jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qinggang Qin | Zhengyu Xu | Wei Chen | Xue Liu | Jiawang Chen | Wenshuai Gao | Liang Li
Source: ACS Applied Materials & Interfaces (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

High-Performance Gate-Voltage-Tunable Photodiodes Based on Nb <sub>2</sub> Pd <sub>3</sub> Se <sub>8</sub> /WSe <sub>2</sub> Mixed-Dimensional Heterojunctions The mixed-dimensional (MD) van der Waals (vdWs) heterojunction for photodetectors has garnered significant attention owing to its exceptional compatibility and superior quality. Low-dimensional material heterojunctions exhibit unique photoelectric properties attributed to their nanoscale thickness and vdWs contact surfaces. In this work, a novel MD vdWs heterojunction composed of one-dimensional (1D) Nb<sub>2</sub>Pd<sub>3</sub>Se<sub>8</sub> nanowires and two-dimensional (2D) WSe<sub>2</sub> nanosheets is proposed. The heterojunction's energy band engineering is accomplished by manipulating the Fermi level of the bipolar 2D material via gate voltage, resulting in a rectification characteristic that can be adjusted with gate voltage. Under 685 nm laser irradiation, it demonstrates exceptional self-powered photodetection performance, attaining a photoresponsivity of 1.45 A W<sup>-1</sup>, an ultrahigh detectivity of 6.8 × 10<sup>12</sup> Jones, and an ultrafast response time of 37/64 μs at zero bias. In addition, a broadband photodetector from 255 to 1064 nm is realized. These results demonstrate the great potential of Nb<sub>2</sub>Pd<sub>3</sub>Se<sub>8</sub>/WSe<sub>2</sub> MD heterostructures for advanced electronic and optoelectronic devices. Materials science ; Photodiode ; Heterojunction ; Optoelectronics ; Photodetector ; Voltage ; Electrical engineering 2D Materials and Applications ; MXene and MAX Phase Materials ; Chalcogenide Semiconductor Thin Films
Authors: Alireza Ghafarollahi | Markus J. Buehler
Source: arXiv (Cornell University) (preprint, 2024-07-13)
Field: Materials Science — Materials Chemistry

AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence The design of alloys is a multi-scale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically reserved for human experts. Machine learning (ML) can help accelerate this process, for instance, through the use of deep surrogate models that connect structural features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges. Here, we overcome these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of large language models (LLM) the dynamic collaboration among AI agents with expertise in various domains, including knowledge retrieval, multi-modal data integration, physics-based simulations, and comprehensive results analysis across modalities that includes numerical data and images of physical simulation results. The concerted effort of the multi-agent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. Our results enable accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of advanced metallic alloys. Our framework enhances the efficiency of complex multi-objective design tasks and opens new avenues in fields such as biomedical materials engineering, renewable energy, and environmental sustainability. Modal ; Artificial intelligence ; Alloy ; Computer science ; Engineering ; Materials science ; Metallurgy ; Composite material Machine Learning in Materials Science
Authors: Fuzhan Rahmanian | Stefan Fuchs | Bojing Zhang | Maximilian Fichtner | Helge S. Stein
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Autonomous millimeter scale high throughput battery research system The high-throughput Auto-MISCHBARES platform streamlines reliable autonomous experimentation across laboratory devices through scheduling, quality control, live feedback, and real-time data management, including measurement, validation and analysis. Throughput ; Battery (electricity) ; Millimeter ; Scale (ratio) ; Computer science ; Environmental science ; Telecommunications ; Physics ; Geography ; Wireless ; Astronomy ; Cartography Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials
Authors: Peiqian Guo | Zhongbei Tian | Zhichang Yuan | Lu Qu | Xiaoyu Zhang | Xiaoping Zhang
Source: IET Renewable Power Generation (article, 2024-06-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Future‐proofing city power grids: FID‐based efficient interconnection strategies for major load‐centred environments Abstract The flexible interconnection device (FID) offers significant advantages for interconnecting different distribution networks flexibly. This paper focuses on the significant advantages offered by the FID for interconnecting different distribution networks flexibly. It specifically delves into FID‐based multi‐voltage and multi‐substation distribution networks, proposing a preferable scheme applicable to major load‐centred cities. Beginning with an analysis of constructed FID‐based flexible interconnected distribution network projects, key configurations and features are summarized. Subsequently, typical configurations, electrical parameters, facilities, relevant power functionalities, and application scenarios of multi‐voltage multi‐substation distribution networks are outlined. Building upon this foundation, a suitable interconnection scheme tailored for current urban use is explored to meet the specific needs of load‐centred cities, while incorporating recent advancements in high‐power‐density IGCT technology. An EMT model of a 10 kV/10 MW IGCT‐based four‐substation distribution network is developed in PSCAD/EMTDC. Through thorough analysis under different conditions, the operational performance and benefits are evaluated, providing insights into the efficiency and resilience of the proposed FID‐based interconnection. Lastly, challenges and prospects are discussed from various perspectives to advance the development of FID‐based flexible interconnection solutions. This study aims to contribute to the advancement and implementation of robust interconnection solutions to meet the evolving needs of major load‐centred cities. Interconnection ; Computer science ; Key (lock) ; Resilience (materials science) ; Power (physics) ; Reliability engineering ; Distributed computing ; Engineering ; Telecommunications Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Semiconductor materials and devices
Authors: Sergio Pablo‐García | A. Garcı́a | Gun Deniz Akkoc | Malcolm Sim | Yang Cao | Maxine Somers | Chance Hattrick | Naruki Yoshikawa | Dominik Dworschak | Han Hao | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2024-02-08)
Field: Materials Science — Materials Chemistry

