jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A S Pawar | Sneha Desai | Prerana Belekar
Source: International Journal of Research and Innovation in Applied Science (article, 2025-01-01)
Field: Materials Science — Biomaterials

Advances in Ai-Driven Bioreactors for Optimized Polyhydroxybutyrate Production Polyhydroxybutyrate (PHB) is a biodegradable biopolymer with promising applications as a sustainable alternative to petroleum-based plastics. However, PHB production is often limited by fluctuating bioreactor conditions, requiring intensive manual control to maintain optimal growth environments for microbial fermentation. This short communication presents a conceptual framework for an AI-driven bioreactor system designed to autonomously monitor and regulate key parameters such as pH, temperature, aeration, and nutrient concentrations. Integrating real-time biosensors with machine learning algorithms, the proposed system enables dynamic optimization of microbial growth and PHB synthesis through feedback-controlled adjustments. This approach addresses the limitations of traditional static bioreactor systems by providing consistent, high-yield outputs with reduced human intervention. While this is a design-based proposal without experimental validation, it is grounded in current trends in bioengineering and artificial intelligence integration. A schematic of the AI-regulated system is included to illustrate its functional architecture. This work contributes a novel direction for future research in smart bioprocessing and scalable PHB production, emphasizing the role of AI in sustainable biomanufacturing. Polyhydroxybutyrate ; Bioreactor ; Production (economics) ; Biochemical engineering ; Process engineering ; Environmental science ; Biology ; Engineering ; Economics biodegradable polymer synthesis and properties
Authors: Quentin Duez (5578733) | Charlotte Lefebvre (21743942) | Julien De Winter (7272578) | Jérôme Cornil (1282404) | Pascal Gerbaux (9678)
Source: Figshare (article, 2025-07-18)
Field: Chemistry — Organic Chemistry

ClassifyingHost–Guest Topology with Ion Mobility-MassSpectrometry and Machine Learning Elucidating the topology of host–guest complexes is essential for the rational design of supramolecular assemblies. Building on the recent success of data-driven approaches, we evaluate the combination of ion mobility–mass spectrometry (IMS–MS), density functional theory (DFT) featurization, and machine learning to predict and classify the binding modes of 1:1 complexes formed between cucurbit[6]uril (CB6) and diamine guests. Training a regression model with DFT-derived molecular descriptors and experimentally determined collisional cross sections (CCS) enables predicting the CCS of host–guest complexes with a diverse set of diamine guests. The predicted values naturally separate in two distinct groups corresponding respectively to inclusion and exclusion complexes, thereby enabling topology classification. This approach demonstrates that DFT-featurization and IMS–MS data capture well host–guest topology and provide a framework for the data-driven design of supramolecular assemblies. Topology (electrical circuits) ; Set (abstract data type) ; Supramolecular chemistry ; Rational design ; Topological data analysis ; Training set Supramolecular Chemistry and Complexes ; Crystallography and molecular interactions ; Mass Spectrometry Techniques and Applications
Authors: Yufeng Zhou | Á. Valfells | Kristinn Torfason | Andrei Manolescu
Source:  (article, 2025-06-15)
Field: Materials Science — Materials Chemistry

Image Charge Approximations for CNT-Fiber Emitters in a Molecular Dynamics Code Molecular dynamics (MD) simulations are a powerful tool for capturing the discrete particle effects involved in electron emission and propagation. They have proven effective in studying how electron emission from microstructures influences beam characteristics, including emittance, current density, and brightness [1], [2]. However, the high computational cost of MD simulations remains a significant challenge. The MD code, RUMDEED, incorporates the image-charge method to evaluate the electric field modified by space-charge effects near the cathode surface. This can be done in an exact manner for specific geometries. By using simplified parametric calculations, the image-charge model can be applied to a broader range of emitter shapes, reducing the computational load while maintaining accuracy in capturing key physical effects. We use machine learning to enable faster and approximate predictions of the electric field near a nonplanar emitter surface. Results for different geometries of CNT-fiber emitters will be presented. Brightness ; Common emitter ; Range (aeronautics) ; Electric field ; Parametric statistics ; Electron ; Cathode ray ; Code (set theory) ; Computation Carbon Nanotubes in Composites ; Diamond and Carbon-based Materials Research ; Molecular Junctions and Nanostructures
Authors: Laiadi Abdelhamid | A. Chentouf | Mostafa Ezziyyani
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2025-05-26)
Field: Materials Science — Materials Chemistry

