jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Samantha Tetef | Niranjan Govind | Gerald T. Seidler
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy We report a comprehensive computational study of unsupervised machine learning for extraction of chemically relevant information in X-ray absorption near edge structure (XANES) and in valence-to-core X-ray emission spectra (VtC-XES) for classification of a broad ensemble of sulphorganic molecules. By progressively decreasing the constraining assumptions of the unsupervised machine learning algorithm, moving from principal component analysis (PCA) to a variational autoencoder (VAE) to t-distributed stochastic neighbour embedding (t-SNE), we find improved sensitivity to steadily more refined chemical information. Surprisingly, when embedding the ensemble of spectra in merely two dimensions, t-SNE distinguishes not just oxidation state and general sulphur bonding environment but also the aromaticity of the bonding radical group with 87% accuracy as well as identifying even finer details in electronic structure within aromatic or aliphatic sub-classes. We find that the chemical information in XANES and VtC-XES is very similar in character and content, although they unexpectedly have different sensitivity within a given molecular class. We also discuss likely benefits from further effort with unsupervised machine learning and from the interplay between supervised and unsupervised machine learning for X-ray spectroscopies. Our overall results, <i>i.e.</i>, the ability to reliably classify without user bias and to discover unexpected chemical signatures for XANES and VtC-XES, likely generalize to other systems as well as to other one-dimensional chemical spectroscopies. XANES ; Artificial intelligence ; Autoencoder ; Valence (chemistry) ; Unsupervised learning ; Spectroscopy ; Chemistry ; Principal component analysis ; Embedding ; Machine learning ; Spectral line ; Pattern recognition (psychology) ; Computer science ; Artificial neural network ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Nobuyuki Yoshioka | Wataru Mizukami | Franco Nori
Source: Communications Physics (article, 2021-05-21)
Field: Materials Science — Materials Chemistry

Solving quasiparticle band spectra of real solids using neural-network quantum states Abstract Establishing a predictive ab initio method for solid systems is one of the fundamental goals in condensed matter physics and computational materials science. The central challenge is how to encode a highly-complex quantum-many-body wave function compactly. Here, we demonstrate that artificial neural networks, known for their overwhelming expressibility in the context of machine learning, are excellent tool for first-principles calculations of extended periodic materials. We show that the ground-state energies in real solids in one-, two-, and three-dimensional systems are simulated precisely, reaching their chemical accuracy. The highlight of our work is that the quasiparticle band spectra, which are both essential and peculiar to solid-state systems, can be efficiently extracted with a computational technique designed to exploit the low-lying energy structure from neural networks. This work opens up a path to elucidate the intriguing and complex many-body phenomena in solid-state systems. Quasiparticle ; Artificial neural network ; Context (archaeology) ; Computer science ; Quantum ; Wave function ; Electronic band structure ; Complex system ; Physics ; Statistical physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Paul S. Clegg
Source: Soft Matter (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Characterising soft matter using machine learning Machine learning is making a major impact in materials research. I review current progress across a selection of areas of ubiquitous soft matter. When applied to particle tracking, machine learning using convolution neural networks is providing impressive performance but there remain some significant problems to solve. Characterising ordered arrangements of particles is a huge challenge and machine learning has been deployed to create the description, perform the classification and tease out an interpretation using a wide array of techniques often with good success. In glass research, machine learning has proved decisive in quantifying very subtle correlations between the local structure around a site and the susceptibility towards a rearrangement event at that site. There are also beginning to be some impressive attempts to deploy machine learning in the design of composite soft materials. The discovery aspect of this new materials design meets the current interest in teaching algorithms to learn to extrapolate beyond the training data. Soft matter ; Computer science ; Machine learning ; Artificial intelligence ; Engineering ; Chemical engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Mohan Surya Raja Elapolu | Md. Imrul Reza Shishir | Alireza Tabarraei
Source: Computational Materials Science (article, 2021-10-05)
Field: Materials Science — Materials Chemistry

