorts of relationships can be modelled computationally but unambiguous characterisation of the porosity of complex and disordered structures requires specialist tools. In this paper we demonstrate the use of PorosityPlus to investigate porosity in the vacancy induced amorphisation of defective crystals of carbon, silicon and germanium. The PorosityPlus software allows for the identification of vacancies, twin vacancies and larger pores, along with their relative locations and their respective populations. Void migration and coalescence along with the associated density changes can also be calculated. We show that, with increasing initial vacancies (reduced density) carbon (diamond), silicon and germanium exhibit characteristic density-dependent porosity profiles, coupled with simultaneous amorphisation. This is an ideal tool for integration into advanced computational workflows, such as creating fingerprints for topological data analysis or machine learning, since the porosity profile for each configuration is unique. Materials science ; Porosity ; Germanium ; Void (composites) ; Crystallinity ; Amorphous solid ; Nanoporous ; Diamond ; Coalescence (physics) ; Silicon ; Vacancy defect ; Chemical physics ; Crystallography ; Nanotechnology ; Optoelectronics ; Composite material ; Chemistry ; Physics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Memory and Neural Computing
Authors: Hiroshi Kajino
Source: arXiv (Cornell University) (preprint, 2018-09-08)
Field: Materials Science — Materials Chemistry

Molecular Hypergraph Grammar with its Application to Molecular Optimization Molecular optimization aims to discover novel molecules with desirable properties. Two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllable way due to hard chemical constraints such as the valency conditions, and (ii) it is often costly to evaluate a property of a novel molecule, and therefore, the number of property evaluations is limited. These challenges are to some extent alleviated by a combination of a variational autoencoder (VAE) and Bayesian optimization (BO). VAE converts a molecule into/from its latent continuous vector, and BO optimizes a latent continuous vector (and its corresponding molecule) within a limited number of property evaluations. While the most recent work, for the first time, achieved 100% validity, its architecture is rather complex due to auxiliary neural networks other than VAE, making it difficult to train. This paper presents a molecular hypergraph grammar variational autoencoder (MHG-VAE), which uses a single VAE to achieve 100% validity. Our idea is to develop a graph grammar encoding the hard chemical constraints, called molecular hypergraph grammar (MHG), which guides VAE to always generate valid molecules. We also present an algorithm to construct MHG from a set of molecules. Autoencoder ; Hypergraph ; Computer science ; Molecular graph ; Theoretical computer science ; Set (abstract data type) ; Encoding (memory) ; Artificial intelligence ; Algorithm ; Graph ; Artificial neural network ; Mathematics ; Discrete mathematics ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: P.L. Narayana | Seong-Woong Kim | Jae‐Keun Hong | N.S. Reddy | Jong‐Taek Yeom
Source: Metals and Materials International (article, 2018-03-13)
Field: Materials Science — Materials Chemistry

Estimation of Transformation Temperatures in Ti–Ni–Pd Shape Memory Alloys Materials science ; Shape-memory alloy ; Transformation (genetics) ; Artificial neural network ; Solid mechanics ; Phase (matter) ; Transformational leadership ; Sputtering ; Metallurgy ; Thermodynamics ; Thin film ; Artificial intelligence ; Composite material ; Nanotechnology ; Computer science Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials ; Titanium Alloys Microstructure and Properties
Authors: Silvia Gabardi | Gabriele C. Sosso | Jörg Behler | Marco Bernasconi
Source: Faraday Discussions (article, 2018-10-31)
Field: Materials Science — Materials Chemistry

Priming effects in the crystallization of the phase change compound GeTe from atomistic simulations Strategies to reduce the incubation time for crystal nucleation and thus the stochasticity of the set process are of relevance for the operation of phase change memories in ultra-scaled geometries. With these premises, in this work we investigate the crystallization kinetics of the phase change compound GeTe. We have performed large scale molecular dynamics simulations using an interatomic potential, generated previously from a neural network fitting of a database of ab initio energies. We have addressed the crystallization of models of amorphous GeTe annealed at different temperatures above the glass transition. The results on the distribution of subcritical nuclei and on the crystal growth velocity of postcritical ones are compared with our previous simulations of the supercooled liquid quenched from the melt. We find that a large population of subcritical nuclei can form at the lower temperatures where the nucleation rate is large. This population partially survives upon fast annealing, which leads to a dramatic reduction of the incubation time at high temperatures where the crystal growth velocity is maximal. This priming effect could be exploited to enhance the speed of the set process in phase change memories. Crystallization ; Molecular dynamics ; Phase change ; Materials science ; Phase (matter) ; Chemical physics ; Priming (agriculture) ; Crystallography ; Thermodynamics ; Nanotechnology ; Chemistry ; Computational chemistry ; Physics ; Organic chemistry Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Quantum Dots Synthesis And Properties
Authors: Jan Jenke | Aparna P. A. Subramanyam | Marc Densow | Thomas Hammerschmidt | D. G. Pettifor | Ralf Drautz
Source: Physical review. B./Physical review. B (article, 2018-10-03)
Field: Materials Science — Materials Chemistry

