jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eduardo Pedraza | Andrés R. Tejedor | Alejandro Feito | Francisco Gámez | Rosana Collepardo‐Guevara | Eduardo Sanz | Jorge R. Espinosa
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-14)
Field: Chemistry — Physical and Theoretical Chemistry

Predicting Saturation Concentrations of Phase-Separating Proteins via Thermodynamic Integration Phase separation of proteins and nucleic acids into biomolecular condensates contributes to the regulation of cellular compartmentalisation in membrane-less environments. A key parameter controlling the onset of biomolecular condensate formation via liquid—liquid phase separation is the saturation concentration ( C sat )— the threshold concentration above which condensation takes place. While measuring C sat for protein solutions in vitro is experimentally accessible, determining this quantity in simulations remains challenging due to the extremely low equilibrium concentrations at which many proteins phase separate. This occurs because the gold standard in simulations consists on combining a residue-resolution coarse-grained model with the Direct Coexistence simulation method, which yields poor estimates of the equilibrium concentrations of the dilute phase due to lack of statistics. In this work, we present two independent thermodynamic integration (TI) schemes which, when combined with Direct Coexistence simulations, enable accurate calculation of saturation concentrations and phase diagrams—facilitating direct comparison with experimental measurements across a wide range of conditions. Our methods, combined with the Mpipi-Recharged residue-resolution coarse-grained model, accurately estimate C sat for a wide range of intrinsically disordered and multi-domain proteins, including disease-associated RNA- and DNA-binding proteins involved in the formation of stress granules and P granules, as well as engineered mutants of hnRNPA1. Furthermore, we compare our TI methods against a computationally efficient machine-learning predictor trained to estimate saturation concentrations at sub-physiological temperatures. While both approaches yield realistic predictions, explicit molecular dynamics simulations enable the calculation of complete phase diagrams and provide insight into the molecular mechanisms and interactions driving phase-separation. Overall, our approach offers a robust, physically grounded framework for improving and validating coarse-grained models of biomolecular phase behaviour, effectively bridging the gap between simulation and experiment. Saturation (graph theory) ; Phase (matter) ; Thermodynamics ; Chemistry ; Materials science ; Biological system ; Statistical physics ; Physics ; Mathematics ; Biology thermodynamics and calorimetric analyses ; nanoparticles nucleation surface interactions ; Proteins in Food Systems
Authors: Shuo Liu | Xiuguo Chen | Shiyuan Liu
Source: Optics Express (article, 2025-11-20)
Field: Materials Science — Surfaces, Coatings and Films

TFSolver: a numerical Python toolkit for parallel electromagnetic calculation of planar multilayer thin films at multi-wavelength and multi-angle TFSolver is a Python toolkit for the electromagnetic calculation of planar isotropic and anisotropic multilayer thin films. This toolkit uses the PyTorch library to implement the 4 × 4 matrix method. One of the key advantages of TFSolver is its ability to perform parallel simulations for multilayer thin-film stacks across a broad spectral range and various incident angles, significantly speeding up the simulation process. Additionally, it supports GPU acceleration, further improving computational efficiency. Another notable feature is its support for automatic differentiation, which enables gradient-based optimization tasks. Furthermore, TFSolver can be integrated with deep learning algorithms, supporting emerging applications such as physics-guided inverse design. Here, we first introduce the formulation and the usage of TFSolver, compare it with existing toolkits and software, and validate its computational accuracy and efficiency. With its accelerated computational performance and automatic differentiation, TFSolver is a valuable toolkit for the characterization and design of various isotropic and anisotropic materials and multilayer thin-film devices. Python (programming language) ; Planar ; Isotropy ; Anisotropy ; Thin film ; Inverse Optical Coatings and Gratings ; Metamaterials and Metasurfaces Applications ; Electromagnetic Scattering and Analysis
Authors: Ariana Beste | Daniel Charles Bufford | Dan Bolintineanu | Kevin Potter
Source: Propellants Explosives Pyrotechnics (article, 2025-01-02)
Field: Chemistry — Analytical Chemistry

