jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bor‐Yann Tseng | You-Cheng Cai | Chen‐Wei Conan Guo | Elena Zhao | Chi‐Hua Yu
Source: Journal of Materials Research and Technology (article, 2023-04-08)
Field: Materials Science — Biomaterials

Reinforcement learning design framework for nacre-like structures optimized for pre-existing crack resistance Nacre is known for its uniquely high toughness and lightweight capabilities. Its unique structure is composed of soft nacre proteins and stiff calcium carbonates, allowing it to deflect cracks that expand in straight lines to increase energy dissipation. However, nacre microstructures are challenging to mimic due to the intractable number of combinations in the design space. We thus propose a reinforcement learning (RL) framework to efficiently design a high-toughness nacre-like structure. By designing local structures at the crack tip, we incorporated reinforcement learning with finite element to optimize the structure by replacing the soft and stiff materials in the design space. Starting from the initial unit cell, where the majority of the unit cell consists of soft materials, our method gradually improves the cell by arranging stiff and soft materials on the unit cell to achieve higher toughness. The optimized designs exhibit crack-insensitive behavior and excellent crack resistance when subjected to finite element simulation and experimental testing. This design framework can be used in synthetic instruments that require rapid construction rearrangements such as biomaterials and unexposed substructures, increasing their mechanical performance. Toughness ; Materials science ; Reinforcement ; Finite element method ; Microstructure ; Fracture toughness ; Composite material ; Reinforcement learning ; Material Design ; Dissipation ; Structural engineering ; Computer science ; Engineering ; Artificial intelligence Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Paleontology and Evolutionary Biology
Authors: Riley J. Hickman | Matteo Aldeghi | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2023-10-04)
Field: Materials Science — Materials Chemistry

Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a suggest-make-measure closed-loop constitutes a self-driving laboratory (SDL), which have been endorsed as a next-generation technology for autonomous scientific experimentation. Despite the promise of early SDL prototypes, a lack of flexible experiment planning algorithms prevents certain prevalent optimization problem types from being addressed. For instance, many experiment planning algorithms are unable to intelligently deal with failed measurements resulting from a priori unknown constraints on the parameter space. Such constraint functions are pervasive in chemistry and materials science research, stemming from unexpected equipment failures, failed/abandoned syntheses, or unstable molecules or materials. In this work, we provide a comprehensive discussion and benchmark of BO strategies to deal with a priori unknown constraints, characterized by learning the constraint function on-the-fly using a variational Gaussian process classifier and combining its predictions with the typical BO regression surrogate to parameterize feasibility-aware acquisition functions. These acquisition functions balance sampling parameter space regions deemed to be promising in terms of optimization objectives with avoidance of regions predicted to be infeasible. In addition to benchmarking feasibility-aware acquisition functions on analytic optimization benchmark surfaces, we conduct two realistic optimization benchmarks derived from previously reported studies: inverse design of hybrid organic-inorganic halide perovskite materials with unknown stability constraints, and the design of BCR-Abl kinase inhibitors with unknown synthetic accessibility constraints. We deliver intuitive recommendations to readers on which strategies work best for various scenarios. Overall, this work contributes to advancing the practicality and efficiency of autonomous experimentation in SDLs. All strategies introduced in this work are implemented as part of the open-source Atlas Python library. Bayesian optimization ; Computer science ; Benchmarking ; Benchmark (surveying) ; A priori and a posteriori ; Optimization problem ; Mathematical optimization ; Flexibility (engineering) ; Bayesian probability ; Gaussian process ; Machine learning ; Artificial intelligence ; Gaussian ; Algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Cheng-Zhou 城洲 Zhang 张 | Xiao-Qian 小倩 Fu 付
Source: Chinese Physics B (article, 2023-10-10)
Field: Materials Science — Materials Chemistry