A novel approach for studying crack propagation in polycrystalline graphene using machine learning algorithms Molecular dynamics ; Brittleness ; Convolutional neural network ; Materials science ; Ultimate tensile strength ; Crystallite ; Graphene ; Computer science ; Artificial intelligence ; Fracture mechanics ; Algorithm ; Fracture (geology) ; Artificial neural network ; Grain boundary ; Process (computing) ; Machine learning ; Composite material ; Nanotechnology ; Chemistry Graphene research and applications ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Yuya Oaki | Yasuhiko Igarashi
Source: Bulletin of the Chemical Society of Japan (article, 2021-08-27)
Field: Materials Science — Materials Chemistry

Materials Informatics for 2D Materials Combined with Sparse Modeling and Chemical Perspective: Toward Small-Data-Driven Chemistry and Materials Science Abstract Application of data-scientific approaches to conventional sciences, such as chemo-informatics, bio-informatics, and materials informatics (MI), has attracted much interest toward data-driven research. The concept enables accelerated discovery of new materials, enhancement of performance, and optimization of processes. However, sufficient bigdata is not always prepared to apply to machine learning. For example, experimental scientists have their own small data including success and failure in their laboratory, whether in academia or industry. If such small data is effectively utilized with a data-scientific approach, research activities can be accelerated without energy, resource, and cost consumption. This account focuses on MI for small data, a recent concept for application of small data, with introduction of model cases, such as control of exfoliation processes to obtain 2D materials. Combination of machine learning and chemical perspective is effective for construction of straightforward and interpretable predictors through the extraction of a limited number of descriptors from small dataset. Although the prediction accuracy is not so precise, the model has enough accuracy to be a guideline reducing the number of the next experiments. The present MI for small data opens potentials of small-data-driven chemistry and materials science. Chemistry ; Perspective (graphical) ; Informatics ; Materials informatics ; Nanotechnology ; Data science ; Health informatics ; Engineering informatics ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Daniil Bash | Yongqiang Cai | Vijila Chellappan | Swee Liang Wong | Yang Xu | Pawan Kumar | Jin Da Tan | Anas Abutaha | Jayce Jian Wei Cheng | Yee‐Fun Lim | Siyu Tian | Zekun Ren | Flore Mekki‐Berrada | Wai Kuan Wong | Jiaxun Xie | Jatin Kumar | Saif A. Khan | Qianxiao Li | Tonio Buonassisi | Kedar Hippalgaonkar
Source: Advanced Functional Materials (article, 2021-06-24)
Field: Materials Science — Materials Chemistry

Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites Abstract Combining high‐throughput experiments with machine learning accelerates materials and process optimization toward user‐specified target properties. In this study, a rapid machine learning‐driven automated flow mixing setup with a high‐throughput drop‐casting system is introduced for thin film preparation, followed by fast characterization of proxy optical and target electrical properties that completes one cycle of learning with 160 unique samples in a single day, a &gt; 10 × improvement relative to quantified, manual‐controlled baseline. Regio‐regular poly‐3‐hexylthiophene is combined with various types of carbon nanotubes, to identify the optimum composition and synthesis conditions to realize electrical conductivities as high as state‐of‐the‐art 1000 S cm −1 . The results are subsequently verified and explained using offline high‐fidelity experiments. Graph‐based model selection strategies with classical regression that optimize among multi‐fidelity noisy input‐output measurements are introduced. These strategies present a robust machine‐learning driven high‐throughput experimental scheme that can be effectively applied to understand, optimize, and design new materials and composites. Materials science ; Throughput ; Carbon nanotube ; High fidelity ; Fidelity ; Conductivity ; Computer science ; Composite material ; Nanotechnology ; Acoustics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Organic Electronics and Photovoltaics
Authors: R. Jenkins | Sawyer D. Campbell | Douglas H. Werner
Source: Nanophotonics (article, 2021-11-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Establishing exhaustive metasurface robustness against fabrication uncertainties through deep learning Abstract Photonic engineered materials have benefitted in recent years from exciting developments in computational electromagnetics and inverse‐design tools. However, a commonly encountered issue is that highly performant and structurally complex functional materials found through inverse‐design can lose significant performance upon being fabricated. This work introduces a method using deep learning (DL) to exhaustively analyze how structural issues affect the robustness of metasurface supercells, and we show how systems can be designed to guarantee significantly better performance. Moreover, we show that an exhaustive study of structural error is required to make strong guarantees about the performance of engineered materials. The introduction of DL into the inverse‐design process makes this problem tractable, enabling optimization runtimes to be measurable in days rather than months and allowing designers to establish exhaustive metasurface robustness guarantees. Robustness (evolution) ; Computer science ; Electromagnetics ; Inverse ; Inverse problem ; Computer engineering ; Photonics ; Deep learning ; Computer architecture ; Electronic engineering ; Artificial intelligence ; Materials science ; Engineering ; Mathematics Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Photonic Crystals and Applications
Authors: Satoshi Noguchi | Junya Inoue
Source: Physical review. E (article, 2021-08-09)
Field: Materials Science — Materials Chemistry

Stochastic characterization and reconstruction of material microstructures for establishment of process-structure-property linkage using the deep generative model In material design, microstructure characterization and reconstruction are indispensable for understanding the role of a structure in a process-structure-property relation. The significant contribution of this paper is to introduce a methodology for the characterization and generation of material microstructures using deep generative networks as the first step in the establishment of a process-structure-property linkage for forward or inverse material design. Our approach can be divided into two parts: (i) characterization of material microstructures by a vector quantized variational auto-encoder, and (ii) determination of the correlation between the extracted microstructure characterizations and the given conditions, such as processing parameters and/or material properties, by a pixel convolutional neural network. As an example, we tested our framework in the generation of low-carbon-steel microstructures from the given material processing. The results were in satisfactory agreement with the experimental observation qualitatively and quantitatively, demonstrating the potential of applying the proposed method to forward or inverse material design. One of the advantages of the proposed methodology lies in the capability to capture the stochastic nature behind the microstructure generation. As a result, this methodology enables us to build a process-structure-property linkage while quantifying uncertainties, which not only makes a prediction more robust but also shows a way toward enhancing our understanding of the stochastic competitive phenomena behind the generation of material microstructures. Characterization (materials science) ; Microstructure ; Linkage (software) ; Process (computing) ; Property (philosophy) ; Computer science ; Material properties ; Materials science ; Nanotechnology ; Composite material Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Welding Techniques and Residual Stresses
Authors: Peiqin Wu | Zeming He | Ming Yang | Jinhai Xu | Na Li | Ziman Wang | Jian Li | Ting Ma | Xing Lu | Hang Zhang | Ting Zhang
Source: International Journal of Thermophysics (review, 2021-05-25)
Field: Materials Science — Materials Chemistry

A Review on Flexible Thermoelectric Technology: Material, Device, and Applications Flexibility (engineering) ; Thermoelectric effect ; Thermoelectric cooling ; Computer science ; Renewable energy ; Thermoelectric generator ; Mechanical engineering ; Field (mathematics) ; Energy harvesting ; Materials science ; Systems engineering ; Energy (signal processing) ; Electrical engineering ; Engineering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Xian Yeow Lee | Joshua R. Waite | Chih-Hsuan Yang | Balaji Sesha Sarath Pokuri | Ameya Joshi | Aditya Balu | Chinmay Hegde | Baskar Ganapathysubramanian | Soumik Sarkar
Source: Nature Computational Science (article, 2021-03-25)
Field: Materials Science — Materials Chemistry

Fast inverse design of microstructures via generative invariance networks Differentiable function ; Computer science ; Inverse ; Deep learning ; Engineering design process ; Process (computing) ; Generative grammar ; Computer engineering ; Artificial intelligence ; Theoretical computer science ; Mechanical engineering ; Mathematics ; Engineering ; Geometry Machine Learning in Materials Science ; Thin-Film Transistor Technologies ; Advanced Memory and Neural Computing
Authors: Aimi Athirah Aznan | Claudia Gonzalez Viejo | Alexis Pang | Sigfredo Fuentes
Source: Sensors (article, 2021-09-23)
Field: Chemistry — Analytical Chemistry

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies. Artificial intelligence ; Artificial neural network ; Machine learning ; Computer science ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Smart Agriculture and AI
Authors: Mercè Deumal | Sergi Vela | Maria Fumanal | Jordi Ribas‐Ariño | Juan J. Novoa
Source: Journal of Materials Chemistry C (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Insights into the magnetism and phase transitions of organic radical-based materials Organic radicals have been consistently regarded as promising building blocks for the next generation of applied materials. Here, we discuss the insight gained from computational modelling into their magnetism and phase transitions. Magnetism ; Materials science ; Radical ; Phase transition ; Phase (matter) ; Chemical physics ; Nanotechnology ; Condensed matter physics ; Organic chemistry ; Physics ; Chemistry Magnetism in coordination complexes ; Organic and Molecular Conductors Research ; Synthesis and Properties of Aromatic Compounds
Authors: Kou Li | Ryoichi Yuasa | Ryogo Utaki | Meiling Sun | Yu Tokumoto | Daichi Suzuki | Yukio Kawano
Source: Nature Communications (article, 2021-05-21)
Field: Materials Science — Polymers and Plastics