Automated Scoring of Morphological Changes in Images of Pentaerythritol Tetranitrate ABSTRACT Recent advances in characterization techniques that generate large datasets of material microstructure images require robust, automated image‐processing. We applied an unsupervised anomaly detection method called feature anomaly detection system (FADS) to automatically detect and quantify microstructure changes in images of the explosive pentaerythritol tetranitrate (PETN) aged at various temperatures. We demonstrated the FADS approach on two‐dimensional images extracted from computed tomography scans, but the same technique can be readily applied to other imaging modalities. FADS calculates anomaly scores on the basis of differences in filter activations of nominal and test data in pretrained convolutional neural networks. The FADS scores successfully differentiated between pristine PETN and PETN aged at a temperature where material coarsening occurred. Morphological metric analysis of segmented images verified observed trends in FADS scores as a function of aging temperature and aging time, specifically by calculating volume fractions, specific boundary lengths, two‐point correlation functions, and local thicknesses. The FADS technique has two important advantages compared to traditional morphological analysis: First, it uses grayscale images as input, rather than images that are segmented to separate the appropriate phases; and second, FADS scores capture any type of changes among image sets, rather than requiring prior knowledge or selection of a relevant set of metrics. Pentaerythritol tetranitrate ; Pentaerythritol ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Computer science ; Chemistry ; Explosive material ; Composite material ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Cell Image Analysis Techniques
Authors: Markus Schumann | Jonas Moske | Antonia Wüst | Kristian Kersting | Peter Groche
Source: Research Square (preprint, 2025-11-21)
Field: Materials Science — Materials Chemistry

Requirements for numeric models as sources of synthetic data for predicting real-world data sets Synthetic data ; Process (computing) ; Transferability ; Field (mathematics) ; SIGNAL (programming language) ; Feature (linguistics) ; Transfer of learning ; Interpretation (philosophy) ; Experimental data ; Domain (mathematical analysis) Machine Learning in Materials Science ; Metal Forming Simulation Techniques ; Metallurgy and Material Forming
Authors: Gregor Maier | Jan Hamaekers | Benedikt Ziebarth
Source: Computational Materials Science (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Multitask learning via task embeddings for glass property prediction with improved sample efficiency Glassy materials are prevalent in many modern-day applications, which place ever new demands on the materials' properties. However, because of their amorphous nature, the design of glasses with specific properties is inherently difficult and necessarily data-driven. Due to the large size of the compositional space and the cost of the manufacturing process, glass data is only sparsely available. It is thus important to design and train models which maximize the benefit of each individual training sample on the models' resulting prediction quality. In this paper, we study a multitask learning approach to meet this challenge. We analyze its effect on the sample efficiency, that is, the dependence of the test error on the training set size of a neural network model for predicting properties of oxide glasses. In contrast to existing encoder-based multitask learning models in computational glass science, we propose a decoder-based model, consisting of task embeddings in the input layer and a subsequent shared network architecture. This allows us to apply well-established single-task models in a multitask learning setting. We demonstrate in a series of numerical experiments, predicting the Young's modulus and the log-viscosity, respectively, that this approach notably improves the model's sample efficiency. To deal with parametric target properties, we complement the task embeddings by a parameter embedding and show that this preserves the advantageous multitask learning effect. In all experiments, we observe an algebraic scaling law for the decay of the test error with respect to the number of training samples. Embedding ; Artificial neural network ; Multi-task learning ; Task (project management) ; Complement (music) ; Property (philosophy) ; Set (abstract data type) ; Parametric statistics ; Sample (material) ; Generalization Phase-change materials and chalcogenides ; Material Dynamics and Properties ; Machine Learning in Materials Science
Authors: Miguel Yescas | Ghassen Ben Salem | Stéphane Marie
Source:  (article, 2025-07-20)
Field: Materials Science — Materials Chemistry

