jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziyuan Wang | Guo Jun | Yu Wang | Yi‐Xing Zhang | Jing Feng | Zhen‐Hua Ge
Source: Small (article, 2023-12-24)
Field: Materials Science — Materials Chemistry

Realizing High Thermoelectric Properties in Bi<sub>2</sub>S<sub>3</sub> Bulk via Band Engineering and Nanorods Compositing Bismuth sulfide is a promising thermoelectric material because of its low cost and toxicity; however, its low electrical conductivity limits its thermoelectric properties. In this study, Bi<sub>2</sub>S<sub>3</sub>+x wt% HfCl<sub>4</sub> (x = 0, 0.25, 0.5, 0.75, and 1.0) bulk samples are fabricated using a combination of melting and spark plasma sintering. The microstructures, electronic structures, and thermoelectric properties of the composites are characterized. The results of electronic structure calculations show that doping with HfCl<sub>4</sub> produces an impurity energy level that narrows the bandgap and allows the Fermi energy level to enter the conduction band, leading to a favorable increase in carrier concentration. By regulating the HfCl<sub>4</sub> doping concentration, the electrical conductivity of the 0.75 wt% doped sample reaches 253 Scm<sup>-1</sup> at 423 K and its maximum ZT value is 0.47 at 673 K. Moreover, the sample is compounded with Bi<sub>2</sub>S<sub>3</sub> nanorods prepared by the hydrothermal method, reducing thermal conductivity by 30% due to the introduction of additional interfaces and pores. This resulted in a final ZT value of 0.61 at 673 K, which is approximately eight times higher than that of pure Bi<sub>2</sub>S<sub>3</sub>. This step-by-step optimization approach provides a valuable methodology for enhancing the performance of other thermoelectric material systems. Nanorod ; Compositing ; Materials science ; Thermoelectric effect ; Nanotechnology ; Engineering physics ; Optoelectronics ; Computer science ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Advanced Thermodynamics and Statistical Mechanics
Authors: Davi Fébba | Kevin R. Talley | K.E. Johnson | Stephen T. Schaefer | Sage R. Bauers | John S. Mangum | Rebecca W. Smaha | Andriy Zakutayev
Source: APL Materials (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

Autonomous sputter synthesis of thin film nitrides with composition controlled by Bayesian optimization of optical plasma emission Autonomous experimentation has emerged as an efficient approach to accelerate the pace of material discovery. Although instruments for autonomous synthesis have become popular in molecular and polymer science, solution processing of hybrid materials, and nanoparticles, examples of autonomous tools for physical vapor deposition are scarce yet important for the semiconductor industry. Here, we report the design and implementation of an autonomous workflow for sputter deposition of thin films with controlled composition, leveraging a highly automated sputtering reactor custom-controlled by Python, optical emission spectroscopy (OES), and a Bayesian optimization algorithm. We modeled film composition, measured by x-ray fluorescence, as a linear function of plasma emission lines monitored during co-sputtering from elemental Zn and Ti targets in an N2 and Ar atmosphere. A Bayesian control algorithm, informed by OES, navigates the space of sputtering power to fabricate films with user-defined compositions by minimizing the absolute error between desired and measured optical emission signals. We validated our approach by autonomously fabricating ZnxTi1−xNy films that deviate from the targeted cation composition by a relative ±3.5%, even for 15 nm thin films, demonstrating that the proposed approach can reliably synthesize thin films with a specific composition and minimal human interference. Moreover, the proposed method can be extended to more difficult synthesis experiments where plasma intensity lines depend non-linearly on pressure, or the elemental sticking coefficients strongly depend on the substrate temperature. Sputtering ; Materials science ; Thin film ; Sputter deposition ; Optoelectronics ; Nanotechnology Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Semiconductor materials and devices
Authors: Jie Chen | Hengrui Zhang | Carolin B. Wahl | Wei Liu | Chad A. Mirkin | Vinayak P. Dravid | Daniel W. Apley | Wei Chen
Source: Proceedings of the National Academy of Sciences (article, 2023-11-09)
Field: Materials Science — Materials Chemistry

Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from crystals with random orientations. Inspired by the human decision-making process, a framework for automated crystal system classification from DPs with arbitrary orientations was developed. A convolutional neural network was trained using evidential deep learning, and the predictive uncertainties were quantified and leveraged to fuse multiview predictions. Using vector map representations of DPs, the framework achieves a testing accuracy of 0.94 in the examples considered, is robust to noise, and retains remarkable accuracy using experimental data. This work highlights the ability of ML to be used to accelerate experimental high-throughput materials data analytics. Bottleneck ; Computer science ; Convolutional neural network ; Throughput ; Fuse (electrical) ; Artificial intelligence ; Machine learning ; Analytics ; Process (computing) ; Support vector machine ; Identification (biology) ; Artificial neural network ; Pattern recognition (psychology) ; Data mining ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Mario Teixeira Parente | Georg Brandl | Christian Franz | U. Stuhr | Marina Ganeva | A. Schneidewind
Source: Nature Communications (article, 2023-04-19)
Field: Materials Science — Materials Chemistry

Active learning-assisted neutron spectroscopy with log-Gaussian processes Neutron scattering experiments at three-axes spectrometers (TAS) investigate magnetic and lattice excitations by measuring intensity distributions to understand the origins of materials properties. The high demand and limited availability of beam time for TAS experiments however raise the natural question whether we can improve their efficiency and make better use of the experimenter's time. In fact, there are a number of scientific problems that require searching for signals, which may be time consuming and inefficient if done manually due to measurements in uninformative regions. Here, we describe a probabilistic active learning approach that not only runs autonomously, i.e., without human interference, but can also directly provide locations for informative measurements in a mathematically sound and methodologically robust way by exploiting log-Gaussian processes. Ultimately, the resulting benefits can be demonstrated on a real TAS experiment and a benchmark including numerous different excitations. Computer science ; Probabilistic logic ; Gaussian ; Benchmark (surveying) ; Neutron ; Spectrometer ; Lattice (music) ; Neutron spectroscopy ; Statistical physics ; Artificial intelligence ; Machine learning ; Physics ; Neutron scattering ; Optics ; Nuclear physics ; Acoustics ; Quantum mechanics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Mass Spectrometry Techniques and Applications
Authors: Luis M. Antunes | Keith T. Butler | Ricardo Grau‐Crespo
Source: arXiv (Cornell University) (preprint, 2023-07-10)
Field: Materials Science — Materials Chemistry

Crystal Structure Generation with Autoregressive Large Language Modeling The generation of plausible crystal structures is often the first step in predicting the structure and properties of a material from its chemical composition. Quickly generating and predicting inorganic crystal structures is important for the discovery of new materials, which can target applications such as energy or electronic devices. However, most current methods for crystal structure prediction are computationally expensive, slowing the pace of innovation. Seeding structure prediction algorithms with quality generated candidates can overcome a major bottleneck. Here, we introduce CrystaLLM, a methodology for the versatile generation of crystal structures, based on the autoregressive large language modeling (LLM) of the Crystallographic Information File (CIF) format. Trained on millions of CIF files, CrystaLLM focuses on modeling crystal structures through text. CrystaLLM can produce plausible crystal structures for a wide range of inorganic compounds unseen in training, as demonstrated by ab initio simulations. The integration with predictors of formation energy permits the use of a Monte Carlo Tree Search algorithm to improve the generation of meaningful structures. Our approach challenges conventional representations of crystals, and demonstrates the potential of LLMs for learning effective 'world models' of crystal chemistry, which will lead to accelerated discovery and innovation in materials science. Bottleneck ; Computer science ; Crystal (programming language) ; Crystal structure prediction ; Autoregressive model ; Algorithm ; Crystal structure ; Artificial intelligence ; Machine learning ; Chemistry ; Crystallography ; Mathematics ; Econometrics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Mahwish Afzia | Rafaqat Ali Khan | Bushra Ismail | Magdi E. A. Zaki | Talal M. Althagafi | Abdulaziz A. Alanazi | Afaq Ullah Khan
Source: Molecules (article, 2023-03-21)
Field: Materials Science — Materials Chemistry

