jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peter Bøggild | Timothy J. Booth | Bjarke S. Jessen | Abhay Shivayogimath | Nolan Lassaline | Stephan Hofmann | Kim Daasbjerg | Anders Smith | Kasper Nørgaard | Amaia Zurutuza | Terrance Barkan | Andrew J. Pollard
Source: arXiv (Cornell University) (preprint, 2024-09-18)
Field: Materials Science — Materials Chemistry

Recommendations and tools to enable reproducibility in 2D materials research Research on 2D materials has achieved significant milestones and fuelled a rapidly growing industrial sector. This progress, however, is accompanied by challenges in reproducibility, arising from the atomic thinness, fragility, and environmental sensitivity of these materials. Subtle variations in methods or materials can lead to drastically different outcomes, undermining reliability and slowing down both scientific and technological advances. At the same time, academic publishing and funding systems continue to place greater value on novelty than on efforts to improve reproducibility. This Expert Recommendation outlines concrete, actions researchers can take to improve reproducibility in 2D materials science. We introduce two tools - STEP (Standardised Template for Experimental Procedures) and ReChart (Reproducibility Charter) - designed to support rigorous documentation and transparent sharing of protocols, failure modes, and raw data. To illustrate the application of STEP, we provide three detailed examples covering key processes in 2D materials research: graphene growth by chemical vapour deposition (CVD) on copper foil, wet transfer of CVD graphene, and dry assembly of van der Waals heterostructures. We offer practical recommendations that spans the full research process and show how researchers can engage constructively with stakeholders across academia, funding, publishing, and industry to create a stronger basis for reproducibility, transparency and trust in the field. Reproducibility ; Closing (real estate) ; Materials science ; Computer science ; Mathematics ; Statistics ; Political science Machine Learning in Materials Science
Authors: 贺晓娴 He Xiaoxian | 周思雨 Zhou Siyu | 赵基翔 Zhao Jixiang | 汪相如 Wang Xiangru
Source: Laser & Optoelectronics Progress (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

激光通信中液晶光学相控阵技术的研究进展（特邀） 作为一种新兴的非机械式波束控制技术，光学相控阵大大提高了系统的效率和稳定性，具有低质量、小尺寸、快速波束赋形和低功耗等优点，在多个领域得到了广泛的应用。本文从波束指向器的角度，综述了液晶空间光学相控阵技术在激光通信中的研究进展。根据激光通信系统对波束指向器的性能要求，从大口径、大角度、快速响应、多波束和偏转效率提升等多方面介绍了液晶光学相控阵领域的研究现状与最新进展，总结了液晶光学相控阵目前面临的问题及未来的发展趋势。 Computer science Liquid Crystal Research Advancements ; Photonic Crystals and Applications ; Photonic and Optical Devices
Authors: Biao Yang | Zhongwei Zhao | Haoran Zhang | Yang Chen | Xiucai Chen
Source: Applied Sciences (article, 2024-08-23)
Field: Chemistry — Organic Chemistry

Temperature Field Optimization for Multi-Microwave Sources Based on Collaborative Switching under Uncertain Communication The multi-microwave sources reactor can significantly reduce energy consumption and processing time with broad application prospects in industrial processing. In order to optimize the temperature field of materials, this paper proposes the heating strategy of multi-microwave sources based on collaborative switching, particularly in distributed combined heat source networks with poor communication conditions. Firstly, simplifying system control variables to enhance the design of the microwave intelligent agent system, and optimizing the time-frequency characteristics of combined power output from multi-microwave sources to emphasize the process of energy partition. Meanwhile, an event-triggered strategy reduces communication frequency and energy consumption between agents. Secondly, a fixed positive lower limit τmin is used in event-triggered to avoid Zeno behavior caused by DoS attacks. Finally, The finite element method was used with the time domain for thermal analysis. The simulation results of SiC show that the energy utilization efficiency of microwave heating equipment is increased by 4.3∼10.7%, temperature uniformity is improved by 25.6∼43.6%, and the results of the potato experiment simulation showed that the multi-microwave source collaborative switching heating strategy can effectively optimize the temperature field distribution of the material. Microwave ; Computer science ; Energy consumption ; Process engineering ; Field (mathematics) ; Distributed computing ; Electronic engineering ; Telecommunications ; Engineering ; Electrical engineering Microwave-Assisted Synthesis and Applications
Authors: Zhen Huang | Haoyang Zou | Xuefeng Li | Yixiu Liu | Yu-Xiang Zheng | Ethan Chern | Shijie Xia | Yiwei Qin | Weizhe Yuan | Pengfei Liu
Source: arXiv (Cornell University) (preprint, 2024-11-25)
Field: Chemistry — Inorganic Chemistry

O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson? This paper presents a critical examination of current approaches to replicating OpenAI's O1 model capabilities, with particular focus on the widespread but often undisclosed use of knowledge distillation techniques. While our previous work explored the fundamental technical path to O1 replication, this study reveals how simple distillation from O1's API, combined with supervised fine-tuning, can achieve superior performance on complex mathematical reasoning tasks. Through extensive experiments, we show that a base model fine-tuned on simply tens of thousands of samples O1-distilled long-thought chains outperforms O1-preview on the American Invitational Mathematics Examination (AIME) with minimal technical complexity. Moreover, our investigation extends beyond mathematical reasoning to explore the generalization capabilities of O1-distilled models across diverse tasks: hallucination, safety and open-domain QA. Notably, despite training only on mathematical problem-solving data, our models demonstrated strong generalization to open-ended QA tasks and became significantly less susceptible to sycophancy after fine-tuning. We deliberately make this finding public to promote transparency in AI research and to challenge the current trend of obscured technical claims in the field. Our work includes: (1) A detailed technical exposition of the distillation process and its effectiveness, (2) A comprehensive benchmark framework for evaluating and categorizing O1 replication attempts based on their technical transparency and reproducibility, (3) A critical discussion of the limitations and potential risks of over-relying on distillation approaches, our analysis culminates in a crucial bitter lesson: while the pursuit of more capable AI systems is important, the development of researchers grounded in first-principles thinking is paramount. Simple (philosophy) ; Replication (statistics) ; Distillation ; Computer science ; Process engineering ; Mathematics ; Engineering ; Philosophy ; Chemistry ; Chromatography ; Epistemology ; Statistics Radioactive element chemistry and processing
Authors: Ernesto Diaz | Renuka Sriram | Jeremy W. Gordon | Avantika Sinha | Xiaoxi Liu | Sule Sahin | Jason C. Crane | Marram P. Olson | Hsin-Yu Chen | Jenna Bernard | Daniel B. Vigneron | Zhen J. Wang | Duan Xu | Peder E. Z. Larson
Source: Journal of Imaging Informatics in Medicine (preprint, 2024-05-06)
Field: Chemistry — Spectroscopy

Data Format Standardization and DICOM Integration for Hyperpolarized 13C MRI DICOM ; Computer science ; Standardization ; Medical imaging ; Pipeline transport ; Python (programming language) ; Medical physics ; Data mining ; Information retrieval ; Medicine ; Artificial intelligence ; Engineering Advanced NMR Techniques and Applications ; Atomic and Subatomic Physics Research ; Solid-state spectroscopy and crystallography
Authors: Stefan Bräse
Source: ChemRxiv (preprint, 2024-05-14)
Field: Materials Science — Materials Chemistry

Digital Chemistry: Navigating the Confluence of Computation and Experimentation – Definition, Status Quo, and Future Perspective Digital chemistry represents a transformative approach integrating computational methods, digital data, and automation within the chemical sciences. It is defined by the use of digital tools and algorithms to simulate, predict, accelerate, and analyze chemical processes and properties, augmenting traditional experimental methods. The current status quo of digital chemistry is marked by rapid advancements in several key areas: high-throughput screening, miniaturization, machine learning models, quantum chemistry, and laboratory automation. These technologies have enabled unprecedented speeds in discovering and optimizing new molecules, materials, and reactions. Retrosynthesis and structure-active prediction tools have supported these endeavors. Furthermore, integrating large-language models and robotics in chemistry labs (self-driving labs) has begun to automate routine tasks and complex decision-making processes. Looking forward, the future of digital and digitalized chemistry is poised for significant growth, driven by the increasing accessibility of computational resources, the expansion of chemical databases, and the refinement of artificial intelligence algorithms. This evolution promises to accelerate innovation in drug discovery, material science, and sustainable manufacturing, ultimately leading to more efficient, cost-effective, and environmentally friendly chemical research and production. The challenge lies in advancing the technology itself, fostering interdisciplinary collaboration, and ensuring the ethical use of digital tools in chemical research. Computer science ; Transformative learning ; Status quo ; Automation ; Data science ; Cyberinfrastructure ; Management science ; Artificial intelligence ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
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: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

An Affordable Platform for Automated Synthesis and Electrochemical Characterization Characterization (materials science) ; Electrochemistry ; Computer science ; Business ; Nanotechnology ; Chemistry ; Materials science ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Shyamal Mondal
Source: Vidyodaya Journal of Science (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

Exploring the Synergistic Potential of Artificial Intelligence and Machine Learning in Chemistry Machine learning (ML) and artificial intelligence (AI) have become specialists in different areas of chemistry. These technologies help to change the standard approaches to data analysis and molecular design along with the properties forecast. This review describes the interesting applications and increasing potential of AI and ML, specifically in drug discovery, chemical synthesis, material science, and computational chemistry. Computationally, the focus was on the application of AI algorithms to quantum chemistry simulations to predict properties of elements within a molecule, or possible reactions of molecules at a rate that would not have been possible manually. Moreover, AI-driven robotic synthesis platforms and experimental techniques have become less labor-intensive. The methods used for the identification of new chemical structures have improved in terms of speed. The benefits and the limitations of integrating AI, as well as the opportunities, are discussed in detail. In this review, it is also reiterated that there are risks that come with the integration of ML in chemistry and how interdisciplinary collaboration and data sharing are crucial to advancing in this field. In a single summary, this review demonstrates how the use of AI and ML can and will expand the horizons of chemical science and discovery.Keywords: Artificial Intelligence, Machine Learning, Drug Discovery, Chemical Synthesis, Materials Science, Computational Chemistry. Artificial intelligence ; Computer science ; Machine learning ; Drug discovery ; Field (mathematics) ; Chemistry ; Identification (biology) ; Nanotechnology ; Data science ; Biochemical engineering ; Materials science ; Mathematics ; Engineering ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Michael A. Stolberg | Jeffrey Lopez | Sawyer Cawthern | Abraham Herzog‐Arbeitman | Ha-Kyung Kwon | Daniel Schweigert | Abraham Anapolosky | Brian D. Storey | Jeremiah A. Johnson | Yang Shao‐Horn
Source: ChemRxiv (preprint, 2024-12-23)
Field: Materials Science — Materials Chemistry

A Data-Driven Platform for Automated Characterization of Polymer Electrolytes Lithium-ion batteries aid in decarbonization through enabling electric vehicles and renewable energy generation, but these applications put increasing demands on the energy density, safety, and cost of the batteries. Polymer electrolytes could improve battery safety but currently do not have sufficient ionic conductivity for ambient operation. To address this challenge, we developed a high throughput platform to increase the speed and scale of polymer electrolyte research by an estimated 100X. We utilized automated formulation and characterization operations, including electrochemical impedance spectroscopy with in situ thickness measurements, to perform a comparison of lithium and sodium salts in poly(ethylene oxide). Our study provides a high-quality, unified reference dataset for the community, and greatly expands available data for sodium-based electrolytes. Secondly, our large dataset allows us to find that the local minima in glass transition temperature that corresponds to maximum ionic conductivity is a colligative property, independent of either anion or cation chemistry. Characterization (materials science) ; Polymer electrolytes ; Computer science ; Polymer ; Electrolyte ; Nanotechnology ; Materials science ; Chemistry ; Composite material Machine Learning in Materials Science
Authors: Xiang Chen | Mingkang Liu | Shun‐Gao Yin | Yuchen Gao | Nan Yao | Qiang Zhang
Source: arXiv (Cornell University) (preprint, 2024-11-30)
Field: Materials Science — Materials Chemistry

Uni-Electrolyte: An Artificial Intelligence Platform for Designing Electrolyte Molecules for Rechargeable Batteries Electrolyte is a very important part of rechargeable batteries such as lithium batteries. However, the electrolyte innovation is facing grand challenges due to the complicated solution chemistry and infinite molecular space (&gt;1060 for small molecules). This work reported an artificial intelligence (AI) platform, namely Uni-Electrolyte, for designing advanced electrolyte molecules, which mainly includes three parts, i.e. EMolCurator, EMolForger, and EMolNetKnittor. New molecules can be designed by combining high-throughput screening and generative AI models from more than 100 million alternative molecules in the EMolCurator module. The molecule properties including frontier molecular orbital information, formation energy, binding energy with a Li ion, viscosity, and dielectric constant, can be adopted as the screening parameters. The EMolForger, and EMolNetKnittor module can predict the retrosynthesis pathway and reaction pathway with electrodes for a given molecule, respectively. With the assist of advanced AI methods, the Uni-Electrolyte is strongly supposed to discover new electrolyte molecules and chemical principles, promoting the practical application of next-generation rechargeable batteries. Electrolyte ; Nanotechnology ; Electrolyte Disorder ; Materials science ; Computer science ; Chemical engineering ; Engineering ; Chemistry ; Electrode ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Yuma Iwasaki | D. Ogawa | Masato Kotsugi | Y. K. Takahashi
Source: arXiv (Cornell University) (preprint, 2024-11-28)
Field: Materials Science — Materials Chemistry

Autonomous materials search using machine learning and ab initio calculations for L10-FePt-based quaternary alloys The efficient exploration of expansive material spaces remains a significant challenge in materials science. To address this issue, autonomous material search methods that combine machine learning with ab initio calculations have emerged as a promising solution. These approaches offer a systematic and rapid means of discovering new materials, particularly when the material space is too large. This requirement is particularly important in the development of L10-structured alloys as magnetic recording media. These materials require a high magnetic moment (M) and magnetocrystalline anisotropy energy (EMCA) to satisfy the demands of next-generation data storage technologies. Although autonomous search methods have been successfully applied to various material systems, quaternary L10 alloys with optimized magnetic properties remain an open and underexplored frontier. In this study, we present a simulation-based autonomous search method aimed at identifying quaternary L10 alloys with enhanced M and EMCA values. Over a continuous 100-day search, our system suggested the FeMnPtEr alloy system as a promising candidate, exhibiting superior values for both M and EMCA. Although further experimental validation is required, this study underscores the potential of autonomous search methods to accelerate the discovery of advanced materials. Keywords: L10, FePt, machine learning, ab initio calculations, Bayesian optimization Ab initio ; Quaternary ; Materials science ; Physics ; Geology ; Quantum mechanics ; Paleontology Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Lin Huang | Rong‐Jun Xie
Source: Chinese Journal of Luminescence (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Strategies and Challenges of Data-driven Research and Discovery in Luminescent Materials Abstract Artificial intelligence has been bringing a great of convenience for human in manufacture life science and technology for its abilities of efficient data analyses accurate prediction automatic task executing and person alized service.Machine learning and high-throughput computing have extensively permeated and successfully ap plied in the field of materials opening up new horizons for innovative research and design methods in luminescent materials.By employing efficient algorithms for mining and processing large-scale data the screening and design process of new materials is accelerated thereby driving the discovery and application progress of novel materials.This article provides an overview of the recent advances in data-driven research on luminescent materials.Based on relevant research cases it outlines the entire process of data-driven material research and elaborates on the impor tance and implementation strategies of data acquisition in the development of luminescent materials.It also conducts a thorough analysis of how to accurately extract core features that characterize material performance while exploring algorithm selection strategies applicable to the field of luminescent materials.Finally the bottleneck of the current research on data-driven luminescent materials is pointed out such as a lack of high-quality data and difficulties in constructing complex structure-performance correlation models.It also provides an outlook on future development di rections with a particular emphasis on the construction of luminescent material database platforms implementation of high-throughput experiments and establishment of corresponding data production standards. Key words data- Materials science ; Luminescence ; Nanotechnology ; Data science ; Systems engineering ; Computer science ; Optoelectronics ; Engineering Machine Learning in Materials Science
Authors: Jieyu Lü | Zhengshuai Song | Qiyuan Zhao | Yuanqi Du | Yirui Cao | Haojun Jia | Chenru Duan
Source: arXiv (Cornell University) (preprint, 2024-10-21)
Field: Materials Science — Materials Chemistry

Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge of Large Language Models Designing functional transition metal complexes (TMCs) faces challenges due to the vast search space of metals and ligands, requiring efficient optimization strategies. Traditional genetic algorithms (GAs) are commonly used, employing random mutations and crossovers driven by explicit mathematical objectives to explore this space. Transferring knowledge between different GA tasks, however, is difficult. We integrate large language models (LLMs) into the evolutionary optimization framework (LLM-EO) and apply it in both single- and multi-objective optimization for TMCs. We find that LLM-EO surpasses traditional GAs by leveraging the chemical knowledge of LLMs gained during their extensive pretraining. Remarkably, without supervised fine-tuning, LLMs utilize the full historical data from optimization processes, outperforming those focusing only on top-performing TMCs. LLM-EO successfully identifies eight of the top-20 TMCs with the largest HOMO-LUMO gaps by proposing only 200 candidates out of a 1.37 million TMCs space. Through prompt engineering using natural language, LLM-EO introduces unparalleled flexibility into multi-objective optimizations, thereby circumventing the necessity for intricate mathematical formulations. As generative models, LLMs can suggest new ligands and TMCs with unique properties by merging both internal knowledge and external chemistry data, thus combining the benefits of efficient optimization and molecular generation. With increasing potential of LLMs as pretrained foundational models and new post-training inference strategies, we foresee broad applications of LLM-based evolutionary optimization in chemistry and materials design. Generative grammar ; Computer science ; Natural language processing ; Linguistics ; Artificial intelligence ; Philosophy Machine Learning in Materials Science
Authors: Yuanlong Zheng | Catherine Blake | Layla Mravac | Fengxue Zhang | Yuxin Chen | Shuolong Yang
Source: arXiv (Cornell University) (preprint, 2024-11-27)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach Capturing Hidden Parameters in Autonomous Thin-Film Deposition The integration of machine learning and robotics into thin film deposition is transforming material discovery and optimization. However, challenges remain in achieving a fully autonomous cycle of deposition, characterization, and decision-making. Additionally, the inherent sensitivity of thin film growth to hidden parameters such as substrate conditions and chamber conditions can compromise the performance of machine learning models. In this work, we demonstrate a fully autonomous physical vapor deposition system that combines in-situ optical spectroscopy, a high-throughput robotic sample handling system, and Gaussian Process Regression models. By employing a calibration layer to account for hidden parameter variations and an active learning algorithm to optimize the exploration of the parameter space, the system fabricates silver thin films with optical reflected power ratios within 2.5% of the target in an average of 2.3 attempts. This approach significantly reduces the time and labor required for thin film deposition, showcasing the potential of machine learning-driven automation in accelerating material development. Deposition (geology) ; Computer science ; Artificial intelligence ; Materials science ; Thin film ; Nanotechnology ; Geology ; Geomorphology Machine Learning in Materials Science