Weldability of a Microalloyed Carbon-Manganese Steel for Nuclear Power Plants: The Hardness Criterion and Fracture Toughness on the HAZ Abstract Microalloyed carbon-manganese steels are utilized in specific applications within the energy industry where stringent mechanical properties are essential to endure service conditions, including temperature and loadings. However, it has been observed that under certain welding parameters, particularly in the low range, the hardness in the heat affected zone (HAZ) near the fusion line can hardly comply with the criterion of 320HV imposed after post-weld heat treatment (PWHT) by the applicable specifications. This is particularly true in the parent metal HAZ induced by the last weld beads of the weld joint if the microalloying elements such as V and Nb approach the upper-bound wt.% contents. This paper deals with the weldability of a microalloyed carbon-manganese steel from the hardness and fracture toughness stand points. This work also proposes a method for predicting the maximum hardness level in the HAZ after PWHT by using an artificial neural networks (ANN) technique. It is demonstrated that despite the presence of local hardness values in the HAZ higher than the hardness requirement imposed by the applicable specification (ISO 15614-1), the component design requirements for fracture toughness level and ductile initiation behavior at the operating temperatures are not compromised. Finally, this work highlights some inconsistencies between the applicable specification and the use of modern micro-alloyed carbon-manganese steels. Weldability ; Welding ; Fracture toughness ; Heat-affected zone ; Toughness ; Fracture (geology) ; Indentation hardness Material Properties and Failure Mechanisms ; Microstructure and Mechanical Properties of Steels ; Welding Techniques and Residual Stresses
Authors: Ziao Tian | Zengzhe Xi | Pengfei Ma | Zhe Guo | Taolin Liu | Tong Zhou | Tian Jiang | Xuan Li
Source:  (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Silicon-based dual-channel optical switches based on GSST phase change materials The deep coupling of optical switches, optical computing, and AI is reshaping the arithmetic infrastructure landscape. Optical switches, as the dynamic reconfiguration core of optical computing systems, realize low-power optical path switching through non-volatile phase-change materials. It can meet the challenging needs of AI for low power consumption, high parallelism and high speed. In this paper, we propose a 1 × 2 reconfigurable optical switch with heterogeneous integration on silicon substrate, which consists of a silicon strip waveguide and a GSST-Bragg (Ge<sub>2</sub>Sb<sub>2</sub>Se<sub>4</sub>Te<sub>1</sub> ) grating waveguide. The results show that the extinction ratio of two-channel switching can reach more than 21dB; for the straight-through and branch ports, the losses at the center wavelength are about 0.24dB and 0.66dB, which are superior to the traditional electro-optical modulation schemes. Combined with a photonic neural network architecture, the optical switch can support dynamic configuration of a wavelength routing matrix. Optical switch ; Control reconfiguration ; Silicon photonics ; Waveguide ; Optical power ; Routing (electronic design automation) ; Optical performance monitoring ; Optical burst switching ; Grating ; Coupling (piping) Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Nonlinear Optical Materials Research
Authors: Yushi Deng | Mario R. Eden | Selen Cremaschi
Source: Computers & Chemical Engineering (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Metrics for evaluating stochastic outputs in machine learning models: Addressing accuracy and uncertainty Metric (unit) ; Process (computing) ; Gaussian process ; Uncertainty analysis ; Uncertainty quantification ; Performance metric ; Measurement uncertainty ; Stochastic modelling Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Sebastiano De Stefano (17402301) | Ofelia Durante (17402292) | Andrea Sessa (22126697) | Antonio Politano (1401358) | Gianluca D’Olimpio (9121042) | Tsotne Dadiani (22126700) | Enver Faella (17402304) | Adrian Dinescu (1731883) | Catalin Parvulescu (22126703) | Crispin Hetherington (10130254) | Chia-Nung Kuo (9121045) | Chin Shan Lue (9121048) | Martino Aldrigo (22126706) | Maurizio Passacantando (814175) | Antonio Di Bartolomeo (3166725)
Source: Figshare (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

