jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Julius Seumer | Nicolai Ree | Jan H. Jensen
Source: ChemRxiv (preprint, 2025-01-15)
Field: Materials Science — Materials Chemistry

Enhancing Chemical Synthesis Planning: Automated Quantum Mechanics-Based Regioselectivity Prediction for C-H Activation with Directing Groups The mild and selective functionalization of carbon-hydrogen (C-H) bonds remains a pivotal challenge in organic synthesis, crucial for developing complex molecular architectures in pharmaceuticals, polymers, and agrochemicals. Despite advancements in directing group (DG) methodologies and computational approaches, predicting accurate regioselectivity in C-H activation poses significant difficulties due to the diversity and complexity of organic compounds. This study introduces a novel quantum mechanics-based computational workflow tailored for the regioselective prediction of C-H activation in the presence of directing groups. Utilizing (semi-empirical) quantum calculations hierarchically, the workflow efficiently predicts outcomes by considering concerted metallation deprotonation mechanisms mediated by common catalysts like Pd(OAc)2. Our methodology not only identifies potential activation sites but also addresses the limitations of existing models by including a broader range of directing groups and reaction conditions while maintaining moderate computational cost. Validation against a comprehensive dataset reveals that the workflow achieves high accuracy, significantly surpassing traditional models in both speed and predictive capability. This development promises substantial advancements in the design of new synthetic routes, offering rapid and reliable regioselectivity predictions that are essential for accelerating innovation in material science and medicinal chemistry. Regioselectivity ; Quantum chemical ; Molecular mechanics ; Chemistry ; Computer science ; Computational chemistry ; Nanotechnology ; Materials science ; Catalysis ; Organic chemistry ; Molecular dynamics ; Molecule Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions
Authors: N. Segal | Aviv Netanyahu | Kevin P. Greenman | Pulkit Agrawal | Rafael Gómez‐Bombarelli
Source: ArXiv.org (preprint, 2025-02-09)
Field: Chemistry — Physical and Theoretical Chemistry

Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules Discovery of high-performance materials and molecules requires identifying extremes with property values that fall outside the known distribution. Therefore, the ability to extrapolate to out-of-distribution (OOD) property values is critical for both solid-state materials and molecular design. Our objective is to train predictor models that extrapolate zero-shot to higher ranges than in the training data, given the chemical compositions of solids or molecular graphs and their property values. We propose using a transductive approach to OOD property prediction, achieving improvements in prediction accuracy. In particular, the True Positive Rate (TPR) of OOD classification of materials and molecules improved by 3x and 2.5x, respectively, and precision improved by 2x and 1.5x compared to non-transductive baselines. Our method leverages analogical input-target relations in the training and test sets, enabling generalization beyond the training target support, and can be applied to any other material and molecular tasks. Property (philosophy) ; Distribution (mathematics) ; Statistical physics ; Mathematics ; Materials science ; Physics ; Mathematical analysis ; Philosophy ; Epistemology History and advancements in chemistry
Authors: Armando Mandlule | Yue Liu | Susanne Schwanz | Yvonne Pieper | H.‐D. SCHARF | Kamila Iskhakova | A.L.C. Conceição | D. C. Florian Wieland | Berit Zeller‐Plumhoff | Francesca M. Toma | Axel T. Neffe
Source: Materials Advances (article, 2025-11-14)
Field: Materials Science — Biomaterials

Correlative characterization of stereocomplex formation in blends of aliphatic polyester P(PCL <sub> <i>m</i> </sub> - <i>b</i> -PLLA <sub> <i>n</i> </sub> ) multiblock-copolymers and PDLA Strategic polymer synthesis and blending allows control of stereocomplexation and property fine-tuning in PLA-based materials as shown by correlative characterization. Correlative ; Polyester ; Characterization (materials science) ; Polymer ; Property (philosophy) biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties
Authors: Kasimir P. Gregory | Amir Karton
Source: Journal of Computational Chemistry (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Big‐Data Analysis of Geometric Descriptors as Efficient Predictors of Energetic Stability in Nonplanar Polycyclic Aromatic Hydrocarbons Accurate, efficient stability predictors are essential for understanding isomer formation in polycyclic aromatic hydrocarbons (PAHs), with implications for pollution toxicity and carbon-material design, holding broad environmental and technological significance. Recently, a benchmark study demonstrated that PBE0-D4 reproduces CCSD(T)-level isomerization energies for 335 PAHs with a mean absolute deviation (MAD) of 0.67 kcal mol<sup>-1</sup>. Here, we apply the PBE0-D4/6-31G(2df,p) level of theory to 38,264 PAH isomers from the COMPAS-3x database and identify fast, geometry-based parameters that predict isomer stability. The total dihedral deviation (Σ<sub>Dihedral</sub>) provides a cost-free nonplanarity metric yielding a mean absolute deviation (MAD) of 3.6 kcal mol<sup>-1</sup>, outperforming maximal z-displacement (MAD = 4.8 kcal mol<sup>-1</sup>) and the Harmonic Oscillator Model of Aromaticity (HOMA; MAD = 5.3 kcal mol<sup>-1</sup>). A combined Σ<sub>Dihedral</sub>-HOMA model reduces the MAD to 2.5 kcal mol<sup>-1</sup>, and adding a fitted semiempirical xTB correction further lowers the MAD to 0.8 kcal mol<sup>-1</sup>. We implement these descriptors in the PAH Automated Property Scanner (PAHAPS) web tool, enabling rapid estimation of PAH isomer energies from molecular coordinates without intensive quantum calculations. This integrated approach facilitates large-scale screening and efficient design of stable PAH isomers for environmental and materials applications. Stability (learning theory) ; Computational chemistry ; Big data ; Chemistry ; Polycyclic aromatic hydrocarbon ; Computer science ; Environmental chemistry ; Data mining ; Machine learning Machine Learning in Materials Science ; Synthesis and Properties of Aromatic Compounds ; Polymer crystallization and properties
Authors: А. Н. Кошев | Valentina Kuzina
Source: Nanotechnologies in Construction A Scientific Internet-Journal (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Distributed reaction surface of carbon-graphite volume-porous cathodes as a parameter for optimizing of metallization process of composite and nanocomposite materials Introduction.The porous composite and nanocomposite materials discussed in this publication are metallized carbon-graphite materials, in particular carbon fiber materials (CFMs).At metallization of CFMs one of the actual problems is the task of applying uniform metal coating on the surface of CFMs fibers over the entire volume of the processed material.One of the most effective ways to coat CFMs with metals and their alloys is the galvanic method.This method allows, among other things, to optimize the process by optimizing both the design of the electrolyzer with flow-through three-dimensional electrodes (FTE) and the choice of electrodeposition modes, such as galvanic, concentration, and hydrodynamic.Materials and methods.Methods of mathematical modeling of porous medium metallization processes and previously published experimental data were used to solve problems on calculation of operating modes and elements of the electrolyzer design, in particular, the specific reaction surface of the UWM distributed over the thickness of the electrolyzer.Results and discussion.The development of mathematical models of the processes of electroplating metallization of CFMs is carried out.Boundary value problems of mathematical physics are formulated and calculation methods are proposed.Problems of optimization of distribution of specific reaction surface of CFMs by thickness of FTE on the basis of electrochemical theory of processes in porous medium are set, ways of their solution are presented.Specific technological problems are solved.The comparison of the most effective values of the distribution of specific reaction surface of CFMs over the thickness of FTE obtained as a result of modeling with the experimental data is given.The workability of models and methods for studying the process of copper electrodeposition from sulfuric acid electrolyte is shown.Conclusion.Calculation of regularities of electrochemical processes of metal extraction in FTE on the basis of the developed mathematical models allows to predict the results of electrolysis, determine the optimal modes of operation and elements of FTE design.The use of FTE for metallization of CFMs with properly distributed reaction surface contributes to obtaining composite materials with specified properties. Materials science ; Graphite ; Nanocomposite ; Composite number ; Porosity ; Carbon fibers ; Volume (thermodynamics) ; Composite material ; Process (computing) ; Cathode ; Chemical engineering ; Chemistry ; Computer science Carbon Nanotubes in Composites ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Nanotechnology research and applications
Authors: Junya Ohyama
Source: Chemical Communications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Toward understanding and development of the three-dimensional atomic structures of supported metal catalysts Understanding of supported metal catalysts, which are indispensable for numerous practical chemical processes including chemical production, environmental purification, and power generation, has deepened alongside advances in analytical techniques. In particular, recent progress in direct atomic-scale and even three-dimensional analysis techniques has revealed that actual structures often differ significantly from conventionally assumed model structures, leading to remarkable catalysis. In this review, our recent studies on supported metal catalysts using such advanced analytical techniques, specifically high-energy-resolution fluorescence-detected X-ray absorption near edge structure (HERFD-XANES) and spherical aberration-corrected scanning transmission electron microscopy (Cs-STEM), are reviewed. First, the complementarity of X-ray absorption fine structure spectroscopy (XAFS) and STEM in characterizing the diverse structures of supported catalysts is described. Representative examples of distinctive catalyst structures and their associated catalytic properties revealed using HERFD-XANES and Cs-STEM are then presented. Finally, the three-dimensional atomic structure of a practical supported metal nanoparticle catalyst, resolved for the first time through the application of atomic-resolution electron tomography, is discussed, and future directions in supported metal catalyst research enabled by 3D structural analysis are explored. Catalysis ; Metal ; Scanning transmission electron microscopy ; Absorption spectroscopy ; Nanoparticle ; Atomic absorption spectroscopy ; Transmission electron microscopy ; Spectroscopy Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: George Luis Morejón Aguila | Julia L. Rodríguez | Tatyana Poznyak | Yair Cruz‐Narváez | Héctor Francisco Mendoza-León | Luis Lartundo‐Rojas | Claudia Jazmín Ramos Torres | José J. Castro‐Arellano
Source: Catalysts (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Naproxen Degradation Using NiO Synthesized via Ultrasonic Spray Pyrolysis on Ni–Fe Foam by Ozone Naproxen (NPX), a nonsteroidal anti-inflammatory drug, is considered an emerging contaminant due to its persistence and potential environmental risks. In this study, NPX degradation was investigated through ozonation using nickel–iron foam (NiFeF) and NiO-modified NiFeF (NiO/NiFeF). The effect of the foam size was investigated using three configurations: S1 (1 cm × 2.5 cm), S2 (2 cm × 2.5 cm), and S3 (2 cm × 5 cm). Complete NPX removal was achieved in all systems, with degradation times of 4 min for ozonation alone, 2 min for NiFeF-S1, and 1 min for NiO/NiFeF-S2 and NiO/NiFeF-S3. The NiO/NiFeF catalyst was synthesized via ultrasonic spray pyrolysis, resulting in a porous structure with abundant active sites. Compared with conventional ozonation, NiO/NiFeF-S1 improved the total organic carbon (TOC) removal rate by 6.2-fold and maintained 87.5% of its activity after five reuse cycles, demonstrating excellent stability. High-resolution mass spectrometry revealed that catalytic ozonation generated fewer by-products (22 vs. 27 for ozonation alone) and promoted more selective pathways, including demethylation, ring-opening oxidation, and partial mineralization to CO2 and H2O. This enhanced performance is attributed to the synergy between NiO and NiFeF, which facilitates reactive oxygen species generation and electron transfer. These results demonstrate the potential of NiO/NiFeF as an efficient and stable catalyst for pharmaceutical removal from water. Degradation (telecommunications) ; Ozone ; Catalysis ; Non-blocking I/O ; Mineralization (soil science) ; Pyrolysis Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Bo Yu | Vincenzo Lordi | Daniel Schwalbe‐Koda
Source: ArXiv.org (preprint, 2025-11-13)
Field: Materials Science — Materials Chemistry

Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets, and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost. Outlier ; Pruning ; Dimensionality reduction ; Data compression ; Curse of dimensionality ; Compression (physics) ; Set (abstract data type) ; Boosting (machine learning) Machine Learning in Materials Science ; Quantum many-body systems ; Protein Structure and Dynamics
Authors: Yingying Huang | Daqian Lu | Sihang Zhang | Shouxiang Jiang | Yonghui Zhou | Jiangtao Xu
Source: Sustainable Food Technology (article, 2025-12-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Portable Raman spectroscopy combined with machine learning for highly sensitive and rapid detection of food pollutants with flexible Ag@TiO <sub>2</sub> @polyester SERS substrates Flexible, Ag NP-decorated porous TiO 2 SERS substrates combined with a portable Raman spectrometer and machine learning algorithms realize the rapid detection and identification of mixed pollutants. Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Spectrometer ; Spectroscopy ; Raman scattering Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Jeongmin Ha | Yingshan Ma | Yulong An | Sung Jin An | Hyunsook Jung | Suvi‐Tuuli Akkanen | Je-Hyun Yoo | Jung‐Wook Min | Hanvit Kim | Faisal Ahmed | Sang Hoon Chae | Young Min Song | Weiwei Cai | Tawfique Hasan | Zhipei Sun | Dong‐Ho Kang | Hyeon‐Jin Shin | Yunyun Dai | Hoon Hahn Yoon
Source: Advanced Functional Materials (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum. Central to this capability are IR photodetectors (PDs) based on 2D materials (2DMs), which offer tunable spectral responsivity and wavelength‐resolved multiparameter optical information. This review examines the fundamental mechanisms and design strategies that enable spectral tunability at the frontier of 2DM‐based IR PDs, elucidating how they offer unique opportunities to tailor spectral responses across a broad wavelength range through symmetry‐breaking induced by geometric (geometrically tunable spectral engineering) and electric‐field (electrically tunable spectral engineering) effects. These approaches collectively enable simultaneous optimization of spectral tunability and sensitivity without compromising wavelength coverage, speed, power efficiency, or scalability, while also providing polarization sensitivity, multiband detection, and self‐powered operation for edge‐integrated AI platforms, including computational spectroscopy, artificial vision, computing, and communications. This review outlines the key processes and integration requirements for scalable manufacturing, which are essential for establishing spectrally tunable 2DM‐based IR PDs as core building blocks of intelligent optoelectronics. Ultimately, the development of spectrally tunable 2DM‐based IR PDs will transform intelligent optoelectronic platforms for or with AI. Photodetector ; Responsivity ; Infrared ; Plasmon ; Microbolometer ; Scalability ; Optical communication ; Spectral sensitivity 2D Materials and Applications ; Ga2O3 and related materials ; Thermal Radiation and Cooling Technologies
Authors: Haibo Cao | Junjie Zeng | Lixin Xue
Source: BMC Chemistry (article, 2025-07-03)
Field: Chemistry — Spectroscopy

Accuracy and feasibility analysis of computational chemistry in drug spectral simulation—a case study of acetylsalicylic acid Comparison of experimental and simulated spectra demonstrated high consistency, with R² values of 0.9933 and 0.9995, confirming the predictive power of the computational model. Solvent effects, such as the redshift of UV absorption in aqueous media, were successfully reproduced. Computational analysis resolved ambiguous IR peak assignments caused by overlap or impurities. While limitations such as the lack of NMR data, use of a single functional (GGA/BLYP), and simplified solvation were acknowledged, the integrated approach significantly improved student engagement and conceptual understanding. This study demonstrates the pedagogical and analytical benefits of combining experimental and computational methods, enhancing interpretative accuracy, supporting green chemistry, and offering a reproducible, resource-efficient framework for pharmaceutical education. Spectral analysis ; Chemistry ; Computer science ; Drug ; Computational chemistry ; Combinatorial chemistry ; Pharmacology ; Medicine ; Physics ; Spectroscopy Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Kamal Choudhary
Source: ChemRxiv (preprint, 2025-06-02)
Field: Materials Science — Materials Chemistry

MicroscopyGPT: Generating Atomic-Structure Captions from Microscopy Images of 2D Materials with Vision-Language Transformers Determining complete atomic structures directly from microscopy images remains a longstanding challenge in materials science. MicroscopyGPT is a vision-language model (VLM) that leverages multimodal generative pre-trained transformers to predict full atomic configurations including lattice parameters, element types, and atomic coordinates, from Scanning Transmission Electron Microscopy (STEM) images. The model is trained on a chemically and structurally diverse dataset of simulated STEM images generated using the AtomVision tool and the JARVIS-DFT as well as the C2DB two-dimensional (2D) materials databases. The training set for finetuning comprises approximately 5000 2D materials, enabling the model to learn complex mappings from image features to crystallographic representations. I fine-tune the 11-billion-parameter LLaMA model, allowing efficient training on resource-constrained hardware. The rise of VLMs and the growth of materials datasets offer a major opportunity for microscopy-based analysis. This work highlights the potential of automated structure reconstruction from microscopy, with broad implications for materials discovery, nanotechnology, and catalysis. Atomic force microscopy ; Microscopy ; Transformer ; Materials science ; Artificial intelligence ; Computer vision ; Computer science ; Nanotechnology ; Optics ; Engineering ; Physics ; Electrical engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Alexandra Day | Carolin B. Wahl | Roberto dos Reis | Wei‐keng Liao | Youjia Li | Muhammed Nur Talha Kilic | Chad A. Mirkin | Vinayak P. Dravid | Alok Choudhary | Ankit Agrawal
Source: Scientific Reports (article, 2025-05-17)
Field: Materials Science — Materials Chemistry

Automated image segmentation for accelerated nanoparticle characterization Recent developments in materials science have made it possible to synthesize millions of individual nanoparticles on a chip. However, many steps in the characterization process still require extensive human input. To address this challenge, we present an automated image processing pipeline that optimizes high-throughput nanoparticle characterization using intelligent image segmentation and coordinate generation. The proposed method can rapidly analyze each image and return optimized acquisition coordinates suitable for multiple analytical STEM techniques, including 4D-STEM, EELS, and EDS. The pipeline employs computer vision and unsupervised learning to remove the image background, segment the particle into areas of interest, and generate acquisition coordinates. This approach eliminates the need for uniform grid sampling, focusing data collection on regions of interest. We validated our approach using a diverse dataset of over 900 high-resolution grayscale nanoparticle images, achieving a 96.0% success rate based on expert-validated criteria. Using established 4D-STEM acquisition times as a baseline, our method demonstrates a 25.0 to 29.1-fold reduction in total processing time. By automating this crucial preprocessing step and optimizing data acquisition, our pipeline significantly accelerates materials characterization workflows while reducing unnecessary data collection. Computer science ; Segmentation ; Characterization (materials science) ; Image segmentation ; Artificial intelligence ; Image (mathematics) ; Nanoparticle ; Computer vision ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Mineral Processing and Grinding ; Spectroscopy Techniques in Biomedical and Chemical Research