jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Taiji MAZDA | Yukihide Kajita | Takashi Akedo | Tsubasa Hazama
Source: IOP Conference Series Materials Science and Engineering (article, 2020-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recognition of Nonlinear Hysteretic Behavior by Neural Network using Deep Learning Abstract In Japan, large earthquakes have caused many damages to structures. Cracks in the reinforced concrete and plasticization of the steel bar and steel components resulted in increased damage. In the seismic design, the dynamic response analysis is carried out by using a mathematical model that appropriately evaluates the nonlinearity of the material and the components and the seismic performance is confirmed by performing a dynamic response analysis. However, when applying new materials and components, much time and much effort are required to select a mathematical model. In this study, we focused on the high pattern recognition capability of the neural network. We attempt to directly model using a neural network without replacing the complex nonlinear hysteretic behavior using the mathematical model. By improvement of learning data and introduction of deep learning, it was confirmed that the recognition ability for nonlinear hysteretic behavior of neural network was greatly improved, and its applicability as a numerical operation subroutine for time history response analysis was drastically improved. Artificial neural network ; Nonlinear system ; Artificial intelligence ; Computer science ; Physics Magnetic Properties and Applications ; Piezoelectric Actuators and Control ; Iterative Learning Control Systems
Authors: Jacob Kerner | Alan B. Dogan | Horst A. von Recum
Source: SSRN Electronic Journal (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research Data science ; Big data ; Computer science ; Nanotechnology ; Materials science ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shobhana Narasimhan
Source: APL Materials (article, 2020-04-01)
Field: Materials Science — Materials Chemistry

A handle on the scandal: Data driven approaches to structure prediction Structure–property relationships play a central role in condensed matter physics, chemistry, and materials science. However, the problem of predicting the structure of a material, given its chemical composition, remains immensely challenging. Here, we review some of the progress that has been made in this area for both crystalline materials and atomic clusters. Early work consisted of heuristic rules-of-thumb or structure maps using descriptors that were obtained largely by inspection. Increasingly, these approaches are being expanded to use descriptors that have been obtained by applying machine learning techniques to big data containing information from the experiment and/or first principles calculations. Improved techniques for global optimization in the multi-dimensional coordinate space have also led to major advances in the field. Heuristic ; Field (mathematics) ; Space (punctuation) ; Property (philosophy) ; Big data ; Rule of thumb ; Computer science ; Work (physics) ; Materials science ; Nanotechnology ; Artificial intelligence ; Data science ; Data mining ; Algorithm ; Mechanical engineering ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Xuemei Liu | XiaoYan SONG | Kai Guo | Dong Liu | Fei Mao
Source: Scientia Sinica Technologica (article, 2020-03-30)
Field: Materials Science — Materials Chemistry

Development of database and information management system for data-driven materials design <p indent="0mm">With the progress of data-driven materials design, the scale and quality of datasets for intelligence algorithms such as machine learning and data mining have actually become the bottleneck for the practical benefit. The massive experimental data reported in the literatures and those unpublished are significant treasure trove for producing high-quality datasets. However, there has been a lack of specific database or information management system for storing and managing data as well as generating high-quality datasets. Using the Sm-Co alloy system as an example, which is a typical representative of the high-temperature permanent magnetic materials, we established an intelligent database and the corresponding information management system, where the data acquisition, data annotation, data extraction and data format conversion are flexibly integrated. We set up correlation models for the elemental composition, phase constitution, crystal structure, processing, measurement, and properties of the materials. From these models, the collected data are structured and the logical associations of related data are explicit. Thus, high-quality datasets can be produced from the developed system according to certain requirements for materials design. In the developed information management system, the online data annotation by multi-user has been realized. Moreover, the system is advanced in high-efficiency data entry and unifying data format, and precise search for redundant data. It has been confirmed that the present specific database and information management system play an important role in the data-driven materials design, and are promising in the applications in the fields of materials genome initiative and materials informatics. Database ; Database design ; Computer science ; Data administration ; Data management ; Data science ; Database model Machine Learning in Materials Science
Authors: Rapela R. Maphanga | Tshepiso Mokoena | Mahlatse Ratsoma
Source: Materials Today Proceedings (article, 2020-05-04)
Field: Materials Science — Materials Chemistry

Estimating DFT calculated voltage using machine learning regression models Battery (electricity) ; Voltage ; Contrast (vision) ; Process (computing) ; Computer science ; Regression ; Characterization (materials science) ; Machine learning ; Artificial intelligence ; Regression analysis ; Linear regression ; Materials science ; Engineering ; Mathematics ; Nanotechnology ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Nathaniel H. Park | Dmitry Yu. Zubarev | James L. Hedrick | Vivien Kiyek | Christiaan Corbet | Simon Lottier
Source: ChemRxiv (preprint, 2020-07-22)
Field: Materials Science — Materials Chemistry

A Recommender System for Inverse Design of Polycarbonates and Polyesters The convergence of artificial intelligence and machine learning with material science holds significant promise to rapidly accelerate development timelines of new high-performance polymeric materials. Within this context, we report an inverse design strategy for polycarbonate and polyester discovery based on a recommendation system that proposes polymerization experiments that are likely to produce materials with targeted properties. Following recommendations of the system driven by the historical ring-opening polymerization results, we carried out experiments targeting specific ranges of monomer conversion and dispersity of the polymers obtained from cyclic lactones and carbonates. The results of the experiments were in close agreement with the recommendation targets with few false negatives or positives obtained for each class.&lt;br&gt; Polyester ; Polycarbonate ; Polymerization ; Context (archaeology) ; Dispersity ; Inverse ; Timeline ; Computer science ; Monomer ; Convergence (economics) ; Class (philosophy) ; Polymer ; Materials science ; Polymer science ; Artificial intelligence ; Polymer chemistry ; Mathematics ; Composite material ; Geology ; Statistics Machine Learning in Materials Science
Authors: Seoin Back | Jonggeol Na | Zachary W. Ulissi
Source: ChemRxiv (preprint, 2020-10-13)
Field: Materials Science — Materials Chemistry

An Efficient Discovery of Active, Selective and Stable Catalysts for Electrochemical H2O2 Synthesis Through Active Motif Screening &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Electrochemical reduction of O2 provides a clean and decentralized pathway to produce H2O2 compared to the current energy-intensive anthraquinone process. As the electrochemical reduction of O2 proceeds via either two-electron or four-electron path- way, it is thus essential to control the selectivity as well as to maximize the catalytic activity. Siahrostami et al. demonstrated a novel approach to control the reaction pathway by optimizing an adsorption ensemble to tune adsorption sites of reaction intermediates, and identified Pt-Hg catalysts from density functional theory (DFT) calculations and experimentally validated this catalyst (Nat. Mater. 2013, 12, 1137). Inspired by this concept, in this work, we apply a state-of-the-art high-throughput screening to develop O2 reduction catalyst for selective H2O2 production. Starting from Materials Project database, we evaluate activity, selectivity and electrochemical stability. To efficiently perform the screening, we introduce an active motif based approach which pre-screens unpromising materials and only performs DFT calculations for promising materials, which significantly reduce the number of the required calculations. We not only provide a list of promising candidates identified by DFT calculations, but also suggest element species to achieve high catalytic activity or H2O2 selectivity for future experimental attempts. Finally, we discuss a strategy for efficient future high-throughput screening using a machine learning pipeline consisting of a non-linear dimension reduction and a density-based clustering. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Catalysis ; Density functional theory ; Electrochemistry ; Selectivity ; Chemistry ; Combinatorial chemistry ; High-throughput screening ; Nanotechnology ; Computer science ; Computational chemistry ; Materials science ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Jan‐Joris Devogelaer | Hugo Meekes | Paul Tinnemans | Elias Vlieg | R. De Gelder
Source: ChemRxiv (preprint, 2020-06-10)
Field: Materials Science — Materials Chemistry

Cocrystal Prediction by Artificial Neural Networks 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. Cocrystal ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Molecule ; Organic chemistry ; Hydrogen bond Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Seunghee Lee | Hyun Woo Kim | Jino Im | Seok Ki Kim | Yong Tae Kim | Hyunju Chang | Tae-Wook Ko | Jonggul Lee | YunKyong Hyon
Source: Journal of the Korean Physical Society (article, 2020-10-01)
Field: Materials Science — Materials Chemistry

Applying Machine Learning Algorithms to Predict Potential Energies and Atomic Forces during C-H Activation Molecular dynamics (MD) simulations are useful in understanding the interaction between solid materials and molecules. However, performing MD simulations is possible only when interatomic potentials are available and constructing such interatomic potentials usually requires additional computational work. Recently, generating interatomic potentials was shown to be much easier when machine learning (ML) algorithms were used. In addition, ML algorithms require new descriptors for improved performance. Here, we present an ML approach with several categories of atomic descriptors to predict the parameters necessary for MD simulations, such as the potential energies and the atomic forces. We propose several atomic descriptors based on structural information and find that better descriptors can be generated from eXtreme gradient boosting (XGBoost). Moreover, we observe fewer descriptors that perform better in predicting the potential energies and the forces during methane activation processes on a catalytic Pt(111) surface. These results were consistently observed in two different ML algorithms: fully-connected neural network (FNN) and XGBoost. Taking into account the advantages of FNN and XGBoost, we propose an efficient ML model for estimating potential energies. Our findings will be helpful in developing new ML potentials for long-time MD simulations. Interatomic potential ; Computer science ; Molecular dynamics ; Artificial neural network ; Algorithm ; Machine learning ; Artificial intelligence ; Work (physics) ; Physics ; Chemistry ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Yu Qi | Zhaolan Zheng
Source: arXiv (Cornell University) (review, 2020-10-31)
Field: Chemistry — Spectroscopy

A review of neural network algorithms and their applications in supercritical extraction Neural network realizes multi-parameter optimization and control by simulating certain mechanisms of the human brain. It can be used in many fields such as signal processing, intelligent driving, optimal combination, vehicle abnormality detection, and chemical process optimization control. Supercritical extraction is a new type of high-efficiency chemical separation process, which is mainly used in the separation and purification of natural substances. There are many influencing factors. The neural network model can quickly optimize the process parameters and predict the experimental results under different process conditions. It is helpful to understand the inner law of the experiment and determine the optimal experimental conditions. This paper briefly describes the basic concepts and research progress of neural networks and supercritical extraction, and summarizes the application of neural network algorithms in supercritical extraction, aiming to provide reference for the development and innovation of industry technology. Artificial neural network ; Supercritical fluid ; Process (computing) ; Computer science ; Extraction (chemistry) ; Supercritical fluid extraction ; SIGNAL (programming language) ; Algorithm ; Artificial intelligence ; Chemistry Analytical Chemistry and Chromatography ; Phase Equilibria and Thermodynamics ; Advanced Control Systems Optimization
Authors: S. Quondam Antonio | Francesco Riganti Fulginei | Hari Prasad Rimal | AbdelRahman M. Ghanim
Source:  (article, 2020-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On the Use of Feedforward Neural Networks to Simulate Magnetic Hysteresis in Electrical Steels The present investigation aims at the definition of an efficient and robust neural network-based model to simulate the magnetic hysteresis in performing magnetic alloys suitable for aircraft applications. Starting from a set of measured hysteresis loops, a convenient and effective method to train the network consists to identify the Preisach model and use it for the generation of the training set. The obtained neural network turned out to be particularly robust and able to reproduce the behaviour of the Preisach model with a significant reduction of the computational time. The comparative analysis between the two approaches takes into account different kinds of excitation waveforms. Artificial neural network ; Hysteresis ; Magnetic hysteresis ; Feedforward neural network ; Computer science ; Feed forward ; Set (abstract data type) ; Waveform ; Reduction (mathematics) ; Excitation ; Control theory (sociology) ; Artificial intelligence ; Control engineering ; Voltage ; Magnetic field ; Engineering ; Physics ; Mathematics ; Magnetization ; Electrical engineering Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Electric Motor Design and Analysis
Authors: Vasiliki Fragkia | Isak Worre Foged
Source: eCAADe proceedings (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Methods for the Prediction and Specification of Functionally Graded Multi-Grain Responsive Timber Composites The paper presents design-integrated methods for high-resolution specification and prediction of functionally graded wood-based thermal responsive composites, using machine learning. The objective is the development of new circular design workflow, employing robotic fabrication, in order to predict fabrication files linked to material performance and design requirements, focused on application for intrinsic responsive and adaptive architectural surfaces. Through an experimental case study, the paper explores how machine learning can form a predictive design framework where low-resolution data can solve material systems at high resolution. The experimental computational and prototyping studies show that the presented image-based machine learning method can be adopted and adapted across various stages and scales of architectural design and fabrication. This in turn allows for a design-per-requirement approach that optimizes material distribution and promotes material economy. Composite material ; Materials science ; Computer science Natural Fiber Reinforced Composites ; Innovations in Concrete and Construction Materials ; Wood Treatment and Properties
Authors: Takafumi Aizawa
Source: Molecules (article, 2020-12-08)
Field: Materials Science — Polymers and Plastics

New Design Method for Fabricating Multilayer Membranes Using CO2-Assisted Polymer Compression Process It was verified that deep learning can be used in creating multilayer membranes with multiple porosities using the CO<sub>2</sub>-assisted polymer compression (CAPC) method. To perform training while reducing the number of experimental data as much as possible, the experimental data of the compression behavior of two layers were expanded to three layers for training, but sufficient accuracy could not be obtained. However, the accuracy was dramatically improved by adding the experimental data of the three layers. The possibility of only simulating process results without the necessity for a model is a merit unique to deep learning. Overall, in this study, the results show that by devising learning data, deep learning is extremely effective in designing multilayer membranes using the CAPC method. Membrane ; Process (computing) ; Materials science ; Computer science ; Compression (physics) ; Polymer ; Experimental data ; Deep learning ; Artificial intelligence ; Composite material ; Mathematics ; Chemistry Polymer Foaming and Composites ; Membrane Separation and Gas Transport ; Advanced Polymer Synthesis and Characterization
Authors: Fabian L. Thiemann | P.N. Rowe | Erich A. Müller | Angelos Michaelides
Source: The Journal of Physical Chemistry C (preprint, 2020-09-14)
Field: Materials Science — Materials Chemistry

Machine Learning Potential for Hexagonal Boron Nitride Applied to Thermally and Mechanically Induced Rippling We introduce an interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology. The potential is based on a training set of configurations collected from density functional theory (DFT) simulations and is capable of treating bulk and multilayer hBN as well as nanotubes of arbitrary chirality. The developed force field faithfully reproduces the potential energy surface predicted by DFT while improving the efficiency by several orders of magnitude. We test our potential by comparing formation energies, geometrical properties, phonon dispersion spectra, and mechanical properties with respect to benchmark DFT calculations and experiments. In addition, we use our model and a recently developed graphene-GAP to analyze and compare thermally and mechanically induced rippling in large scale two-dimensional (2D) hBN and graphene. Both materials show almost identical scaling behavior with an exponent of η ≈ 0.85 for the height fluctuations agreeing well with the theory of flexible membranes. On the basis of its lower resistance to bending, however, hBN experiences slightly larger out-of-plane deviations both at zero and finite applied external strain. Upon compression, a phase transition from incoherent ripple motion to soliton-ripples is observed for both materials. Our potential is freely available online at [http://www.libatoms.org]. Graphene ; Materials science ; Phonon ; Density functional theory ; Boron nitride ; Scaling ; Condensed matter physics ; Interatomic potential ; Nanotechnology ; Physics ; Molecular dynamics ; Quantum mechanics ; Mathematics ; Geometry Graphene research and applications ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Amir Hajibabaei | Chang Woo Myung | Kwang S. Kim
Source: arXiv (Cornell University) (preprint, 2020-09-28)
Field: Materials Science — Materials Chemistry

Towards Universal Sparse Gaussian Process Potentials: Application to Lithium Diffusivity in Superionic Conducting Solid Electrolytes For machine learning of interatomic potentials the sparse Gaussian process regression formalism is introduced with a data-efficient adaptive sampling algorithm. This is applied for dozens of solid electrolytes. As a showcase, experimental melting and glass-crystallization temperatures are reproduced for Li7P3S11 and an unchartered infelicitous phase is revealed with much lower Li diffusivity which should be circumvented. By hierarchical combinations of the expert models universal potentials are generated, which pave the way for modeling large-scale complexity by a combinatorial approach. Thermal diffusivity ; Fast ion conductor ; Gaussian ; Kriging ; Electrolyte ; Statistical physics ; Crystallization ; Computer science ; Gaussian process ; Thermodynamics ; Materials science ; Machine learning ; Chemistry ; Physics ; Physical chemistry ; Computational chemistry Machine Learning in Materials Science
Authors: L. Amigues | Valérie Pommier‐Budinger | J. Bordeneuve
Source: IFAC-PapersOnLine (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hysteresis Dispersion Compensation with Neural Network based Controller Hysteresis is a commonly encountered physical phenomenon in many systems. It results in a dependence of the state of a system to its history. This non-linearity makes it particularly difficult to control accurately. There are many ways for compensating hysteresis, one of them consists of building an inverse model of the hysteresis and using it as a feedforward controller. Coupled to a feedback mechanism, the hysteresis impact can thus be minimized. However, the performance of these controllers decreases when exposed to dispersion in the hysteresis quantity or shape. The capacity of neural networks to model non-linear phenomena is not to be proven and will be put at use. In this paper, an artificial neural network model was trained to replace the conventional hysteresis inverse model. The controller performance was evaluated on a limited-angle torque motor, which exhibits hysteresis due to the magnetization saturation of the ferromagnetic materials. The experimental results pointed out the superior robustness to system dispersion of the Neural Network based controller for time and frequency response. Control theory (sociology) ; Hysteresis ; Feed forward ; Artificial neural network ; Robustness (evolution) ; Computer science ; Dispersion (optics) ; Controller (irrigation) ; Control engineering ; Physics ; Engineering ; Artificial intelligence ; Control (management) ; Condensed matter physics ; Chemistry ; Optics Magnetic Properties and Applications ; Piezoelectric Actuators and Control ; Electric Motor Design and Analysis