NeuromorphicPhotoresponse in Ultrathin SnS<sub>2</sub>‑Based Field EffectTransistor As artificial intelligence continues to evolve, neuromorphic technologies, which emulate biological neural networks, are increasingly seen as a promising direction. Two-dimensional materials are considered promising for neuromorphic applications due to their tunable electrical and optoelectronic properties. In this work, a back-gated tin disulfide (SnS<sub>2</sub>) field-effect transistor (FET) is electrically and optoelectronically characterized at different temperatures (80, 295, and 380 K), pressures (ambient and 10<sup>–4</sup> mbar), and illumination conditions (dark and laser light from 420 to 800 nm). Responsivity peaks of up to ∼100 A/W are recorded. Persistent photoconductivity is observed, with current retention after illumination ranging from 0% to ∼30% of the initial dark current, depending on temperature and gate voltage. The underlying microscopic mechanisms are analyzed, revealing a key role for trap states and ambient adsorbates, and a qualitative model is proposed to explain the observed effects. Trap states within the bandgap, often considered detrimental, are exploited to induce synaptic plasticity, with synaptic weight changes tunable from 0.001 to 3000. Temperature and gate voltage are found to be effective parameters for modulating plasticity, enabling smooth transitions between short-term and long-term behavior. These results clarify the microscopic origin of plasticity in SnS<sub>2</sub>, demonstrate its robustness under realistic conditions, and lay the foundation for the integration of this two-dimensional material into next-generation neuromorphic architectures. Neuromorphic engineering ; Transistor ; Responsivity ; Synaptic weight ; Photoconductivity ; Field-effect transistor ; Artificial neural network ; Robustness (evolution) ; Voltage 2D Materials and Applications ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: J. G. Duarte | A. Di Leva | R. Buompane | A. Formicola | J. T. Harke | D. Rapagnani | C. Santonastaso | L. Gialanella
Source: The European Physical Journal A (article, 2025-01-25)
Field: Chemistry — Spectroscopy

A new way to unravel the $$^{12}$$C($$\alpha $$,$$\gamma $$)$$^{16}$$O cross section components using neural networks Section (typography) ; Alpha (finance) ; Artificial neural network ; Cross section (physics) ; Computer science ; Artificial intelligence ; Mathematics ; Physics ; Statistics ; Astronomy ; Operating system Advanced NMR Techniques and Applications ; Medical Imaging Techniques and Applications ; Nuclear physics research studies
Authors: Kamogelo M Mmereke | Adewale O. Oladipo | Tracy Masebe | Fulufhelo J Nemavhola | Thanyani Pandelani
Source: Computational and Structural Biotechnology Journal (review, 2025-01-01)
Field: Materials Science — Biomaterials

Computational fluid dynamics modeling and simulation of nanoparticle-tumor interaction: Systematic literature review Cancer therapy mediated by nanoparticles is gaining recognition for shifting the paradigm of targeted and/or personalized cancer therapy. Despite the great promise, only a few nanoformulations have been clinically approved due to the complexities that limit effective and efficient nanodrug development. Moreover, the preparation of cancer nanodrug has not yet been optimized for clinical approval in patient treatment. Computational fluid dynamics (CFD) is a new technique that simulates and analyzes fluid flows and their interactions with surfaces using computer algorithms and numerical analysis. This simulation and modeling tool provides a distinct advantage in understanding tumor-host mechano-biology and mechanisms that help identify the main factors affecting the transport of tumor-targeting nanoagents. Taking these factors into consideration, the advent of computational fluid dynamics simulation and modeling represents a shift in the optimization of cancer nanoagents' fluidics. This review briefly introduces the fluid mechanism along with its principles and foundations relating to cancer drug delivery. Key components of tumor microenvironments relating to temperature, flow velocity, fluid pressure, and tumor rheology, as well as physicochemical properties of nanoparticles modulating fluid mechanics, were discussed. It also includes a thorough examination of the advantages and challenges of using nanoformulations such as liposomes, polymers, and extracellular matrix in exploring the progress made in computational fluid dynamics simulation to study the mechanism of nanoparticle delivery and interactions with cancerous tumors. The convergence of Machine Learning algorithms and CFD simulation in tumor-nanodrug interactions. The application of ML algorithms provides high predictive accuracy of nanodrug delivery that can benefit cancer biomedicine research by predicting how flow affects drug efficacy. The future of the ML-CFD is detailed to include imaging and 3D-CFD simulations to increase the credibility of these models and advancement to translational clinical research. This review concluded by urging collaborative efforts for a multiscale approach by biomedical engineers and scientists, as well as oncologists, to develop a modeling framework that advances precision medical care for effective cancer treatment. Standardization of the model and approaches, together with nanoparticle synthesis, is recommended to advance this research to the translational and clinical stage. Computational fluid dynamics ; Biomedicine ; Fluid dynamics ; Mechanism (biology) ; Modeling and simulation ; Cancer ; Fluid simulation ; Convergence (economics) Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Micro and Nano Robotics
Authors: Rafał
A. Bachorz (21511618) | Joanna Pastwińska (21511621) | Michael S. Lawless (21511624) | David W. Miller (21511627) | Anna Sałkowska (6178073) | Kaja Karaś (21511630) | Iwona Karwaciak (21511633) | Jeremy O. Jones (773341) | Marcin Ratajewski (6178079)
Source: Figshare (article, 2025-06-09)
Field: Chemistry — Organic Chemistry

Identificationof a Novel Indolizine RORγT InverseAgonist Using the AI-Driven Drug Design Platform Automated multiparameter optimization (MPO) at the point of initial drug design is a powerful emerging approach to improve and expedite drug development. We employed the AI-driven drug design (AIDD) platform to design novel RORγT ligands optimized using QSAR activity models, machine learning ADMET properties, 3D pharmacophore similarity, and synthetic difficulty predictions. We calculated several measures of novelty postdesign and then employed multicriteria decision analysis (MCDA) to select compounds for synthesis for this important drug target. We found that 19/27 (70%) of the selected compounds inhibited RORγT activity in a cell-based assay by at least 25% at 20 μM. The most potent compound had a measured IC<sub>50</sub> of 1.51 μM (the predicted IC<sub>50</sub> was 1.29 μM) and demonstrated activity in human T cells. The logP, thermodynamic solubility, liver microsome clearance, fraction unbound in plasma, and MDCK permeability of this compound were measured <i>in vitro</i> and shown to be close to or better, e.g., higher <i>in vitro</i> solubility, than the values predicted by ADMET Predictor. This compound has an indolizine scaffold that has not yet been reported in the context of the RORγT receptor, demonstrating the power of MPO in the earliest stages of drug design to create novel, active molecules already possessing ADMET properties suitable for advanced lead optimization. Pharmacophore ; Drug ; Context (archaeology) ; Indolizine ; Quantitative structure–activity relationship ; Drug discovery ; Lipinski's rule of five ; Identification (biology) Synthesis and Reactivity of Heterocycles ; Computational Drug Discovery Methods ; Synthesis and bioactivity of alkaloids
Authors: A. L. Zhiznyakov | Yaroslav Kulkov | V. A. Emel’yanov
Source:  (article, 2025-03-24)
Field: Materials Science — Surfaces, Coatings and Films

Recognition Algorithm of Carbon Chains in Nanoparticle Assemblies from Electron Microscopy Images This paper presents the results of the development and application of neural networks for the automated analysis of images obtained from an electron microscope to recognize gold nanoparticles and carbyne chains. The YOLOv4 architecture, trained on 5 annotated images, was used to solve the nanoparticle detection task. The model demonstrated stable results with a loss function of 1.6, indicating its high accuracy in object localization. To detect connections between particles, the YOLOv8 architecture was applied, trained on 13 images with polygonal masks. The model exhibited high efficiency in segmenting connections, as confirmed by the Precision, Recall, and F1-score metrics. However, difficulties were encountered in detecting small particles and connections under low contrast conditions, requiring further optimization. Testing on test data confirmed the models' functionality, although cases of false positives and missed detections were noted. Future work suggests employing more advanced architectures, such as YOLO11, as well as increasing the volume and diversity of training data to enhance model accuracy and robustness. The developed solutions can be used to accelerate the analysis of nanostructures in materials science, nanoengineering, and other scientific fields. Electron microscope ; Carbon Nanoparticles ; Nanoparticle ; Microscopy ; Materials science ; Carbon fibers ; Computer science ; Electron ; Nanotechnology ; Algorithm ; Artificial intelligence ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques ; Carbon Nanotubes in Composites ; Machine Learning in Materials Science