jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jatin Sarabu | Sohail Zaidi
Source:  (article, 2025-03-15)
Field: Materials Science — Materials Chemistry

AI/ML Approach for Predictive Modeling of RNA Structures and Chemical Reactivity Profiles RNA, predominantly a single-stranded molecule, is integral to gene expression, with its folding into secondary structures playing a vital role in processes such as transcription, translation, and splicing. These secondary structures are essential for RNA's biological functions and are closely linked to various genetic diseases and malignancies. Understanding these structures is critical for identifying therapeutic targets and advancing disease treatment, with the potential to make significant societal impacts.Traditional bioinformatics methods for predicting RNA secondary structures often face limitations in accuracy and scalability. In contrast, machine learning, particularly neural network models, has emerged as a game-changing approach, capable of identifying complex patterns in large datasets with improved precision.This study leverages a convolutional neural network (CNN) to predict RNA secondary structures and chemical reactivity profiles. Using a dataset of 1,118,5113 RNA sequences obtained from Kaggle, the CNN model was trained to detect key structural features. The model achieved a mean absolute error of 0.28, demonstrating superior predictive performance compared to traditional methods.The results underscore the transformative potential of machine learning in RNA research. By enhancing our ability to predict RNA secondary structures, this work provides valuable insights into RNA functionality and its implications in disease treatment. The study highlights how cutting-edge technologies like machine learning can accelerate biomedical discoveries, ultimately paving the way for innovative therapeutic strategies targeting RNArelated diseases. RNA ; Convolutional neural network ; Nucleic acid structure ; Nucleic acid secondary structure ; Folding (DSP implementation) ; Artificial neural network ; Deep learning Machine Learning in Materials Science ; RNA and protein synthesis mechanisms ; Fuel Cells and Related Materials
Authors: Jeyapaul Angel Ida Chellam | Bright Brailson Mansingh | Daniel Stalin Alex | Joseph Selvi Binoj
Source: JOURNAL OF POLYMER MATERIALS (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Prediction and Validation of Mechanical Properties of &lt;i&gt;Areca catechu/Tamarindus indica&lt;/i&gt; Fruit Fiber with Nano Coconut Shell Powder Reinforced Hybrid Composites Machine learning models can predict material properties quickly and accurately at a low computational cost. This study generated novel hybridized nanocomposites with unsaturated polyester resin as the matrix and Areca fruit husk fiber (AFHF), tamarind fruit fiber (TFF), and nano-sized coconut shell powder (NCSP). It is challenging to determine the optimal proportion of raw materials in this composite to achieve maximum mechanical properties. This task was accomplished with the help of ML techniques in this study. The tensile strength of the hybridized nanocomposite was increased by 134.06% compared to the neat unsaturated polyester resin at a 10:5:2 wt.% ratio, AFHF:TFF:NCSP. The stiffness and impact behavior of hybridized nanocomposites were similar. The scanning electron microscope showed homogeneous reinforcement and nanofiller distribution in the matrix. However, the hybridized nanocomposite with a 20:5:0 wt.% combination ratio had the highest strain at break of 5.98%, AFHF:TFF:NCSP. The effectiveness of recurrent neural networks and recurrent neural networks with Levenberg’s algorithm was assessed using R2, mean absolute errors, and minimum squared errors. Tensile and impact strength of hybridized nanocomposites were well predicted by the recurrent neural network with Levenberg’s model with 2 and 3 hidden layers, 80 neurons and 80 neurons, respectively. A recurrent neural network model with 4 hidden layers, 60 neurons, and 2 hidden layers, 100 neurons predicted hybridized nanocomposites’ Young’s modulus and elongation at break with maximum R2 values. The mean absolute errors and minimum squared errors were evaluated to ensure the reliability of the machine learning algorithms. The models optimize hybridized nanocomposites’ mechanical properties, saving time and money during experimental characterization. Ultimate tensile strength ; Scanning electron microscope ; Nanocomposite ; Fiber ; Composite number ; Husk ; Modulus ; Shell (structure) Natural Fiber Reinforced Composites ; Coconut Research and Applications
Authors: P. Reddy | Nagaiah Kambhala
Source: ChemistrySelect (article, 2025-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Green Carbon Quantum Dots: State‐of‐the‐Art Strategies on Performance Enhancement of Supercapacitor Applications—A Review ABSTRACT Carbon quantum dots (CQDs) synthesized from green biomass and biowaste have swiftly found their place as sustainable materials for high‐performance supercapacitors due to their unique structural, optical, and electronic properties. In this regard, this review appraises cutting‐edge green synthesis techniques such as hydrothermal, microwave‐assisted, and sand bath reflux treatments, taken on from the viewpoint of scalability concerning energy consumption and yield and also that regarding CQD physicochemical properties. Specific capacitance values up to 808 F/g and energy densities approaching 78.6 Wh/kg have been recorded for an optimized synthesis and heteroatom doping approach, with cycling stability greater than 98% retention with 10,000 cycles. The synergistic integration of CQDs with a hybrid architecture of metal oxides, MXenes, and conductive polymers brings about an enhancement of electrical double‐layer capacitance and pseudocapacitance. The electrolyte compatibility is discussed throughout aqueous, nonaqueous, and ionic liquid systems, highlighting the common trade‐offs of ionic conductivity, voltage window, and long‐term stability. The comparative tables highlight some benchmarks related to recent literature for a more objective orientation regarding green CQD‐based electrode materials. Some trends that appear include growing use of artificial intelligence and machine learning to accelerate optimization of precursor‐dopant pairs, scaling‐up methods, and structural engineering toward tailoring electrochemical properties. In summary, this review gives the forefront challenges and opportunities with green CQD as materials of the next generation for green and sustainable energy storage devices. Supercapacitor ; Capacitance ; Electrochemical energy storage ; Ionic liquid ; Energy storage ; Battery (electricity) ; Electrolyte ; Renewable energy ; Heteroatom Supercapacitor Materials and Fabrication ; Carbon and Quantum Dots Applications ; Advancements in Battery Materials
Authors: Li, Lucas | Puel, Jean-Baptiste | Carton, Florence | Barrit, Dounya | Giraldo, Jhony H.
Source: arXiv (Cornell University) (preprint, 2025-11-24)
Field: Materials Science — Materials Chemistry

Solar-GECO: Perovskite Solar Cell Property Prediction with Geometric-Aware Co-Attention Perovskite solar cells are promising candidates for next-generation photovoltaics. However, their performance as multi-scale devices is determined by complex interactions between their constituent layers. This creates a vast combinatorial space of possible materials and device architectures, making the conventional experimental-based screening process slow and expensive. Machine learning models try to address this problem, but they only focus on individual material properties or neglect the important geometric information of the perovskite crystal. To address this problem, we propose to predict perovskite solar cell power conversion efficiency with a geometric-aware co-attention (Solar-GECO) model. Solar-GECO combines a geometric graph neural network (GNN) - that directly encodes the atomic structure of the perovskite absorber - with language model embeddings that process the textual strings representing the chemical compounds of the transport layers and other device components. Solar-GECO also integrates a co-attention module to capture intra-layer dependencies and inter-layer interactions, while a probabilistic regression head predicts both power conversion efficiency (PCE) and its associated uncertainty. Solar-GECO achieves state-of-the-art performance, significantly outperforming several baselines, reducing the mean absolute error (MAE) for PCE prediction from 3.066 to 2.936 compared to semantic GNN (the previous state-of-the-art model). Solar-GECO demonstrates that integrating geometric and textual information provides a more powerful and accurate framework for PCE prediction. Perovskite (structure) ; Probabilistic logic ; Energy conversion efficiency ; Process (computing) ; Property (philosophy) ; Convolutional neural network ; Focus (optics) ; Perovskite solar cell Machine Learning in Materials Science ; Perovskite Materials and Applications ; Data Visualization and Analytics
Authors: Yifan Zhang | Tao Ban | Juan Chen | Hongyi Gao | Jingjing Wang | Jitti Kasemchainan | Ping Yang | Ge Wang
Source: Chemical Engineering Journal (article, 2025-10-04)
Field: Chemistry — Inorganic Chemistry

