jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Theodore Tyrikos‐Ergas | Sevasti Agiakloglou | Antonio J. LaPorte | Wesley Wang | Chieh‐Kai Chan | C. Wells | Christopher K. Rakowski | Rachel I. Hammond | Junyi Qiu | Jane E. Raymond | Tiago Vieira | John Limanto | Marc N. Feiglin | Daniel J. Blair | Martin D. Burke
Source: ChemRxiv (preprint, 2025-06-13)
Field: Chemistry — Inorganic Chemistry

Automated Iterative N-C and C-C Bond Formation Small molecule solutions to many contemporary societal challenges await discovery, but the artisanal and manual process via which this class of chemical matter is typically accessed limits the discovery of new functions. Automated assembly of (N-methyl iminodiacetic acid) MIDA or (tetramethyl N-methyl iminodiacetic acid) TIDA boronate building blocks via iterative C-C bond formation, an approach we call “block chemistry”, alternatively enables generalized and automated preparation of many different types of small molecules in a modular fashion. But in its current form, this engine cannot also leverage nitrogen atoms as iteration handles. Here, we disclose a new iteration-enabling group, CbzT, that reversibly attenuates the reactivity of nitrogen atoms and enables generalized catch-and-release purification. CbzT is leveraged to achieve the automated modular synthesis of Imatinib (Gleevec), an archetypical clinically approved kinase inhibitor, in which building blocks are iteratively linked by both N-C and C-C bonds. This work substantially expands the types of small molecules that can be iteratively assembled in an automated modular fashion. It also advances the concept of intentionally developing chemistry that machines can do. Bond ; Computer science ; Mathematics ; Economics ; Finance Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science
Authors: Theodore Tyrikos‐Ergas | Sevasti Agiakloglou | Antonio J. LaPorte | Wesley Wang | Chieh‐Kai Chan | C. Wells | Christopher K. Rakowski | Rachel I. Hammond | Junyi Qiu | Jane E. Raymond | Tiago Vieira | John Limanto | Marc N. Feiglin | Daniel J. Blair | Martin D. Burke
Source: ChemRxiv (preprint, 2025-05-09)
Field: Chemistry — Inorganic Chemistry

Automated Iterative N-C and C-C Bond Formation Small molecule solutions to many contemporary societal challenges await discovery, but the artisanal and manual process via which this class of chemical matter is typically accessed limits the discovery of new functions. Automated iterative cross-coupling with (N-methyl iminodiacetic acid) MIDA or (tetramethyl N-methyl iminodiacetic acid) TIDA boronate building blocks alternatively enables generalized and automated preparation of many different types of small molecules in a modular fashion. But in its current form, this engine cannot leverage nitrogen atoms as iteration handles. Here, we disclose a new iteration-enabling group, CbzT, that reversibly attenuates the reactivity of nitrogen atoms and enables generalized catch-and-release purification. CbzT is leveraged to achieve the automated modular synthesis of Imatinib (Gleevec), an archetypical clinically approved kinase inhibitor, in which building blocks are iteratively linked by both N-C and C-C bonds. This work substantially expands the types of small molecules that can be made in an automated modular fashion. It also advances the concept of intentionally developing chemistry that machines can do. Bond ; Computer science ; Economics ; Finance Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science
Authors: Ramender Singh
Source:  (preprint, 2025-10-22)
Field: Materials Science — Materials Chemistry

Generative AI for 3D Molecular Structure Prediction Using Diffusion Models The ability to generate stable molecular structures is crucial for advancements in computational chemistry and materials science. This study explores a generative AI framework that leverages diffusion-based learning to construct threedimensional molec Construct (python library) ; Generative grammar ; Generative model ; Diffusion ; Computational model Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jeff L. Zhang | Jingru Gan | Xiaoxuan Wang | Zhiyan Jia | Cong Gu | Jianpeng Chen | Yanqiao Zhu | Mingyu Derek Ma | Dawei Zhou | Ling Li | Wei Wang
Source: ArXiv.org (preprint, 2025-10-14)
Field: Materials Science — Materials Chemistry