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives Optoelectronic materials are essential for today’s scientific and technological development, and machine learning provides new ideas and tools for their research. In this paper, we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices. Then, we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods. We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices, including the methods related to crystal structure, properties (defects, electronic structure) research, materials and devices optimization, material characterization, and process optimization. In summarizing the algorithms and feature representations used in different studies, it is noted that prior knowledge can improve optoelectronic materials design, research, and decision-making processes. Finally, the prospect of machine learning applications in optoelectronic materials is discussed, along with current challenges and future directions. This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field. Computer science ; Materials informatics ; Process (computing) ; Characterization (materials science) ; Artificial intelligence ; Materials science ; Machine learning ; Nanotechnology ; Health informatics ; Engineering informatics Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Chalcogenide Semiconductor Thin Films
Authors: Jeffrey I. Seeman
Source: JACS Au (review, 2023-08-28)
Field: Chemistry — Physical and Theoretical Chemistry

Revolutions in Chemistry: Assessment of Six 20th Century Candidates (The Instrumental Revolution; Hückel Molecular Orbital Theory; Hückel’s 4<i>n</i> + 2 Rule; the Woodward–Hoffmann Rules; Quantum Chemistry; and Retrosynthetic Analysis) Six 20th century candidates for revolutions in chemistry are examined, using a definitional scheme published recently by the author. Six groupings of 13 characteristics of revolutions in science are considered: causes and birthings of revolutions, relationships between the old and the new, conceptual qualities of the candidate revolutions, instrumental and methodological functions, social construction of knowledge and practical considerations, and testimonials. The Instrumental Revolution was judged to be a revolution in chemistry because of the enormous increase in community-wide knowledge provided by the new instruments and the intentionality in the identification of specific target instruments, in the mindfulness in their design, manufacture, testing, use, and ultimately commercialization. The Woodward-Hoffmann rules were judged to precipitate the Quantum Chemistry Revolution because of theoretical, practical, and social construction of knowledge characteristics. Neither Hückel molecular orbital theory nor Hückel's 4<i>n</i> + 2 rule was considered an initiator of a revolution in chemistry but rather participants in the Quantum Chemistry Revolution. Retrosynthetic analysis was not judged to initiate a revolution in chemistry. Chemistry ; Hückel method ; Epistemology ; Molecular orbital ; Philosophy ; Organic chemistry History and advancements in chemistry ; Chemistry and Chemical Engineering ; Various Chemistry Research Topics
Authors: Ziming Wang | Xiaotong Liu | Haotian Chen | Tao Yang | Yurong He
Source: Applied Sciences (article, 2023-12-12)
Field: Materials Science — Materials Chemistry

Exploring Multi-Fidelity Data in Materials Science: Challenges, Applications, and Optimized Learning Strategies Machine learning techniques offer tremendous potential for optimizing resource allocation in solving real-world problems. However, the emergence of multi-fidelity data introduces new challenges. This paper offers an overview of the definition, applications, data preprocessing methodologies, and learning approaches associated with multi-fidelity data. To validate the algorithms, we examine three widely-used learning methods relevant to multi-fidelity data through the design of multi-fidelity datasets that encompass various types of noise. As we expected, employing multi-fidelity data learning methods yields better results compared to solely using high-fidelity data learning methods. Additionally, considering the inherent various types of noise within datasets, the comprehensive correction strategy proves to be the most effective. Moreover, multi-fidelity learning methods facilitate effective decision-making processes by enabling the combination of datasets from various sources. They extract knowledge from lower fidelity data, improving model accuracy compared to models solely relying on high-fidelity data. Fidelity ; Computer science ; Preprocessor ; Machine learning ; Data pre-processing ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Farhana Naaz | Preeti Lahiri | Chanda Kumari | Hemant Kumar Dubey
Source: Physics and Chemistry of Solid State (article, 2023-06-29)
Field: Materials Science — Materials Chemistry

Spectroscopic, Magnetic and Morphological studies of MgFe2O4 Nanopowder Spinel type nano ferrite compound MgFe2O4 was synthesized through sol gel technique using metal nitrates as precursors. The phase composition, morphology and elemental analysis of magnesium ferrite (MgFe2O4) were performed by X-ray diffraction, fourier transform infrared, atomic force microscopy, energy dispersive x-ray and scanning electron microscopy, analyses. The sample's X-ray diffraction pattern verifies the existence of single phase material, with the size of its crystallites estimated to be 39.9 nm. Fourier transform infrared examination supported metal-oxygen vibrations corresponding to tetrahedral and octahedral sites, respectively. From scanning electron microscopy image, grain size obtained about 97.7 nm. Raman spectra of the sample shows five Raman active modes (A1g + Eg + 3F2g), which is compatible with the spinel structure. Magnetic measurement study at room temperature shows a hysteresis loop behaviour with a low saturation magnetization value, 27.192 emu g-1 and a small coercivity value. The optical band gap determined using UV-visible transmittance spectra. Additionally, X-ray photoelectron spectroscopy are used to confirm oxidation states and explore the chemical composition of the sample. Coercivity ; Raman spectroscopy ; Analytical Chemistry (journal) ; Scanning electron microscope ; Materials science ; Crystallite ; Spinel ; Ferrite (magnet) ; X-ray photoelectron spectroscopy ; Fourier transform infrared spectroscopy ; Nuclear magnetic resonance ; Chemistry ; Optics ; Condensed matter physics ; Metallurgy ; Physics Magnetic Properties and Synthesis of Ferrites
Authors: Zhiding Liang | Jinglei Cheng | Rui Yang | Hang Ren | Zhixin Song | Di Wu | Xuehai Qian | Tongyang Li | Yiyu Shi
Source: arXiv (Cornell University) (preprint, 2023-07-17)
Field: Materials Science — Materials Chemistry

Unleashing the Potential of LLMs for Quantum Computing: A Study in Quantum Architecture Design Large Language Models (LLMs) contribute significantly to the development of conversational AI and has great potentials to assist the scientific research in various areas. This paper attempts to address the following questions: What opportunities do the current generation of generative pre-trained transformers (GPTs) offer for the developments of noisy intermediate-scale quantum (NISQ) technologies? Additionally, what potentials does the forthcoming generation of GPTs possess to push the frontier of research in fault-tolerant quantum computing (FTQC)? In this paper, we implement a QGAS model, which can rapidly propose promising ansatz architectures and evaluate them with application benchmarks including quantum chemistry and quantum finance tasks. Our results demonstrate that after a limited number of prompt guidelines and iterations, we can obtain a high-performance ansatz which is able to produce comparable results that are achieved by state-of-the-art quantum architecture search methods. This study provides a simple overview of GPT's capabilities in supporting quantum computing research while highlighting the limitations of the current GPT at the same time. Additionally, we discuss futuristic applications for LLM in quantum research. Quantum computer ; Computer science ; Quantum ; Supercomputer ; Architecture ; Generative grammar ; Ansatz ; Computational science ; Computer architecture ; Computer engineering ; Systems engineering ; Management science ; Software engineering ; Artificial intelligence ; Parallel computing ; Engineering ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Scientific Computing and Data Management
Authors: Yaoyao Tang | Quan Xu | Peide Zhu | Rongye Zhu | Juncheng Wang
Source: Materials Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Utilizing machine learning to expedite the fabrication and biological application of carbon dots This review introduces machine learning into imaging, sensing, and cancer therapy for CDs, demonstrating the great potential for ML to accelerate developments in materials science and provide new insight into the biological application field. Nanotechnology ; Computer science ; Field (mathematics) ; Artificial intelligence ; Data science ; Machine learning ; Materials science Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Graphene research and applications
Authors: Magdalena Valentina Lungu
Source: Materials (review, 2023-05-14)
Field: Materials Science — Materials Chemistry

