jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thomas M. Dixon | Jeanine Williams | Maximilian O. Besenhard | Roger M. Howard | James MacGregor | Philip Peach | Adam D. Clayton | Nicholas J. Warren | Richard A. Bourne
Source: Digital Discovery (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Operator-free HPLC automated method development guided by Bayesian optimization Automated, closed-loop HPLC method optimization using single and multi-objective Bayesian optimization algorithms. Computer science ; Bayesian probability ; Operator (biology) ; Bayesian optimization ; Artificial intelligence ; Machine learning ; Chemistry Analytical Chemistry and Chromatography ; Water Quality Monitoring and Analysis ; Spectroscopy and Chemometric Analyses
Authors: Tong Xie | Yuwei Wan | Yufei Zhou | Wei Huang | Yixuan Liu | Qingyuan Linghu | Shaozhou Wang | Chunyu Kit | Clara Grazian | Wenjie Zhang | Bram Hoex
Source: Patterns (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

Creation of a structured solar cell material dataset and performance prediction using large language models Materials scientists usually collect experimental data to summarize experiences and predict improved materials. However, a crucial issue is how to proficiently utilize unstructured data to update existing structured data, particularly in applied disciplines. This study introduces a new natural language processing (NLP) task called structured information inference (SII) to address this problem. We propose an end-to-end approach to summarize and organize the multi-layered device-level information from the literature into structured data. After comparing different methods, we fine-tuned LLaMA with an F1 score of 87.14% to update an existing perovskite solar cell dataset with articles published since its release, allowing its direct use in subsequent data analysis. Using structured information, we developed regression tasks to predict the electrical performance of solar cells. Our results demonstrate comparable performance to traditional machine-learning methods without feature selection and highlight the potential of large language models for scientific knowledge acquisition and material development. Computer science ; Inference ; Task (project management) ; Feature selection ; Artificial intelligence ; Machine learning ; Natural language processing ; Selection (genetic algorithm) ; Performance prediction ; Data science ; Data mining ; Systems engineering ; Engineering ; Simulation Machine Learning in Materials Science ; Software Engineering Research ; Scientific Computing and Data Management
Authors: Zhuo Chen | Ri-gui Zhou | Pengju Ren
Source: RSC Advances (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Spectraformer: deep learning model for grain spectral qualitative analysis based on transformer structure This study delves into the use of compact near-infrared spectroscopy instruments for distinguishing between different varieties of barley, chickpeas, and sorghum, addressing a vital need in agriculture for precise crop variety identification. This identification is crucial for optimizing crop performance in diverse environmental conditions and enhancing food security and agricultural productivity. We also explore the potential application of transformer models in near-infrared spectroscopy and conduct an in-depth evaluation of the impact of data preprocessing and machine learning algorithms on variety classification. In our proposed spectraformer multi-classification model, we successfully differentiated 24 barley varieties, 19 chickpea varieties, and ten sorghum varieties, with the highest accuracy rates reaching 85%, 95%, and 86%, respectively. These results demonstrate that small near-infrared spectroscopy instruments are cost-effective and efficient tools with the potential to advance research in various identification methods, but also underscore the value of transformer models in near-infrared spectroscopy classification. Furthermore, we delve into the discussion of the influence of data preprocessing on the performance of deep learning models compared to traditional machine learning models, providing valuable insights for future research in this field. Sorghum ; Preprocessor ; Artificial intelligence ; Computer science ; Machine learning ; Agriculture ; Data pre-processing ; Agricultural engineering ; Near-infrared spectroscopy ; Engineering ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ahmed M. Gomaa | Yixing Huang | Amr Hagag | Charlotte Schmitter | Daniel Höfler | Thomas Weißmann | Katharina Breininger | Manuel Schmidt | Jenny Stritzelberger | Daniel Delev | Roland Coras | Arnd Dörfler | Oliver Schnell | Benjamin Frey | Udo S. Gaipl | Sabine Semrau | Christoph Bert | Peter Hau | Rainer Fietkau | Florian Putz
Source: Neuro-Oncology Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Comprehensive multimodal deep learning survival prediction enabled by a transformer architecture: A multicenter study in glioblastoma The proposed transformer-based survival prediction model integrates complementary information from diverse input modalities, contributing to improved glioblastoma survival prediction compared to state-of-the-art methods. Consistent performance was observed across institutions supporting model generalizability. Glioblastoma ; Architecture ; Transformer ; Deep learning ; Computer science ; Artificial intelligence ; Medicine ; Engineering ; Geography ; Cancer research ; Electrical engineering Machine Learning in Materials Science ; Glioma Diagnosis and Treatment ; Radiomics and Machine Learning in Medical Imaging
Authors: Junhao Yuan | Zhen Li | Yujia Yang | Anyi Yin | Wenjie Li | Dan Sun | Qing Wang
Source: Journal of Materials Informatics (review, 2024-09-26)
Field: Materials Science — Materials Chemistry

Applications of machine learning method in high-performance materials design: a review As a generalized method of mathematical statistics, machine learning (ML) is playing an increasingly significant role in the realm of materials design. More sophisticated methodologies for in-depth understandings and wide applications have been developed from initially simple data relation mappings. The present work first summarizes the basic technical issues of ML and then systematically reviews the main implementation strategies for ML methods in accelerating materials research and development process in recent years, encompassing three primary aspects. Firstly, it is necessary to establish the relationship between the key characteristic parameters and properties in any given materials system for a better prediction and exploration of new materials. Then, the computational algorithms in materials science need to be optimized to replace complex calculations with model-predicted data. Finally, the ML methods are applied to summarize the one-dimensional property data and two-dimensional microstructural images of materials to establish standardized analysis methods. During this process, the domain knowledge in a specific system is of great significance to improving the prediction accuracy and efficiency of ML methods, whether pre-processing experimental or computational databases. The powerful capability of ML methods to handle high-dimensional data will enable researchers to make more effective decisions in materials design. In the future, the relationship between the microstructure and mechanical properties, which is necessary to establish a more effective search engine for alloys with targeted mechanical properties, will be the focus of ML mechanical properties of alloy materials. Computer science ; Engineering drawing ; Engineering Machine Learning in Materials Science ; Advanced materials and composites ; X-ray Diffraction in Crystallography
Authors: Syed Abdur Rahman | Abhinav Chandraker | Om Prakash | Ankur Chauhan
Source: Engineering Fracture Mechanics (article, 2024-06-04)
Field: Materials Science — Materials Chemistry

Data-driven machine learning approach for predicting dwell fatigue life in two classes of Titanium alloys Titanium alloy ; Materials science ; Titanium ; Computer science ; Structural engineering ; Metallurgy ; Machine learning ; Artificial intelligence ; Reliability engineering ; Engineering ; Alloy Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Metallurgy and Material Forming
Authors: Varad Daoo | Jayant K. Singh
Source: ACS Applied Materials & Interfaces (article, 2024-01-30)
Field: Chemistry — Inorganic Chemistry

Accelerating In Silico Discovery of Metal–Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning Cryogenic distillation, a currently employed method for C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>6</sub> and C<sub>3</sub>H<sub>6</sub>/C<sub>3</sub>H<sub>8</sub> mixture separation, is energy-intensive, prompting the research toward alternative technologies, including adsorbent-based separation. In this work, we combine machine learning (ML) technique with high-throughput screening to screen ∼23,000 hypothetical metal-organic frameworks (MOFs) for paraffin (C<sub>2</sub>H<sub>6</sub> and C<sub>3</sub>H<sub>8</sub>) selective adsorbent separation. First, structure-based prescreening was employed to remove MOFs with undesired geometric properties. Further, a random forest model built upon the multicomponent grand canonical Monte Carlo (m-GCMC) simulation data of training set MOFs was found to be the most successful in learning the relationship between MOF features and olefin/paraffin mixture separation. Using this technique, the separation performance of the remaining (test set) MOFs was predicted, and the top-performing MOFs were identified. We also employed active learning (AL) to evaluate its effectiveness in improving the prediction of olefin/paraffin selectivity. AL was discovered to be ∼29 times more efficient than the best-supervised ML model, as it was able to identify the top materials in limited training data and at a fraction of computational cost and time as compared to ML techniques. Among the top selected materials, framework chemistry was found to be the most important parameter. Nickel and copper (as a metal node) in a tfzd and hms topological arrangement respectively, were discovered to be a prevalent attribute in high-performing MOFs, further demonstrating the prominent significance of framework chemistry. Additionally, the top MOFs discovered were studied in detail and further compared to the previously reported MOFs. These MOFs show the highest selectivity for C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>6</sub> and C<sub>3</sub>H<sub>6</sub>/C<sub>3</sub>H<sub>8</sub> mixture separation, as reported until date. The hierarchical strategy devised in this study will facilitate the quick screening of MOFs across multiple databases toward industrially significant separation processes by leveraging molecular simulations and AL. Propane ; Metal-organic framework ; Materials science ; Olefin fiber ; Adsorption ; Computer science ; Process engineering ; Organic chemistry ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Haolin Zhan | Jiawei Liu | Qiyuan Fang | Xinyu Chen | Yang Ni | Lingling Zhou
Source: Advanced Science (article, 2024-06-05)
Field: Chemistry — Spectroscopy

