jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Salam Himika Devi
Source: Advances in Clinical Toxicology (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Molecular-Engineered Nanoparticle’s Intensive Use in Nanotechnology Challenges like Nanotoxicity and Breakthrough Opportunities This critical review focuses on Engineered nanoparticles (ENPs) types, properties, toxicities, applications, and future scopes. ENPs are specially designed chemical substances or materials with a particle size between 1 and 100 nm in at least one dimension, while the term Engineered nanomaterials (ENMs) is used collectively for ENPs and nanostructured materials. They can be classified into different classes based on their properties, shapes, or sizes. The different groups include fullerenes, metal NPs, ceramic NPs, and polymeric NPs. NPs possess unique physical and chemical properties due to their high surface area and nanoscale size. NPs like carbon nanotubes (CNTs), quantum dots (QDs), fullerenes, nanocomposites, metal-oxide nanoparticles (TiO2, ZnO, and iron oxides), and fibrous nanomaterials (NMs). Exposure to such ENP increases as nanotechnology advances, though it is an emerging field with a wide range of applications as an interdisciplinary, integrated science with huge potential. Aims to present a comprehensive review of nanotoxicity to human health and impact on the environment, future aspects breakthrough opportunities. Nanowaste as a new and challenging research topic is also highlighted. NPs’ intensive use, persistence, and retention in the environment need to evaluate nanogenotoxicity to humans and other organisms. ENMs used in therapeutic targeted drug delivery, their toxicokinetic, surface chemistry, mechanism of cellular interactions, effect on chromosomes, and DNA started being studied by researchers, however, not fully understood. Nano genotoxicity assessment should be done thoroughly following regulatory guidelines in order to safeguard humans from its harmful effects like cancer and reduce fertility. ENM retention time in the environment should be traced keeping in mind Eco nanotoxicity, bioaccumulation, and biomagnification in aquatic and forest ecosystems. The unique properties of NPs can be utilized fully by knowing all useful characteristics for breakthrough inventions in health care, cancer treatment, anti-aging (longevity), aeronautics, illuminating diodes, nanofertilizers and Artificial Intelligence (AI)-powered robotics for noble innovations for human use. Finally, the challenges of nanowaste management and gaps in breakthrough opportunities are highlighted to reflect the need for more serious action and advancement in nanoscience with novel inventions. Nanotechnology ; Nanotoxicology ; Nanomaterials ; Nanoparticle ; Applications of nanotechnology ; Materials science ; Carbon nanotube Nanoparticles: synthesis and applications
Authors: Zikai Xie | Xenophon Evangelopoulos | Ömer H. Omar | Alessandro Troisi | Andrew I. Cooper | Linjiang Chen
Source: ChemRxiv (preprint, 2023-08-23)
Field: Materials Science — Materials Chemistry

Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules We evaluate the effectiveness of fine-tuning GPT-3 for the prediction of electronic and functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can successfully identify and distinguish between chemically meaningful patterns, and discern subtle differences among them, exhibiting robust predictive performance for the prediction of molecular properties. We focus on assessing the fine-tuned models' resilience to information loss, resulting from the absence of atoms or chemical groups, and to noise that we introduce via random alterations in atomic identities. We discuss the challenges and limitations inherent to the use of GPT-3 in molecular machine-learning tasks and suggest potential directions for future research and improvements to address these issues. Computer science ; Organic molecules ; Resilience (materials science) ; Noise (video) ; Focus (optics) ; Artificial intelligence ; Machine learning ; Biological system ; Molecule ; Nanotechnology ; Materials science ; Chemistry ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Luke P. J. Gilligan | Matteo Cobelli | Valentin Taufour | Stefano Sanvito
Source: arXiv (Cornell University) (preprint, 2023-01-27)
Field: Materials Science — Materials Chemistry