Random forest method for predicting discharge current waveform and mode of dielectric barrier discharges This study addresses the classification of Homogeneous and Filamentary discharge modes in dielectric barrier discharge (DBD) systems and predicts the Homogeneous current waveform using machine learning (ML). The motivation stems from the need for accurate modelling in non-thermal plasma systems. The problem tackled is distinguishing between these two modes and predicting the current waveform for Homogeneous discharge. A random forest classification algorithm is applied, using experimental features such as applied voltage, frequency, gas gap, dielectric material, and gas type. An exponential model is proposed for the discharge current, with Gaussian regression transforming the model’s parameters. The classification results are evaluated through a confusion matrix, showcasing 80% accuracy in distinguishing discharge modes. The regression analysis reveals strong Pearson correlation coefficients between predicted and experimental waveforms. In conclusion, the results demonstrate the efficacy of ML techniques in enhancing DBD system modelling, though improvements can be made by expanding the dataset and refining feature selection for better classification and prediction performance. Waveform ; Current (fluid) ; Dielectric barrier discharge ; Mode (computer interface) ; Partial discharge ; Materials science ; Random forest ; Acoustics ; Environmental science ; Electrical engineering ; Dielectric ; Optoelectronics ; Voltage ; Computer science ; Engineering ; Physics ; Artificial intelligence High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Advanced Sensor and Control Systems
Authors: Chloe B. Starkey | Saptarsi Mondal | Carlos R. Baiz
Source: The Journal of Chemical Physics (article, 2025-12-17)
Field: Chemistry — Spectroscopy

Machine learning potentials accurately reproduce vibrational dynamics in complex environments Vibrational spectroscopy provides a bond-specific view of molecular structure and dynamics, but translating spectroscopic observables to local environments requires the development of accurate models to compute spectroscopic observables from simulations. Recent advances in machine learning interatomic potentials (MLIPs) provide ab-initio-like accuracy at low computational cost, opening new opportunities for developing general, transferable models that can predict spectroscopic observables without system-specific parameterization. Here, we benchmark the performance of the Universal Model of Atoms (UMA), a recently developed MLIP, for predicting IR absorption spectra and picosecond frequency fluctuations of an ester carbonyl in a range of solvents. UMA results are compared with two established approaches: an empirical frequency map parameterized for the ester carbonyl and the semiempirical tight-binding method, GFN2-xTB. We find that UMA reproduces experimental observables with accuracy comparable to traditional methods, while offering broader generality and efficiency. Observable ; Benchmark (surveying) ; Parameterized complexity ; Generality ; Molecular dynamics ; Experimental data ; Range (aeronautics) ; Picosecond Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Machine Learning in Materials Science
Authors: Rohan Shorey | Elnaz Esmizadeh | Tizazu H. Mekonnen
Source: Progress in Polymer Science (article, 2025-12-11)
Field: Materials Science — Biomaterials

Progress in bioplastics blends, compatibilization, modifications, and AI-driven innovations for material applications Many bioplastics offer potential advantages over petroleum-based plastics, such as renewability, improved sustainability, and, in some cases, biodegradability or lower toxicity. However, in most cases, their limited mechanical performance, processing stability, or higher production costs hinder widespread adoption. Blending is a key strategy to overcome these limitations; however, the inherent immiscibility of most biopolymers leads to challenges like coarse morphology and poor interfacial adhesion. This review aims to provide an in-depth analysis of bioplastic blends by examining the fundamental principles (thermodynamic interactions, process kinematics, and morphology development) that control their behavior. It critically evaluates a broad spectrum of compatibilization strategies that span non-reactive and reactive methods and those utilizing nanoﬁllers, aimed at stabilizing blend microstructures and enhancing material performance. A novel aspect of this work is its integration of these material science concepts with important end-of-life considerations, including biodegradability and recyclability challenges. Furthermore, it highlights the transformative role of artiﬁcial intelligence (AI) and machine learning (ML) as novel instruments for accelerating the design and optimization of next-generation bioplastic formulations. Overall, this review concludes that unlocking the full potential of bioplastics for high-performance industrial applications necessitates a holistic approach that integrates tailored blending strategies with advanced computational design, thus paving the way for the realization of a circular bioeconomy. Bioplastic ; Process (computing) ; Compatibilization ; Work (physics) ; Key (lock) ; Biomimetics biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Nanocomposite Films for Food Packaging
Authors: Wenrou Yu (13911049) | Quanwei Lv (13911052) | Zhaoxian Chen (3217152) | Xiang Wang (127867) | Yuanyuan Liu (136992) | Li Wang (15202) | Yingzhou Huang (2300041)
Source: Figshare (article, 2025-10-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spatial Trackingof Water Pollutions Based on SERSSpectra with Machine Learning Water pollution detection technology is key to safeguarding the ecological security of river basins. However, traditional water pollution tracking methods have shown limitations with the development of industry and urbanization. Surface-enhanced Raman spectroscopy (SERS) has long been recognized as an optical method that can provide fingerprint-like chemical information at the single-molecule level. In this work, a new attention neural network (aNN) combined with random forest (RF) approach was constructed to be applied to accurately identify the SERS spectral signature of wastewater for spatial tracking of water pollutions. A total of 34,520 SERS spectra were collected from 96 combinations of three types of wastewater after data augmentation. The data set was used to train the aNN-RF model, with the identification accuracy of over 88% and predicted mixing ratios of wastewater types showed the same step-down trend as actual mixing ratios. As a proof-of-concept, a one-dimensional watershed scenario was simulated, and wastewater outlet and pollution pathway were identified by the SERS-aNN-RF platform. Results suggested that even in complex and unknown watersheds, the SERS-aNN-RF platform can accurately track pollutions, exhibiting excellent potential in spatial tracking of water pollutions. Wastewater ; Pollution ; Watershed ; Artificial neural network ; Tracking (education) ; Identification (biology) ; Water pollution ; Water quality Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Spectroscopy and Chemometric Analyses
Authors: Hongfu Huang | Junhao Peng | Kaiqi Li | Jian Zhou | Zhimei Sun
Source: Computer Physics Communications (article, 2025-12-13)
Field: Materials Science — Materials Chemistry

Efficient GPU-accelerated training of a neuroevolution potential with analytical gradients Transferability ; Convergence (economics) ; Training (meteorology) ; Inefficiency ; Limit (mathematics) ; Training set ; Observable Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Electron Microscopy Techniques and Applications
Authors: Adair, Christopher W
Source: ScholarsArchive  (Brigham Young University) (article, 2025-04-15)
Field: Materials Science — Materials Chemistry

Using Human Computation and Harmonic Expansions for Characterization and Design of Grain Boundary Networks Grain Boundary Networks (GBNs) are a high-dimensional feature in polycrystalline microstructural materials that are formed from the crystallographic degrees of freedom. This feature affects multiple material properties such as diffusivity, fracture, elasticity, heat transfer, and others. The link between the crystallography and the material properties is called the structure-property linkage, and allows for simulation and design of materials. While it is possible to use the structure-property linkage to design materials for better performance, the high-dimensional nature of the interconnected grain boundaries preclude the use of many common optimization algorithms for efficiency or local minima considerations. This work shows how the high-dimensional GBN can be understood and optimized using a combination of Human Computation and Harmonic Expansions. The GBN design problem is formulated as a "Game with a Purpose," "Operation: Forge the Deep." This was used to show how and when human players are more efficient and effective at solving the problem than a common global optimization algorithm, through a statistics driven experiment. To understand the differences in resulting GBN solutions, a Harmonic Expansion representation was created to quantitatively differentiate GBN configurations from each other. This representation shows new insights into the similarities and differences created directly from the long range connected nature of GBNs, and describes differences in material properties not seen before with local GBN measures such as Triple Junction Fractions. Finally, the player decisions collected from "Operation: Forge the Deep" were used to generate a GBN Decision Transformer; a machine learning model that predicts optimization decisions like players. This model was able to generate good optimization solutions for GBNs when sufficient training data was available, but was unable to extrapolate to larger structures. However, this model was also capable of inference on multiple different structure-property models, even when only trained on a single model type. This allows for training on one physics model, but use on many different material property predictions. In total, this work helped expand optimization and characterization of long-range GBN structures for use in structure-property linkages. Maxima and minima ; Representation (politics) ; Computation ; Feature (linguistics) ; Range (aeronautics) ; Grain boundary ; Boundary (topology) ; Harmonic ; Work (physics) Machine Learning in Materials Science ; Composite Material Mechanics ; Topology Optimization in Engineering
Authors: Xiaoyan Sun | Jiankun Fu | Hao Min | Madeleine Bussemaker | Jiang Jun-cheng | Ru Zhou | Lian X. Liu
Source: Polymer Degradation and Stability (article, 2025-12-04)
Field: Materials Science — Polymers and Plastics

Machine learning-guided optimization of flame retardancy in rigid polyurethane foams Polyurethane ; Fire retardant ; Curing (chemistry) ; Limiting oxygen index ; Curse of dimensionality ; Polymer ; Limiting ; Thresholding Flame retardant materials and properties ; Fire dynamics and safety research ; Polymer composites and self-healing
Authors: Dylan Stewart Barker | Adam Sweetman
Source: Beilstein Journal of Nanotechnology (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Automated collection and categorisation of STM images and STS spectra with and without machine learning Atomic resolution scanning probe microscopy, and in particular scanning tunnelling microscopy (STM) allows for high-spatial-resolution imaging and also spectroscopic analysis of small organic molecules. However, preparation and characterisation of the probe apex in situ by a human operator is one of the major barriers to high-throughput experimentation and to reproducibility between experiments. Characterisation of the probe apex is usually accomplished via assessment of the imaging quality on the target molecule and also the characteristics of the scanning tunnelling spectra (STS) on clean metal surfaces. Critically for spectroscopic experiments, assessment of the spatial resolution of the image is not sufficient to ensure a high-quality tip for spectroscopic measurements. The ability to automate this process is a key aim in development of high resolution scanning probe materials characterisation. In this paper, we assess the feasibility of automating the assessment of imaging quality, and spectroscopic tip quality, via both machine learning (ML) and deterministic methods (DM) using a prototypical tin phthalocyanine on Au(111) system at 4.7 K. We find that both ML and DM are able to classify images and spectra with high accuracy, with only a small amount of prior surface knowledge. We highlight the practical advantage of DM not requiring large training datasets to implement on new systems and demonstrate a proof-of-principle automated experiment that is able to repeatedly prepare the tip, identify molecules of interest, and perform site-specific STS experiments using DM, in order to produce large numbers of spectra with different tips suitable for statistical analysis. Deterministic methods can be easily implemented to classify the imaging and spectroscopic quality of a STM tip for the purposes of high-resolution STM and STS on small organic molecules. Via automated classification of the tip state, we demonstrate an automated experiment that can collect a high number of spectra on multiple molecules without human intervention. The technique can be easily extended to most metal-adsorbate systems and is promising for the development of automated, high-throughput, STM characterisation of small adsorbate systems. Computer science ; Artificial intelligence ; Spectral line ; Nanotechnology ; Pattern recognition (psychology) ; Human–computer interaction ; Materials science ; Physics Machine Learning in Materials Science ; Mineral Processing and Grinding ; Neural Networks and Applications
Authors: Xuhui Chen | Fei Hou | Wen-Cheng Wang | Hong Qin | Ying He
Source: ArXiv.org (preprint, 2025-06-03)
Field: Materials Science — Materials Chemistry

MIND: Material Interface Generation from UDFs for Non-Manifold Surface Reconstruction Unsigned distance fields (UDFs) are widely used in 3D deep learning due to their ability to represent shapes with arbitrary topology. While prior work has largely focused on learning UDFs from point clouds or multi-view images, extracting meshes from UDFs remains challenging, as the learned fields rarely attain exact zero distances. A common workaround is to reconstruct signed distance fields (SDFs) locally from UDFs to enable surface extraction via Marching Cubes. However, this often introduces topological artifacts such as holes or spurious components. Moreover, local SDFs are inherently incapable of representing non-manifold geometry, leading to complete failure in such cases. To address this gap, we propose MIND (Material Interface from Non-manifold Distance fields), a novel algorithm for generating material interfaces directly from UDFs, enabling non-manifold mesh extraction from a global perspective. The core of our method lies in deriving a meaningful spatial partitioning from the UDF, where the target surface emerges as the interface between distinct regions. We begin by computing a two-signed local field to distinguish the two sides of manifold patches, and then extend this to a multi-labeled global field capable of separating all sides of a non-manifold structure. By combining this multi-labeled field with the input UDF, we construct material interfaces that support non-manifold mesh extraction via a multi-labeled Marching Cubes algorithm. Extensive experiments on UDFs generated from diverse data sources, including point cloud reconstruction, multi-view reconstruction, and medial axis transforms, demonstrate that our approach robustly handles complex non-manifold surfaces and significantly outperforms existing methods. The source code is available at https://github.com/jjjkkyz/MIND. Polygon mesh ; Point cloud ; Workaround ; Surface (topology) ; Field (mathematics) ; Spurious relationship ; Surface reconstruction ; Point (geometry) ; Distance transform ; Marching cubes Diamond and Carbon-based Materials Research ; Laser-induced spectroscopy and plasma ; Plasma Diagnostics and Applications
Authors: Peilin Kang | Jintu Zhang | Enrico Trizio | Tingjun Hou | Michele Parrinello
Source: ArXiv.org (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

Committors without Descriptors The study of rare events is one of the major challenges in atomistic simulations, and several enhanced sampling methods towards its solution have been proposed. Recently, it has been suggested that the use of the committor, which provides a precise formal description of rare events, could be of use in this context. We have recently followed up on this suggestion and proposed a committor-based method that promotes frequent transitions between the metastable states of the system and allows extensive sampling of the process transition state ensemble. One of the strengths of our approach is being self-consistent and semi-automatic, exploiting a variational criterion to iteratively optimize a neural-network-based parametrization of the committor, which uses a set of physical descriptors as input. Here, we further automate this procedure by combining our previous method with the expressive power of graph neural networks, which can directly process atomic coordinates rather than descriptors. Besides applications on benchmark systems, we highlight the advantages of a graph-based approach in describing the role of solvent molecules in systems, such as ion pair dissociation or ligand binding. Dissociation (chemistry) ; Parametrization (atmospheric modeling) ; Benchmark (surveying) ; Rare events ; Metastability ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks