jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Min Zhao | Xuanhua Yan | Jitao Sun | Jing Wu
Source: Analytica Chimica Acta (article, 2025-10-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molecular enrichment integrated with soft voting for efficient SERS detection of multiple drugs Analyte ; Voting ; Classifier (UML) ; Support vector machine ; Detection limit ; Fingerprint (computing) ; Substrate (aquarium) ; Pattern recognition (psychology) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Igor O. Sokolov | Gert-Jan Both | Art D. Bochevarov | Pavel A. Dub | Daniel S. Levine | Christopher T. Brown | Shaheen Acheche | Panagiotis Kl. Barkoutsos | Vincent E. Elfving
Source: Physical review. A/Physical review, A (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Quantum-enhanced neural exchange-correlation functionals Kohn-Sham density functional theory (KS-DFT) provides the exact ground-state energy and electron density of a molecule, contingent on the as-yet-unknown universal exchange-correlation (XC) functional. Recent research has demonstrated that neural networks can efficiently learn to represent approximations to that functional, offering accurate generalizations to molecules not present during the training process. With the latest advancements in quantum-enhanced machine learning (ML), evidence is growing that quantum neural network (QNN) models may offer advantages in ML applications. In this work, we explore the use of QNNs for representing XC functionals, enhancing and comparing them to classical ML techniques. We present QNNs based on differentiable quantum circuits as quantum (hybrid) models for XC in KS-DFT, implemented across various architectures. We assess their performance on one-dimensional (1D) and 3D systems. To that end, we expand existing differentiable KS-DFT frameworks and propose strategies for efficient training of such functionals, highlighting the importance of fractional orbital occupation for accurate results. We prove that, under specific assumptions, noise-induced errors do not accumulate across SCF iterations, and we confirm this result through numerical experiments. Our best QNN-based XC functional yields energy profiles of the <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:msub> <a:mi mathvariant="normal">H</a:mi> <a:mn>2</a:mn> </a:msub> </a:math> and planar <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"> <c:msub> <c:mi mathvariant="normal">H</c:mi> <c:mn>4</c:mn> </c:msub> </c:math> molecules that deviate by no more than 1 mHa from the reference DMRG and FCI/6-31G results, respectively. Moreover, they reach chemical precision on a system, <e:math xmlns:e="http://www.w3.org/1998/Math/MathML"> <e:mrow> <e:msub> <e:mi mathvariant="normal">H</e:mi> <e:mn>2</e:mn> </e:msub> <e:msub> <e:mi mathvariant="normal">H</e:mi> <e:mn>2</e:mn> </e:msub> </e:mrow> </e:math> , not present in the training dataset, using only a few variational parameters. This work lays the foundation for the integration of quantum models in KS-DFT, thereby opening new avenues for expressing XC functionals in a differentiable way and facilitating computations of various properties. Differentiable function ; Artificial neural network ; Quantum ; Energy (signal processing) ; Density functional theory ; Generalization ; Deep neural networks Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Aanish Paruchuri | Carl J. Thrasher | A. John Hart | Robert Macfarlane | Arthi Jayaraman
Source: arXiv (Cornell University) (preprint, 2025-01-07)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning for Identifying Grain Boundaries in Scanning Electron Microscopy (SEM) Images of Nanoparticle Superlattices Nanoparticle superlattices consisting of ordered arrangements of nanoparticles exhibit unique optical, magnetic, and electronic properties arising from nanoparticle characteristics as well as their collective behaviors. Understanding how processing conditions influence the nanoscale arrangement and microstructure is critical for engineering materials with desired macroscopic properties. Microstructural features such as grain boundaries, lattice defects, and pores significantly affect these properties but are challenging to quantify using traditional manual analyses as they are labor-intensive and prone to errors. In this work, we present a machine learning workflow for automating grain segmentation in scanning electron microscopy (SEM) images of nanoparticle superlattices. This workflow integrates signal processing techniques, such as Radon transforms, with unsupervised learning methods like agglomerative hierarchical clustering to identify and segment grains without requiring manually annotated data. In the workflow we transform the raw pixel data into explainable numerical representation of superlattice orientations for clustering. Benchmarking results demonstrate the workflow's robustness against noisy images and edge cases, with a processing speed of four images per minute on standard computational hardware. This efficiency makes the workflow scalable to large datasets and makes it a valuable tool for integrating data-driven models into decision-making processes for material design and analysis. For example, one can use this workflow to quantify grain size distributions at varying processing conditions like temperature and pressure and using that knowledge adjust processing conditions to achieve desired superlattice orientations and grain sizes. Superlattice ; Scanning electron microscope ; Materials science ; Nanoparticle ; Grain boundary ; Electron microscope ; Nanotechnology ; Microscopy ; Optics ; Metallurgy ; Composite material ; Physics ; Optoelectronics ; Microstructure Electron and X-Ray Spectroscopy Techniques
Authors: Rik S. Breebaart | Gianmarco Lazzeri | Roberto Covino | Peter G. Bolhuis
Source: ArXiv.org (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

Understanding Reaction Mechanisms from Start to Finish Understanding mechanisms of rare but important events in complex molecular systems, such as protein folding or ligand (un)binding, requires accurately mapping transition paths from an initial to a final state. The committor is the ideal reaction coordinate for this purpose, but calculating it for high-dimensional, nonlinear systems has long been considered intractable. Here, we introduce an iterative path sampling strategy for computing the committor function for systems with high free energy barriers. We start with an initial guess to define isocommittor interfaces for transition interface sampling. The resulting path ensemble is then reweighted and used to train a neural network, yielding a more accurate committor model. This process is repeated until convergence, effectively solving the long-standing circular problem in enhanced sampling where a good reaction coordinate is needed to generate efficient sampling, and vice-versa. The final, converged committor model can be interrogated to extract mechanistic insights. We demonstrate the power of our method on a benchmark 2D potential and a more complex host-guest (un)binding process in explicit solvent. Reaction coordinate ; Process (computing) ; Path (computing) ; Nonlinear system ; Benchmark (surveying) ; Folding (DSP implementation) ; Sampling (signal processing) ; Ideal (ethics) ; Iterative and incremental development Machine Learning in Materials Science
Authors: Li Ma | Ming Ding | Yu Zhang | Tao Huang | Zhen-Feng Tong | Lu Sun
Source: Annals of Nuclear Energy (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Predicting swelling in austenitic steels under PWR environments using machine learning Swelling ; Austenite ; Generalization ; Nonlinear system ; Austenitic stainless steel Fusion materials and technologies ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Wenqi Zeng (21923) | Lu Zhang (50563) | Yuan Yao (31223)
Source: Figshare (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Leveraging TransformerModels to Capture Multi-ScaleDynamics in Biomolecules by Nano-GPT Long-term biomolecular dynamics is critical for understanding key evolutionary transformations in molecular systems. However, capturing these processes requires extended simulation timescales that often exceed the practical limits of conventional models. To address this, shorter simulations, initialized with diverse perturbations, are commonly used to sample the phase space and explore a wide range of behaviors. Recent advances have leveraged language models to infer long-term behavior from short trajectories, but methods such as long short-term memory (LSTM) networks are constrained to low-dimensional reaction coordinates, limiting their applicability to complex systems. In this work, we present nano-GPT, a novel deep learning model inspired by the GPT architecture specifically designed to capture long-term dynamics in molecular systems with fine-grained conformational states and complex transitions. The model employs a two-pass training mechanism that incrementally replaces molecular dynamics (MD) tokens with model-generated predictions, effectively mitigating the accumulation errors inherent in the training window. We validate nano-GPT on three distinct systems: a four-state model potential, the alanine dipeptide, a well-studied simple molecule, and the Fip35 WW domain, a complex biomolecular system. Our results show that nano-GPT effectively captures long-time scale dynamics by learning high-order dependencies through an attention mechanism, offering a novel perspective for interpreting biomolecular processes. Limiting ; Key (lock) ; Complex system ; Molecular dynamics ; Complex dynamics ; Dynamics (music) ; Simple (philosophy) ; Process (computing) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Machine Learning in Bioinformatics
Authors: Mia Gitter | David Yaron
Source: American journal of student research. (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Exploring the Transfer of Neural Network Potentials with Tight Binding Models of Conjugated Systems Neural network potentials (NNPs) have achieved impressive accuracy on local, short-range chemical environments, yet their ability to capture long-range electronic effects remains unclear. We investigate this question using a controlled testbed for electron delocalization: 1D tight-binding chains with 2–10 atoms. We generated synthetic datasets by drawing nearest-neighbor coupling values from a narrow normal distribution and determining the resulting ground-state energies using Hamiltonian diagonalization. We benchmark two architectures: (1) a “full chain” feed-forward MLP that ingests the entire coupling vector and directly predicts the total energy, and (2) a Behler–Parrinello–style “atom environment” model that sums per-atom energies from local windows (sizes 1–3). Across chain lengths, the full-chain model consistently attains lower error and cleaner predicted-vs-true alignments, though accuracy degrades with system size for all models. For 10-atom chains, mean errors are 0.0279 (full-chain) versus 0.0613, 0.0524, and 0.0417 for window sizes 1, 2, and 3, respectively. Loss curves show stable optimization with limited overfitting on small and mid-sized systems. These results indicate that even simple global-feature MLPs can learn delocalization effects that challenge purely local descriptors. Key limitations include the idealized 1D tight-binding ground truth, nearest-neighbor couplings only, and the absence of symmetry/equivariance constraints, which limit transferability to realistic 3D chemistry. Nonetheless, the findings motivate combining global receptive fields (or longer range descriptors) with physics-aware architectures to improve generalization in delocalized electronic systems. Delocalized electron ; Coupling (piping) ; Overfitting ; Artificial neural network ; Hamiltonian (control theory) ; Limit (mathematics) ; Benchmark (surveying) ; Coupling strength ; Key (lock) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Renquan Lv (21259155) | Weiwei Han (286593) | Zixu Zeng (21259158) | Yi He (408132) | Lecheng Lei (1645309) | Ping Li (31981) | Xingwang Zhang (587405)
Source: Figshare (article, 2025-08-14)
Field: Materials Science — Materials Chemistry

Multimodal Modelingfor Polymer Property Predictionand Decoupling of Structure–Property Relationship The key challenge in the development of a target property polymer stems from the unclear structure–property relationship (SPR). A multimodal graph neural network (GNN) framework, GeoALBEF, is proposed to address this problem. GeoALBEF introduces a training process based on an “Align before Fuse” architecture that optimizes a three-stage loss, which deeply fuses polymer graph information and text information. Benchmark tests constructed based on 5,000 polymer samples showed that GeoALBEF reduced the mean relative RMSE by 8.6% compared to the suboptimal model in the prediction task of 24 key properties in 6 categories. It is especially worth pointing out that the model achieves functional group level interpretability through the attention mechanism and the functional group averaging strategy. The interpretability was mapped using visualization methods. Additionally, we used reinforcement learning to quantify this interpretability and successfully decoupled the SPR of the polymers. This multimodal system, which combines high-precision prediction capability and mechanism analysis, establishes an intelligent mapping paradigm from polymer structure to property and is expected to promote the accelerated optimization of target property polymers. Interpretability ; Property (philosophy) ; Decoupling (probability) ; Visualization ; Graph ; Key (lock) ; Benchmark (surveying) ; Reinforcement learning Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Polymer Synthesis and Characterization
Authors: Jakob Johnson | Luther W. McDonald | Tolga Taşdizen
Source: Journal of Radioanalytical and Nuclear Chemistry (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

An exploration of data fusion techniques applied to nuclear forensics tasks Fusion ; Pattern recognition (psychology) ; Sensor fusion ; Information fusion ; Baseline (sea) ; Fidelity Machine Learning in Materials Science ; Nuclear Physics and Applications ; Cold Fusion and Nuclear Reactions
Authors: Pavel V. Afonine | Małgorzata Biczysko | Hatice Gökcan | Olexandr Isayev | Holger Kruse | Nigel W. Moriarty | Mark P. Waller | R.I. Zubatyuk | Adrián E. Roitberg
Source: Structural Dynamics (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Quantum-based atomic model refinement becomes reality Owing to the limited quality of experimental data, such as resolution, atomic model refinement using cryo-EM or crystallographic experimental data is often a challenging task. To make refinement practical, a priori information about model geometry is always used as chemical restraints. These restraints originate from standard libraries that are used across most structure solution software in the field. Geometric restraints derived from these libraries suffer from at least three limitations. First, they lack information about novel molecules, such as ligands. Second, they are agnostic to non-covalent interactions such as hydrogen bonds, salt bridges, pi-interactions, and electrostatics in general. Third, the use of molecule-specific information as a source of geometric restraints, such as polypeptide chain conformations (Ramachandran plot), secondary structure, or rotameric states of amino acid side chains, requires a geometrically sound atomic model in the first place to be defined. Using quantum mechanical (QM) calculations as a source of restraints can eliminate the need for these libraries as well as the use of molecule-specific restraints altogether. However, these calculations are known to be computationally intractable for large molecules unless divide-and-conquer methods are used in conjunction with specialized QM software, some of which (the best one) is not free even for academic users. We present a novel deep learning implementation of a neural network potential that allows performing QM calculations for biomacromolecules in a timescale ranging from seconds to minutes. This, in turn, enables the use of QM-based geometry restraints in standard atomic model refinements. Preliminary results show that these methods produce refined models with much superior geometries compared to refinement using standard restraints while maintaining comparable fit to the experimental data. Computer science ; Quantum ; Theoretical computer science ; Statistical physics ; Theoretical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; History and advancements in chemistry ; Atomic and Molecular Physics
Authors: Peter R. Spackman (4858141)
Source: Figshare (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

Switching Perspectiveson Hirshfeld Partitioning Schemes We explore the foundations and applications of Hirshfeld partitioning, a fundamental technique used in the analysis of atoms in molecules, and the related Hirshfeld surface used to study molecules in crystals. Hirshfeld partitioning has direct connections to information theory, machine learning, and statistical mechanics, and this work aims to deepen our understanding of why these techniques are successful and so generally applicable, highlighting the connections of such methods to our physical understanding of the systems to which they are applied. We further investigate practical considerations in the implementation and interpretation of Hirshfeld surfaces and Voronoi partitioning. Interpretation (philosophy) ; Surface (topology) ; Voronoi diagram ; Work (physics) ; Field (mathematics) ; Statistical analysis ; Visualization Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: T. Ramesh | Thamarai Selvan | M. Prabakaran | Chithras Thangavel | U. Karthick | P. Ashok Kumar
Source: Springer Link (Chiba Institute of Technology) (article, 2025-09-26)
Field: Materials Science — Polymers and Plastics

AI-Optimized Tensile Strength Prediction of Biocomposites Using Agricultural Waste The increasing demand for green and environmentally friendly composite materials have triggered a marked interest in the utilization of agricultural waste as reinforcement in polymer matrices. This paper shares a fresh and unique way of predicting the tensile strength of bio-composites of Rice Husk, Groundnut Shell, and Santa Maria Feverfew when they are dipped in epoxy resin and hardener. We propose a new machine learning model that combines XGBoost and Long Short-Term Memory (LSTM) networks as a new way to reduce the error for the prediction of the tensile strength of a given material. Data were extracted from the process of making composite samples in a laboratory, and then the samples have been tested for their tensile strengths. The XGBoost model can identify non-linear connections, and decrease the number of characteristics, while the LSTM model uses the information from the XGBoost predictions to make the output more accurate. The metrics that are used in the evaluation like MSE, MAE, and R² Score are the evidence that the proposed approach is working. The residual laboratory results, and information extracted from the charts about the most important features, are also the types of visual techniques that can be used as evidence of the models' robustness. This AI-fueled system is a very useful device for designing composites more efficiently and cutting the costs of the experiment, while at the same time it gets the limited development of the green materials and sustainable engineering fields speeded up. Ultimate tensile strength ; Agricultural waste ; Composite number ; Residual ; Process (computing) ; Agriculture ; Epoxy Natural Fiber Reinforced Composites ; Machine Learning in Materials Science ; Fiber-reinforced polymer composites
Authors: Kwangjin An | Jihun Kim | Yunkyung Kim | Daewon Oh | Seokhyun Choung | Hyeongeon Lee | Eonu Nam | Shinjae Lee | Jeongjin Kim | Sinmyung Yoon | Jeong Woo Han
Source: Research Square (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

TiO2-facet-dependent reconstruction of Pt nanoparticles during CO oxidation Facet (psychology) ; Density functional theory ; Catalysis ; Exergonic reaction ; Nanoparticle ; Molecular dynamics ; Nanocrystal ; Reactivity (psychology) Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Muthaiah Chintha | Raghavendar Uppari | Balaswamy Puligilla
Source: International Journal For Multidisciplinary Research (review, 2025-10-08)
Field: Materials Science — Materials Chemistry

AI-Driven Insights into Antimicrobial Nanoparticles: A Brief Review Antimicrobial resistance (AMR) remains a global health emergency, demanding innovative and effective alternatives to traditional antibiotics. Nanoparticles (NPs), particularly metal, metal-oxide, and plant-derived variants, offer robust antimicrobial capabilities. However, optimization and safety assessment persist as major obstacles. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown promise for predictive formulation, mechanistic understanding, and toxicity modeling of antimicrobial NPs. This systematic review presents a synthesis of cutting-edge AI-assisted methodologies covering predictive modeling, data-driven design, and high-throughput screening highlighted by examples and nanostructured surfaces. Challenges and future directions are also discussed. Antimicrobial ; Antibiotic resistance ; Systematic review ; Anti-Infective Agents ; Risk assessment Advanced Nanomaterials in Catalysis
Authors: Vadim Volochaev | Arshak A. Tsaturyan
Source: The Analyst (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Multimodal analytical approaches to nanomaterials: TEM, diffraction, image processing, and fractal analysis In the field of materials science, using diverse experimental and computational methods is a well-known approach as the most effective route to comprehensive material characterization. Combining high-resolution transmission electron microscopy (TEM) techniques and X-ray diffraction (XRD) data analysis enables simultaneous integration of multifaceted data, improving research productivity, accuracy and application development. The pathway from qualitative to quantitative results requires advanced data analysis approaches with a capability of extracting meaningful physical parameters from all complex datasets. While there is no universal method presently existing to derive physical properties directly from the chemical composition and TEM images alone, traditional theoretical approaches and modern machine learning (ML) methods, particularly zero-code artificial intelligence AI/ML platforms, seem to be promising in this area. The actual review deals with current cases of TEM data analysis, particularly where combining TEM and diffraction methods enables enhanced nanoparticle characterization, emphasizing parameters like particle size, coherent domain size, agglomeration, and complicated fractal-like shape, all of which could be rather crucial for basic materials science and further industrial applications. Field (mathematics) ; Fractal ; Experimental data ; Fractal analysis ; Domain (mathematical analysis) ; Image (mathematics) X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications