jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fusataka Kuniyoshi | Kohei Makino | Jun Ozawa | Makoto Miwa
Source: arXiv (Cornell University) (preprint, 2020-02-18)
Field: Materials Science — Materials Chemistry

Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature The synthesis process is essential for achieving computational experiment design in the field of inorganic materials chemistry. In this work, we present a novel corpus of the synthesis process for all-solid-state batteries and an automated machine reading system for extracting the synthesis processes buried in the scientific literature. We define the representation of the synthesis processes using flow graphs, and create a corpus from the experimental sections of 243 papers. The automated machine-reading system is developed by a deep learning-based sequence tagger and simple heuristic rule-based relation extractor. Our experimental results demonstrate that the sequence tagger with the optimal setting can detect the entities with a macro-averaged F1 score of 0.826, while the rule-based relation extractor can achieve high performance with a macro-averaged F1 score of 0.887. Extractor ; Computer science ; Process (computing) ; Representation (politics) ; Macro ; Reading (process) ; Relation (database) ; Heuristic ; Artificial intelligence ; Sequence (biology) ; Natural language processing ; Field (mathematics) ; Simple (philosophy) ; Machine learning ; Data mining ; Programming language ; Process engineering ; Chemistry ; Engineering ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Software Engineering Research
Authors: Małgorzata Rabiej | Stanisław Rabiej
Source: Computational Materials Science (article, 2020-09-08)
Field: Chemistry — Analytical Chemistry

Application of the artificial neural network for identification of polymers based on their X-ray diffraction curves Curve fitting ; Polymer ; Artificial neural network ; Diffraction ; Biological system ; Standard curve ; Similarity (geometry) ; Materials science ; Identification (biology) ; Chemistry ; Computer science ; Mathematics ; Physics ; Artificial intelligence ; Statistics ; Optics ; Composite material ; Chromatography ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Industrial Vision Systems and Defect Detection
Authors: Evgenii Malitckii | Eric Fangnon | Pedro Vilaça
Source: Materials (article, 2020-12-02)
Field: Materials Science — Metals and Alloys

Evaluation of Steels Susceptibility to Hydrogen Embrittlement: A Thermal Desorption Spectroscopy-Based Approach Coupled with Artificial Neural Network A novel approach has been developed for quantitative evaluation of the susceptibility of steels and alloys to hydrogen embrittlement. The approach uses a combination of hydrogen thermal desorption spectroscopy (TDS) analysis with recent advances in machine learning technology to develop a regression artificial neural network (ANN) model predicting hydrogen-induced degradation of mechanical properties of steels. We describe the thermal desorption data processing, artificial neural network architecture development, and the learning process beneficial for the accuracy of the developed artificial neural network model. A data augmentation procedure was proposed to increase the diversity of the input data and improve the generalization of the model. The study of the relationship between thermal desorption spectroscopy data and the mechanical properties of steel evidences a strong correlation of their corresponding parameters. A prototype software application based on the developed model is introduced and is openly available. The developed prototype based on TDS analysis coupled with ANN is shown to be a valuable engineering tool for steel characterization and quantitative prediction of the degradation of steel properties caused by hydrogen. Artificial neural network ; Hydrogen embrittlement ; Materials science ; Hydrogen ; Desorption ; Thermal desorption spectroscopy ; Embrittlement ; Biological system ; Artificial intelligence ; Computer science ; Metallurgy ; Corrosion ; Adsorption ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Corrosion Behavior and Inhibition
Authors: Pavlo Solokha | Roman A. Eremin | Tilmann Leisegang | Davide Μ. Proserpio | Tatiana G. Akhmetshina | Albina Gurskaya | A. Saccone | Serena De Negri
Source: Chemistry of Materials (article, 2020-01-15)
Field: Materials Science — Materials Chemistry

New Quasicrystal Approximant in the Sc–Pd System: From Topological Data Mining to the Bench Intermetallics contribute significantly to our current demand for high-performance functional materials. However, understanding their chemistry is still an open and debated topic, especially for complex compounds such as approximants and quasicrystals. In this work, targetedtopological data mining succeeded in (i) selecting all known Mackay-type approximants, (ii)uncovering the most important geometrical and chemical factors involved in their formation, and(iii) guiding the experimental work to obtain a new binary Sc−Pd 1/1 approximant for icosahedral quasicrystals containing the desired cluster. Single-crystal X-ray diffraction data analysis supplemented by electron density reconstruction using the maximum entropy method, showed fine structural peculiarities, that is, smeared electron densities in correspondence to some crystallographic sites. These characteristics have been studied through a comprehensive density functional theory modeling based on the combination of point defects such as vacancies and substitutions. It was confirmed that the structural disorder occurs in the shell enveloping the classical Mackay cluster, so that the real structure can be viewed as an assemblage of slightly different, locally ordered, four shell nanoclusters. Results obtained here open up broader perspectives for machine learning with the aim of designing novel materials inthe fruitful field of quasicrystals and their approximants. This might become an alternative and/or complementary way to the electronic pseudogap tuning, often used before explorative synthesis. Quasicrystal ; Icosahedral symmetry ; Nanoclusters ; Density functional theory ; Intermetallic ; Pseudogap ; Cluster (spacecraft) ; Materials science ; Topology (electrical circuits) ; Crystallography ; Physics ; Chemistry ; Condensed matter physics ; Nanotechnology ; Computer science ; Mathematics ; Quantum mechanics ; Doping ; Combinatorics ; Alloy X-ray Diffraction in Crystallography ; Quasicrystal Structures and Properties ; Crystallography and molecular interactions
Authors: Amanda S. Barnard
Source: Matter (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Best Practice Leads to the Best Materials Informatics Informatics ; Artificial intelligence ; Computer science ; Field (mathematics) ; Best practice ; Data science ; Materials informatics ; Machine learning ; Health informatics ; Engineering informatics ; Engineering ; Management ; Political science ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Computational Drug Discovery Methods
Authors: Yuxuan Chen | Bailin Tian | Zheng Cheng | Xiaoshan Li | Min Huang | Yuxia Sun | Shuai Liu | Xu Cheng | Shuhua Li | Mengning Ding
Source: Angewandte Chemie (article, 2020-11-12)
Field: Chemistry — Electrochemistry

Electro‐Descriptors for the Performance Prediction of Electro‐Organic Synthesis Abstract Electrochemical organic synthesis has attracted increasing attentions as a sustainable and versatile synthetic platform. Quantitative assessment of the electro‐organic reactions, including reaction thermodynamics, electro‐kinetics, and coupled chemical processes, can lead to effective analytical tool to guide their future design. Herein, we demonstrate that electrochemical parameters such as onset potential, Tafel slope, and effective voltage can be utilized as electro‐descriptors for the evaluation of reaction conditions and prediction of reactivities (yields). An “electro‐descriptor‐diagram” is generated, where reactive and non‐reactive conditions/substances show distinct boundary. Successful predictions of reaction outcomes have been demonstrated using electro‐descriptor diagram, or from machine learning algorithms with experimentally‐derived electro‐descriptors. This method represents a promising tool for data‐acquisition, reaction prediction, mechanistic investigation, and high‐throughput screening for general organic electro‐synthesis. Tafel equation ; Electrochemistry ; Biological system ; Reaction conditions ; Organic synthesis ; Throughput ; Chemistry ; Computer science ; Biochemical engineering ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Electrode ; Organic chemistry ; Physical chemistry ; Catalysis ; Engineering Electrochemical Analysis and Applications ; Radical Photochemical Reactions ; Ionic liquids properties and applications
Authors: Kotheril Ashwathy Thomas | Swati Dey | Nashrin Sultana | Koustav Sarkar | Shubhabrata Datta
Source: Materials and Manufacturing Processes (article, 2020-04-25)
Field: Materials Science — Materials Chemistry

Design of Ti composite with bioactive surface for dental implant Artificial neural network (ANN) and genetic algorithm (GA) are used for designing Ti alloy and composite suitable for dental implant. Hydroxyapatite (HAp), having similarity to the human hard tissues, helps the bone growth. A bioactive surface of the implant with the presence of HAp helps to make the implant last for a longer period of time. A layered Ti alloy – HAp composite is designed in this work. A Ti alloy – HAp composite-based two-layered implant with high strength and low elastic modulus, the inner part (core) of the implant consisting only the Ti alloy, and the outer part (shell) of the implant made of the composite is designed. The objectives of achieving low elastic modulus (EM) and high yield strength (YS) are conflicting in nature, and dealt with multi-objective optimization, where cost of the alloy is included as the third objective, to make the material affordable to the common people. The designed alloy and composites are designed is developed experimentally, and a preliminary study on the microstructure and mechanical properties is conducted. Materials science ; Alloy ; Composite number ; Implant ; Microstructure ; Composite material ; Titanium alloy ; Modulus ; Elastic modulus ; Dental implant ; Biomedical engineering ; Engineering ; Surgery ; Medicine Titanium Alloys Microstructure and Properties ; Bone Tissue Engineering Materials ; Orthopaedic implants and arthroplasty
Authors: Eric L. Buehler | Isabelle Su | Markus J. Buehler
Source: Extreme Mechanics Letters (article, 2020-11-02)
Field: Materials Science — Biomaterials

WebNet: A biomateriomic three-dimensional spider web neural net Spider ; Salient ; Computer science ; Artificial neural network ; Spider silk ; Artificial intelligence ; Bridge (graph theory) ; Theoretical computer science ; Ecology ; Biology Silk-based biomaterials and applications ; Spider Taxonomy and Behavior Studies ; Adhesion, Friction, and Surface Interactions
Authors: Qifan Chen | Mingwei Chen | Linggang Zhu | Naihua Miao | Jian Zhou | Graeme J. Ackland | Zhimei Sun
Source: ACS Applied Materials & Interfaces (article, 2020-09-11)
Field: Materials Science — Materials Chemistry

Composition-Gradient-Mediated Semiconductor–Metal Transition in Ternary Transition-Metal-Dichalcogenide Bilayers The semiconductor-metal transition (SMT) enables multiple applications of one single material, especially in modern devices. How to control it remains one of the most intriguing questions in material physics/chemistry, especially in two-dimensional layered materials. In this work, we report realization of SMT in MoS<sub>2-<i>x</i></sub>O<i><sub>x</sub></i> bilayers, driven by the concentration gradient of the chalcogen atom across the van der Waals (vdW) gap of the disordered bilayers. Using the cluster expansion method, we determined that either semiconducting (stable) or metallic states (metastable) can be realized in MoS<sub>2-<i>x</i></sub>O<i><sub>x</sub></i> bilayers with the same composition. Machine learning analysis revealed that the concentration gradient of the chalcogen atom across the vdW gap is the leading fingerprint of SMT, with structural distortion induced by atom mixing being a significant secondary factor. The electronic origin of the SMT is the broadening of the Mo d<sub><i>z</i><sup>2</sup></sub> and O p<sub><i>z</i></sub> bands, accompanied by the redistribution of the d electrons. This in-vdW-gap composition-gradient-driven SMT phenomenon also applies to MoSe<sub>2-<i>x</i></sub>O<i><sub>x</sub></i> and MoTe<sub>2-<i>x</i></sub>O<i><sub>x</sub></i> bilayers. The present work provides an alternative mechanism of SMT and demonstrates that the composition gradient across the vdW gap in the bilayer materials can be another degree of freedom to tune the band gaps without introducing extrinsic elements. Our findings will benefit the material design for small-scale and energy-efficient electronic devices. Materials science ; Transition metal ; Ternary operation ; Semiconductor ; Metal ; Nanotechnology ; Chemical physics ; Metallurgy ; Optoelectronics ; Catalysis 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Organic and Molecular Conductors Research
Authors: Onur Çaylak | O. Anatole von Lilienfeld | Björn Baumeier
Source: Machine Learning Science and Technology (article, 2020-06-26)
Field: Materials Science — Materials Chemistry

Wasserstein metric for improved quantum machine learning with adjacency matrix representations We study the Wasserstein metric to measure distances between molecules represented by the atom index dependent adjacency 'Coulomb' matrix, used in kernel ridge regression based supervised learning. Resulting machine learning models of quantum properties, a.k.a. quantum machine learning models exhibit improved training efficiency and result in smoother predictions of energies related to molecular distortions. We first illustrate smoothness for the continuous extraction of an atom from some organic molecule. Learning curves, quantifying the decay of the atomization energy's prediction error as a function of training set size, have been obtained for tens of thousands of organic molecules drawn from the QM9 data set. In comparison to conventionally used metrics (L 1 and L 2 norm), our numerical results indicate systematic improvement in terms of learning curve off-set for random as well as sorted (by norms of row) atom indexing in Coulomb matrices. Our findings suggest that this metric corresponds to a favorable similarity measure which introduces index-invariance in any kernel based model relying on adjacency matrix representations. Adjacency matrix ; Metric (unit) ; Measure (data warehouse) ; Matrix norm ; Distance matrix ; Mathematics ; Matrix (chemical analysis) ; Computer science ; Algorithm ; Artificial intelligence ; Discrete mathematics ; Quantum mechanics ; Eigenvalues and eigenvectors ; Physics ; Graph ; Data mining ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Raphael J.L. Townshend | Martin Vögele | Patricia Suriana | Alexander Derry | Alexander S. Powers | Yianni Laloudakis | Sidhika Balachandar | Bowen Jing | Brandon Anderson | Stephan Eismann | Risi Kondor | Russ B. Altman | Ron O. Dror
Source: arXiv (Cornell University) (preprint, 2020-01-01)
Field: Materials Science — Materials Chemistry

ATOM3D: Tasks On Molecules in Three Dimensions Computational methods that operate on three-dimensional molecular structure have the potential to solve important questions in biology and chemistry. In particular, deep neural networks have gained significant attention, but their widespread adoption in the biomolecular domain has been limited by a lack of either systematic performance benchmarks or a unified toolkit for interacting with molecular data. To address this, we present ATOM3D, a collection of both novel and existing benchmark datasets spanning several key classes of biomolecules. We implement several classes of three-dimensional molecular learning methods for each of these tasks and show that they consistently improve performance relative to methods based on one- and two-dimensional representations. The specific choice of architecture proves to be critical for performance, with three-dimensional convolutional networks excelling at tasks involving complex geometries, graph networks performing well on systems requiring detailed positional information, and the more recently developed equivariant networks showing significant promise. Our results indicate that many molecular problems stand to gain from three-dimensional molecular learning, and that there is potential for improvement on many tasks which remain underexplored. To lower the barrier to entry and facilitate further developments in the field, we also provide a comprehensive suite of tools for dataset processing, model training, and evaluation in our open-source atom3d Python package. All datasets are available for download from https://www.atom3d.ai . Computer science ; Python (programming language) ; Suite ; Machine learning ; Artificial intelligence ; Deep learning ; Data science ; Benchmark (surveying) ; Field (mathematics) ; Key (lock) ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: James Chapman | Rampi Ramprasad
Source: The Journal of Physical Chemistry C (article, 2020-09-09)
Field: Materials Science — Materials Chemistry

Nanoscale Modeling of Surface Phenomena in Aluminum Using Machine Learning Force Fields The study of nanoscale surface phenomena is essential in understanding the physical processes that aid in technologically relevant applications, such as catalysis, material growth, and failure nucleation. While experimental observations, such as those based on various forms of microscopy, can be used to better understand surface diffusion mechanisms, the resulting information is often limited in both its spatial and temporal resolution. Therefore, computational methodologies have become critical in the study of the processes that occur in these domains. Until recently, these methodologies have fallen into two broad categories: quantum mechanics (QM) based methods and semiempirical/classical methods. The former are computationally demanding, but accurate and versatile, while the latter are computationally inexpensive, but are significantly limited in their versatility. Machine learning (ML) methods have shown the potential to bridge these two domains by combining the cost of classical methods with the accuracy of QM. In this work, we employ recently developed ML models to simulate a variety of surface phenomena of aluminum. Adatom diffusion barriers were predicted via nudged elastic band calculations. Surface dynamics were also considered by studying the melting temperature of Al slabs and nanoparticles, along with the epitaxial growth of the Al (110) surface. Nucleation ; Nanoscopic scale ; Surface diffusion ; Nanotechnology ; Materials science ; Surface (topology) ; Diffusion ; Multiscale modeling ; Computer science ; Statistical physics ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Ion-surface interactions and analysis
Authors: Naila Al Hasan | Huilong Hou | Tieren Gao | Jonathan D. P. Counsell | Suchismita Sarker | Sigurd Thienhaus | Edward Walton | Peer Decker | Apurva Mehta | Alfred Ludwig | Ichiro Takeuchi
Source: ACS Combinatorial Science (article, 2020-08-04)
Field: Materials Science — Materials Chemistry

Combinatorial Exploration and Mapping of Phase Transformation in a Ni–Ti–Co Thin Film Library Combinatorial synthesis and high-throughput characterization of a Ni-Ti-Co thin film materials library are reported for exploration of reversible martensitic transformation. The library was prepared by magnetron co-sputtering, annealed in vacuum at 500 °C without atmospheric exposure, and evaluated for shape memory behavior as an indicator of transformation. Composition, structure, and transformation behavior of the 177 pads in the library were characterized using high-throughput wavelength dispersive spectroscopy (WDS), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and four-point probe temperature-dependent resistance (<i>R</i>(<i>T</i>)) measurements. A new, expanded composition space having phase transformation with low thermal hysteresis and Co > 10 at. % is found. Unsupervised machine learning methods of hierarchical clustering were employed to streamline data processing of the large XRD and XPS data sets. Through cluster analysis of XRD data, we identified and mapped the constituent structural phases. Composition-structure-property maps for the ternary system are made to correlate the functional properties to the local microstructure and composition of the Ni-Ti-Co thin film library. X-ray photoelectron spectroscopy ; Materials science ; Thin film ; Sputter deposition ; Ternary operation ; Microstructure ; Diffusionless transformation ; Spectroscopy ; Phase (matter) ; Analytical Chemistry (journal) ; Chemical engineering ; Sputtering ; Nanotechnology ; Computer science ; Martensite ; Chemistry ; Metallurgy ; Physics ; Organic chemistry Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Xiang Gao | Rong Tan | Guanghui Li
Source: IOP Conference Series Materials Science and Engineering (article, 2020-03-01)
Field: Materials Science — Materials Chemistry

Research on Text Mining of Material Science Based on Natural Language Processing Abstract Facing large-scale and rapidly growing material science literature data, text mining has become a research hotspot of material science. In recent years, natural language processing technology and machine learning methods have become the main technical means of text mining in materials science. The main task of text mining is to transform unstructured text data into structured material data by information extraction methods such as Named Entity Recognition and entity relationship extraction. This research proposes a general solution framework for material information extraction tasks, and introduces the main concepts and processes of text processing, text annotation, entity relationship extraction, etc., and discusses the current research progress and possible future research directions. Computer science ; Information extraction ; Information retrieval ; Natural language processing ; Biomedical text mining ; Data science ; Temporal annotation ; Task (project management) ; Text processing ; Hotspot (geology) ; Annotation ; Natural language ; Text mining ; Artificial intelligence ; Language technology ; Engineering Machine Learning in Materials Science ; Topic Modeling