An affordable platform for automated synthesis and electrochemical characterization Electrochemical techniques are pivotal for material discovery and renewable energy; however, often the extensive chemical spaces to be explored require high-throughput experimentation (HTE) to ensure timely results, which are costly both for instrument and materials/consumables. While self-driving laboratories (SDL) promise efficient chemical exploration, most contemporary implementations demand significant time, economic investment, and expertise. This study introduces an open and cost-effective autonomous electrochemical setup, comprising a synthesis platform and a custom-designed potentiostat device. We present an SDL platform that offers rapid deployment and extensive control over electrochemical experiments compared to commercial alternatives. Using ChemOS 2.0 for orchestration, we showcase our setup's capabilities through a campaign in which different metal ions reacts with ligands to form coordination compounds., yielding a database of 400 electrochemical measurements. Committed to open science, we provide a potentiostat design, campaign software, and raw data, aiming to democratize customized components in SDLs and ensure transparent data sharing and reproducibility. Characterization (materials science) ; Electrochemistry ; Computer science ; Chemistry ; Nanotechnology ; Materials science ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications
Authors: Eduardo Soares | Vidushi Sharma | Emílio Vital Brazil | Young-Hye Na | Renato Cerqueira
Source: Research Square (preprint, 2024-02-02)
Field: Materials Science — Materials Chemistry

Capturing Formulation Design of Battery Electrolytes with Chemical Large Language Model <title>Abstract</title> Recent progress in large transformers-based foundation models have demonstrated impressive capabilities in mastering complex chemical language representations. These models show promise in learning task-agnostic chemical language representations through a two-step process: pre-training on extensive unlabeled corpora and fine-tuning on specific downstream tasks. By utilizing self-supervised learning capabilities, foundation models have significantly reduced the reliance on labeled data and task-specific features, streamlining data acquisition and pushing the boundaries of chemical language representation. However, their practical implementation in further downstream tasks is still in its early stages and largely limited to sequencing problems. The proposed multimodal approach using MoLFormer, a chemical large language model, aims to demonstrate the capabilities of transformer based models to non-sequencing applications such as capturing design space of liquid formulations. Multimodal MoLFormer utilizes the extensive chemical information learned in pre-training from unlabeled corpora for predicting performance of battery electrolytes and showcases superior performance compared to state-of-the-art algorithms. The potential of foundation models in designing mixed material systems such as liquid formulations presents a groundbreaking opportunity to accelerate the discovery and optimization of new materials and formulations across various industries. Battery (electricity) ; Electrolyte ; Computer science ; Chemistry ; Thermodynamics ; Physics ; Electrode ; Physical chemistry Machine Learning in Materials Science
Authors: Vinay I. Hegde | Miroslava Peterson | Sarah I. Allec | Xiaonan Lu | Thiruvillamalai Mahadevan | Thanh Son Nguyen | Jayani Kalahe | Jared M. Oshiro | Robert J. Seffens | Ethan K. Nickerson | Jincheng Du | Brian J. Riley | John D. Vienna | James E. Saal
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Towards informatics-driven design of nuclear waste forms Informatics-driven approaches, such as machine learning and sequential experimental design, have shown the potential to drastically impact next-generation materials discovery and design. Informatics ; Computer science ; Materials informatics ; Systems engineering ; Data science ; Artificial intelligence ; Health informatics ; Engineering ; Engineering informatics ; Electrical engineering Machine Learning in Materials Science ; Nuclear materials and radiation effects ; Radioactive element chemistry and processing
Authors: Qianxiang Ai | Fanwang Meng | Jiale Shi | Brenden G. Pelkie | Connor W. Coley
Source: ChemRxiv (preprint, 2024-04-08)
Field: Materials Science — Materials Chemistry