A rule-free workflow for the automated generation of databases from scientific literature In recent times, transformer networks have achieved state-of-the-art performance in a wide range of natural language processing tasks. Here we present a workflow based on the fine-tuning of BERT models for different downstream tasks, which results in the automated extraction of structured information from unstructured natural language in scientific literature. Contrary to existing methods for the automated extraction of structured compound-property relations from similar sources, our workflow does not rely on the definition of intricate grammar rules. Hence, it can be adapted to a new task without requiring extensive implementation efforts and knowledge. We test our data-extraction workflow by automatically generating a database for Curie temperatures and one for band gaps. These are then compared with manually-curated datasets and with those obtained with a state-of-the-art rule-based method. Furthermore, in order to showcase the practical utility of the automatically extracted data in a material-design workflow, we employ them to construct machine-learning models to predict Curie temperatures and band gaps. In general we find that, although more noisy, automatically extracted datasets can grow fast in volume and that such volume partially compensates for the inaccuracy in downstream tasks. Workflow ; Computer science ; Information extraction ; Natural language processing ; Transformer ; Artificial intelligence ; Database ; Data mining ; Volume (thermodynamics) ; Natural language ; Information retrieval Machine Learning in Materials Science ; Topic Modeling ; Software Engineering Research
Authors: David Moss
Source:  (preprint, 2023-09-25)
Field: Materials Science — Materials Chemistry

Graphene oxide 2D films for photonic and electronic integrated circuits As a derivative of graphene, graphene oxide (GO) was initially developed by chemists to emulate some of the key properties of graphene, but it was soon recognized as a unique material in its own right, addressing an application space that is not accessible to chemical vapor deposition based materials. Over the past decade, GO has emerged as a new frontier material with tremendous advances in its material fabrication and quality control over its properties. These in turn have led to rapid progress in GO based photonics, electronics, and optoelectronics concepts and devices, evoking new science and paving the way for many technological breakthroughs with exceptional performance. Here, we review the unique fundamental characteristics of GO, its thin film fabrication methods, patterning techniques, and mechanisms for tuning its material properties. This latter capability in particular has enabled novel advanced functional photonic, electronic, and optoelectronic devices. Understanding these insights is essential for designing and tailoring GO properties for state-of-the-art applications including solar energy harvesting, energy storage, medical diagnosis, image displays, and optical communications. We conclude by discussing the open challenges and exciting opportunities of this field, together with future prospects for major technological advancements and breakthroughs. Graphene ; Nanotechnology ; Photonics ; Electronics ; Materials science ; Fabrication ; Engineering physics ; Computer science ; Optoelectronics ; Electrical engineering ; Engineering Graphene research and applications ; 2D Materials and Applications ; Plasmonic and Surface Plasmon Research
Authors: Daniel Wines | Ramya Gurunathan | Kevin F. Garrity | Brian DeCost | Adam J. Biacchi | Francesca Tavazza | Kamal Choudhary
Source: arXiv (Cornell University) (preprint, 2023-05-19)
Field: Materials Science — Materials Chemistry

Recent progress in the JARVIS infrastructure for next-generation data-driven materials design The Joint Automated Repository for Various Integrated Simulations (JARVIS) infrastructure at the National Institute of Standards and Technology (NIST) is a large-scale collection of curated datasets and tools with more than 80000 materials and millions of properties. JARVIS uses a combination of electronic structure, artificial intelligence (AI), advanced computation and experimental methods to accelerate materials design. Here we report some of the new features that were recently included in the infrastructure such as: 1) doubling the number of materials in the database since its first release, 2) including more accurate electronic structure methods such as Quantum Monte Carlo, 3) including graph neural network-based materials design, 4) development of unified force-field, 5) development of a universal tight-binding model, 6) addition of computer-vision tools for advanced microscopy applications, 7) development of a natural language processing tool for text-generation and analysis, 8) debuting a large-scale benchmarking endeavor, 9) including quantum computing algorithms for solids, 10) integrating several experimental datasets and 11) staging several community engagement and outreach events. New classes of materials, properties, and workflows added to the database include superconductors, two-dimensional (2D) magnets, magnetic topological materials, metal-organic frameworks, defects, and interface systems. The rich and reliable datasets, tools, documentation, and tutorials make JARVIS a unique platform for modern materials design. JARVIS ensures openness of data and tools to enhance reproducibility and transparency and to promote a healthy and collaborative scientific environment. Computer science ; Workflow ; Benchmarking ; Documentation ; Data science ; Outreach ; NIST ; Artificial intelligence ; Database Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: X. L. Lei | Edward Kim | Viktoriia Baibakova | Shijing Sun
Source: arXiv (Cornell University) (preprint, 2023-07-28)
Field: Materials Science — Materials Chemistry

Lessons in Reproducibility: Insights from NLP Studies in Materials Science Natural Language Processing (NLP), a cornerstone field within artificial intelligence, has been increasingly utilized in the field of materials science literature. Our study conducts a reproducibility analysis of two pioneering works within this domain: "Machine-learned and codified synthesis parameters of oxide materials" by Kim et al., and "Unsupervised word embeddings capture latent knowledge from materials science literature" by Tshitoyan et al. We aim to comprehend these studies from a reproducibility perspective, acknowledging their significant influence on the field of materials informatics, rather than critiquing them. Our study indicates that both papers offered thorough workflows, tidy and well-documented codebases, and clear guidance for model evaluation. This makes it easier to replicate their results successfully and partially reproduce their findings. In doing so, they set commendable standards for future materials science publications to aspire to. However, our analysis also highlights areas for improvement such as to provide access to training data where copyright restrictions permit, more transparency on model architecture and the training process, and specifications of software dependency versions. We also cross-compare the word embedding models between papers, and find that some key differences in reproducibility and cross-compatibility are attributable to design choices outside the bounds of the models themselves. In summary, our study appreciates the benchmark set by these seminal papers while advocating for further enhancements in research reproducibility practices in the field of NLP for materials science. This balance of understanding and continuous improvement will ultimately propel the intersecting domains of NLP and materials science literature into a future of exciting discoveries. Computer science ; Field (mathematics) ; Artificial intelligence ; Transparency (behavior) ; Data science ; Workflow ; Natural language processing Machine Learning in Materials Science ; Software Engineering Research ; Scientific Computing and Data Management
Authors: Zhiling Zheng | Zichao Rong | Nakul Rampal | Christian Borgs | Jennifer Chayes | Omar M. Yaghi
Source: arXiv (Cornell University) (preprint, 2023-06-20)
Field: Materials Science — Materials Chemistry

A GPT-4 Reticular Chemist for Guiding MOF Discovery We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in-context learning of AI in the next iteration. This iterative human-AI interaction enabled GPT-4 to learn from the outcomes, much like an experienced chemist, by a prompt-learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine-tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT-4 to enhance the feasibility and efficiency of research activities. Chemist ; Workflow ; Reticular connective tissue ; Computer science ; Context (archaeology) ; Process (computing) ; Iterative and incremental development ; Artificial intelligence ; Chemistry ; Software engineering ; Programming language ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Zhiling Zheng | Oufan Zhang | Christian Borgs | Jennifer Chayes | Omar M. Yaghi
Source: arXiv (Cornell University) (preprint, 2023-06-20)
Field: Materials Science — Materials Chemistry

ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information -- an issue that previously made the use of Large Language Models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different tradeoffs between labor, speed, and accuracy. We deploy this system to extract 26,257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the dataset built by text mining, we constructed a machine-learning model with over 86% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions on chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format, while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry sub-disciplines. Workflow ; Computer science ; Automatic summarization ; Parsing ; Unification ; Process (computing) ; Data science ; Documentation ; Automation ; Precision and recall ; Information retrieval ; Artificial intelligence ; Engineering ; Database ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Tim Mueller | Joseph H. Montoya | Weike Ye | X. L. Lei | Linda Hung | Jens S. Hummelshøj | Michael Puzon | Daniel Martinez | Chris Fajardo | Rachel Abela
Source: ChemRxiv (preprint, 2023-11-02)
Field: Materials Science — Materials Chemistry

An Electrochemical Series for Materials The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which are measured in aqueous solutions. We have used machine learning to create an electrochemical series for inorganic materials from tens of thousands of entries in the Inorganic Crystal Structure Database. We demonstrate that this approach enables the prediction of oxidation states directly from composition in a way that is physically justified, human-interpretable, and more accurate than a state-of-the-art transformer-based neural network model. We present applications of our model to structure prediction, materials discovery, and materials electrochemistry, and we discuss possible additional applications and areas for improvement. To facilitate the use of our approach, we introduce a freely available web site and API. Series (stratigraphy) ; Electrochemistry ; Computer science ; Transformer ; Set (abstract data type) ; Electrode ; Chemistry ; Electrical engineering ; Voltage ; Engineering ; Physical chemistry Machine Learning in Materials Science
Authors: Jiangcheng Xu | An Su | Panyi Huang | Wenbo Yu | Kui Du | Zhidan Fan | Bin Sun | Zhicheng Zhong | Can Jin | Weike Su
Source: ChemRxiv (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

PhotoCat: An Artificial Intelligence-Driven Synthesis Planning Platform for Photocatalysis Photocatalysis is becoming increasingly important in modern chemistry for efficient multicomponent one-pot synthesis. However, predicting the results of photocatalytic reactions using artificial intelligence remains challenging, mostly due to the insufficient number of photocatalytic reactions and the incomplete information on reaction conditions in existing reaction databases. In this study, we curated the Photocatalysis Database (PhotoCatDB), which consists of 6,523 photocatalytic reactions (of which 6,175 are multicomponent) containing reaction condition information such as photocatalysts, bases or acids, additives, and solvents. Before adding reaction conditions to the training data, the attention-based deep learning model PhotoCat pre-trained on USPTO and fine-tuned on PhotoCatDB had a Top-1 accuracy of 78.16%, which was 77.70% higher than the same model trained only on the USPTO database and 14.53% higher than the model fine-tuned by the photocatalytic reactions from Reaxys. After adding reaction conditions to the training data, the Top-1 accuracy of PhotoCat was further increased to 82.25%. In addition, the interpretability of the model was reflected in its attention weights, which can infer the model’s understanding of photocatalytic chemistry. Furthermore, five previously unreported photocatalytic reactions predicted by PhotoCat were successfully validated by wet-lab experiments, demonstrating the potential of the model in identifying and verifying novel photocatalysis reactions of real-world significance. Photocatalysis ; Interpretability ; Catalysis ; Reaction conditions ; Computer science ; Chemistry ; Biochemical engineering ; Materials science ; Nanotechnology ; Database ; Photochemistry ; Biological system ; Artificial intelligence ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Text and Document Classification Technologies
Authors: Cameron Gruich | Varun Madhavan | Yixin Wang | Bryan R. Goldsmith
Source: arXiv (Cornell University) (preprint, 2023-02-06)
Field: Materials Science — Materials Chemistry

Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantification It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimation of the trustworthiness of an ML model, but these methods have not been well explored in the field of heterogeneous catalysis. Herein, we investigate different UQ methods applied to a crystal graph convolutional neural network (CGCNN) to predict adsorption energies of molecules on alloys from the Open Catalyst 2020 (OC20) dataset, the largest existing heterogeneous catalyst dataset. We apply three UQ methods to the adsorption energy predictions, namely k-fold ensembling, Monte Carlo dropout, and evidential regression. The effectiveness of each UQ method is assessed based on accuracy, sharpness, dispersion, calibration, and tightness. Evidential regression is demonstrated to be a powerful approach for rapidly obtaining tunable, competitively trustworthy UQ estimates for heterogeneous catalysis applications when using neural networks. Recalibration of model uncertainties is shown to be essential in practical screening applications of catalysts using uncertainties. Trustworthiness ; Uncertainty quantification ; Monte Carlo method ; Computer science ; Calibration ; Artificial neural network ; Convolutional neural network ; Heterogeneous catalysis ; Artificial intelligence ; Catalysis ; Machine learning ; Chemistry ; Mathematics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Carlos Fernando Castro–Guerrero | Andrea González-Benítez | Abisaí Rodríguez-Rodríguez | Filemón Delgado-Arroyo | Ulises León‐Silva | Ana Beatriz Morales–Cepeda | Francisco A. López-González | Arturo Rodas-Grapain | M.R. Díaz-Guillén
Source: International Journal of Polymer Science (article, 2023-07-07)
Field: Materials Science — Biomaterials

Optimization of the Synthesis Parameters and Application of Cellulose Nanocrystals as Binders in Capacitors Cellulose nanocrystals (CNCs) are a very versatile material, and optimizing the reaction conditions to obtain them is vital for cost savings, purity, selectivity, or performance. In this study, the reaction conditions of the CNCs were tested, as well as their application as binders for the fabrication of electrodes of a symmetric capacitor (based on activated carbon). The resulting CNCs were physicochemically characterized using Fourier transform infrared spectroscopy, thermogravimetric analysis, X-ray diffraction, atomic force microscopy, and its capacitive properties using cyclic voltammetry (CV). It was found that the best reaction conditions were at 45°C, 30 and 45 minutes, and 64 wt%. The CNCs were used as a binder, as they conferred stability to the electrodes and prevented the crumbling of the activated carbon electrodes. The CV measurements showed a capacitor behavior; CNCs can be used in energy storage applications. Materials science ; Thermogravimetric analysis ; Cyclic voltammetry ; Fourier transform infrared spectroscopy ; Electrode ; Chemical engineering ; Cellulose ; Capacitor ; Nanocrystal ; Fabrication ; Nanotechnology ; Electrochemistry ; Chemistry ; Physical chemistry ; Electrical engineering Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Lignin and Wood Chemistry