Effects of Dopants and Processing Parameters on the Properties of ZnO-V2O5-Based Varistors Prepared by Powder Metallurgy: A Review This article reviews the progress in developing ZnO-V<sub>2</sub>O<sub>5</sub>-based metal oxide varistors (MOVs) using powder metallurgy (PM) techniques. The aim is to create new, advanced ceramic materials for MOVs with comparable or superior functional properties to ZnO-Bi<sub>2</sub>O<sub>3</sub> varistors using fewer dopants. The survey emphasizes the importance of a homogeneous microstructure and desirable varistor properties, such as high nonlinearity (α), low leakage current density (J<sub>L</sub>), high energy absorption capability, reduced power loss, and stability for reliable MOVs. This study investigates the effect of V<sub>2</sub>O<sub>5</sub> and MO additives on the microstructure, electrical and dielectric properties, and aging behavior of ZnO-based varistors. The findings show that MOVs with 0.25-2 mol.% V<sub>2</sub>O<sub>5</sub> and MO additives sintered in air over 800 °C contain a primary phase of ZnO with a hexagonal wurtzite structure and several secondary phases that impact the MOV performance. The MO additives, such as Bi<sub>2</sub>O<sub>3</sub>, In<sub>2</sub>O<sub>3</sub>, Sb<sub>2</sub>O<sub>3</sub>, transition element oxides, and rare earth oxides, act as ZnO grain growth inhibitors and enhance the density, microstructure homogeneity, and nonlinearity. Refinement of the microstructure of MOVs and consolidation under appropriate PM conditions improve their electrical properties (J<sub>L</sub> ≤ 0.2 mA/cm<sup>2</sup>, α of 22-153) and stability. The review recommends further developing and investigating large-sized MOVs from the ZnO-V<sub>2</sub>O<sub>5</sub> systems using these techniques. Materials science ; Varistor ; Dopant ; Powder metallurgy ; Metallurgy ; Doping ; Microstructure ; Optoelectronics ; Electrical engineering ; Engineering ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Transition Metal Oxide Nanomaterials
Authors: Pieter Samyn | Joey Bosmans | Patrick Cosemans
Source: Polymers (article, 2023-09-22)
Field: Materials Science — Polymers and Plastics

Role of Bio-Based and Fossil-Based Reactive Diluents in Epoxy Coatings with Amine and Phenalkamine Crosslinker The properties of epoxy can be adapted depending on the selection of bio-based diluents and crosslinkers to balance the appropriate viscosity for processing and the resulting mechanical properties for coating applications. This work presents a comprehensive study on the structure-property relationships for epoxy coatings with various diluents of mono-, di-, and bio-based trifunctional glycidyl ethers or bio-based epoxidized soybean oil added in appropriate concentration ranges, in combination with a traditional fossil-based amine or bio-based phenalkamine crosslinker. The viscosity of epoxy resins was already reduced for diluents with simple linear molecular configurations at low concentrations, while higher concentrations of more complex multifunctional diluents were needed for a similar viscosity reduction. The curing kinetics were evaluated through the fitting of data from differential scanning calorimetry to an Arrhenius equation, yielding the lowest activation energies for difunctional diluents in parallel with a balance between viscosity and reactivity. While the variations in curing kinetics with a change in diluent were minor, the phenalkamine crosslinkers resulted in a stronger decrease in activation energy. For cured epoxy resins, the glass transition temperature was determined as an intrinsic parameter that was further related to the mechanical coating performance. Considerable effects of the diluents on coating properties were investigated, mostly showing a reduction in abrasive wear for trifunctional diluents in parallel with the variations in hardness and ductility. The high hydrophobicity for coatings with diluents remained after wear and provided good protection. In conclusion, the coating performance could be related to the intrinsic mechanical properties independently of the fossil- or bio-based origin of diluents and crosslinkers, while additional lubricating properties are presented for vegetable oil diluents. Diluent ; Epoxy ; Materials science ; Curing (chemistry) ; Differential scanning calorimetry ; Coating ; Chemical engineering ; Activation energy ; Composite material ; Polymer chemistry ; Chemistry ; Organic chemistry ; Thermodynamics Polymer composites and self-healing ; Epoxy Resin Curing Processes ; Lignin and Wood Chemistry
Authors: Gregory Hunt | Michael Gahagan | Mitchell A. Peplow
Source: Lubrication Science (article, 2023-01-04)
Field: Materials Science — Materials Chemistry

The influence of model organosulfur extreme pressure additives and analogues on the corrosion of copper as measured by a wire corrosion test method Abstract A wire corrosion test (WCT) is used to investigate the influence of model organosulfur extreme pressure additives and analogues. The corrosion response from these compounds is discussed with regards to chemical structural type. Corrosion rates were calculated using the guide in ASTM G31 to provide a measurement in mm/year. Organosulfur compounds ; Corrosion ; Copper ; Metallurgy ; Materials science ; Erosion corrosion of copper water tubes ; Sulfur Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Electronic Packaging and Soldering Technologies
Authors: Han‐Don Um | Deokjae Choi | Amit Solanki | Emerald Huang | Kwanyong Seo | F. Habbal
Source: Nanophotonics (article, 2023-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dynamic selection of visible wavelengths using resonant TiO <sub>2</sub> nanostructures All-dielectric nanoarrays have strong electromagnetic resonances with various interesting applications and are tuned by adjusting their geometrical parameters. However, their optical properties are permanently encoded during fabrication. This study presents robust dynamically tunable all-dielectric nanoresonators for controllable, reversible, and reproducible color filtering. Our design uses an array of TiO<sub>2</sub> nanodiscs embedded in a transparent, stretchable polydimethylsiloxane (PDMS) membrane and exhibits a narrow spectral response due to Mie magnetic and electric dipole resonances hybridized with the TiO<sub>2</sub> nanodiscs lattice modes. By mechanically stretching the PDMS membrane, the pitch of the TiO<sub>2</sub> nanodiscs was increased and the spectral location of the resonances was altered. Additionally, an optically asymmetric structure was fabricated by partially embedding TiO<sub>2</sub> nanodiscs in PDMS. Thus, the magnitude of the Rayleigh anomaly diffraction, which could interrupt the dipole resonances, was reduced. Our design has sharp, frequency-tunable resonances in the visible spectrum, and we demonstrated dynamic tunability by stretching the metasurfaces. Nanomaterials ; Nanostructure ; Materials science ; Selection (genetic algorithm) ; Nanotechnology ; Wavelength ; Visible spectrum ; Optoelectronics ; Computer science Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Optical Wireless Communication Technologies
Authors: Philipp Brendel | Vlad Medvedev | Andreas Roßkopf
Source: IEEE Journal of Emerging and Selected Topics in Industrial Electronics (article, 2023-12-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-Informed Neural Networks for Magnetostatic Problems on Axisymmetric Transformer Geometries Physics-Informed Neural Networks (PINNs) have shown their potential for solving direct problems in many engineering domains including electromagnetics. We employ fully connected and convolutional PINNs in order to predict the magnetic vector potential and resulting inductances and coupling for axisymmetric transformer geometries commonly used in inductive power systems. Both approaches are compared quantitatively and validated against reference solutions obtained from a numerical solver. Fully connected PINNs tend to train faster and more accurate for single geometries, whereas convolutional PINNs show an outperformance in terms of their generalization capability and are able to predict inductances and coupling for a wide range of transformer geometries accurately in a matter of milliseconds. The combination of high accuracy and fast inference paves the way for PINN-based topology optimization in the field of power electronics. Transformer ; Topology (electrical circuits) ; Electromagnetics ; Solver ; Rotational symmetry ; Artificial neural network ; Inference ; Computer science ; Convolutional neural network ; Power electronics ; Physics ; Electronic engineering ; Power (physics) ; Artificial intelligence ; Engineering ; Electrical engineering ; Mechanics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Neural Networks and Applications