Extracting Structured Data from Organic Synthesis Procedures Using a Fine-Tuned Large Language Model The popularity of data-driven approaches and machine learning (ML) techniques in the field of organic chemistry and its various subfields has increased the value of structured reaction data. Most data in chemistry is represented by unstructured text, and due to the vastness of the organic chemistry literature (papers, patents), manual conversion from unstructured text to structured data remains a largely manual endeavor. Software tools for this task would facilitate downstream applications such as reaction prediction and condition recommendation. In this study, we leverage the power of fine-tuned large language models (LLMs) to extract reaction information from organic synthesis procedure text into structured data following the Open Reaction Database (ORD) schema, a comprehensive data structure designed for organic reactions. The fine-tuned model produces syntactically correct ORD records with an average accuracy of 91.25% for ORD “messages” (e.g., full compound, workups, or condition definitions) and 92.25% for individual data fields (e.g., compound identifiers, mass quantities), with the ability to recognize compound-referencing tokens and to infer reaction roles. We investigate its failure modes and evaluate performance on specific subtasks such as reaction role classification. Leverage (statistics) ; Computer science ; Schema (genetic algorithms) ; Identifier ; Bridging (networking) ; Information retrieval ; Data mining ; Natural language processing ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Computational Drug Discovery Methods
Authors: Sarbaz Mohammed Qader | Azhin hamad mohammed | Akar Mahmood Muhammed | Rebaz Anwar Omer | Eman Ibraheem Abdulkareem | Rzgar Faruq Rashid
Source: Reviews in Inorganic Chemistry (article, 2024-10-11)
Field: Chemistry — Organic Chemistry

Potential of organometallic complexes in medicinal chemistry Abstract Organometallic complexes, which include ligands such as carbon monoxide (CO), carbenes, alkyls, phenyls, p-bound alkynes, alkenes, cyclopentadienyls, and arenes, have been extensively utilized in fields like materials chemistry and catalysis. These complexes also offer opportunities for the development of new medications with unique modes of action. Specifically, we are interested in anticancer drugs that can enhance the effectiveness of platinum treatments, broaden their range of action, reduce adverse effects, and prevent resistance. The distinct physiochemical properties of organometallic complexes have made them valuable in homogeneous catalysis, including the production of lead compounds and therapeutic possibilities. Over the past 20 years, a small group of researchers worldwide has explored the medical applications of these compounds’ unique characteristics, such as their structural diversity, potential for ligand exchange, and redox and catalytic properties. The results have been remarkable, and it is anticipated that numerous other organometallic compounds will undergo clinical trials in the coming years in addition to those already underway. In this brief study, we outline the advantages that organometallic metal complexes have over coordination compounds and pure organic molecules. Chemistry ; Organometallic chemistry ; Organic chemistry ; Computational chemistry ; Crystal structure Ferrocene Chemistry and Applications ; Metal complexes synthesis and properties ; Synthesis and Biological Evaluation
Authors: Shengguo Hu | Jiawen Xu | Ming‐Yi Li | Tie Jun Cui | Lianlin Li
Source: Nanophotonics (article, 2024-01-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Language‐controllable programmable metasurface empowered by large language models Programmable metasurface has become a prominent tool in various areas including control, communication, computing, and so on, due to its unique capability in the electromagnetic (EM) manipulation. However, it is lack of the intelligence in the sense that it usually requires the manual intervention, and thus makes it hard to behavior as the human process. To endow the programmable metasurface with the intelligence, we here proposed the concept of the language-controllable programmable metasurface for autonomous EM manipulations by exploring the notable capability of large language models (LLMs) in attaining the human-like intelligence. We have established a proof-of-principle system of language-controllable programmable metasurface, where, for illustration, the programmable metasurface is designed to have 32 × 24 binary electronically controllable meta-atoms and work at around 5.5 GHz. In addition, we have constructed a visual-semantic map to facilitate the language-controllable EM manipulation in three-dimensional (3D) physical environments. We have experimentally demonstrated that our language-controllable programmable metasurface is capable of decomposing autonomously an ambiguous task of EM manipulation into a sequence of executable ones and implementing them individually in real-world indoor settings. We expect that the presented strategy could hold promising potential in pushing programmable metasurfaces towards human-level autonomous agents, which are capable of accomplishing the smart EM-involved multi-modality manipulations through self-directed planning and actions. Computer science ; Computer architecture Metamaterials and Metasurfaces Applications ; Energy Harvesting in Wireless Networks ; Antenna Design and Analysis
Authors: Sheng Gong | Yumin Zhang | Zhenliang Mu | Zhichen Pu | Hongyi Wang | Zhiao Yu | Mengyi Chen | Tianze Zheng | Zhi Wang | Lifei Chen | Yang, Zhenze | Xiaojie Wu | Shaochen Shi | Weihao Gao | Wen Yan | Liang Xiang
Source: arXiv (Cornell University) (preprint, 2024-04-10)
Field: Materials Science — Materials Chemistry

