jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fuzhan Rahmanian | Stefan Fuchs | Bojing Zhang | Maximilian Fichtner | Helge S. Stein
Source: ChemRxiv (preprint, 2024-01-10)
Field: Materials Science — Materials Chemistry

Autonomous millimeter scale high throughput battery research system (Auto-MISCHBARES) Discoveries of novel electrolyte-electrode combinations require comprehensive structure-property-interface correlations. Herein, we present an autonomous millimeter scale high-throughput battery research system (MISCHBARES) operated by hierarchical autonomous laboratory automation and orchestration (HELAO) which integrates modular research instrumentation and AI control. This paper will present cathode electrolyte interphase (CEI) formation in lithium-ion batteries at various potentials by correlating high-throughput electrochemistry and spectroscopy. We believe quality control and complex data analysis to be the missing puzzle piece towards more complex workflow automation. Auto-MISCHBARES integrates automatic quality control for both hardware and software to ensure high reliability through an on-the-fly fidelity assessment of each individual experiment. Data analysis is achieved through our Modular and Autonomous Data Analysis Platform (MADAP) in our presented platform, which is capable of performing a fully automated analysis of various voltammetry measurements in real-time. Integration of MISCHBARES and MADAP through HELAO enables versatile and complex active learning workflows in the discovery of new materials. We demonstrate this integrated workflow for reliable charging/discharging protocols. Workflow ; Throughput ; Automation ; Modular design ; Computer science ; Interface (matter) ; Orchestration ; Battery (electricity) ; Software ; Laboratory automation ; Plug and play ; Instrumentation (computer programming) ; Embedded system ; Engineering ; Operating system ; Database Machine Learning in Materials Science ; Machine Learning and Algorithms ; Fault Detection and Control Systems
Authors: Izabel Caldeira | Brangre Toury | Mathieu Maillard | Valentin Gt | Daniel Turover | Christian Brylinski | Frantiek Lofaj | Lenka Kvetkov | Petra Hviov | Margita Kabtov | Tom Roch | V Girman | H Heuermann | St Terbrack | Mariana Pajtov | Silvia uriov | Darina Ondruov | Petra Skalkov | Rbert Jank | Simona Brigantov | Alexander Dubek | Elena Seran | Sebastien Hess | Michel Godefroy | Ivan Stojanovi | Mirta Logar | Ivan Fatovi | Ivan Juraga | Vesna Alar | Marin Kurtela | Samuel Cupillard | C Credi | M Levi | S Turri | G Simeone | A Mhringer | J Rotter | D Medina | W Barthlott | M Mail | B Bhushan | K Koch | W Tian | G Primc | M Mozeti | A Aguilar-Morales | S Alamri | T Kunze | A Lasagni | J Durret | N Frolet | C Gourgon | M Kwiat | S Cohen | A Pevzner | F Patolsky | H Lan | Y Ding | J Zhao | X Gao | S Chen | H Lin | Z Li | X Lin | A Marmur | H Liu | A Kirnbauer | A Kretschmer | S.-C Liang | Z.-C Chang | V Braic | M Balaceanu | O Sobol | ' | A Andreev | A Vladescu | A Pogrebnjak | I Yakushchenko | S Grigoriev | Y Xu | G Li | Y Xia | S Bachani | C.-J Wang | F Lofaj | L Kvetkov | E Lewin | P Hviov | J Hay | B Crawford
Source:  (article, 2024-03-25)
Field: Materials Science — Surfaces, Coatings and Films

Study of Wettability: Textured Surfaces Prepared with UV-Crosslinked Hybrid Solutions The most recent developments around sol-gel processes to produce hybrid solutions (organic/inorganic) allowed us to find alternative routes to replace commonly used resins in nanoimprinting lithography processes. Another concern is the high temperatures needed in conventional reproduction processes, so the solution addressed in this work is using UV light to polymerize the coatings. Here, we present a study to compare the wetting of smooth and patterned sures. The latter has been obtained using a simple process to produce surfaces with superhydrophobic patterns via the Soft UV-NIL. The process involves the synthesis of a transparent hybrid silica-based solution that coats the substrate, structuration with micrometric patterns, and cross-linking under UV light. Finally, static and dynamic wetting characterizations were performed to understand wetting behaviors. Wetting ; Materials science ; Contact angle ; Chemical engineering ; Composite material ; Engineering Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions
Authors: Christopher Nguyen | William Nguyen | Atsushi Suzuki | Daisuke Oku | Hong An Phan | Dinh Viet Sang | Zooey Nguyen | Ha Cam Anh | Shruti Raghavan | Huy Vo | Thang Duc Nguyen | Lan Thi Mai Nguyen | Yoshikuni Hirayama
Source: arXiv (Cornell University) (preprint, 2024-11-21)
Field: Materials Science — Materials Chemistry

SemiKong: Curating, Training, and Evaluating A Semiconductor Industry-Specific Large Language Model Large Language Models (LLMs) have demonstrated the potential to address some issues within the semiconductor industry. However, they are often general-purpose models that lack the specialized knowledge needed to tackle the unique challenges of this sector, such as the intricate physics and chemistry of semiconductor devices and processes. SemiKong, the first industry-specific LLM for the semiconductor domain, provides a foundation that can be used to develop tailored proprietary models. With SemiKong 1.0, we aim to develop a foundational model capable of understanding etching problems at an expert level. Our key contributions include (a) curating a comprehensive corpus of semiconductor-related texts, (b) creating a foundational model with in-depth semiconductor knowledge, and (c) introducing a framework for integrating expert knowledge, thereby advancing the evaluation process of domain-specific AI models. Through fine-tuning a pre-trained LLM using our curated dataset, we have shown that SemiKong outperforms larger, general-purpose LLMs in various semiconductor manufacturing and design tasks. Our extensive experiments underscore the importance of developing domain-specific LLMs as a foundation for company- or tool-specific proprietary models, paving the way for further research and applications in the semiconductor domain. Code and dataset will be available at https://github.com/aitomatic/semikong Training (meteorology) ; Semiconductor industry ; Computer science ; Engineering ; Manufacturing engineering ; Geography Machine Learning in Materials Science
Authors: Yutong Li | Yuechen Yang | Chaoran Zhao | Chunling Cao
Source: Preprints.org (preprint, 2024-09-14)
Field: Materials Science — Materials Chemistry

Large Language Models in Entrepreneurship: A Survey Large Language Models (LLMs) have emerged as powerful tools in the domain of artificial intelligence, specifically enhancing natural language processing capabilities. These models leverage extensive datasets and sophisticated algorithms to automate and enhance tasks traditionally performed by humans, thereby revolutionizing innovation and entrepreneurship. LLMs accelerate product development, streamline business operations, and enable precise and rapid decision-making, all of which are crucial for maintaining competitiveness in dynamic markets. This paper categorizes the applications of LLMs in innovation and entrepreneurship into three main areas: technological innovation, strategic decision-making, and process automation. By exploring various integrations of LLMs in entrepreneurial ventures, this work offers both theoretical insights and practical examples, underscoring the transformative impact of LLMs in shaping modern business landscapes. Entrepreneurship ; Transformative learning ; Leverage (statistics) ; Business ; Computer science ; Sociology ; Artificial intelligence Machine Learning in Materials Science
Authors: Y.A. Khatsrinova | А. И. Хацринов
Source:  (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

COMPLEX UTILIZATION OF COBALT-MOLYBDENUM CATALYSTS Molybdenum ; Cobalt ; Catalysis ; Computer science ; Materials science ; Metallurgy ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Hongxing Hua | H. J. Yang | Wanyu Lin | Zhou, Pan
Source: arXiv (Cornell University) (preprint, 2024-10-03)
Field: Materials Science — Materials Chemistry

Revisiting the Canonicalization for Fast and Accurate Crystal Tensor Property Prediction Predicting the tensor properties of crystalline materials is a fundamental task in materials science. Unlike scalar property prediction, which requires invariance, tensor property prediction requires maintaining O(3) group tensor equivariance. Achieving such equivariance typically demands specialized architectural designs, which substantially increase computational cost. Canonicalization, a classical technique for geometry, has recently been explored for efficient learning with symmetry.In this work, we revisit the problem of crystal tensor property prediction through the lens of canonicalization. Specifically, we demonstrate how polar decomposition, a simple yet efficient algebraic method, can serve as a form of canonicalization and be leveraged to ensure equivariant tensor property prediction. Building upon this insight, we propose a general O(3)-equivariant framework for fast and accurate crystal tensor property prediction, referred to as GoeCTP. By utilizing canonicalization, GoeCTP achieves high efficiency without requiring the explicit incorporation of equivariance constraints into the network architecture.Experimental results indicate that GoeCTP achieves the high prediction accuracy and runs up to 13 times faster compared to existing state-of-the-art methods, underscoring its effectiveness and efficiency. Equivariant map ; Polar ; Polar decomposition ; Tensor (intrinsic definition) ; Decomposition ; Property (philosophy) ; Pure mathematics ; Tensor decomposition ; Mathematics ; Physics ; Computer science ; Chemistry ; Philosophy ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yuang Shi | Nakul Rampal | Chengbin Zhao | Christian Borgs | Jennifer Chayes | Omar M. Yaghi
Source: ChemRxiv (preprint, 2024-12-25)
Field: Materials Science — Materials Chemistry

Comparison of LLMs in Extracting Synthesis Conditions and Generating Q&amp;A Datasets for Metal-Organic Frameworks Artificial intelligence, represented by large language models (LLMs), has demonstrated tremendous capabilities in natural language recognition and extraction. To further evaluate the performance of various LLMs in extracting information from academic papers, this study explores the application of LLMs in reticular chemistry, focusing on their effectiveness in generating Q&A datasets and extracting synthesis conditions from scientific literature. The models evaluated include OpenAI's GPT-4 Turbo, Anthropic’s Claude 3 Opus, and Google's Gemini 1.5 Pro. Key results indicate that Claude excelled in providing complete synthesis data, while Gemini outperformed others in accuracy, characterization-free compliance(obedience), and proactive structuring of responses. Although GPT-4 was less effective in quantitative metrics, it demonstrated strong logical reasoning and contextual inference capabilities. Overall, Gemini and Claude achieved the highest scores in accuracy, groundedness, and adherence to prompt requirements, making them suitable benchmarks for future studies. The findings reveal the potential of LLMs to aid in scientific research, particularly in the efficient construction of structured datasets, which can help train models, predict, and assist in the synthesis of new metal-organic frameworks (MOFs). Inference ; Obedience ; Computer science ; Key (lock) ; Structuring ; Artificial intelligence ; Psychology ; Political science ; Social psychology ; Computer security Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yuyang Ye | Lu‐An Tang | Haoyu Wang | Runlong Yu | Wenchao Yu | Erhu He | Haifeng Chen | Hui Xiong
Source:  (article, 2024-08-24)
Field: Materials Science — Materials Chemistry

PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict following actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy between generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is designed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality. Adversarial system ; Computer science ; Imitation ; Artificial intelligence ; Psychology Machine Learning in Materials Science ; Energy, Environment, and Transportation Policies ; Advanced Multi-Objective Optimization Algorithms
Authors: Fang Sun | Zijie Huang | Haixin Wang | Tang, Huacong | Xiao Luo | Wei Wang | Yizhou Sun
Source: arXiv (Cornell University) (preprint, 2024-11-03)
Field: Materials Science — Materials Chemistry

Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics Accurately predicting long-horizon molecular dynamics (MD) trajectories remains a significant challenge, as existing deep learning methods often struggle to retain fidelity over extended simulations. We hypothesize that one key factor limiting accuracy is the difficulty of capturing interactions that span distinct spatial and temporal scales, ranging from high-frequency local vibrations to low-frequency global conformational changes. To address these limitations, we propose Graph Fourier Neural ODEs (GF-NODE), integrating a graph Fourier transform for spatial frequency decomposition with a Neural ODE framework for continuous-time evolution. Specifically, GF-NODE first decomposes molecular configurations into multiple spatial frequency modes using the graph Laplacian, then evolves the frequency components in time via a learnable Neural ODE module that captures both local and global dynamics, and finally reconstructs the updated molecular geometry through an inverse graph Fourier transform. By explicitly modeling high- and low-frequency phenomena in this unified pipeline, GF-NODE captures long-range correlations and local fluctuations more effectively. We provide theoretical insight through heat equation analysis on a simplified diffusion model, demonstrating how graph Laplacian eigenvalues can determine temporal dynamics scales, and crucially validate this correspondence through comprehensive empirical analysis on real molecular dynamics trajectories showing quantitative spatial-temporal correlations across diverse molecular systems. Experimental results on challenging MD benchmarks demonstrate that GF-NODE achieves state-of-the-art accuracy while preserving essential geometrical features over extended simulations. These findings highlight the promise of bridging spectral decomposition with continuous-time modeling to improve the robustness and predictive power of MD simulations. Bridging (networking) ; Ode ; Fourier transform ; Graph ; Computer science ; Mathematics ; Theoretical computer science ; Applied mathematics ; Mathematical analysis Machine Learning in Materials Science
Authors: Mark Zakharov | Farzaneh Rabiei Kashanaki | Jose Renau
Source: arXiv (Cornell University) (preprint, 2024-12-31)
Field: Chemistry — Analytical Chemistry

Enabling New HDLs with Agents Large Language Models (LLMs) based agents are transforming the programming language landscape by facilitating learning for beginners, enabling code generation, and optimizing documentation workflows. Hardware Description Languages (HDLs), with their smaller user community, stand to benefit significantly from the application of LLMs as tools for learning new HDLs. This paper investigates the challenges and solutions of enabling LLMs for HDLs, particularly for HDLs that LLMs have not been previously trained on. This work introduces HDLAgent, an AI agent optimized for LLMs with limited knowledge of various HDLs. It significantly enhances off-the-shelf LLMs. Computer science Analytical Methods in Pharmaceuticals
Authors: P. Chris Broekema | Rob V. van Nieuwpoort
Source: arXiv (Cornell University) (preprint, 2024-12-11)
Field: Materials Science — Materials Chemistry

SuperCode: Sustainability PER AI-driven CO-DEsign Currently, data-intensive scientific applications require vast amounts of compute resources to deliver world-leading science. The climate emergency has made it clear that unlimited use of resources (e.g., energy) for scientific discovery is no longer acceptable. Future computing hardware promises to be much more energy efficient, but without better optimized software this cannot reach its full potential. In this vision paper, we propose a generic AI-driven co-design methodology, using specialized Large Language Models (like ChatGPT), to effectively generate efficient code for emerging computing hardware. We describe how we will validate our methodology with two radio astronomy applications, with sustainability as the key performance indicator. This paper is a modified version of our accepted SuperCode project proposal. We present it here in this form to introduce the vision behind this project and to disseminate the work in the spirit of Open Science and transparency. An additional aim is to collect feedback, invite potential collaboration partners and use-cases to join the project. Sustainability ; Biology Machine Learning in Materials Science ; Vehicle emissions and performance
Authors: Elizabeth A. Holm | Ryan Cohn
Source: Research Square (preprint, 2024-09-30)
Field: Materials Science — Materials Chemistry

Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution Monte Carlo method ; Graph ; Statistical physics ; Computer science ; Theoretical computer science ; Statistics ; Mathematics ; Physics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels
Authors: Daniele Grandi | Yash Jain | Allin Groom | Brandon Cramer | Christopher McComb
Source: arXiv (Cornell University) (preprint, 2024-04-23)
Field: Materials Science — Materials Chemistry

Evaluating Large Language Models for Material Selection Material selection is a crucial step in conceptual design due to its significant impact on the functionality, aesthetics, manufacturability, and sustainability impact of the final product. This study investigates the use of Large Language Models (LLMs) for material selection in the product design process and compares the performance of LLMs against expert choices for various design scenarios. By collecting a dataset of expert material preferences, the study provides a basis for evaluating how well LLMs can align with expert recommendations through prompt engineering and hyperparameter tuning. The divergence between LLM and expert recommendations is measured across different model configurations, prompt strategies, and temperature settings. This approach allows for a detailed analysis of factors influencing the LLMs' effectiveness in recommending materials. The results from this study highlight two failure modes, and identify parallel prompting as a useful prompt-engineering method when using LLMs for material selection. The findings further suggest that, while LLMs can provide valuable assistance, their recommendations often vary significantly from those of human experts. This discrepancy underscores the need for further research into how LLMs can be better tailored to replicate expert decision-making in material selection. This work contributes to the growing body of knowledge on how LLMs can be integrated into the design process, offering insights into their current limitations and potential for future improvements. Selection (genetic algorithm) ; Computer science ; Natural language processing ; Artificial intelligence Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Emrah AYDIN | Husayin BULAT | Aydın Güllü
Source: The Eurasia Proceedings of Science Technology Engineering and Mathematics (article, 2024-08-13)
Field: Materials Science — Biomaterials

Water Retaining Nylon Rolling Application in the Automotive Industry with Collaborative Robot (COBOT) This study focuses on the prototype design and preliminary experiments regarding the applicability of robots capable of collaborating with humans in the automotive sector. In standard factories, production is mostly carried out by humans. The use of robots is more suitable for tasks that require a certain standard. Performing operations with cooperative robots working alongside humans reduces costs and increases efficiency. A Cost/Benefit analysis was conducted in this study to assess the feasibility of using robots. The application will be carried out by a cobot. The surface beneath the upholstery on the side doors in the automobile body assembly line is covered with nylon to prevent liquid permeability. Currently, this process is performed by two operators for four doors. To validate the application in cases of quality issues, marking is done along the line with acetate and a pen during the rolling process. In the study, positioning for the doors on the conveyor line, moving at a speed of 3,2 m/min, was achieved using cameras mounted on two cobots equipped with grippers on the right and left sides. By applying a compressive force of 50N with the pressure roller at the gripper end, the adhesive on the nylon is ensured to bond with the sheet metal. The stability of the system was observed when comparing animation data with the application created in the experimental environment. As a result of all these processes, operational precision increased, and continuous robot-door synchronization was achieved. According to cost analyses, it is projected that the system will amortize itself within approximately 4 years. Automotive industry ; Manufacturing engineering ; Business ; Computer science ; Automotive engineering ; Environmental science ; Engineering biodegradable polymer synthesis and properties