jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shi Xuan Leong | Sergio Pablo‐García | Zijian Zhang | Alán Aspuru‐Guzik
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs) Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prominent MLLMs against two critical tasks: (i) reaction diagram parsing and (ii) resolving cross-modality data interdependencies. The frontrunner MLLM achieved ≥96% accuracy in both tasks, with the strategic integration of single-shot visual prompts and image pre-processing techniques. We integrate this capability into a toolkit named MERMES (multimodal reaction mining pipeline for electrosynthesis). Our toolkit functions as an end-to-end MLLM-powered pipeline that integrates article retrieval, information extraction and multimodal analysis for streamlining and automating knowledge extraction. This work lays the groundwork for the increased utilization of MLLMs to accelerate the digitization of chemistry knowledge for data-driven research. Electrosynthesis ; Computer science ; Natural language processing ; Data science ; Chemistry ; Electrochemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Service-Oriented Architecture and Web Services
Authors: Rhyan Barrett | Julia Westermayr
Source: The Journal of Physical Chemistry Letters (article, 2024-01-03)
Field: Materials Science — Materials Chemistry

Reinforcement Learning for Traversing Chemical Structure Space: Optimizing Transition States and Minimum Energy Paths of Molecules In recent years, deep learning has made remarkable strides, surpassing human capabilities in tasks, such as strategy games, and it has found applications in complex domains, including protein folding. In the realm of quantum chemistry, machine learning methods have primarily served as predictive tools or design aids using generative models, while reinforcement learning remains in its early stages of exploration. This work introduces an actor-critic reinforcement learning framework suitable for diverse optimization tasks, such as searching for molecular structures with specific properties within conformational spaces. As an example, we show an implementation of this scheme for calculating minimum energy pathways of a Claisen rearrangement reaction and a number of S<sub>N</sub>2 reactions. The results show that the algorithm is able to accurately predict minimum energy pathways and, thus, transition states, providing the first steps in using actor-critic methods to study chemical reactions. Traverse ; Reinforcement learning ; Space (punctuation) ; Reinforcement ; Chemical space ; Energy (signal processing) ; Molecule ; Computer science ; Materials science ; Physics ; Artificial intelligence ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Zhe Sun | Yuhang Cheng | Boyao Wan | Xiaoming Jin | Tianfu Zhang | Zhu Hongyi | Qi Xue | Lei Xiao | Guigao Liu | Wei Jiang | Guangpu Zhang
Source: SmartMat (article, 2024-01-31)
Field: Materials Science — Polymers and Plastics

Constructing mechanically robust, efficient self‐healing, high‐energy, and recyclable energetic composites by hybrid dynamic lock strategy Abstract It is still a huge challenge to introduce effective crack‐healing ability into energetic composites with a high oxidizer content. In this article, a poly(urea‐urethane) energetic elastomer was prepared by the polycondensation reaction of glycidyl azido polymer (GAP), isophorone diisocyanate (IPDI), and 2‐aminophenyl disulfide (2‐APD). In the poly(urea‐urethane) elastomer structure, the hybrid dynamic lock, including multilevel H‐bonds and disulfide bonds, not only provides abundant dynamic interactions and promotes chain diffusion, but also enhances physical crosslinking density. Such a unique design fabricated the energetic elastomer with robust tensile strength (0.72 MPa), high stretchability (1631%), and outstanding toughness (8.95 MJ/m 3 ) in the field of energetic polymers. Meanwhile, this energetic elastomer exhibited high self‐healing efficiency (98.4% at 60 °C) and heat release ( Q = 1750.46 J/g). Experimental and theoretical results adequately explain the self‐healing mechanism, particularly the role of azido units. The high‐solid content (80 wt%) energetic composites based on the energetic elastomer presented outstanding micro‐defect self‐healing (97.8%) and recycling without loss of mechanical performance. The development of smart energetic composites with excellent self‐healing and recyclable ability provides a meaningful way for a wide range of applications in the field of energetic materials. Self-healing ; Composite material ; Materials science ; Lock (firearm) ; Structural engineering ; Engineering Polymer composites and self-healing ; Polydiacetylene-based materials and applications ; Energetic Materials and Combustion
Authors: Lucas Busta | D Hall | Braidon Johnson | Madelyn Schaut | Caroline M. Hanson | Anika Gupta | Megan Gundrum | Yuer Wang | Hiroshi Maéda
Source: The Plant Journal (article, 2024-07-08)
Field: Chemistry — Organic Chemistry

Mapping of specialized metabolite terms onto a plant phylogeny using text mining and large language models Plants produce a staggering array of chemicals that are the basis for organismal function and important human nutrients and medicines. However, it is poorly defined how these compounds evolved and are distributed across the plant kingdom, hindering a systematic view and understanding of plant chemical diversity. Recent advances in plant genome/transcriptome sequencing have provided a well-defined molecular phylogeny of plants, on which the presence of diverse natural products can be mapped to systematically determine their phylogenetic distribution. Here, we built a proof-of-concept workflow where previously reported diverse tyrosine-derived plant natural products were mapped onto the plant tree of life. Plant chemical-species associations were mined from literature, filtered, evaluated through manual inspection of over 2500 scientific articles, and mapped onto the plant phylogeny. The resulting "phylochemical" map confirmed several highly lineage-specific compound class distributions, such as betalain pigments and Amaryllidaceae alkaloids. The map also highlighted several lineages enriched in dopamine-derived compounds, including the orders Caryophyllales, Liliales, and Fabales. Additionally, the application of large language models, using our manually curated data as a ground truth set, showed that post-mining processing can largely be automated with a low false-positive rate, critical for generating a reliable phylochemical map. Although a high false-negative rate remains a challenge, our study demonstrates that combining text mining with language model-based processing can generate broader phylochemical maps, which will serve as a valuable community resource to uncover key evolutionary events that underlie plant chemical diversity and enable system-level views of nature's millions of years of chemical experimentation. Phylogenetics ; Phylogenetic tree ; Workflow ; Biology ; Computer science ; Computational biology ; Evolutionary biology ; Database ; Genetics ; Gene Chemical synthesis and alkaloids ; Botanical Research and Applications ; Plant biochemistry and biosynthesis
Authors: Yue Peng | Zhengshuang Yang | Hui‐Chuan Sun | Jinling Li | Xiuwan Lan | Sijia Liu
Source: Aging and Disease (review, 2024-02-22)
Field: Materials Science — Biomaterials

Nanomaterials in Medicine: Understanding Cellular Uptake, Localization, and Retention for Enhanced Disease Diagnosis and Therapy Nanomaterials (NMs) have emerged as promising tools for disease diagnosis and therapy due to their unique physicochemical properties. To maximize the effectiveness and design of NMs-based medical applications, it is essential to comprehend the complex mechanisms of cellular uptake, subcellular localization, and cellular retention. This review illuminates the various pathways that NMs take to get from the extracellular environment to certain intracellular compartments by investigating the various mechanisms that underlie their interaction with cells. The cellular uptake of NMs involves complex interactions with cell membranes, encompassing endocytosis, phagocytosis, and other active transport mechanisms. Unique uptake patterns across cell types highlight the necessity for customized NMs designs. After internalization, NMs move through a variety of intracellular routes that affect where they are located subcellularly. Understanding these pathways is pivotal for enhancing the targeted delivery of therapeutic agents and imaging probes. Furthermore, the cellular retention of NMs plays a critical role in sustained therapeutic efficacy and long-term imaging capabilities. Factors influencing cellular retention include nanoparticle size, surface chemistry, and the cellular microenvironment. Strategies for prolonging cellular retention are discussed, including surface modifications and encapsulation techniques. In conclusion, a comprehensive understanding of the mechanisms governing cellular uptake, subcellular localization, and cellular retention of NMs is essential for advancing their application in disease diagnosis and therapy. This review provides insights into the intricate interplay between NMs and biological systems, offering a foundation for the rational design of next-generation nanomedicines. Internalization ; Intracellular ; Endocytosis ; Extracellular ; Nanotechnology ; Cell ; Cell biology ; Chemistry ; Biology ; Materials science ; Biochemistry Nanoparticle-Based Drug Delivery ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Juan Manuel Parrilla Gutiérrez | Jarosław M. Granda | Jean‐François Ayme | Michał D. Bajczyk | Liam Wilbraham | Leroy Cronin
Source: Nature Computational Science (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Electron density-based GPT for optimization and suggestion of host–guest binders Here we present a machine learning model trained on electron density for the production of host-guest binders. These are read out as simplified molecular-input line-entry system (SMILES) format with >98% accuracy, enabling a complete characterization of the molecules in two dimensions. Our model generates three-dimensional representations of the electron density and electrostatic potentials of host-guest systems using a variational autoencoder, and then utilizes these representations to optimize the generation of guests via gradient descent. Finally the guests are converted to SMILES using a transformer. The successful practical application of our model to established molecular host systems, cucurbit[n]uril and metal-organic cages, resulted in the discovery of 9 previously validated guests for CB[6] and 7 unreported guests (with association constant K<sub>a</sub> ranging from 13.5 M<sup>-1</sup> to 5,470 M<sup>-1</sup>) and the discovery of 4 unreported guests for [Pd<sub>2</sub>1<sub>4</sub>]<sup>4+</sup> (with K<sub>a</sub> ranging from 44 M<sup>-1</sup> to 529 M<sup>-1</sup>). Host (biology) ; Ranging ; Autoencoder ; Gradient descent ; Electron density ; Molecule ; Characterization (materials science) ; Computer science ; Chemistry ; Electron ; Materials science ; Biological system ; Nanotechnology ; Physics ; Artificial intelligence ; Deep learning ; Biology ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Supramolecular Chemistry and Complexes
Authors: Jules Vermeiren | Nicole Dilissen | Vincent Goovaerts | Jef Vleugels
Source: Construction and Building Materials (review, 2024-04-24)
Field: Chemistry — Organic Chemistry

Electrification of clinker and calcination treatments in the cement sector by microwave technology – A review Electrification of clinker and calcination treatments is one of the decarbonation options for the cement industry. In this review, the technological potential of microwave heating to realise this electrification is assessed. In microwave heating, electricity is converted into electromagnetic radiation that can directly and volumetrically be absorbed by the product. A theoretical discussion on basic microwave-material interactions is provided, and the relevant technological aspects are considered. Early feasibility studies are reviewed and interpreted, complemented by a set of dielectric measurements of the relevant raw materials collected from literature. Based on these insights, and supported by relevant journal publications or research project results, different pathways towards scaling-up are discussed, like a combination of conventional and microwave heating, susceptor-assisted microwave heating, and direct microwave heating. • Microwave heating has the potential to electrify clinker and calcination treatments in the cement sector. • Microwave absorption of most raw materials increases with temperature. • Microwave susceptors and combinations with conventional heating methods can assist microwave heating at lower temperatures. • Demonstrator systems up to 40 kg/h have been positively validated. Calcination ; Electrification ; Clinker (cement) ; Cement ; Materials science ; Microwave ; Waste management ; Business ; Metallurgy ; Engineering ; Electricity ; Electrical engineering ; Telecommunications ; Portland cement ; Chemistry Microwave-Assisted Synthesis and Applications ; Concrete and Cement Materials Research ; Chemical Synthesis and Characterization
Authors: Juanli Jing | Menglin Zhai | Shiqing Dou | Lin Wang | Binghai Lou | Jichi Yan | Shixin Yuan
Source: Agriculture (article, 2024-02-13)
Field: Chemistry — Analytical Chemistry

Optimizing the YOLOv7-Tiny Model with Multiple Strategies for Citrus Fruit Yield Estimation in Complex Scenarios The accurate identification of citrus fruits is important for fruit yield estimation in complex citrus orchards. In this study, the YOLOv7-tiny-BVP network is constructed based on the YOLOv7-tiny network, with citrus fruits as the research object. This network introduces a BiFormer bilevel routing attention mechanism, which replaces regular convolution with GSConv, adds the VoVGSCSP module to the neck network, and replaces the simplified efficient layer aggregation network (ELAN) with partial convolution (PConv) in the backbone network. The improved model significantly reduces the number of model parameters and the model inference time, while maintaining the network’s high recognition rate for citrus fruits. The results showed that the fruit recognition accuracy of the modified model was 97.9% on the test dataset. Compared with the YOLOv7-tiny, the number of parameters and the size of the improved network were reduced by 38.47% and 4.6 MB, respectively. Moreover, the recognition accuracy, frames per second (FPS), and F1 score improved by 0.9, 2.02, and 1%, respectively. The network model proposed in this paper has an accuracy of 97.9% even after the parameters are reduced by 38.47%, and the model size is only 7.7 MB, which provides a new idea for the development of a lightweight target detection model. Yield (engineering) ; Estimation ; Citrus fruit ; Horticulture ; Agronomy ; Biological system ; Mathematics ; Environmental science ; Biology ; Statistics ; Physics ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Arif Ullah | Yuxinxin Chen | Pavlo O. Dral
Source: Machine Learning Science and Technology (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

Molecular quantum chemical data sets and databases for machine learning potentials Abstract The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials. In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs . This resource also has the overview in a machine-readable database format with the Jupyter notebook example for analysis. Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases. Quantum chemical ; Computer science ; Quantum ; Database ; Chemistry ; Physics ; Molecule ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Yixi Zhang | Jin‐Da Luo | Hong‐Bin Yao | Bin Jiang
Source: Artificial Intelligence Chemistry (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Size dependent lithium-ion conductivity of solid electrolytes in machine learning molecular dynamics simulations Solid-state electrolytes are key ingredients in next-generation devices for energy storage and release. Machine learning molecular dynamics (MLMD) has shown great promise in studying the diffusivity of mobile ions in solid-state electrolytes, with much higher efficiency than conventional ab initio molecular dynamics (AIMD). In this work, we combine an efficient embedded atom neural network (EANN) approach and an uncertainty-driven active learning algorithm that optimally selects data points from high-temperature AIMD trajectories to construct ML potentials for solid-state electrolytes and validate this strategy in a benchmark system, Li3YCl6, for which several controversy theoretical results exist. Through systematic MLMD simulations, we find that a typically used small supercell in AIMD simulations fails to predict the supersonic transition at a critical temperature, leading to a significant overestimation of the Li+ conductivity in Li3YCl6 at room temperature. Fortunately, thanks to the scalability of the EANN potential, extended MLMD simulations in a sufficiently large cell does yield a notable change of temperature-dependence in conductivity at ~420 K and a much lower room-temperature conductivity in excellent with experiment. Interestingly, our results are all based on a semi-local PBE density functional, which was argued unable to predict the superionic transition. We analyze possible reasons of the seemingly inconsistent MLMD results reported in literature with different ML potentials. This work paves the way of simply using high-temperature AIMD data to generate more reliable MLMD results of low-temperature ionic conductivities in solid-state electrolytes. Conductivity ; Ionic conductivity ; Molecular dynamics ; Electrolyte ; Lithium (medication) ; Fast ion conductor ; Materials science ; Work (physics) ; Ion ; Chemical physics ; Statistical physics ; Chemistry ; Thermodynamics ; Physics ; Computational chemistry ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies
Authors: Mohammad Hossein Golbabaei | Mohammadreza Saeidi Varnoosfaderani | Farshid Hemmati | Mohammad Reza Barati | Fatemehsadat Pishbin | S.A. Seyyed Ebrahimi
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Biomaterials

Machine learning-guided morphological property prediction of 2D electrospun scaffolds: the effect of polymer chemical composition and processing parameters Among various methods for fabricating polymeric tissue engineering scaffolds, electrospinning stands out as a relatively simple technique widely utilized in research. Numerous studies have delved into understanding how electrospinning processing parameters and specific polymeric solutions affect the physical features of the resulting scaffolds. However, owing to the complexity of these interactions, no definitive approaches have emerged. This study introduces the use of Simplified Molecular Input Line Entry System (SMILES) encoding method to represent materials, coupled with machine learning algorithms, to model the relationships between material properties, electrospinning parameters and scaffolds' physical properties. Here, the scaffolds' fiber diameter and conductivity have been predicted for the first time using this approach. In the classification task, the voting classifier predicted the fibers diameter with a balanced accuracy score of 0.9478. In the regression task, a neural network regressor was architected to learn the relations between parameters and predict the fibers diameter with <i>R</i><sup>2</sup> = 0.723. In the case of fibers conductivity, regressor and classifier models were used for prediction, but the performance fluctuated due to the inadequate information in the published data and the collected dataset. Finally, the model prediction accuracy was validated by experimental electrospinning of a biocompatible polymer (<i>i.e.</i>, polyvinyl alcohol and polyvinyl alcohol/polypyrrole). Field-emission scanning electron microscope (FE-SEM) images were used to measure fiber diameter. These results demonstrated the efficacy of the proposed model in predicting the polymer nanofiber diameter and reducing the parameter space prior to the scoping exercises. This data-driven model can be readily extended to the electrospinning of various biopolymers. Materials science ; Polymer ; Artificial neural network ; Property (philosophy) ; Fiber ; Artificial intelligence ; Computer science ; Composite material Electrospun Nanofibers in Biomedical Applications ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials