jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Felix Winterer | Fabian R. Geisenhof | Noelia Fernandez | Α. Seiler | Fan Zhang | R. Thomas Weitz
Source: arXiv (Cornell University) (preprint, 2023-05-08)
Field: Materials Science — Materials Chemistry

Ferroelectric and anomalous quantum Hall states in bare rhombohedral trilayer graphene Nontrivial interacting phases can emerge in elementary materials. As a prime example, continuing advances in device quality have facilitated the observation of a variety of spontaneous quantum Hall-like states, a cascade of Stoner-like magnets, and an unconventional superconductor in bilayer graphene. Its natural extension, rhombohedral trilayer graphene is predicted to be even more susceptible to interactions given its even flatter low-energy bands and larger winding number. Theoretically, five spontaneous quantum Hall phases have been proposed to be candidate ground states. Here, we provide transport evidence for observing four of the five competing ordered states in interaction-maximized, dually-gated, rhombohedral trilayer graphene. In particular, at vanishing but finite magnetic fields, two states with Chern numbers 3 and 6 can be stabilized at elevated and low electric fields, respectively, and both exhibit clear magnetic hysteresis. We also reveal that the quantum Hall ferromagnets of the zeroth Landau level are ferroelectrics with spontaneous layer polarizations even at zero electric field, as evidenced by electric hysteresis. Our findings exemplify the possible birth of rich interacting electron physics in a simple elementary material. Condensed matter physics ; Bilayer graphene ; Quantum Hall effect ; Graphene ; Landau quantization ; Physics ; Ferromagnetism ; Magnetic field ; Ferroelectricity ; Electric field ; Quantum spin Hall effect ; Quantum mechanics Graphene research and applications ; Quantum and electron transport phenomena ; Topological Materials and Phenomena
Authors: Arash Davoudvand | Hadi Arvin
Source: Mechanics of Composite Materials (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

Size-Dependent Thermal Backbone Curves for Nanocomposite Reddy Microbeams Reinforced with Graphene Platelets and Resting on Elastic Foundation Materials science ; Graphene ; Nanocomposite ; Nonlinear system ; Thermal expansion ; Composite material ; Transverse plane ; Boundary value problem ; Mathematical analysis ; Physics ; Mathematics ; Nanotechnology ; Structural engineering Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Ahmed Khalifa | Ganpathy Murthy | Ribhu K. Kaul
Source: Physical review. B./Physical review. B (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

Absence of edge states in the valley Chern insulator in moiré graphene We study the edge spectrum of twisted sheets of single layer and bilayer graphene in cases where the continuum model predicts a valley Chern insulator---an insulating state in which the occupied moir\'e mini-bands from each valley have a net Chern number, but both valleys together have no net Chern number, as required by time-reversal symmetry. In a simple picture, such a state might be expected to have chiral valley polarized counterpropagating edge states. We present results from exact diagonalization of the tight-binding model of commensurate structures in the ribbon geometry. We find that for both the single-layer and bilayer moir\'e ribbons robust edge modes are generically absent. We attribute this lack of edge modes to the fact that the edge induces valley mixing. Ribbon ; Condensed matter physics ; Bilayer graphene ; Physics ; Insulator (electricity) ; Enhanced Data Rates for GSM Evolution ; Graphene ; Moiré pattern ; Bilayer ; Geometry ; Quantum mechanics ; Optics ; Mathematics Graphene research and applications ; Topological Materials and Phenomena ; Quantum and electron transport phenomena
Authors: Pelin Gürkan Ünal | Rıza Atav | Uğur Ergünay
Source: Journal of Natural Fibers (article, 2023-05-20)
Field: Materials Science — Polymers and Plastics

Determining the Permeability and Handle Properties of Wool, Yak and Cashmere Hand-knitted Fabrics Luxury fibers are used for their tactile properties, comfortable feeling for the users, and sometimes for status in society. These fibers are all protein-based and have always been compared to the properties of wool. Cashmere is known for its incredibly soft and warm yet lightweight and breathable qualities, making it a must-have fabric for any fall and winter closet. Besides labored and time-wasting cashmere production makes the fiber very expensive. Yak fiber is an alternative to expensive cashmere with similar properties, which costs only a quarter of cashmere. In this study, air and water vapor permeability properties, which are important for consumer comfort were investigated in addition to handle properties of the fabrics compared with the properties of the fabrics made of wool fibers. It was found that yarns made of wool fibers were easy to spin in fine yarn counts whereas yarns made of yak fibers were the coarsest. Hand-knitted fabrics made of cashmere fibers had the softest feeling even if the fabrics were produced with similar technical specifications. The permeability results of the fabrics were different, especially the porosity of the fabrics had a higher impact on the permeability of the fabrics. Wool ; Materials science ; Composite material ; YAK ; Fiber ; Air permeability specific surface ; Animal science Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Color perception and design
Authors: Yu Qin Song | Santiago Miret | Huan Zhang | Bang Liu
Source: arXiv (Cornell University) (preprint, 2023-10-12)
Field: Materials Science — Materials Chemistry

HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science We propose an instruction-based process for trustworthy data curation in materials science (MatSci-Instruct), which we then apply to finetune a LLaMa-based language model targeted for materials science (HoneyBee). MatSci-Instruct helps alleviate the scarcity of relevant, high-quality materials science textual data available in the open literature, and HoneyBee is the first billion-parameter language model specialized to materials science. In MatSci-Instruct we improve the trustworthiness of generated data by prompting multiple commercially available large language models for generation with an Instructor module (e.g. Chat-GPT) and verification from an independent Verifier module (e.g. Claude). Using MatSci-Instruct, we construct a dataset of multiple tasks and measure the quality of our dataset along multiple dimensions, including accuracy against known facts, relevance to materials science, as well as completeness and reasonableness of the data. Moreover, we iteratively generate more targeted instructions and instruction-data in a finetuning-evaluation-feedback loop leading to progressively better performance for our finetuned HoneyBee models. Our evaluation on the MatSci-NLP benchmark shows HoneyBee's outperformance of existing language models on materials science tasks and iterative improvement in successive stages of instruction-data refinement. We study the quality of HoneyBee's language modeling through automatic evaluation and analyze case studies to further understand the model's capabilities and limitations. Our code and relevant datasets are publicly available at \url{https://github.com/BangLab-UdeM-Mila/NLP4MatSci-HoneyBee}. Computer science ; Benchmark (surveying) ; Construct (python library) ; Trustworthiness ; Process (computing) ; Language model ; Soundness ; Code (set theory) ; Quality (philosophy) ; Artificial intelligence ; Data science ; Programming language Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Göknil Pelin Coşkun | Yağmur Özhan | Vladimir Dobričić | Jelena Bošković | Rengin Reis | Hande Si̇pahi̇ | Zafer Şahin | Şeref Demirayak
Source: Pharmaceutics (article, 2023-05-09)
Field: Chemistry — Organic Chemistry

Discovery of Novel Thiophene/Hydrazones: In Vitro and In Silico Studies against Pancreatic Cancer Cancer is the disease with the highest mortality. Drug studies contribute to promising treatments; however there is an urgent need for selective drug candidates. Pancreatic cancer is difficult to treat and the cancer progresses rapidly. Unfortunately, current treatments are ineffective. In this study, ten new diarylthiophene-2-carbohydrazide derivatives were synthesized and evaluated for their pharmacological activity. The 2D and 3D anticancer activity studies suggested the compounds <b>7a</b>, <b>7d</b>, and <b>7f</b> were promising. Among these, <b>7f</b> (4.86 µM) showed the best 2D inhibitory activity against PaCa-2 cells. Compounds <b>7a</b>, <b>7d</b> and <b>7f</b> were also tested for their cytotoxic effects on healthy cell line but only compound <b>7d</b> showed selectivity. Compounds <b>7a</b>, <b>7d</b>, and <b>7f</b> showed the best 3D cell line inhibitory effect according to spheroid diameters. The compounds were screened for their COX-2 and 5-LOX inhibitory activity. For COX-2, the best IC<sub>50</sub> value was observed for <b>7c</b> (10.13 µM) and all compounds showed significantly lower inhibition compared to standard. In the 5-LOX inhibition study, compounds <b>7a</b> (3.78 µM), <b>7c</b> (2.60 µM), <b>7e</b> (3.3 µM), and <b>7f</b> (2.94 µM) demonstrated influential activity compared to standard. Regarding molecular docking studies, binding mode of compounds <b>7c</b>, <b>7e</b>, and <b>7f</b> to the 5-LOX enzyme were non-redox or redox types, but not the iron-binding type. As dual inhibitors of 5-LOX and pancreatic cancer cell line, <b>7a</b> and <b>7f</b> were identified as the most promising compounds. Pancreatic cancer ; IC50 ; Chemistry ; In silico ; Enzyme ; Cell culture ; Docking (animal) ; Pharmacology ; In vitro ; Cytotoxicity ; Drug ; Cancer cell ; Cancer ; Biochemistry ; Cancer research ; Medicine ; Biology ; Internal medicine ; Gene Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Click Chemistry and Applications
Authors: Arun Mannodi‐Kanakkithodi | Austin McDannald | Shijing Sun | Saaketh Desai | Keith A. Brown | A. Gilad Kusne
Source: MRS Bulletin (review, 2023-05-01)
Field: Materials Science — Materials Chemistry

A framework for materials informatics education through workshops Informatics ; Curriculum ; Field (mathematics) ; Computer science ; Engineering ethics ; Engineering ; Pedagogy ; Sociology Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Andy S. Anker | Keith T. Butler | Raghavendra Selvan | Kirsten M. Ø. Jensen
Source: ChemRxiv (preprint, 2023-08-30)
Field: Materials Science — Materials Chemistry

Machine Learning for Analysis of Experimental Scattering and Spectroscopy Data in Materials Chemistry The rapid growth of materials chemistry data, driven by advancements in large-scale radiation facilities as well as laboratory instruments, has outpaced conventional data analysis and modelling methods, which can require enormous manual effort. To address this bottleneck, we investigate the application of supervised and unsupervised machine learning (ML) techniques for scattering and spectroscopy data analysis in materials chemistry research. Our perspective focuses on ML applications in powder diffraction (PD), pair distribution function (PDF), small-angle scattering (SAS), inelastic neutron scattering (INS), and X-ray absorption spectroscopy (XAS) data, but the lessons that we learn are generally applicable across materials chemistry. We review the ability of ML to efficiently and accurately identify physical and structural models and extract information from experimental data. Furthermore, we discuss the challenges associated with supervised ML and highlight how unsupervised ML can mitigate these limitations, thus enhancing experimental materials chemistry data analysis. Our perspective emphasises the transformative potential of ML in materials chemistry characterisation and identifies promising directions for future applications. The perspective aims to guide newcomers to ML-based experimental data analysis, alerting them to the potential pitfalls and offering guidance for success. Transformative learning ; Neutron scattering ; Perspective (graphical) ; Experimental data ; Bottleneck ; Scattering ; Computer science ; Spectroscopy ; Data science ; Materials science ; Nanotechnology ; Artificial intelligence ; Physics ; Optics ; Mathematics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Nikita Janakarajan | Tim Erdmann | Sarath Swaminathan | Teodoro Laino | Jannis Born
Source: arXiv (Cornell University) (preprint, 2023-09-28)
Field: Materials Science — Materials Chemistry

Language models in molecular discovery The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to "scientific language models" that operate on small molecules, proteins or polymers. In chemistry, language models contribute to accelerating the molecule discovery cycle as evidenced by promising recent findings in early-stage drug discovery. Here, we review the role of language models in molecular discovery, underlining their strength in de novo drug design, property prediction and reaction chemistry. We highlight valuable open-source software assets thus lowering the entry barrier to the field of scientific language modeling. Last, we sketch a vision for future molecular design that combines a chatbot interface with access to computational chemistry tools. Our contribution serves as a valuable resource for researchers, chemists, and AI enthusiasts interested in understanding how language models can and will be used to accelerate chemical discovery. Sketch ; Drug discovery ; Computer science ; Language model ; Scientific discovery ; Modeling language ; Data science ; Software ; Field (mathematics) ; Artificial intelligence ; Chemistry ; Cognitive science ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Wei Zhang | Qinggong Wang | Xiangtai Kong | Jiacheng Xiong | Shengkun Ni | Duanhua Cao | Buying Niu | Mingan Chen | Runze Zhang | Yitian Wang | Lehan Zhang | Xutong Li | Zhaoping Xiong | Qian Shi | Feng Cheng | Zunyun Fu | Mingyue Zheng
Source: ChemRxiv (preprint, 2023-11-15)
Field: Materials Science — Materials Chemistry

Fine-tuning ChatGPT Achieves State-of-the-Art Performance for Chemical Text Mining Extracting knowledge from complex and diverse chemical texts is a pivotal task for both experimental and computational chemists. The task is still considered to be extremely challenging due to the complexity of the chemical language and scientific literature. This study fine-tuned ChatGPT for five intricate chemical text mining tasks: compound entity recognition, reaction role labelling, metal-organic framework (MOF) synthesis information extraction, nuclear magnetic resonance spectroscopy (NMR) data extraction, and the conversion of reaction paragraph to action sequence. The fine-tuned ChatGPT demonstrated impressive performance, significantly reducing the need for repetitive and extensive prompt engineering experiments. It achieved exact accuracy levels ranging from 69% to 95% on these tasks with minimal annotated data. For comparison, we fine-tuned open-source pre-trained large language models (LLMs) such as Llama2, T5, and BART. The results showed that the fine-tuned ChatGPT excelled in all tasks. It even outperformed those task-adaptive pre-training and fine-tuning models that were based on a significantly larger amount of in-domain data. Given its versatility, robustness, and low-code capability, leveraging fine-tuned LLMs as toolkits for automated data acquisition could revolutionize chemical knowledge extraction. Computer science ; Paragraph ; Robustness (evolution) ; Natural language processing ; Task (project management) ; Artificial intelligence ; Information extraction ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Jingtao Huang | Jingteng Xue | Mingwei Li | Yuan Cheng | Zhonghong Lai | Jin Hu | Fei Zhou | Nan Qu | Yong Liu | Jingchuan Zhu
Source: Materials (article, 2023-10-19)
Field: Materials Science — Materials Chemistry

Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations In this paper, we studied the effects of a series of alloying atoms on the stability and micromechanical properties of aluminum alloy using a machine learning accelerated first-principles approach. In our preliminary work, high-throughput first-principles calculations were explored and the solution energy and theoretical stress of atomically doped aluminum substrates were extracted as basic data. By comparing five different algorithms, we found that the Catboost model had the lowest RMSE (0.24) and lowest MAPE (6.34), and this was used as the final prediction model to predict the solid solution strengthening of the aluminum matrix by the elements. Calculations show that alloying atoms such as K, Na, Y and Tl are difficult to dissolve in the aluminum matrix, whereas alloy atoms like Sc, Cu, B, Zr, Ni, Ti, Nb, V, Cr, Mn, Mo, and W exerted a strengthening influence. Theoretical studies on solid solutions and the strengthening effect of various alloy atoms in an aluminum matrix can offer theoretical guidance for the subsequent selection of suitable alloy elements. The theoretical investigation of alloy atoms in an aluminum matrix unveils the fundamental aspects of the solution strengthening effect, contributing significantly to the expedited development of new aluminum alloys. Alloy ; Materials science ; Aluminium ; Density functional theory ; Matrix (chemical analysis) ; Solid solution ; Work (physics) ; Alonizing ; Metallurgy ; Thermodynamics ; Computational chemistry ; Composite material ; Chemistry ; Physics Machine Learning in Materials Science ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Babak Mahjour | Jillian Hoffstadt | Tim Cernak
Source: ChemRxiv (preprint, 2023-02-28)
Field: Materials Science — Materials Chemistry

Designing Chemical Reaction Arrays using phactor and ChatGPT High throughput experimentation (HTE) is a common practice in the pharmaceutical industry. Medicinal chemists design reaction arrays to optimize the yield of couplings between building blocks and/or pharmacophores. Popular reactions attempted by medicinal chemists include the amide coupling, Suzuki coupling, and Buchwald-Hartwig coupling. We show how the artificial intelligence (AI) language model ChatGPT can automatically formulate reaction arrays for these common reactions based on the literature corpus it was trained on. Furthermore, we showcase how ChatGPT results can be directly translated into inputs for the HTE management software phactor, which enables automated execution and analysis of assays. This workflow is experimentally demonstrated. Workflow ; Pharmacophore ; Computer science ; Coupling (piping) ; Yield (engineering) ; Software ; Throughput ; Coupling reaction ; Artificial intelligence ; Combinatorial chemistry ; Toolbox ; Amide ; Chemistry ; Software engineering ; Programming language ; Engineering ; Database ; Materials science ; Catalysis ; Organic chemistry ; Stereochemistry ; Mechanical engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Tupan Das | S M Noor | Suman Kumari | Jyotirekha Mallick | Anant Shukla | Subhadeep Datta | Murli Kumar Manglam | Manoranjan Kar
Source: Physica Scripta (article, 2023-07-17)
Field: Materials Science — Materials Chemistry

Tuning of magnetic properties of Al-doped cobalt ferrite nanofiber prepared by electrospinning technique Abstract The Al-doped cobalt ferrite (CoFe 2-x Al x O 4 ) nanofiber has been prepared by employing the electrospinning technique. All the nanofibers are characterized by the XRD technique. The Rietveld refinement of the XRD pattern reveals the spinel cubic structure with the Fd <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mover accent="true"> <mml:mn>3</mml:mn> <mml:mo>¯</mml:mo> </mml:mover> </mml:math> m space group. The TEM micrographs confirm the formation of nanofiber with a nearly uniform diameter of ∼137 ± 10 nm. The magnetic properties of all the nanofibers have been investigated by measuring the M-H hysteresis loops with the help of a Vibrating sample magnetometer (VSM). The M-H loops analysis demonstrates a reduction in the saturation magnetization, and coercivity from 106.88 emu g −1 to 40.01 emu g −1 and 0.897 kOe to 0.324 kOe, respectively, with the increase in Al concentration in the nanofibers. The ‘Law of Approach to Saturation’ technique has been employed to understand the magnetocrystalline anisotropy constant in the samples. The magnetocrystalline anisotropy constant ( K 1 ) decreases with the increase in Al 3+ substitution in place of Fe 3+ in the spinel cobalt ferrite nanofibers. A squeezing effect on the M-H loop near M = 0 is observed and, it has been analyzed by a hysteresis loop width ( ∆H ) versus magnetization ( M ) plot. The squeezing effect has been observed due to the exchange interaction phenomenon between the magnetic and non-magnetic atoms in the Al-doped cobalt ferrite nanofiber. The magnetic properties study demonstrates that Al-doped cobalt ferrite nanofiber exhibits soft magnetic nature. Coercivity ; Materials science ; Magnetocrystalline anisotropy ; Nanofiber ; Magnetic hysteresis ; Magnetization ; Ferrite (magnet) ; Spinel ; Rietveld refinement ; Analytical Chemistry (journal) ; Electrospinning ; Nuclear magnetic resonance ; Magnetic anisotropy ; Composite material ; Condensed matter physics ; Crystal structure ; Crystallography ; Metallurgy ; Magnetic field ; Physics Magnetic Properties and Synthesis of Ferrites ; Magnetic properties of thin films ; Multiferroics and related materials
Authors: Qian Wan | Siying Hu | Yu Zhang | Piaohong Wang | Bo Wen | Zhicong Lu
Source: arXiv (Cornell University) (preprint, 2023-07-20)
Field: Materials Science — Materials Chemistry

"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Models Prewriting is the process of discovering and developing ideas before a first draft, which requires divergent thinking and often implies unstructured strategies such as diagramming, outlining, free-writing, etc. Although large language models (LLMs) have been demonstrated to be useful for a variety of tasks including creative writing, little is known about how users would collaborate with LLMs to support prewriting. The preferred collaborative role and initiative of LLMs during such a creativity process is also unclear. To investigate human-LLM collaboration patterns and dynamics during prewriting, we conducted a three-session qualitative study with 15 participants in two creative tasks: story writing and slogan writing. The findings indicated that during collaborative prewriting, there appears to be a three-stage iterative Human-AI Co-creativity process that includes Ideation, Illumination, and Implementation stages. This collaborative process champions the human in a dominant role, in addition to mixed and shifting levels of initiative that exist between humans and LLMs. This research also reports on collaboration breakdowns that occur during this process, user perceptions of using existing LLMs during Human-AI Co-creativity, and discusses design implications to support this co-creativity process. Prewriting ; Creativity ; Psychology ; Process (computing) ; Writing process ; Collaborative writing ; Pedagogy ; Computer science ; Social psychology ; Teaching method ; Cooperative learning Machine Learning in Materials Science ; Software Engineering Research ; Design Education and Practice