Microenvironment modulation of metal-organic frameworks for catalytic hydrogenation reaction Catalysis ; Bimetallic strip ; Ligand (biochemistry) ; Heterogeneous catalysis ; Precious metal ; Reaction conditions ; Metal-organic framework ; Active site Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Ruixin Li | Qianlong Li | Xiaojie Zhang | Wei Zhang
Source: Journal of Chemical Information and Modeling (article, 2025-11-08)
Field: Chemistry — Organic Chemistry

A Graph-to-Sequence Method for C–H Activation Retrosynthesis C-H activation reactions are widely used in organic synthesis but pose significant challenges for retrosynthesis due to the high bond energy, chemical inertness, and complex regioselectivity of C-H bonds. Traditional models struggle to identify reactive centers and predict plausible disconnection paths in such systems. We propose TransGraphEdit, a template-free framework that combines a graph neural network (GNN) encoder and a Transformer decoder to model stepwise structural edits based on the product's molecular graph. The model captures long-range dependencies and synergistic effects between distant functional groups, enabling interpretable and mechanism-aware retrosynthesis predictions. To improve generalization on low-resource C-H activation reactions, we applied a SMILES augmentation strategy to expand the training space. TransGraphEdit achieves 58.06% Top-1 accuracy on a curated C-H Arylation data set and 53.8% on USPTO-50K, demonstrating its robustness and domain adaptability. Retrosynthetic analysis ; Robustness (evolution) ; Generalization ; Encoder ; Training set ; Domain (mathematical analysis) ; Artificial neural network Catalytic C–H Functionalization Methods ; Machine Learning in Materials Science ; Advanced Graph Neural Networks
Authors: Gabriel D. Barbosa (16735312) | Alberto Striolo (1609168)
Source: Figshare (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Critical Micellar ConcentrationUsing Electrostatic and Structural Properties as Descriptors Understanding and predicting surfactants’ critical micelle concentration (CMC) remains a key challenge for the rational design of functional amphiphiles. In this work, we develop a deep learning framework to predict CMCs using quantum chemically derived descriptors, focusing on electrostatic surface potential (ESP) and structural features. We employ a comprehensive temperature-dependent data set comprising over 1300 CMC values across diverse surfactant classes. Fourteen molecular descriptors are extracted via density functional theory (DFT) calculations and used as input, alongside temperature. A fully connected neural network trained on these features yields accurate predictions, achieving performance comparable to previously published graph-based models. To support model interpretability, we explicitly assessed ESP distributions for representative surfactants. SHapley Additive exPlanations (SHAP) and partial dependence analyses reveal that molecular volume, ESP variance, and solvation free energy are the dominant predictors, aligning with established thermodynamic theories. These results demonstrate that DFT-derived electrostatic and geometric descriptors can enable robust and interpretable CMC prediction, offering a physically grounded alternative to black-box models. The methodology and insights presented here may also inform the design of nanostructured soft materials, including surfactant-assisted platforms for hydrogen storage. Density functional theory ; Artificial neural network ; Solvation ; Set (abstract data type) ; Electrostatics ; Key (lock) ; Energy (signal processing) ; Molecular descriptor ; Rational design Machine Learning in Materials Science ; Supramolecular Self-Assembly in Materials ; Advanced Polymer Synthesis and Characterization
Authors: William C. Tucker | Lauren J. Abbott | Justin B. Haskins | Simran Kumari | Masanobu Uchimura | Balachandran GadaguntlaRadhakrishnan | Shigemasa Kuwata | Yasushi Ichikawa
Source: Computational Materials Science (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

High-throughput discovery of large barocaloric effects Weighting ; Entropy (arrow of time) ; Plastic crystal ; Random forest ; Feature (linguistics) ; Crystal (programming language) ; Phase transition Machine Learning in Materials Science ; Shape Memory Alloy Transformations ; Inorganic Chemistry and Materials
Authors: Yunyi Yan | Yaochen Yu | Junwei Hu | Xuecheng Sun | Qinlan Luo | Zengyong Chu | Jiayu Dai | Haiyang Niu
Source: Physical Chemistry Chemical Physics (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Atomistic simulation study of diamond doping based on machine learning potential Diamond is regarded as a highly promising semiconductor material due to its outstanding physical properties. However, achieving efficient p-type and n-type doping remains a significant challenge. In this work, we systematically investigate the B-O and B-S co-doping behaviors in diamond using molecular dynamics and Monte Carlo hybrid simulations based on machine learning potentials. The results indicate that the dopant ratio determines the formation type of impurity complexes, thereby governing the overall electronic properties of the material. Our simulations reveal that a B : O ratio of 4 : 1 leads to the exclusive formation of B<sub>4</sub>O complexes, which exhibit the lowest formation energy (- 1.92 eV) and a low ionization energy (0.11 eV), making them a promising co-doping strategy for high-quality p-type diamond. For B-S co-doping, a B : S ratio of 1 : 1 results in BS complexes with significantly reduced formation energy compared to S single-doping and a low ionization energy (0.56 eV), meeting the requirements for n-type diamond, while a B : S ratio of 4 : 1 is more suitable for p-type diamond. These findings emphasize the critical role of precise dopant ratio control in obtaining high-performance p/n-type diamond semiconductors and provide atomic-scale mechanistic insights for the controllable synthesis of co-doping diamond. Dopant ; Diamond ; Doping ; Semiconductor ; Impurity ; Ionization ; Monte Carlo method Diamond and Carbon-based Materials Research ; Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science
Authors: Mona Amrihesari (20421304) | Blair Brettmann (11883511)
Source: Figshare (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

IntroducingData-Driven Materials Informatics intoUndergraduate Courses through a Polymer Science Workshop With the rapid growth of artificial intelligence and machine learning across scientific disciplines from materials discovery to data-driven problem solving, there is increasing opportunity to integrate these tools into a broad range of applications. Successful adoption of these approaches in research can be enhanced by foundational exposure during undergraduate education. The objective of this study is to introduce fundamental machine learning concepts to undergraduate students through a hands-on, application-focused workshop during a polymer science and engineering course. Students were guided through key steps of the machine learning workflow, including data cleaning, model training, performance evaluation, and result interpretation, using a polymer solubility data set generated via visual inspection. The effectiveness of the workshop was assessed through pre- and postworkshop student surveys, which indicated a measurable improvement in students’ understanding and confidence in applying machine learning techniques. The integration of this workshop into a materials course introduces the students to the new concepts while extending the application of the course material. Informatics ; Set (abstract data type) ; Materials informatics ; Undergraduate research ; Engineering education ; Key (lock) ; Science and engineering Machine Learning in Materials Science ; Various Chemistry Research Topics ; Material Selection and Properties
Authors: Mudassir Hussain Tahir | Muhammad Sulaman | Mohammed A. Amin | Zaki I. Zaki | Mohamed Mohamed Soliman
Source: ChemistrySelect (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Data‐Driven Design of Polymers with High Melting Point: Chemical Space Generation and Property Prediction Abstract This work is presenting a new method utilizing machine learning to perform the designing of polymers. 10 machine learning models are developed to predict melting points, from which the best‐performing model is chosen for further investigation. An automated process generates 10,000 polymers, and their chemical space is visually explored to uncover underlying patterns. Synthetic accessibility assessment done to select the polymers. Structural characteristics of selected polymers are examined through cluster analysis. This research significantly advances polymer design techniques, providing valuable insights for the development of efficient polymers for various industrial applications. Process (computing) ; Chemical space ; Polymer ; Property (philosophy) ; Space (punctuation) ; Work (physics) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Organic Electronics and Photovoltaics
Authors: Jonas A. Finkler | Yan Lin | Tao Du | Jilin Hu | Morten M. Smedskjær
Source: ArXiv.org (preprint, 2025-09-17)
Field: Chemistry — Inorganic Chemistry

Inverse Design of Amorphous Materials with Targeted Properties Disordered (amorphous) materials, such as glasses, are emerging as promising candidates for applications within energy storage, nonlinear optics, and catalysis. Their lack of long-range order and complex short- and medium-range orderings, which depend on composition as well as thermal and pressure history, offer a vast materials design space. To this end, relying on machine learning methods instead of trial and error is promising, and among these, inverse design has emerged as a tool for discovering novel materials with desired properties. Although inverse design methods based on diffusion models have shown success for crystalline materials and molecules, similar methods targeting amorphous materials remain less developed, mainly because of the limited availability of large-scale datasets and the requirement for larger simulation cells. In this work, we propose and validate an inverse design method for amorphous materials, introducing AMDEN (Amorphous Material DEnoising Network), a diffusion model-based framework that generates structures of amorphous materials. These low-energy configurations are typically obtained through a thermal motion-driven random search-like process that cannot be replicated by standard denoising procedures. We therefore introduce an energy-based AMDEN variant that implements Hamiltonian Monte Carlo refinement for generating these relaxed structures. We further introduce several amorphous material datasets with diverse properties and compositions to evaluate our framework and support future development. Amorphous solid ; Inverse ; Monte Carlo method ; Thermal ; Inverse problem ; Design of experiments ; Diffusion Pigment Synthesis and Properties
Authors: Sergiy Plankovsky
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-22)
Field: Materials Science — Metals and Alloys

Methodology for conducting experimental studies of material properties during electrochemical treatment The experimental research methodology is presented, formed taking into account the requirements of modern international standards DIN SPEC 91399 and VDE SPEC 90028 V1.0 and adapted to the real conditions of industrial equipment. In contrast to the widespread internal instructions of enterprises, the proposed approach ensures the reproducibility of measurements, the traceability of parameters and the suitability of data for further use in digital models and machine learning algorithms. A structured database linking the chemical composition of materials, electrolyte characteristics, pulse current modes and experimentally determined dissolution rates forms the basis for systematizing knowledge about ECHO at a qualitatively new level. Traceability ; Dissolution ; Contrast (vision) ; Electrolyte ; Spec# ; Reproducibility ; Experimental data Hydrogen embrittlement and corrosion behaviors in metals ; Molten salt chemistry and electrochemical processes ; Electrodeposition and Electroless Coatings
Authors: Atefe Shahsavaripour | Mohammad Hossein Badiei | Leila Yousefi | Ahmad Kalhor
Source:  (article, 2025-05-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

GAN-Driven Image Generation for Metamaterial Absorbers Using Mean and Variance Encoding In this work, we introduce an efficient methodology in the generation of complex images within a deep learning framework that combines Variational Autoencoders with Generative Adversarial Networks. In this work, the proposed methodology is applied in electromagnetics for the design of wideband metamaterial absorbers. This greatly simplifies the design process by encoding key image properties into compact low-dimensional representations, and each image can be represented with four numerical values that guide the GAN to generate high-quality outputs. This approach is very suitable in applications that demand efficient, accurate image generation, like in antenna design, electromagnetic imaging, and metamaterial structures. The present method will be used in the encoding of structural parameters within the latent space via deep VAE architecture while creating new feature vectors through the utilization of GANs in allowing creative and optimized designs concerning metamaterials. Simulated structures decoded and validated in CST Microwave Studio showed excellent absorption in a wide frequency range and for different angles of incidence. Such simplification of the design provides highperformance absorbing materials that could be used in critical applications of radar and telecommunications. Our results underline the transformative potential of machine learning, especially GANs, for solving complex electromagnetic design problems, opening new frontiers in material design and applications of neural networks. Metamaterial ; Encoding (memory) ; Electromagnetics ; Process (computing) ; Key (lock) ; Feature (linguistics) ; Image (mathematics) ; Range (aeronautics) ; Feature vector Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Scattering and Analysis