A predictive machine learning force field framework for liquid electrolyte development Despite the widespread applications of machine learning force fields (MLFF) in solids and small molecules, there is a notable gap in applying MLFF to simulate liquid electrolyte, a critical component of the current commercial lithium-ion battery. In this work, we introduce BAMBOO (\textbf{B}yteDance \textbf{A}I \textbf{M}olecular Simulation \textbf{Boo}ster), a predictive framework for molecular dynamics (MD) simulations, with a demonstration of its capability in the context of liquid electrolyte for lithium batteries. We design a physics-inspired graph equivariant transformer architecture as the backbone of BAMBOO to learn from quantum mechanical simulations. Additionally, we introduce an ensemble knowledge distillation approach and apply it to MLFFs to reduce the fluctuation of observations from MD simulations. Finally, we propose a density alignment algorithm to align BAMBOO with experimental measurements. BAMBOO demonstrates state-of-the-art accuracy in predicting key electrolyte properties such as density, viscosity, and ionic conductivity across various solvents and salt combinations. The current model, trained on more than 15 chemical species, achieves the average density error of 0.01 g/cm$^3$ on various compositions compared with experiment. Electrolyte ; Molecular dynamics ; Computer science ; Materials science ; Ionic liquid ; Density functional theory ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Organic chemistry ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Tavus Hosseinabad | Gholamreza Nabiyouni | Kambiz Hedayati
Source: Polymer Composites (article, 2024-06-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis, characterization and microwave absorption properties of <scp>BaFe<sub>12</sub>O<sub>19</sub>/Ni<sub>0.5</sub>Mn<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub>@</scp> polyaniline nanocomposites with different ferrite percentages Abstract In this paper, BaFe 12 O 19 /Ni 0.5 Mn 0.5 Fe 2 O 4 @PANI core‐shell nanocomposites were synthesized with different ferrite/PANI ratios. The Ferrite nanoparticles were prepared by the auto‐combustion sol–gel method. Chemical oxidative polymerization of aniline in acidic solution along with adding ferrite nanoparticles to the composition was used for the synthesis of nanocomposites. The product then was studied by X‐ray diffractometer)XRD(, field emission scanning electron microscope (FE‐SEM), transmission electron microscope (TEM), and Fourier transform infrared spectroscopy (FTIR) analyses. The magnetic properties of samples were investigated with vibrating sample magnetometer (VSM). Also, nanocomposite samples were examined in terms of the ability to absorb microwaves. Using Vector Network Analyzer (VNA). the reflection loss (RL) of nanocomposites was measured at frequencies between 2 and 18 GHz. The minimum reflection loss (RL min ) was obtained at −36.98 dB at the frequency of 13.8 GHz with an effective absorption bandwidth of 4.87 GHz for the sample with the equal ratio of ferrite/PANI. Synthesized nanocomposite as an effective compound has very high reflection loss in a wide frequency range. Highlights Thermal stability is improved by adding ferrite nanoparticles to polyaniline. The polymer shell reduces the magnetic properties of ferrite nanoparticles. There is a strong exchange coupling between hard and soft ferrite. Ferrite/polymer ratio of 1:1 has the lowest amount of reflection loss. In the X and K u band, magnetic loss is mainly caused by eddy currents. Materials science ; Characterization (materials science) ; Microwave ; Absorption (acoustics) ; Analytical Chemistry (journal) ; Mineralogy ; Metallurgy ; Composite material ; Nanotechnology ; Chemistry Electromagnetic wave absorption materials ; Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials
Authors: Buta Singh | Jinchen Han | Mohammed J. Meziani | Li Cao | Subhadra Yerra | J.V. Collins | Simran Dumra | Ya‐Ping Sun
Source: Nanomaterials (review, 2024-07-27)
Field: Materials Science — Materials Chemistry

Polymeric Nanocomposites of Boron Nitride Nanosheets for Enhanced Directional or Isotropic Thermal Transport Performance Polymeric composites with boron nitride nanosheets (BNNs), which are thermally conductive yet electrically insulating, have been pursued for a variety of technological applications, especially those for thermal management in electronic devices and systems. Highlighted in this review are recent advances in the effort to improve in-plane thermal transport performance in polymer/BNNs composites and also the growing research activities aimed at composites of enhanced cross-plane or isotropic thermal conductivity, for which various filler alignment strategies during composite fabrication have been explored. Also highlighted and discussed are some significant challenges and major opportunities for further advances in the development of thermally conductive composite materials and their mechanistic understandings. Boron nitride ; Materials science ; Nanocomposite ; Thermal conductivity ; Fabrication ; Composite number ; Composite material ; Isotropy ; Electrical conductor ; Thermal ; Nanotechnology ; Polymer nanocomposite ; Filler (materials) Thermal properties of materials ; Graphene research and applications ; Thermal Expansion and Ionic Conductivity