Electronic structure based descriptor for characterizing local atomic environments A quantitative descriptor of local atomic environments is often required for the analysis of atomistic data. Descriptors of the local atomic environment ideally provide physically and chemically intuitive insight. This requires descriptors that are low-dimensional representations of the interplay between atomic geometry and electronic bond formation. The moments of the local density of states relate the atomic structure to the electronic structure and bond chemistry. This makes it possible to construct electronic structure based descriptors of the local atomic environment that have an immediate relation to the binding energy. We show that a low-dimensional moments-descriptor is sufficient as the lowest moments, calculated from the closest atomic neighborhood, carry the largest contributions to the local bond energy. Here, we construct moments-descriptors that project the space of local atomic environments on a two-dimensional map. We discuss in detail the separation of various atomic environments and their connections in the map. The distances in the map may be related to energy differences between local atomic environments as we show by analytic considerations based on analytic bond-order potentials and by numerical assessment using tight-binding and density-functional theory calculations. Possible applications of the proposed moments-descriptors include the classification of local atomic environments in molecular-dynamic simulations, the selection of structure sets for developing and testing interatomic potentials, as well as the construction of descriptors for machine-learning applications. Construct (python library) ; Electronic structure ; Space (punctuation) ; Statistical physics ; Computer science ; Chemistry ; Artificial intelligence ; Physics ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Michael T. Tang | Zachary W. Ulissi | Karen Chan
Source: The Journal of Physical Chemistry C (article, 2018-05-30)
Field: Materials Science — Materials Chemistry

Theoretical Investigations of Transition Metal Surface Energies under Lattice Strain and CO Environment An understanding of the relative stability of surface facets is crucial to develop predictive models of catalyst activity and to fabricate catalysts with a controlled morphology. In this work, we present a systematic density functional theory study of the effect of lattice strain and CO environment on the surface formation energies of Cu, Pt, and Ni. First, we show that both compressive and tensile lattice strains favor the formation of stepped versus low-index terraces such as (111) and (100). Then, we investigate the effect of the CO environment using configurations of CO at various coverages, determined using a greedy, systematic approach, inspired by forward stepwise feature selection. We find that the CO environment favors stepped facets on Ni, Cu, and Pt. These trends are illustrated with the corresponding equilibrium Wulff shapes at various strains and CO pressures. In general, the surface energies of the studied transition metals are highly sensitive to strain and CO coverage, which should be considered when rationalizing trends in the catalytic activity. Transition metal ; Lattice (music) ; Materials science ; Catalysis ; Density functional theory ; Ultimate tensile strength ; Strain (injury) ; Tensile strain ; Metal ; Chemical physics ; Chemistry ; Computational chemistry ; Composite material ; Metallurgy ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Catalytic Processes in Materials Science
Authors: Ting Wu | Nan Zhong | Ling Yang
Source: Journal of Spectroscopy (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon The cold storage time of salmon has a significant impact on its freshness, which is an important factor for consumers to evaluate the quality of salmon. The efficient, accurate, and convenient protocol is urgent to appraise the freshness for quality checking. In this paper, the ability of visible/near-infrared (VIS/NIR) spectroscopy was evaluated to predict the cold storage time of salmon meat and skin, which were stored at low-temperature box for 0~12 days. Meanwhile, a double-layer stacked denoising autoencoder neural network (SDAE-NN) algorithm was introduced to establish the prediction model without spectral pre-preprocessing. The results showed that, compared with the common methods such as partial least squares regression (PLSR) and back propagation neural network (BP-NN), the SDAE-NN method had a better performance due to its high efficiency in decreasing noise and optimizing the initial weights. The determination coefficient of test sets ( R 2 test ) and root mean square error of test sets (RMSEP) have been calculated based on SDAE-NN, for the salmon meat (skin), the R 2 test can reach 0.98 (0.92), and the RMSEP can reach 0.93 (1.75), respectively. It is highlighted that the algorithm is efficient and accurate and that the salmon meat would be more suitable for predicting freshness than the salmon skin. VIS/NIR spectroscopy combined with the SDAE-NN algorithm can be widely used to predict the freshness of various agricultural products. Partial least squares regression ; Artificial neural network ; Algorithm ; Near-infrared spectroscopy ; Mean squared error ; Preprocessor ; Computer science ; Noise reduction ; Mathematics ; Artificial intelligence ; Statistics ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Yang Chen | Zheng Yin | Hong Yan | Guohua Zhou | Xiaoquan Wu | Zhi Hu
Source: Materials (article, 2018-11-20)
Field: Materials Science — Biomaterials

Effect of Samarium on the Microstructure and Corrosion Resistance of AZ91 Magnesium Alloy Treated by Ultrasonic Vibration The effects of samarium (Sm) on the microstructure and corrosion behavior of AZ91 magnesium alloy treated by ultrasonic vibration were investigated by scanning electron microscopy, X-ray diffraction, transmission electron microscopy, and electrochemical measurements. The results showed that the addition of Sm resulted in the formation of Al₂Sm, which reduced the volume fraction of the β-Mg<sub>17</sub>Al<sub>12</sub> phase and changed its morphology to fine granular. The AZ91⁻Sm alloys treated by ultrasonic vibration revealed relatively lower weight loss, hydrogen evolution, and corrosion current density values compared to the ultrasonic-treated AZ91 alloy prepared without Sm. Locally, a coarse β phase in the ultrasonic-treated AZ91 alloy accelerated the possibility of micro-galvanic corrosion growing into the matrix. In the prepared AZ91⁻Sm alloys treated by ultrasonic vibration, the fine β and Al₂Sm phases reduced the probability of micro-galvanic corrosion growth and, therefore, formed a uniform corrosion layer on the surface of the alloys. Microstructure ; Corrosion ; Materials science ; Samarium ; Magnesium ; Magnesium alloy ; Metallurgy ; Alloy ; Ultrasonic sensor ; Vibration ; Chemistry ; Inorganic chemistry ; Acoustics ; Physics Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties