jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Robert W. Epps | Milad Abolhasani
Source: Applied Physics Reviews (article, 2021-11-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modern nanoscience: Convergence of AI, robotics, and colloidal synthesis Autonomous experimentation and chemical discovery strategies are rapidly rising across multiple fields of science. However, closed-loop material development approaches have not been widely employed in colloidal nanoscience mainly due to the challenges in synthesis space size, sensitivity to reaction conditions, and the complexity of monitoring multiple synthesis outputs. Recent advancements in automated reactor designs for controlled and reproducible nanocrystal synthesis and intelligent experiment selection algorithms are leading to wider propagation of artificial intelligence-guided autonomous experimentation techniques in colloidal nanoscience. This review will cover the current literature on closed-loop, autonomous platforms for accelerated development of colloidal nanomaterials and discuss the critical features and strategies for developing autonomous robotic experimentation systems suitable to problems in colloidal nanoscience, while providing the context, effectiveness, and prospects of each technique. Then, we will discuss some immediate opportunities in the field for more rapid technological advancement and colloidal nanomaterial discovery. Context (archaeology) ; Nanotechnology ; Computer science ; Robotics ; Artificial intelligence ; Systems engineering ; Robot ; Engineering ; Materials science Gold and Silver Nanoparticles Synthesis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; Quantum Dots Synthesis And Properties
Authors: Saba Ayub | Beh Hoe Guan | Faiz Ahmad | Muhammad Faisal Javed | Amir Mosavi | Imre Felde
Source: Metals (article, 2021-07-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Preparation Methods for Graphene Metal and Polymer Based Composites for EMI Shielding Materials: State of the Art Review of the Conventional and Machine Learning Methods Advancement of novel electromagnetic inference (EMI) materials is essential in various industries. The purpose of this study is to present a state-of-the-art review on the methods used in the formation of graphene-, metal- and polymer-based composite EMI materials. The study indicates that in graphene- and metal-based composites, the utilization of alternating deposition method provides the highest shielding effectiveness. However, in polymer-based composite, the utilization of chemical vapor deposition method showed the highest shielding effectiveness. Furthermore, this review reveals that there is a gap in the literature in terms of the application of artificial intelligence and machine learning methods. The results further reveal that within the past half-decade machine learning methods, including artificial neural networks, have brought significant improvement for modelling EMI materials. We identified a research trend in the direction of using advanced forms of machine learning for comparative analysis, research and development employing hybrid and ensemble machine learning methods to deliver higher performance. EMI ; Graphene ; Materials science ; Electromagnetic shielding ; Electromagnetic interference ; Composite number ; Polymer ; Machine learning ; Composite material ; Artificial intelligence ; Computer science ; Nanotechnology ; Telecommunications Electromagnetic wave absorption materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Material Selection and Properties
Authors: Andreas Leitherer | Angelo Ziletti | Luca M. Ghiringhelli
Source: arXiv (Cornell University) (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates, which are found to be correlated with crystalline order of metallic nanoparticles in electron tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments. Artificial intelligence ; Computer science ; Deep learning ; Artificial neural network ; Machine learning ; Bayesian probability ; Bayesian network ; Noise (video) ; Probabilistic logic ; Pattern recognition (psychology) ; Unsupervised learning ; Image (mathematics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Georgios Katsikas | C. Sarafidis | Joseph Kioseoglou
Source: physica status solidi (b) (article, 2021-05-05)
Field: Materials Science — Materials Chemistry

Machine Learning in Magnetic Materials The technological advancements of every era of human civilization owe themselves to the materials available at the time. Despite the substantial interest in the discovery of novel materials, materials research remains a very delicate and time‐exhaustive procedure. Over the last 30 years, ab initio computational methods based on density functional theory (DFT) have allowed researchers to explore materials with ease and expect above‐experiment measurement precision. However, DFT‐based detailed investigation of novel materials is generally computationally intensive and greatly time‐consuming. This review presents machine learning methods applied to DFT simulation data to uncover connections between material structure, chemical composition, and magnetization, a target property chosen for its great industrial demand. Models are developed that can partially circumvent the need for simulation, guiding researchers in the design of magnetic materials. Specifically, the Materials Project database is examined and it is concluded that Eu, Gd, Pu, Fe, Np, Mn, U, Cr, Co, and Ce are amongst the most common elements found in magnetic materials, and that materials of the same composition may have different magnetization depending on their space group. A neural network capable of predicting magnetization with a standard error of 8.3 × 10 −3 μ B Å −3 is created. Magnetization ; Density functional theory ; Property (philosophy) ; Computer science ; Ab initio ; Chemical space ; Artificial intelligence ; Space (punctuation) ; Materials science ; Machine learning ; Computational chemistry ; Chemistry ; Physics ; Magnetic field Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Andrew J. Lew | Markus J. Buehler
Source: Applied Physics Reviews (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

A deep learning augmented genetic algorithm approach to polycrystalline 2D material fracture discovery and design The gestalt of computational methods including physics-based molecular dynamics simulations, data-driven machine learning (ML) models, and biologically-inspired genetic algorithms affords a powerful toolbox for tackling materials mechanism discovery and design problems. Here, we leverage these methods to investigate the complex multidimensional problem of polycrystalline 2D material fracture. We focus first on graphene and in doing so, demonstrate a practical workflow for exploring the structural dependencies of fracture energy. Despite training our ML model on exclusively single crystal fracture in increments of 10° orientations, we can identify a crack branching mechanism responsible for high bicrystal toughness centered at initial crystal orientation angles of 19° and 41°. These high peaks span only a few degrees in range and are completely overlooked by a search with stride 10°. Furthermore, we can discover qualitative physical phenomena such as collective fracture branch termination and extract quantitative trends relating angular dispersion and mis-orientation angles of crystal grains to fracture energy. None of these complex polycrystalline behaviors were presented in the training data, and the predictive power of the model ultimately allows us to expeditiously generate polycrystalline graphene structures with bespoke fracture paths, a task with great implications in industrial design applications and mechanism discovery. Furthermore, the approach is not limited to graphene specifically, as we demonstrate by retraining the model for another more complex 2D material—MoS2—and achieve polycrystalline fracture predictions of comparable accuracy. Artificial intelligence ; Materials science ; Crystallite ; Computer science ; Leverage (statistics) ; Fracture toughness ; Algorithm ; Machine learning ; Composite material Machine Learning in Materials Science ; Graphene research and applications ; Fuel Cells and Related Materials
Authors: Ruihan Wang | Yurong Zou | Chunchun Zhang | Xin Wang | Mingli Yang | Dingguo Xu
Source: Microporous and Mesoporous Materials (article, 2021-12-30)
Field: Chemistry — Inorganic Chemistry

Combining crystal graphs and domain knowledge in machine learning to predict metal-organic frameworks performance in methane adsorption Methane ; Adsorption ; Computer science ; Metal-organic framework ; Embedding ; Graph ; Porosity ; Force field (fiction) ; Correlation coefficient ; Materials science ; Algorithm ; Chemistry ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis
Authors: Luis A. Miccio | Gustavo A. Schwartz
Source: Macromolecules (article, 2021-02-05)
Field: Materials Science — Materials Chemistry

Mapping Chemical Structure–Glass Transition Temperature Relationship through Artificial Intelligence Artificial neural networks (ANNs) have been successfully used in the past to predict different properties of polymers based on their chemical structure and to localize and quantify the intramonomer contributions to these properties. In this work, we propose to move forward in order to use the mathematical framework of the ANN for embedding the chemical structure of monomers into a high-dimensional abstract space. This approach allows us not only to accurately predict the glass transition temperature (Tg) of polymers but, even more important, also to encode their chemical structure as m-dimensional vectors in a mathematical space. For this aim, we employed a fully connected neural network trained with a set of more than 200 atactic acrylates that provide the coordinates of the vectorized chemical structures into the m-dimensional space. These data points were then treated with a hierarchical nonparametric clusterization method in order to automatically group similar chemical structures into clusters with alike properties. These clusters were then projected into a human-readable three-dimensional space using principal component analysis. This approach allows us to deal with chemical structures as if they were mathematical entities and therefore to perform quantitative operations, so far hardly imaginable, being essential for both the design of new materials and the understanding of the structure-property relationships. Chemical space ; Artificial neural network ; Computer science ; Biological system ; Space (punctuation) ; Principal component analysis ; Glass transition ; Set (abstract data type) ; Mathematical structure ; Chemical structure ; Artificial intelligence ; Polymer ; Algorithm ; Chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Zak E. Hughes | Michelle Nguyen | Jialei Wang | Yang Liu | Mark T. Swihart | Matthias Poloczek | Peter I. Frazier | Marc R. Knecht | Tiffany R. Walsh
Source: ACS Nano (article, 2021-11-06)
Field: Materials Science — Biomaterials

