jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luis Angel Mayoral Astorga | Masoud Shabaninezhad | Howard Northfield | Spyridon Ntais | Sabaa Rashid | Ewa Lisicka-Skrzek | Hamid Mehrvar | Éric Bernier | Dominic Goodwill | Lora Ramunno | Pierre Berini
Source: Nanophotonics (article, 2024-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrically tunable plasmonic metasurface as a matrix of nanoantennas We report the fabrication and characterization of a plasmonic metasurface comprising electrically-contacted sub-wavelength gold dipole nanoantennas, conformally coated by a thin hafnia film, an indium tin oxide layer and a backside mirror, forming metal-oxide-semiconductor (MOS) capacitors, for use as an electrically-tunable reflectarray or metasurface. By voltage biasing the nanoantennas through metallic connectors and leveraging the carrier refraction effect in the MOS capacitors, our measurements demonstrate phase control in reflection over a range of about 30°, with a constant magnitude of reflection coefficient of 0.5, and the absence of secondary lobes. Comprehensive electromagnetic and quantum carrier models of the structure are developed and are in excellent agreement with the measurements. The metasurface holds promise for use as an optical phased array. Materials science ; Plasmon ; Optoelectronics ; Metamaterial ; Reflection (computer programming) ; Reflection coefficient ; Capacitor ; Indium tin oxide ; Optics ; Thin film ; Nanotechnology ; Voltage ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Ritesh Gupta | Gaurav Mittal | Krishna Kumar | Upender Pandel
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-05-17)
Field: Materials Science — Materials Chemistry

Analysing the shape memory behaviour of GnP-enhanced nanocomposites: a comparative study between experimental and finite element analysis Abstract Shape memory polymers (SMPs) are capable of enduring significant deformations and returning to their original form upon activation by certain external stimuli. However, their restricted mechanical and thermal capabilities have limited their broader application in engineering fields. To address this, the integration of graphene nanoplatelets (GnPs) with SMPs has proven effective in enhancing their mechanical and thermal properties while maintaining inherent shape memory functions. The study evaluated shape memory nanocomposites (SMNCs) using dynamic mechanical, thermogravimetric, and static tensile, flexural, and shape memory tests, along with scanning electron microscopy to analyse tensile fractures. The results indicate that the optimal content of GnP is 0.6 wt%, resulting in excellent shape memory, thermal, and mechanical properties. Specifically, this composition demonstrates a shape recovery ratio of 94.02%, a storage modulus of 4580.07 MPa, a tensile strength of 61.42 MPa, and a flexural strength of 116.37 MPa. Additionally, the incorporation of GnPs into epoxy reduces recovery times by up to 52% at the 0.6 wt% concentration. While there is a slight decrease in the shape fixity ratio from 98.77% to 93.02%, the shape recoverability remains consistently high across all samples. Current finite element (FE) models often necessitate complex, problem-specific user subroutines, which can impede the straightforward application of research findings in real-world settings. To address this, the current study introduces an innovative finite element simulation method using the widely used ABAQUS software to model the thermomechanical behaviour of SMNCs, importantly incorporating the time-dependent viscoelastic behaviour of the material. The effectiveness of this new approach was tested by comparing experimental results from bending test of SMNCs cantilever beam with outcomes derived from FE simulations. The strong agreement between the experimental data and simulation results confirmed the precision and reliability of this novel technique. Materials science ; Finite element method ; Nanocomposite ; Composite material ; Shape-memory alloy ; Structural engineering ; Engineering Magnesium Oxide Properties and Applications
Authors: Subrat Kumar Jena | S. Pradyumna | Snehashish Chakraverty | Mohamed A. Eltaher
Source: ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

Chebyshev–Ritz and Navier's methods for hygro‐magneto vibration of Euler–Bernoulli nanobeam resting on Winkler–Pasternak elastic foundation Abstract This research employs Chebyshev–Ritz method along with Navier's method to investigate the vibration characteristics of a nanobeam subject to a longitudinal magnetic field and linear hygroscopic environment. The nanobeam is characterized by a Winkler–Pasternak elastic foundation and follows the nonlocal Euler–Bernoulli beam theory. The governing equation of motion is derived using Hamilton's principle, and non‐dimensional frequency parameters are computed for Simply Supported‐Simply Supported (SS), Clamped‐Clamped (CC), and Clamped‐Free (CF) boundary conditions. The motivation behind this study is to provide a comprehensive and efficient analytical framework for understanding the dynamic behavior of nanobeams in complex environments. By investigating the influence of magnetic and hygroscopic factors on the vibration characteristics of nanobeams, this research aims to offer valuable insights for the design and optimization of nanoscale structures. Employing shifted Chebyshev polynomials as shape functions in Chebyshev–Ritz method offers several advantages in the proposed model. Firstly, these polynomials possess orthogonal properties, which can significantly enhance computational efficiency. The orthogonality of shifted Chebyshev polynomials allow for simpler and more streamlined numerical computations compared to non‐orthogonal basis functions. Additionally, the orthogonality ensures that the resulting system of equations is well‐conditioned, even for higher‐order polynomial approximations. A closed‐form solution for SS boundary condition is obtained through Navier's method. Convergence analysis is performed to validate the accuracy and effectiveness of the proposed model against existing models. The non‐dimensional frequency parameters obtained using both Navier's method and Chebyshev–Ritz method demonstrate strong agreement, further validating the proposed nanobeam model. Additionally, a comprehensive parametric study evaluates the impact of various characteristics, including the small‐scale parameter, Winkler modulus, shear modulus, magnetic parameter, and hygroscopic parameter. The findings contribute to a nuanced understanding of nanobeam vibrations under the influence of a magnetic field and hygroscopic environment, providing valuable insights for the design and optimization of nanoscale structures in practical applications. Chebyshev polynomials ; Boundary value problem ; Ritz method ; Orthogonality ; Mathematics ; Chebyshev filter ; Vibration ; Euler's formula ; Mathematical analysis ; Timoshenko beam theory ; Orthogonal polynomials ; Geometry ; Physics Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Karthik Balasubramanian | Suvo Banik | Sukriti Manna | Srilok Srinivasan | Subramanian K. R. S. Sankaranarayanan
Source: APL Machine Learning (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Learning the stable and metastable phase diagram to accelerate the discovery of metastable phases of boron Boron, an element of captivating chemical intricacy, has been surrounded by controversies ever since its discovery in 1808. The complexities of boron stem from its unique position between metals and insulators in the Periodic Table. Recent computational studies have shed light on some of the stable boron allotropes. However, the demand for multifunctionality necessitates the need to go beyond the stable phases into the realm of metastability and explore the potentially vast but elusive metastable phases of boron. Traditional search for stable phases of materials has focused on identifying materials with the lowest enthalpy. Here, we introduce a workflow that uses reinforcement learning coupled with decision trees, such as Monte Carlo tree search, to search for stable and metastable boron phases, with enthalpy as the objective. We discover new boron metastable phases and construct a phase diagram that locates their phase space (T, P) at different levels of metastability (ΔG) from the ground state and provides useful information on the domains of relative stability of the various stable and metastable boron phases. Metastability ; Phase diagram ; Boron ; Materials science ; Chemical physics ; Phase (matter) ; Thermodynamics ; Chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Ferroelectric and Negative Capacitance Devices
Authors: Brandon J. Furey | Z. Wu | Mariano Isaza-Monsalve | Stefan Walser | Elyas Mattivi | René Nardi | Philipp Schindler
Source: Quantum (article, 2024-12-27)
Field: Chemistry — Spectroscopy

Strategies for implementing quantum error correction in molecular rotation The rotation of trapped molecules offers a promising platform for quantum technologies and quantum information processing. In parallel, quantum error correction codes that can protect quantum information encoded in rotational states of a single molecule have been developed. These codes are currently an abstract concept, as no implementation strategy is yet known. Here, we present a step towards experimental implementation of one family of such codes, namely absorption-emission codes. We first construct architecture-agnostic check and correction operators. These operators are then decomposed into elements of the quantum logic spectroscopy toolbox that is available for molecular ions. We then describe and analyze a measurement-based sequential as well as an autonomous implementation strategy in the presence of thermal background radiation, a major noise source for rotation in polar molecules. The presented strategies and methods might enable robust sensing or even fault-tolerant quantum computing using the rotation of individual molecules. Rotation (mathematics) ; Quantum ; Error detection and correction ; Quantum error correction ; Computer science ; Physics ; Quantum mechanics ; Quantum computer ; Algorithm ; Artificial intelligence Molecular spectroscopy and chirality ; Atomic and Subatomic Physics Research ; Spectroscopy and Laser Applications
Authors: Ziqi Ni | Yahao Li | Kai Hu | Ke Han | Ming Xu | Xingyu Chen | Fengqi Liu | Yicong Ye | Shuxin Bai
Source: arXiv (Cornell University) (preprint, 2024-11-10)
Field: Materials Science — Materials Chemistry

MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration The rapid evolution of artificial intelligence, particularly large language models, presents unprecedented opportunities for materials science research. We proposed and developed an AI materials scientist named MatPilot, which has shown encouraging abilities in the discovery of new materials. The core strength of MatPilot is its natural language interactive human-machine collaboration, which augments the research capabilities of human scientist teams through a multi-agent system. MatPilot integrates unique cognitive abilities, extensive accumulated experience, and ongoing curiosity of human-beings with the AI agents' capabilities of advanced abstraction, complex knowledge storage and high-dimensional information processing. It could generate scientific hypotheses and experimental schemes, and employ predictive models and optimization algorithms to drive an automated experimental platform for experiments. It turns out that our system demonstrates capabilities for efficient validation, continuous learning, and iterative optimization. Human–machine system ; Computer science ; Artificial intelligence ; Engineering ethics ; Data science ; Engineering Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Tao Song | Man Luo | Linjiang Chen | Yan Huang | Qing Zhu | Daobin Liu | Baicheng Zhang | Gang Zou | Fei Zhang | Weiwei Shang | Jun Jiang | Yi Luo
Source: ChemRxiv (preprint, 2024-07-30)
Field: Materials Science — Materials Chemistry

A multi-agent-driven robotic AI chemist enabling autonomous chemical research on demand The successful integration of large language models (LLMs) into laboratory workflows has demonstrated robust capabilities in natural language processing, autonomous task execution, and collaborative problem-solving. This offers an exciting opportunity to realize the dream of autonomous chemical research on demand. Here, we report a robotic AI chemist powered by a hierarchical multi-agent system, ChemAgents, based on an on-board Llama-3-70B LLM, capable of executing complex, multi-step experiments with minimal human intervention. It operates through a Task Manager agent that interacts with human researchers and coordinates four role-specific agents—Literature Reader, Experiment Designer, Computation Performer, and Robot Operator—each leveraging one of four foundational resources: a comprehensive Literature Database, an extensive Protocol Library, a versatile Model Library, and a state-of-the-art Automated Lab. We demonstrate its versatility and efficacy through six experimental tasks of varying complexity, ranging from straightforward synthesis and characterization to more complex exploration and screening of experimental parameters, culminating in the discovery and optimization of functional materials. Our multi-agent-driven robotic AI chemist showcases the potential of on-demand autonomous chemical research to drive unprecedented efficiencies, accelerate discovery, and democratize access to advanced experimental capabilities across academic disciplines and industries. Workflow ; Task (project management) ; Computer science ; Chemist ; Robot ; Human–computer interaction ; On demand ; Matching (statistics) ; Artificial intelligence ; Systems engineering ; Engineering ; Multimedia ; Database Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Chemical Synthesis and Analysis
Authors: Andrew McNaughton | Gautham Krishna Sankar Ramalaxmi | Agustin Kruel | Carter R. Knutson | Rohith Varikoti | Neeraj Kumar
Source: arXiv (Cornell University) (preprint, 2024-05-02)
Field: Chemistry — Physical and Theoretical Chemistry

CACTUS: Chemistry Agent Connecting Tool-Usage to Science Large language models (LLMs) have shown remarkable potential in various domains, but they often lack the ability to access and reason over domain-specific knowledge and tools. In this paper, we introduced CACTUS (Chemistry Agent Connecting Tool-Usage to Science), an LLM-based agent that integrates cheminformatics tools to enable advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama2-7b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b and Mistral-7b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with domain-specific tools, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment. Furthermore, CACTUS represents a significant milestone in the field of cheminformatics, offering an adaptable tool for researchers engaged in chemistry and molecular discovery. By integrating the strengths of open-source LLMs with domain-specific tools, CACTUS has the potential to accelerate scientific advancement and unlock new frontiers in the exploration of novel, effective, and safe therapeutic candidates, catalysts, and materials. Moreover, CACTUS's ability to integrate with automated experimentation platforms and make data-driven decisions in real time opens up new possibilities for autonomous discovery. Cactus ; Plant science ; Chemistry ; Botany ; Biology Various Chemistry Research Topics
Authors: Mengming Li | Wenji Fang | Qijun Zhang | Zhiyao Xie
Source: arXiv (Cornell University) (preprint, 2024-01-24)
Field: Materials Science — Materials Chemistry

SpecLLM: Exploring Generation and Review of VLSI Design Specification with Large Language Model The development of architecture specifications is an initial and fundamental stage of the integrated circuit (IC) design process. Traditionally, architecture specifications are crafted by experienced chip architects, a process that is not only time-consuming but also error-prone. Mistakes in these specifications may significantly affect subsequent stages of chip design. Despite the presence of advanced electronic design automation (EDA) tools, effective solutions to these specification-related challenges remain scarce. Since writing architecture specifications is naturally a natural language processing (NLP) task, this paper pioneers the automation of architecture specification development with the advanced capabilities of large language models (LLMs). Leveraging our definition and dataset, we explore the application of LLMs in two key aspects of architecture specification development: (1) Generating architecture specifications, which includes both writing specifications from scratch and converting RTL code into detailed specifications. (2) Reviewing existing architecture specifications. We got promising results indicating that LLMs may revolutionize how these critical specification documents are developed in IC design nowadays. By reducing the effort required, LLMs open up new possibilities for efficiency and accuracy in this crucial aspect of chip design. Computer science ; Architecture ; Electronic design automation ; Software engineering ; Key (lock) ; Automation ; Specification language ; Process (computing) ; Computer architecture ; Embedded system ; Programming language ; Engineering ; Computer security Machine Learning in Materials Science ; Semiconductor materials and devices ; Advancements in Photolithography Techniques
Authors: Shayan Azizi | Ehsan Asadi | Shaun C. Howard | Benjamin W. Muir | Riley O’Shea | Alireza Bab‐Hadiashar
Source: IEEE Transactions on Automation Science and Engineering (article, 2024-06-05)
Field: Chemistry — Analytical Chemistry

Autonomous Hyperspectral Characterisation Station: Robot Aided Measuring of Polymer Degradation This paper addresses a gap between the capabilities and utilisation of robotics and automation in laboratory settings and builds upon the concept of Self Driving Labs (SDL). We introduce an innovative approach to the temporal characterisation of materials. The article discusses the challenges posed by manual methods involving established laboratory equipment and presents an automated hyperspectral characterisation station. This station integrates robot-aided hyperspectral imaging (HSI), complex material characterisation modelling, and automated data analysis, offering a non-destructive and comprehensive approach. This work explains how the proposed assembly can automatically measure the half-life of biodegradable polymers with higher throughput and accuracy than manual methods. The investigation explores the effect of pH, number of average molecular weight (Mn), end groups, and blends on the degradation rate of polylactic acid (PLA). The novel contributions of the paper lie in introducing an adaptable classification station for characterisation and presenting an innovative methodology for polymer degradation rate measurements. The proposed system holds promise for expediting the development of high-throughput screening and characterisation methods within advanced material and chemistry laboratories. Note to Practitioners—The characterisation and classification of materials hold significant importance within the realms of materials science, chemistry, manufacturing and the circular economy. We introduce an innovative approach by employing robotics to automate hyperspectral imaging and processing to accelerate and improve material characterisation within a laboratory environment. This methodology facilitates the time-dependent characterisation of materials. This automated system encompasses sample manipulation and handling for hyperspectral scanning, accompanied by automated image and data processing procedures to minimise manual interventions effectively. The developed system can be seamlessly integrated into lab settings, offering objective, accurate, and automated measurement of biodegradable polymer degradation rates. Hyperspectral imaging ; Robot ; Degradation (telecommunications) ; Computer science ; Remote sensing ; Artificial intelligence ; Computer vision ; Engineering ; Materials science ; Geology ; Electronic engineering Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Industrial Vision Systems and Defect Detection
Authors: Holland Hysmith | Elham Foadian | Shakti P. Padhy | Sergei V. Kalinin | R. G. Moore | Olga S. Ovchinnikova | Mahshid Ahmadi
Source: ChemRxiv (preprint, 2024-01-12)
Field: Materials Science — Materials Chemistry

The future of self-driving laboratories: From Human in the Loop Interactive AI to Gamification Recent developments in artificial intelligence (AI) and machine learning (ML), implemented through self-driving laboratories (SDLs), are rapidly creating unprecedented opportunities for the accelerated discovery and optimization of materials. This paper provides a joint analysis of SDLs from both academic and industry perspectives, highlighting the importance of integrating human intelligence in these systems. It discusses the necessity of careful planning in SDL design across physical, data, and workflow dimensions, including instrumental setup, experimental workflow, data management, and human-SDL interaction. The significance of integrating human input within SDLs, especially as the focus shifts from individual tools and tasks to the creation and management of complex workflows, is emphasized. The paper stresses the crucial role of reward function design in developing forward-looking workflows and examines the interplay between hardware evolution, ML application across chemical processes, and the influence of reward systems in research. Ultimately, the article advocates for a future where SDLs blend human intuition in hypothesis formulation with AI's precision, speed, and data-handling capabilities. Workflow ; Intuition ; Computer science ; Human-in-the-loop ; Human intelligence ; Data science ; Artificial intelligence ; Human–computer interaction ; Knowledge management ; Psychology ; Cognitive science ; Database Machine Learning in Materials Science