Robot-assisted, source-camera-coupled multi-view broadband imagers for ubiquitous sensing platform Multi-functional photo-imaging garners attention towards the development of universal safety-net sensor networks. Although there are urgent needs to comprehensively address the optical information from arbitrarily structured and located targets, investigations on multi-view sensitive broadband monitoring, being independent of the operating environment, are yet to be completed. This study presents a robot-assisted, photo-source and imager implanted, multi-view stereoscopic sensitive broadband photo-monitoring platform with reflective and transmissive switchable modes. A multifaceted photo-thermoelectric device design based on flexible carbon nanotube films facilitates the prototype demonstrations of non-destructive, target-structure-independent, free-form multi-view examinations on actual three-dimensional industrial components. Further functionalisation, namely, a portable system utilising three-dimensional printing and ultraviolet processing, achieves the unification of freely attachable photo-imagers and miniature photo-sources, enabling location-independent operation. Consequently, the non-destructive unmanned, remote, high-speed, omni-directional testing of a defective aerial miniature model winding road-bridge with a robot-assisted photo-source imager built into a multi-axis movable photo-thermoelectric monitor arm is demonstrated. Broadband ; Computer science ; Stereoscopy ; Robot ; Computer hardware ; Light source ; Embedded system ; Real-time computing ; Artificial intelligence ; Optics ; Telecommunications ; Physics Transition Metal Oxide Nanomaterials ; Photoacoustic and Ultrasonic Imaging ; Gas Sensing Nanomaterials and Sensors
Authors: Ibrahim Goda | Jérémie Girardot
Source: International Journal of Damage Mechanics (article, 2021-02-11)
Field: Materials Science — Materials Chemistry

Numerical modeling and analysis of the ballistic impact response of ceramic/composite targets and the influence of cohesive material parameters Hybrid ceramic/composite targets are acknowledged to provide effective impact protection against armor piercing projectiles, which is why the research on this topic is continuously developing further. In this work, a nonlinear dynamic finite element (FE) simulation method is developed to systematically explore the ballistic perforation behaviors of hybrid ceramic/woven-fabric reinforced polymer (WFRP) composite when impacted by a non-deformable projectile. The hybrid system is composed by an alumina ceramic plate forming the front surface and glass or carbon WFRP composite back-up plate. The simulations are carried out using ABAQUS/Explicit FE code, wherein three different constitutive material models are formulated and implemented. The Johnson–Holmquist and composite damage models are used for alumina and composite material behaviors, respectively. The brittle fracture and fragmentation of the ceramic plate and the failure criteria based on fracture of fibers or matrices of composite materials during perforation are considered. Besides, interlaminar delamination between composite plies as well as ceramic/composite interfacial decohesion are modeled using a cohesive surface method, and the behaviors of interlayer degradation and failure are described using a traction-separation law. The accuracy of the developed model is validated with available experimental and analytical results. What’s more, the perforation process against the projectile and the ballistic mechanism of each layer in the composite backplate and in the ceramic as well are profoundly explored. Meanwhile, the numerical simulations are used to evaluate the changes of energy of the projectile and ceramic/composite panels. The influence of key parameters, such as interface cohesive properties and friction, on the ballistic performance in terms of energy absorption capability is additionally addressed. For the preliminary and early design phase, the present dynamic model could provide an efficient approach for numerical predictions of ballistic impact responses of the hybrid ceramic/FRP composites. Projectile ; Materials science ; Composite material ; Ceramic ; Composite number ; Ballistic limit ; Brittleness ; Perforation ; Delamination (geology) ; Finite element method ; Fracture (geology) ; Structural engineering ; Engineering High-Velocity Impact and Material Behavior ; Mechanical Behavior of Composites ; Structural Response to Dynamic Loads