Fast Pure Shift NMR Spectroscopy Using Attention‐Assisted Deep Neural Network Pure shift NMR spectroscopy enables the robust probing on molecular structure and dynamics, benefiting from great resolution enhancements. Despite extensive application landscapes in various branches of chemistry, the long experimental times induced by the additional time dimension generally hinder its further developments and practical deployments, especially for multi-dimensional pure shift NMR. Herein, this study proposes and implements the fast, reliable, and robust reconstruction for accelerated pure shift NMR spectroscopy with lightweight attention-assisted deep neural network. This deep learning protocol allows one to regain high-resolution signals and suppress undersampling artifacts, as well as furnish high-fidelity signal intensities along with the accelerated pure shift acquisition, benefitting from the introduction of the attention mechanism to highlight the spectral feature and information of interest. Extensive results of simulated and experimental NMR data demonstrate that this attention-assisted deep learning protocol enables the effective recovery of weak signals that are almost drown in the serious undersampling artifacts, and the distinction and recognition of close chemical shifts even though using merely 5.4% data, highlighting its huge potentials on fast pure shift NMR spectroscopy. As a result, this study affords a promising paradigm for the AI-assisted NMR protocols toward broader applications in chemistry, biology, materials, and life sciences, and among others. Undersampling ; Paradigm shift ; Nuclear magnetic resonance spectroscopy ; Computer science ; Deep learning ; Spectroscopy ; Protocol (science) ; Artificial neural network ; Artificial intelligence ; Biological system ; Materials science ; Chemistry ; Nuclear magnetic resonance ; Physics Advanced NMR Techniques and Applications ; Advanced MRI Techniques and Applications ; NMR spectroscopy and applications
Authors: Ziduo Yang | Yi-Ming Zhao | Xian Wang | Xiaoqing Liu | Xiuying Zhang | Yifan Li | Qiujie Lv | Calvin Yu‐Chian Chen | Lei Shen
Source: Nature Communications (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and super large twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax's reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science. Scalability ; Generative model ; Relaxation (psychology) ; Computer science ; Uncertainty quantification ; Generative grammar ; Statistical physics ; Biological system ; Artificial intelligence ; Physics ; Machine learning ; Biology ; Neuroscience Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Arthi Jayaraman | Bradley D. Olsen
Source: Macromolecules (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Convergence of Artificial Intelligence, Machine Learning, Cheminformatics, and Polymer Science in <i>Macromolecules</i> Cheminformatics ; Macromolecule ; Convergence (economics) ; Artificial intelligence ; Polymer science ; Computer science ; Polymer ; Chemistry ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Zhongheng Fu | Guangtong Hai | Xia‐Xia Ma | Dominik Legut | Yongchao Zheng | Xiang Chen
Source: Journal of Energy Chemistry (article, 2024-07-17)
Field: Materials Science — Materials Chemistry

Rational design of MXene-based vacancy-confined single-atom catalyst for efficient oxygen evolution reaction Catalysis ; Vacancy defect ; Rational design ; Atom (system on chip) ; Oxygen atom ; Materials science ; Oxygen ; Chemical physics ; Chemical engineering ; Nanotechnology ; Chemistry ; Computational chemistry ; Crystallography ; Computer science ; Organic chemistry ; Molecule ; Engineering ; Embedded system MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Wei Zhuang | Xiao Zhao | Qianqian Luo | Xinyuan Lv | Zhilin Zhang | Lihua Zhang | Minghao Sui
Source: Water Research (article, 2024-09-26)
Field: Chemistry — Analytical Chemistry

Task decomposition strategy based on machine learning for boosting performance and identifying mechanisms in heterogeneous activation of peracetic acid process Peracetic acid ; Boosting (machine learning) ; Task (project management) ; Decomposition ; Process (computing) ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Engineering ; Systems engineering ; Biochemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Machine Learning in Materials Science
Authors: Seyedsaeed Mehrabi-Kalajahi | Ahmad Ostovari Moghaddam | Fahimeh Hadavimoghaddam | Rana Salari | Mikhail A. Varfolomeev | Almaz L. Zinnatullin | Kamil R. Minnebaev | Dmitrii A. Emelianov | Д.А. Учаев | Rahele Fereidonnejad | O.V. Zaitseva | Gulgena R. Khasanova | Evgeny Trofimov | Alexei Rozhenko | Andreu Cabot | F. G. Vagizov
Source: ACS Applied Nano Materials (article, 2024-02-21)
Field: Materials Science — Materials Chemistry

(CoFeMnCuNiCr)<sub>3</sub>O<sub>4</sub> High-Entropy Oxide Nanoparticles Immobilized on Reduced Graphene Oxide as Heterogeneous Catalysts for Solvent-Free Aerobic Oxidation of Benzyl Alcohol The development of noble metal-free heterogeneous catalysts holds promise for the solvent-free and selective aerobic oxidation of organic compounds. However, the moderate activity of these catalysts under atmospheric conditions limits their industrial use. In this work, the synthesis of noble metal-free (CoFeMnCuNiCr)3O4 high-entropy oxide (HEO) nanoparticles and their grafting on reduced graphene oxide (rGO) to produce a HEO–rGO nanocomposite is detailed. X-ray diffraction (XRD), scanning electron microscopy (SEM), and Raman and Mössbauer spectroscopy analyses confirm the formation of the spinel HEO phase. HEO–rGO nanocomposites are used for aerobic and solvent-free oxidation of benzyl alcohol, displaying excellent catalytic performance. Up to 10.36% conversion and 78.5% selectivity of benzaldehyde can be achieved in only 4 h. An extensive analytical study shows that the excellent performance of HEO–rGO nanocomposites is attributed to the synergistic effect between the rGO active sites and the abundant oxygen vacancies within the HEO nanoparticles. Moreover, four robust machine learning models including Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) are applied to predict the selectivity of the oxidation reactions. The XGBoost is demonstrated as the best-fitting model for all data with an error of less than 2.5%. Overall, both experimental and numerical data suggest the potential application of the HEO–rGO nanocomposites in chemical industries for the selective oxidation of alcohols to added-value products. Benzyl alcohol ; Graphene ; Oxide ; Catalysis ; Nanoparticle ; Alcohol oxidation ; Solvent ; Inorganic chemistry ; Materials science ; Chemical engineering ; Chemistry ; Nanotechnology ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Daisy Rabbitt | Victor M. Villapún | Luke N. Carter | Kenny Man | Morgan Lowther | Peter O’Kelly | Alexander J. Knowles | Alessandro Mottura | Yuanbo T. Tang | Lorenzo Luerti | Roger C. Reed | Sophie C. Cox
Source: Advanced Healthcare Materials (review, 2024-12-23)
Field: Materials Science — Materials Chemistry

Rethinking Biomedical Titanium Alloy Design: A Review of Challenges from Biological and Manufacturing Perspectives Current biomedical titanium alloys have been repurposed from other industries, which has contributed to several biologically driven implant failure mechanisms. This review highlights the added value that may be gained by building an appreciation of implant biological responses at the onset of alloy design. Specifically, the fundamental mechanisms associated with immune response, angiogenesis, osseointegration and the potential threat of infection are discussed, including how elemental selection can modulate these pivotal systems. With a view to expedite inclusion of these interactions in alloy design criteria, methods to analyze these performance characteristics are also summarized. While machine learning techniques are being increasingly used to unearth complex relationships between alloying elements and material properties, much is still unknown about the correlation between composition and some bio-related properties. To bridge this gap, high-throughput methods are also reviewed to validate biological response along with cutting edge manufacturing approaches that may support rapid discovery. Taken together, this review encourages the alloy development community to rethink their approach to enable a new generation of biomedical implants intrinsically designed for a life in the body, including functionality to tackle biological challenges thereby offering improved patient outcomes. Osseointegration ; Nanotechnology ; Titanium alloy ; Computer science ; Biochemical engineering ; Materials science ; Risk analysis (engineering) ; Alloy ; Engineering ; Implant ; Medicine Titanium Alloys Microstructure and Properties ; Bone Tissue Engineering Materials ; Orthopaedic implants and arthroplasty