Correlation between Magnetic and Dielectric Response of CoFe2O4:Li1+/Zn2+ Nanopowders Having Improved Structural and Morphological Properties The vast applicability of spinel cobalt ferrite due to its unique characteristics implies the need for further exploration of its properties. In this regard, structural modification at the O-site of spinel with Li<sup>1+</sup>/Zn<sup>2+</sup> was studied in detail for exploration of the correlation between structural, magnetic, and dielectric properties of the doped derivatives. The CTAB-assisted coprecipitation method was adopted for the synthesis of the desired compositions owing to its cost effectiveness and size controlling ability. Redistribution of cations at T- and O-sites resulted in the expansion of the crystal lattice, but no distortion of the cubic structure was observed, which further supports the flexible crystal structure of spinel for accommodating larger Li<sup>1+</sup>/Zn<sup>2+</sup> cations. Moreover, an XPS analysis confirmed the co-existence of the most stable oxidation states of Zn<sup>2+</sup>, Li<sup>1+</sup>, Co<sup>2+</sup>, and Fe<sup>3+</sup> ions with unstable Co<sup>3+</sup> and Fe<sup>2+</sup> ions as well, which induces the probability of hopping mechanisms to a certain extent and is a well-established behavior of cobalt ferrite nanoparticles. The experimental results showed that Li<sup>1+</sup>/Zn<sup>2+</sup> co-doped samples exhibit the best magnetic properties at dopant concentration <i>x</i> = 0.3. However, increasing the dopant content causes disturbance at both sites, resulting in decreasing magnetic parameters. It is quite evident from the results that dielectric parameters are closely associated with each other. Therefore, dopant content at <i>x</i> = 0.1 is considered the threshold value exhibiting the highest dielectric parameters, whereas any further increase would result in decreasing the dielectric parameters. The reduced dielectric properties and enhanced magnetic properties make the investigated samples a potential candidate for magnetic recording devices. Spinel ; Dopant ; Materials science ; Dielectric ; Coprecipitation ; Crystal structure ; Doping ; Ion ; Cobalt ; Superparamagnetism ; Analytical Chemistry (journal) ; Chemical engineering ; Inorganic chemistry ; Magnetization ; Crystallography ; Chemistry ; Metallurgy ; Magnetic field ; Optoelectronics Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Advancements in Battery Materials
Authors: Dazi Li | Yi Ru | Zhudan Chen | Caibo Dong | Yining Dong | Jun Liu
Source: APL Machine Learning (article, 2023-04-07)
Field: Materials Science — Materials Chemistry

Accelerating the design and development of polymeric materials via deep learning: Current status and future challenges The design and development of polymeric materials have been a hot domain for decades. However, traditional experiments and molecular simulations are time-consuming and labor-intensive, which no longer meet the requirements of new materials development. With the rapid advances of artificial intelligence and materials informatics, machine learning algorithms are increasingly applied in materials science, aiming to shorten the development period of new materials. With the evolution of polymeric materials, the structure of polymers has become more and more complex. Traditional machine learning algorithms often do not perform satisfactorily when dealing with complex data. Presently, deep learning algorithms, including deep neural networks, convolutional neural networks, generative adversarial networks, recurrent neural networks, and graph neural networks, show their uniquely excellent learning capabilities for large and complex data, which will be a powerful tool for the design and development of polymeric materials. This Review introduces principles of several currently popular deep learning algorithms and discusses their multiple applications in the materials field. Applications range from property prediction and molecular generation at the molecular level to structure identification and material synthesis in polymers. Finally, future challenges and opportunities for the application of deep learning in polymeric materials are discussed. Deep learning ; Artificial intelligence ; Computer science ; Convolutional neural network ; Materials informatics ; Generative grammar ; Machine learning ; Field (mathematics) ; Artificial neural network ; Data science ; Engineering informatics ; Health informatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Péter Pál Fehér | Ádám Madarász | András Stirling
Source: Journal of Chemical Theory and Computation (article, 2023-06-29)
Field: Materials Science — Materials Chemistry

Prediction of Redox Power for Photocatalysts: Synergistic Combination of DFT and Machine Learning The accurate prediction of excited state properties is a key element of rational photocatalyst design. This involves the prediction of ground and excited state redox potentials, for which an accurate description of electronic structures is needed. Even with highly sophisticated computational approaches, however, a number of difficulties arise from the complexity of excited state redox potentials, as they require the calculation of the corresponding ground state redox potentials and the estimation of the 0-0 transition energies (<i>E</i><sub>0,0</sub>). In this study, we have systematically evaluated the performance of DFT methods for these quantities on a set of 37 organic photocatalysts representing 9 different chromophore scaffolds. We have found that the ground state redox potentials can be predicted with reasonable accuracy that can be further improved by rationally minimizing the systematic underestimations. The challenging part is to obtain <i>E</i><sub>0,0</sub>, as calculating it directly is highly demanding and its accuracy depends strongly on the DFT functional employed. We have found that approximating <i>E</i><sub>0,0</sub> with appropriately scaled vertical absorption energies offers the best compromise between accuracy and computational effort. An even more accurate and cost-effective approach, however, is to predict <i>E</i><sub>0,0</sub> with machine learning and avoid the use of DFT for excited state calculations. Indeed, the best excited state redox potential predictions are achieved with the combination of M062X for ground state redox potentials and machine learning (ML) for <i>E</i><sub>0,0</sub>. With this protocol, the excited state redox potential windows of the photocatalyst frameworks could be adequately predicted. This shows the potential of combining DFT with ML in the computational design of photocatalysts with preferred photochemical properties. Excited state ; Redox ; Chromophore ; Computer science ; Ground state ; Reduction (mathematics) ; Chemistry ; State (computer science) ; Biological system ; Algorithm ; Atomic physics ; Physics ; Mathematics ; Photochemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; CO2 Reduction Techniques and Catalysts
Authors: Tian Tan | Wah Hoon Siew | Lu Han | M.J. Given | Christina Mckendry | John J. Liggat | Qi Li | Jinliang He
Source: ACS Applied Polymer Materials (article, 2023-08-22)
Field: Materials Science — Polymers and Plastics

Self-Healing of Electrical Damage in Microphase-Separated Polyurethane Elastomers with Robust Dielectric Strength Utilizing Dynamic Hydrogen Bonding Networks Polymer dielectrics are predominantly used as primary insulating materials in various electrical and electronic systems. Recently, self-healing dielectrics have been designed to heal microdamage in polymers to repair discharge channels and restore their critical insulating properties. However, the low dielectric strength of extrinsic self-healing polymers is a critical disadvantage in preventing them from practical application. In this work, we designed and implemented an insulating robust self-healing polyurethane (PU) utilizing a microphase-separating structure with a shape memory effect. The microphase separation structure in the PUs allows hard segments (HSs) to provide high dielectric strength while achieving self-healing through the soft segments (SSs) with high flowability. The analysis of dielectric relaxation behavior and quantum chemical calculations in combination indicated that the energy barrier at the interface of SS and HS is the key factor in improving the dielectric strength of polyurethanes. Observations from three-dimensional computed micro-X-ray tomography and optical microscopy showed that the PU developed can fully heal the damaged area under moderate thermal stimulation while restoring its electrical performance. The calculation of conformational entropy and the verification of the reversibility of hydrogen bonds further illustrated the reason for efficient self-healing capability of the designed PU. This work opened up opportunities for reliable applications of self-healing dielectrics in electrical and electronic apparatus. Materials science ; Dielectric ; Self-healing ; Composite material ; Polymer ; Elastomer ; Dielectric elastomers ; Self-healing material ; Dielectric strength ; Optoelectronics Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Elisa Calabrese | Marialuigia Raimondo | Michelina Catauro | Luigi Vertuccio | Patrizia Lamberti | Raffaele Raimo | Vincenzo Tucci | Liberata Guadagno
Source: Polymers (article, 2023-03-09)
Field: Materials Science — Polymers and Plastics

Thermal and Electrical Characterization of Polyester Resins Suitable for Electric Motor Insulation This paper undertakes the thermal and electrical characterization of three commercial unsaturated polyester imide resins (UPIR) to identify which among them could better perform the insulation function of electric motors (high-power induction motors fed by pulse-wide modulation (PWM) inverters). The process foreseen for the motor insulation using these resins is Vacuum Pressure Impregnation (VPI). The resin formulations were specially selected because they are one-component systems; hence, before the VPI process, they do not require mixing steps with external hardeners to activate the curing process. Furthermore, they are characterized by low viscosity and a thermal class higher than 180 °C and are Volatile Organic Compound (VOC)-free. Thermal investigations using Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC) techniques prove their excellent thermal resistance up to 320 °C. Moreover, impedance spectroscopy in the frequency range of 100 Hz-1 MHz was analyzed to compare the electromagnetic performance of the considered formulations. They manifest an electrical conductivity starting from 10<sup>-10</sup> S/m, a relative permittivity around 3, and a loss tangent value lower than 0.02, which appears almost stable in the analyzed frequency range. These values confirm their usefulness as impregnating resins in secondary insulation material applications. Polyester ; Materials science ; Characterization (materials science) ; Composite material ; Thermal ; Thermal insulation ; Nanotechnology Polymer composites and self-healing ; High voltage insulation and dielectric phenomena ; Epoxy Resin Curing Processes
Authors: Alexandra Elena Stoica | Alexandra Cătălina Bîrcă | Oana Gherasim | Anton Ficai | Alexandru Mihai Grumezescu | Ovidiu Oprea | Bogdan Ştefan Vasile | Cornel Baltă | Ecaterina Andronescu | Anca Hermenean
Source: Pharmaceutics (article, 2023-06-14)
Field: Materials Science — Biomaterials

Electrospun Fibrous Silica for Bone Tissue Engineering Applications The production of highly porous and three-dimensional (3D) scaffolds with biomimicking abilities has gained extensive attention in recent years for tissue engineering (TE) applications. Considering the attractive and versatile biomedical functionality of silica (SiO<sub>2</sub>) nanomaterials, we propose herein the development and validation of SiO<sub>2</sub>-based 3D scaffolds for TE. This is the first report on the development of fibrous silica architectures, using tetraethyl orthosilicate (TEOS) and polyvinyl alcohol (PVA) during the self-assembly electrospinning (ES) processing (a layer of flat fibers must first be created in self-assembly electrospinning before fiber stacks can develop on the fiber mat). The compositional and microstructural characteristics of obtained fibrous materials were evaluated by complementary techniques, in both the pre-ES aging period and post-ES calcination. Then, in vivo evaluation confirmed their possible use as bioactive scaffolds in bone TE. Tetraethyl orthosilicate ; Electrospinning ; Polyvinyl alcohol ; Materials science ; Fiber ; Porosity ; Calcination ; Tissue engineering ; Nanotechnology ; Nanofiber ; Biomedical engineering ; Polymer ; Composite material ; Chemistry Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials ; Silk-based biomaterials and applications