Tuning Materials-Binding Peptide Sequences toward Gold- and Silver-Binding Selectivity with Bayesian Optimization Peptide sequence engineering can potentially deliver materials-selective binding capabilities, which would be highly attractive in numerous biotic and abiotic nanomaterials applications. However, the number of known materials-selective peptide sequences is small, and identification of new sequences is laborious and haphazard. Previous attempts have sought to use machine learning and other informatics approaches that rely on existing data sets to accelerate the discovery of materials-selective peptides, but too few materials-selective sequences are known to enable reliable prediction. Moreover, this knowledge base is expensive to expand. Here, we combine a comprehensive and integrated experimental and modeling effort and introduce a Bayesian Effective Search for Optimal Sequences (BESOS) approach to address this challenge. Through this combined approach, we significantly expand the data set of Au-selective peptide sequences and identify an additional Ag-selective peptide sequence. Analysis of the binding motifs for the Ag-binders offers a roadmap for future prediction with machine learning, which should guide identification of further Ag-selective sequences. These discoveries will enable wider and more versatile integration of Ag nanoparticles in biological platforms. Identification (biology) ; Computer science ; Sequence (biology) ; Computational biology ; Bayesian probability ; Peptide ; Nanotechnology ; Machine learning ; Set (abstract data type) ; Artificial intelligence ; Chemistry ; Biology ; Materials science ; Biochemistry Diatoms and Algae Research ; Advanced biosensing and bioanalysis techniques ; Chemical Synthesis and Analysis
Authors: Marco Arrigoni | Georg K. H. Madsen
Source: npj Computational Materials (article, 2021-05-20)
Field: Materials Science — Materials Chemistry

Evolutionary computing and machine learning for discovering of low-energy defect configurations Abstract Density functional theory (DFT) has become a standard tool for the study of point defects in materials. However, finding the most stable defective structures remains a very challenging task as it involves the solution of a multimodal optimization problem with a high-dimensional objective function. Hitherto, the approaches most commonly used to tackle this problem have been mostly empirical, heuristic, and/or based on domain knowledge. In this contribution, we describe an approach for exploring the potential energy surface (PES) based on the covariance matrix adaptation evolution strategy (CMA-ES) and supervised and unsupervised machine learning models. The resulting algorithm depends only on a limited set of physically interpretable hyperparameters and the approach offers a systematic way for finding low-energy configurations of isolated point defects in solids. We demonstrate its applicability on different systems and show its ability to find known low-energy structures and discover additional ones as well. Computer science ; CMA-ES ; Artificial intelligence ; Machine learning ; Hyperparameter ; Heuristic ; Set (abstract data type) ; Adaptation (eye) ; Domain (mathematical analysis) ; Energy (signal processing) ; Point (geometry) ; Task (project management) ; Function (biology) ; Evolution strategy ; Evolutionary algorithm ; Mathematics Machine Learning in Materials Science ; 2D Materials and Applications ; Corrosion Behavior and Inhibition
Authors: Ye Wei | Rama Srinivas Varanasi | Torsten Schwarz | Leonie Gomell | Huan Zhao | David J. Larson | Binhan Sun | Geng Liu | Hao Chen | Dierk Raabe | Baptiste Gault
Source: Patterns (article, 2021-01-21)
Field: Chemistry — Spectroscopy

Machine-learning-enhanced time-of-flight mass spectrometry analysis Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis. Mass spectrometry ; Mass spectrum ; Time-of-flight mass spectrometry ; Time of flight ; Computer science ; Standardization ; Analytical Chemistry (journal) ; Artificial intelligence ; Chemistry ; Chromatography ; Ion Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Isotope Analysis in Ecology
Authors: Tao Wang | Xiaoxing Tan | Yadong Wei | Hao Jin
Source: Materials Today Communications (article, 2021-10-28)
Field: Materials Science — Materials Chemistry

Accurate bandgap predictions of solids assisted by machine learning Materials science ; Hybrid functional ; Density functional theory ; Mean squared error ; Band gap ; Biological system ; Classifier (UML) ; Machine learning ; Computer science ; Artificial intelligence ; Statistics ; Mathematics ; Physics ; Optoelectronics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Jing Wu | Liyi Bai | Jiawei Huang | Liyang Ma | Jian Liu | Shi Liu
Source: arXiv (Cornell University) (article, 2021-09-15)
Field: Materials Science — Materials Chemistry

Accurate force field of two-dimensional ferroelectrics from deep learning The discovery of two-dimensional (2D) ferroelectrics with switchable out-of-plane polarization such as monolayer $\alpha$-In$_2$Se$_3$ offers a new avenue for ultrathin high-density ferroelectric-based nanoelectronics such as ferroelectric field effect transistors and memristors. The functionality of ferroelectrics depends critically on the dynamics of polarization switching in response to an external electric/stress field. Unlike the switching dynamics in bulk ferroelectrics that have been extensively studied, the mechanisms and dynamics of polarization switching in 2D remain largely unexplored. Molecular dynamics (MD) using classical force fields is a reliable and efficient method for large-scale simulations of dynamical processes with atomic resolution. Here we developed a deep neural network-based force field of monolayer In$_2$Se$_3$ using a concurrent learning procedure that efficiently updates the first-principles-based training database. The model potential has accuracy comparable with density functional theory (DFT), capable of predicting a range of thermodynamic properties of In$_2$Se$_3$ polymorphs and lattice dynamics of ferroelectric In$_2$Se$_3$. Pertinent to the switching dynamics, the model potential also reproduces the DFT kinetic pathways of polarization reversal and 180$^\circ$ domain wall motions. Moreover, isobaric-isothermal ensemble MD simulations predict a temperature-driven $\alpha \rightarrow \beta$ phase transition at the single-layer limit, as revealed by both local atomic displacement and Steinhardt's bond orientational order parameter $Q_4$. Our work paves the way for further research on the dynamics of ferroelectric $\alpha$-In$_2$Se$_3$ and related systems. Ferroelectricity ; Polarization (electrochemistry) ; Molecular dynamics ; Condensed matter physics ; Materials science ; Nanoelectronics ; Force field (fiction) ; Monolayer ; Density functional theory ; Physics ; Statistical physics ; Nanotechnology ; Optoelectronics ; Chemistry ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Advanced Thermoelectric Materials and Devices
Authors: Jörg Schaarschmidt | Jie Yuan | Timo Strunk | Ivan Kondov | Sebastiaan P. Huber | Giovanni Pizzi | Leonid Kahle | Felix T. Bölle | Ivano E. Castelli | Tejs Vegge | Felix Hanke | Tilmann Hickel | Jörg Neugebauer | Celso R. C. Rêgo | Wolfgang Wenzel
Source: Advanced Energy Materials (article, 2021-12-16)
Field: Materials Science — Materials Chemistry

Workflow Engineering in Materials Design within the BATTERY 2030<b>+</b> Project Abstract In recent years, modeling and simulation of materials have become indispensable to complement experiments in materials design. High‐throughput simulations increasingly aid researchers in selecting the most promising materials for experimental studies or by providing insights inaccessible by experiment. However, this often requires multiple simulation tools to meet the modeling goal. As a result, methods and tools are needed to enable extensive‐scale simulations with streamlined execution of all tasks within a complex simulation protocol, including the transfer and adaptation of data between calculations. These methods should allow rapid prototyping of new protocols and proper documentation of the process. Here an overview of the benefits and challenges of workflow engineering in virtual material design is presented. Furthermore, a selection of prominent scientific workflow frameworks used for the research in the BATTERY 2030 + project is presented. Their strengths and weaknesses as well as a selection of use cases in which workflow frameworks significantly contributed to the respective studies are discussed. Workflow ; Materials science ; Battery (electricity) ; Systems engineering ; Manufacturing engineering ; Computer science ; Engineering ; Thermodynamics ; Database ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Manufacturing Process and Optimization
Authors: Pulkit Garg | Zhiliang Pan | Vladyslav Turlo | Timothy J. Rupert
Source: Acta Materialia (article, 2021-08-03)
Field: Materials Science — Materials Chemistry

Segregation competition and complexion coexistence within a polycrystalline grain boundary network Materials science ; Grain boundary ; Crystallite ; Amorphous solid ; Condensed matter physics ; Chemical physics ; Dopant ; Microstructure ; Molecular dynamics ; Work (physics) ; Crystallography ; Thermodynamics ; Metallurgy ; Computational chemistry ; Doping ; Physics Microstructure and mechanical properties ; nanoparticles nucleation surface interactions ; Ion-surface interactions and analysis
Authors: Heng Wang | Ramya Gurunathan | Chenguang Fu | Runzi Cui | Tiejun Zhu | G. Jeffrey Snyder
Source: Materials Advances (article, 2021-11-16)
Field: Materials Science — Materials Chemistry

Thermoelectric transport effects beyond single parabolic band and acoustic phonon scattering we review strategies to understand thermoelectric systems that are more complex than a single parabolic band with acoustic phonon scattering. Thermoelectric effect ; Phonon ; Condensed matter physics ; Scattering ; Phonon scattering ; Materials science ; Physics ; Thermoelectric materials ; Optics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Advanced Thermodynamics and Statistical Mechanics