MatSciBench: Benchmarking the Reasoning Ability of Large Language Models in Materials Science Large Language Models (LLMs) have demonstrated remarkable abilities in scientific reasoning, yet their reasoning capabilities in materials science remain underexplored. To fill this gap, we introduce MatSciBench, a comprehensive college-level benchmark comprising 1,340 problems that span the essential subdisciplines of materials science. MatSciBench features a structured and fine-grained taxonomy that categorizes materials science questions into 6 primary fields and 31 sub-fields, and includes a three-tier difficulty classification based on the reasoning length required to solve each question. MatSciBench provides detailed reference solutions enabling precise error analysis and incorporates multimodal reasoning through visual contexts in numerous questions. Evaluations of leading models reveal that even the highest-performing model, Gemini-2.5-Pro, achieves under 80% accuracy on college-level materials science questions, highlighting the complexity of MatSciBench. Our systematic analysis of different reasoning strategie--basic chain-of-thought, tool augmentation, and self-correction--demonstrates that no single method consistently excels across all scenarios. We further analyze performance by difficulty level, examine trade-offs between efficiency and accuracy, highlight the challenges inherent in multimodal reasoning tasks, analyze failure modes across LLMs and reasoning methods, and evaluate the influence of retrieval-augmented generation. MatSciBench thus establishes a comprehensive and solid benchmark for assessing and driving improvements in the scientific reasoning capabilities of LLMs within the materials science domain. Benchmarking ; Benchmark (surveying) ; Scientific reasoning ; Visual reasoning ; Taxonomy (biology) ; Qualitative reasoning ; Automated reasoning Machine Learning in Materials Science
Authors: Xinwen Yang | Xuan Li | Qin Zhao
Source: Mathematics (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

A Multi-View Fusion Data-Augmented Method for Predicting BODIPY Dye Spectra Fluorescent molecules, particularly BODIPY dyes, have found wide applications in fields such as bioimaging and optoelectronics due to their excellent photostability and tunable spectral properties. In recent years, artificial intelligence methods have enabled more efficient screening of molecules, allowing the required molecules to be quickly obtained. However, existing methods remain inadequate to meet research needs, primarily due to incomplete molecular feature extraction and the scarcity of data under small-sample conditions. In response to the aforementioned challenges, this paper introduces a spectral prediction method that integrates multi-view feature fusion and data augmentation strategies. The proposed method consists of three modules. The molecular feature engineering module constructs a multi-view molecular fusion feature that includes molecular fingerprints, molecular descriptors, and molecular energy gaps, which can more comprehensively obtain molecular feature information. The data augmentation module introduces strategies such as SMILES randomization, molecular fingerprint bit-level perturbation, and Gaussian noise injection to enhance the performance of the model in small sample environments. The spectral prediction module captures the complex mapping relationship between molecular structure and spectrum. It is demonstrated that the proposed method provides considerable advantages in the virtual screening of organic fluorescent molecules and offers valuable support for the development of novel BODIPY derivatives based on data-driven strategies. Feature (linguistics) ; Fusion ; Feature extraction ; Sensor fusion ; Pattern recognition (psychology) ; BODIPY ; Molecular spectroscopy Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques ; Advanced Chemical Sensor Technologies
Authors: Ivan Grega | Félix Therrien | Abhishek Soni | Karry Ocean | Kevan E. Dettelbach | Ribwar Ahmadi | Mehrdad Mokhtari | Curtis P. Berlinguette | Yoshua Bengio
Source: arXiv (Cornell University) (preprint, 2025-02-10)
Field: Chemistry — Electrochemistry

A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories The electrochemical reduction of atmospheric CO$_2$ into high-energy molecules with renewable energy is a promising avenue for energy storage that can take advantage of existing infrastructure especially in areas where sustainable alternatives to fossil fuels do not exist. Automated laboratories are currently being developed and used to optimize the composition and operating conditions of gas diffusion electrodes (GDEs), the device in which this reaction takes place. Improving the efficiency of GDEs is crucial for this technology to become viable. Here we present a modeling framework to efficiently explore the high-dimensional parameter space of GDE designs in an active learning context. At the core of the framework is an uncertainty-aware physics model calibrated with experimental data. The model has the flexibility to capture various input parameter spaces and any carbon products which can be modeled with Tafel kinetics. It is interpretable, and a Gaussian process layer can capture deviations of real data from the function space of the physical model itself. We deploy the model in a simulated active learning setup with real electrochemical data gathered by the AdaCarbon automated laboratory and show that it can be used to efficiently traverse the multi-dimensional parameter space. Diffusion ; Gaseous diffusion ; Electrode ; Physics ; Computer science ; Thermodynamics Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies
Authors: Theodore Tyrikos‐Ergas | Sevasti Agiakloglou | Antonio J. LaPorte | Wesley Wang | Chieh‐Kai Chan | C. Wells | Christopher K. Rakowski | Rachel I. Hammond | Junyi Qiu | Jane E. Raymond | Tiago Vieira | John Limanto | Marc N. Feiglin | Daniel J. Blair | Martin D. Burke
Source: ChemRxiv (preprint, 2025-05-29)
Field: Chemistry — Inorganic Chemistry

Automated Iterative N-C and C-C Bond Formation Small molecule solutions to many contemporary societal challenges await discovery, but the artisanal and manual process via which this class of chemical matter is typically accessed limits the discovery of new functions. Automated assembly of (N-methyl iminodiacetic acid) MIDA or (tetramethyl N-methyl iminodiacetic acid) TIDA boronate building blocks via iterative C-C bond formation, an approach we call “block chemistry”, alternatively enables generalized and automated preparation of many different types of small molecules in a modular fashion. But in its current form, this engine cannot also leverage nitrogen atoms as iteration handles. Here, we disclose a new iteration-enabling group, CbzT, that reversibly attenuates the reactivity of nitrogen atoms and enables generalized catch-and-release purification. CbzT is leveraged to achieve the automated modular synthesis of Imatinib (Gleevec), an archetypical clinically approved kinase inhibitor, in which building blocks are iteratively linked by both N-C and C-C bonds. This work substantially expands the types of small molecules that can be iteratively assembled in an automated modular fashion. It also advances the concept of intentionally developing chemistry that machines can do. Bond ; Computer science ; Economics ; Finance Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science
Authors: Jie Yu | Tong An | De-Ling Chen | Shixiang Zong | Dingrong Bai | Dawei Qi | L.C. Zhang | J.L. Shi
Source: Research Square (preprint, 2025-09-04)
Field: Chemistry — Spectroscopy

Correction of Chromatography-Mass Spectrometry Long-term Instrumental Drift using Quality Control Samples over 155 days Term (time) ; Mass spectrometry ; Chromatography ; Quality (philosophy) ; Chemistry ; Analytical Chemistry (journal) ; Physics Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Pesticide Residue Analysis and Safety
Authors: Jie Yu | Tong An | D Chen | Shixiang Zong | Dingrong Bai | Dawei Qi | Luning Zhang | J.L. Shi
Source: Scientific Reports (article, 2025-11-19)
Field: Chemistry — Analytical Chemistry

Correction of gas chromatography–mass spectrometry long-term instrumental drift using quality control samples over 155 days Long-term instrumental data drift is a critical challenge for ensuring process reliability and product stability. In this study, we conducted 20 repeated tests on the smoke of six commercial tobacco products using gas chromatography-mass spectrometry (GC-MS) instrument over 155 days. We propose a simple, cost-effective, and reliable peak-area correction approach to address long-term data drift, especially on GC-MS data. Using 20 pooled quality control (QC) samples, we establish correction algorithm data set, and achieved reliable peak correction even for compositions exhibiting large fluctuations. Three algorithms - spline interpolation (SC), support vector regression (SVR), and Random Forest (RF) - were applied to normalise 178 target chemicals in 20 repeated measurements on six samples. Two key innovative approaches were introduced. First, we established a "virtual QC sample" by incorporating chromatographic peaks from all 20 QC results, via retention time and mass spectrum verification. This virtual QC served as the meta reference for analyzing and normalizing the test sample. Second, we took translated batch effects and injection order effects into two numerical indices in the algorithms, minimizing artificial parameterization of experiments. For chemical components found in the test samples but absent in the QC samples, normalisation was achieved using either adjacent chromatography peak for correction or by applying average correction coefficients derived from all QC data. Our results show that Random Forest algorithm provided the most stable and reliable correction model for long-term, highly variable data. Principal component analysis (PCA) and standard deviation analysis confirmed the robustness of the correction procedure. In contrast, models based on SC and SVR algorithms exhibited less stability with SC being the lowest. For data with large variation, SVR tends to over-fit and over-correct. This study shows that for long-term data measurements by GC-MS, periodic QC sample measurements combined with appropriate algorithm for correction can effectively compensate for long-term measurement variability, enabling reliable data tracking and quantitative comparison over extended periods. Principal component analysis ; Robustness (evolution) ; Random forest ; Mass spectrometry ; Random error ; Spline interpolation ; Interpolation (computer graphics) ; Reliability (semiconductor) ; Quality assurance Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Metabolomics and Mass Spectrometry Studies
Authors: Maryame Lakrade | Zakaria Mighouar | Laidi Zahiri | Khalifa Mansouri
Source: E3S Web of Conferences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

An Integrated FEM-AI Methodology for Predictive Modelling and Optimization of Filled Bio-Composites This paper proposes a unified approach merging finite element modelling (FEM) and artificial intelligence (AI) to forecast the mechanical behavior of filled bio-composites, which are growing in importance for sustainable material applications. The proposed method applies Digimat software for multi-scale simulations to account for anisotropic behavior and interfacial effects, with AI algorithms, such as neural networks and nonlinear regression, creating predictive correlations between microstructural parameters and mechanical performance. The FEM simulations produce high-fidelity data on stress-strain distributions, which are subsequently employed to train the AI models, establishing a synergistic feedback loop that improves predictive accuracy. In contrast, differences between AI forecasts and FEM findings direct adjustments to the simulation framework, which leads to reliable modelling results. This cyclical merging resolves the constraints of isolated methods, where in finite element analysis by itself may be inadequate for investigating extensive parameter domains, and artificial intelligence frameworks may be compromised by limited or noisy numerical data. The hybrid FEM-AI model achieves high accuracy across mechanical properties, outperforming standalone FEM and AI approaches. Overall, the integrated approach offers a powerful and generalizable framework for efficient exploration of complex material behaviors. Finite element method ; Artificial neural network ; Nonlinear system ; Software ; Model predictive control Machine Learning in Materials Science ; Composite Material Mechanics ; Model Reduction and Neural Networks
Authors: Enrico Jardim Clemente Santos | Angela Mazzeo
Source: Revista Multidisciplinar do Nordeste Mineiro (article, 2025-12-05)
Field: Materials Science — Biomaterials

INTELIGÊNCIA ARTIFICIAL NA OTIMIZAÇÃO E MODELAGEM DE BIOMATERIAIS DE FIBROÍNA DA SEDA A fibroína da seda (FS), uma proteína natural derivada do Bombyx mori, é um biomaterial promissor devido à sua biocompatibilidade, biodegradabilidade e notáveis propriedades mecânicas. No entanto, a otimização de suas propriedades e aplicações biomédicas como engenharia de tecidos, liberação de fármacos e biossensores, permanecem desafios complexos. Este artigo explora a intersecção emergente entre a FS e a Inteligência Artificial (IA) e como esta pode acelerar a descoberta, a otimização de processos de fabricação e a modelagem preditiva das propriedades da FS. Discutimos o potencial da IA para correlacionar a estrutura molecular da FS com suas propriedades macroscópicas, otimizar a desgomagem e a solubilização, e prever o desempenho de scaffolds e nanopartículas. Concluímos que a integração da IA representa um paradigma transformador, permitindo a criação de biomateriais de FS de próxima geração com funcionalidade e desempenho aprimorados. Production (economics) ; Work (physics) ; Sustainability ; Quality (philosophy) Silk-based biomaterials and applications ; Insect Utilization and Effects ; Plant-Derived Bioactive Compounds
Authors: Wan‐Jian Yin | Miao Liu | Xingao Gong
Source: Chinese Science Bulletin (Chinese Version) (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Paradigm shift in materials science driven by artificial intelligence: methods, platforms, and frontiers of applications Paradigm shift ; Computer science ; Data science ; Artificial intelligence ; Epistemology Machine Learning in Materials Science
Authors: Ida Granö | Olli‐Pekka Kahilakoski | Mikael Laine | Miriam Kirchhoff | Oskari Ahola | Ana M. Soto | Renan H. Matsuda | Jaakko O Nieminen | Giulia Pieramico | Heikki Sinisalo | Ilkka Rissanen | Timo Tommila | Timo Roine | Victor H. Souza | Risto J. Ilmoniemi | Pantelis Lioumis | Tuomas P. Mutanen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-25)
Field: Chemistry — Spectroscopy

Fast and standardized motor-hotspot determination with automated TMS mapping Abstract Determining the optimal stimulation target for motor responses (motor hotspot) and the required intensity for reliably eliciting said responses (motor threshold) are common procedures in transcranial magnetic stimulation (TMS) research and treatments. However, the procedures for determining them are user-dependent, slow, and lack standardization, leading to long stimulation sessions with potentially inadequate outcomes. Partially automated algorithms for determining the motor threshold have been developed, but the motor hotspot is still largely mapped by hand. Automating the hotspot mapping will accelerate the process and improve standardization and accuracy. We developed a fully automated algorithm for finding the motor hotspot with multi-locus TMS and Bayesian optimization. Tested online in five healthy participants, the algorithm located motor hotspots with (mean ± 95% CI) 2.1 ± 0.7 mm and 6 ± 2° difference from the global best target with only (mean) 47 stimuli. This is a significant improvement from previous motor-mapping algorithms, which do not optimize for stimulation location and orientation simultaneously. This accurate, fast, and user-independent procedure paves the way for faster experimental processes and more streamlined clinical applications. Hotspot (geology) ; Transcranial magnetic stimulation ; Standardization ; Bayesian probability ; Automated method Mass Spectrometry Techniques and Applications
Authors: Haibin Liu | Yingchao Wu | Wenpeng Zhang | Hui Xu | Zhenqian Guo | Fanhao Meng | Xinyu Men | Allen Chunlong Guo
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-09)
Field: Materials Science — Surfaces, Coatings and Films

SMART: An Integrated Platform Revolutionizing Cryo-Electron Microscopy Workflow from Data Acquisition to Atomic Modeling Abstract Recent advancements in cryo-electron microscopy (cryo-EM) have revolutionized structural biology, yet persistent workflow fragmentation between data acquisition, reconstruction, and modeling limits its full potential. This paper presents SMART, the first fully integrated cryo-EM platform unifying three critical AI-driven modules: DataSmart (automated data collection), CryoSmart (3D reconstruction), and ModelSmart (end-to-end atomic modeling). The platform embeds more than 10 distinct AI algorithms across its workflow, achieving unprecedented automation levels. These advancements not only lower the expertise barrier for cryo-EM adoption but also establish a new paradigm for AI-empowered structural biology, with broad implications for high-throughput drug discovery. This protocol provides a comprehensive delineation of the operational procedures for the SMART platform. As an example, this protocol enabled the determination of the 2.1 Å resolution structure of TRPML1, a Ca2 + -permeable, nonselective, six-transmembrane tetrameric cation channel found in late endosomes and lysosomes (LELs) of mammalian cells. This integrated workflow enables users, including those with minimal cryo-EM experience, to efficiently complete the entire pipeline from data collection to atomic model building. Workflow ; Nanotechnology ; Electron microscope ; Computer science ; Cryo-electron microscopy ; Systems engineering ; Materials science ; Engineering ; Database ; Physics ; Optics ; Nuclear magnetic resonance Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques