jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Artur Grigoryan | Stefan Helfrich | Valentin Lequeux | Benjamine Lapras | Chloé Marchand | Camille Merienne | Fabien Bruno | Roseline Mazet | Fabrice Pirot
Source: Preprints.org (preprint, 2025-06-30)
Field: Chemistry — Analytical Chemistry

Smart Formulation: AI-Driven Web Platform for Optimization and Stability Prediction of Compounded Pharmaceuticals Using KNIME Smart Formulation is a machine learning based platform designed to predict the Beyond Use Dates (BUDs) of compounded oral solid dosage forms by integrating molecular, formulation, and environmental parameters. Using a curated dataset of 3,166 active pharmaceutical ingredients (APIs), the model combines molecular descriptors with experimental stability data from Stabilis to train a tree 35 ensemble regression algorithm. This approach enables accurate prediction of API degradation under varying storage conditions, offering a scalable and cost-effective alternative to traditional stability testing. The analysis reveals a significant influence of formulation variables including the nature and number of excipients, and storage temperature on predicted stability. A negative correlation between LogP and BUD was identified, suggesting that hydrophilic APIs generally exhibit greater stability, especially when 40 formulated with a single excipient. Certain excipients such as cellulose, silica, sucrose, and mannitol were associated with enhanced stability. In contrast, excipients like HPMC and lactose, which have higher hydrogen bond donor and acceptor counts, were linked to faster degradation. The combination of two excipients instead of one often resulted in decreased stability, potentially due to moisture redistribution or phase separation effects. 45 Smart Formulation contributes to the field of computational pharmaceutics by bridging theoretical design and practical compounding. Its implementation in hospital and community pharmacies could mitigate drug shortages, streamline workflows, and support high-quality patient care. Future development will explore real-time stability monitoring and adaptive machine learning to further enhance predictive capabilities. Computer science ; Stability (learning theory) ; Machine learning Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; 3D Printing in Biomedical Research
Authors: Hengyuan Kong | Jianing Wang | Guanyu Lin | Jianbo Chen | Zhigang Xie
Source: Photonics (article, 2025-05-14)
Field: Chemistry — Analytical Chemistry

Analysis of Nutritional Content in Rice Seeds Based on Near-Infrared Spectroscopy The nutritional quality of rice seeds is mainly determined by the content of key components such as protein, fat, and starch. Traditional chemical detection methods are time-consuming, labor-intensive, inefficient, and harmful to the environment. To overcome these limitations, this study developed a non-destructive detection method using near-infrared spectroscopy (1000–2200 nm) combined with linear regression modeling to achieve efficient and simultaneous multi-component analysis through the principle of anharmonic molecular vibration. By combining nutrient data from chemical analysis with spectroscopic measurements, we established a comprehensive rice seed composition dataset. After preprocessing with Gaussian denoising, first-order derivative transformation, SPA wavelength selection, and multiplicative scatter correction (MSC), we constructed partial least squares regression (PLS) and orthogonal partial least squares (OPLS), as well as artificial neural network (ANN) models. The OPLS model performed well in fat prediction (R2 = 0.971, Q2 = 0.926, RMSE = 0.175, RMSECV = 0.186), followed by starch (R2 = 0.956, Q2 = 0.907, RMSE = 0.159, RMSECV = 0.146) and protein (R2 = 0.967, Q2 = 0.936, RMSE = 0.164, RMSECV = 0.156). Our results confirm that the combination of the moving average, first order derivative, SPA, and MSC preprocessing of the OPLS model significantly improves the prediction. The developed non-destructive testing equipment provides a practical solution for automated, high-precision sorting of rice seeds based on nutrient composition. Infrared spectroscopy ; Spectroscopy ; Infrared ; Near-infrared spectroscopy ; Content (measure theory) ; Materials science ; Optics ; Chemistry ; Physics ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; GABA and Rice Research
Authors: Guojie Li | Ying Wang | Haipeng Lu | Zhifeng Huang
Source: Advanced Materials Interfaces (review, 2025-05-05)
Field: Chemistry — Spectroscopy

Amplification of Chiral Raman Scattering: A Review of Resonance Raman Optical Activity and Surface Enhanced Raman Optical Activity Abstract Raman Optical Activity (ROA) is a specialized form of Raman scattering that offers unique insights into chiral and stereochemical properties of molecules, bio‐molecules, nanoclusters, and nanostructures, even though chiral molecules usually have extremely weak ROA signals. By measuring the differential intensities of Raman scattering associated with right‐handed and left‐handed circularly polarized incident or scattering, ROA incisively provides critical stereochemical information across various fields, including biology, chemistry, and material science, with particular emphasis on analyzing protein conformation. This review presents a comprehensive overview of the historical development and fundamental principles of ROA, tracing its evolution from a niche technique to a powerful tool in chiroptical spectroscopies. The current limitations are discussed in both experimental measurements and theoretical modeling of ROA. Importantly, the cutting‐edge methods aimed at amplifying ROA signals and sensitivity is highlighted by means of resonance and surface‐enhanced approaches. Looking ahead, potential applications of modern technologies are explored, such as “superchiral near‐field” techniques, “lab‐on‐a‐chip” platform, and deep machine‐learning methods that have already been successfully applied in surface‐enhanced Raman scattering, to extend the working range of ROA spectroscopies through amplifying ROA signals and detection sensitivity. Materials science ; Raman optical activity ; Raman scattering ; Raman spectroscopy ; Coherent anti-Stokes Raman spectroscopy ; Resonance (particle physics) ; Raman cooling ; X-ray Raman scattering ; Optoelectronics ; Optics ; Atomic physics ; Physics Molecular spectroscopy and chirality ; Spectroscopy Techniques in Biomedical and Chemical Research ; Spectroscopy and Quantum Chemical Studies
Authors: André Orth | H Hofer | Leonie Wildersinn | Fabian Jeschull | Markus Reischl
Source: Current Directions in Biomedical Engineering (article, 2025-09-01)
Field: Materials Science — Surfaces, Coatings and Films

ML-Driven Contamination Classification for XPS Analysis of PLA Surfaces Abstract As materials such as Poly-Lactic Acid (PLA) have become a significant part of medical production and research, it is important that the resulting products adhere to strict regulations concerning contaminations. A wide variety of methods are currently used to comply with these regulations depending on the products and their intended use case. We propose a standardized workflow for high-sensitivity contamination detection in X-ray photoelectron spectroscopy (XPS) survey scans that helps enhance detection reliability, reduce throughput constraints associated with manual analysis and streamline an automated analysis. This makes XPS a more viable option for process monitoring and reduces the need for additional analysis steps. The workflow uses a neural network trained on synthetic signals to predict contamination at%. These are then compared with individually variable contamination limits to classify each contaminant. Workflow ; Contamination ; X-ray photoelectron spectroscopy ; Throughput ; Process (computing) Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: S. Azhagarsamy | N. Pannirselvam
Source: Journal of Polymer Research (article, 2025-10-28)
Field: Materials Science — Polymers and Plastics

Optimization of graphene oxide–nanosilica hybrid nanofillers for enhanced basalt fiber–reinforced polyester composites using machine learning Ultimate tensile strength ; Flexural strength ; Graphene ; Nanocomposite ; Izod impact strength test ; Flexural modulus ; Thermal stability ; Modulus ; Polyester Polymer Nanocomposites and Properties ; Fiber-reinforced polymer composites ; Graphene research and applications
Authors: Nicola Casati | E.V. Boldyreva
Source: Journal of Applied Crystallography (review, 2025-07-22)
Field: Materials Science — Materials Chemistry

Powder diffraction data beyond the pattern: a practical review We share personal experience in the fields of materials science and high-pressure research, discussing which parameters, in addition to positions of peak maxima and intensities, may be important to control and to document in order to make deposited powder diffraction data reusable, reproducible and replicable. We discuss, in particular, which data can be considered as 'raw' and some challenges of revisiting deposited powder diffraction data. We consider procedures such as identifying ('fingerprinting') a known phase in a sample, solving a bulk crystal structure from powder data, and analyzing the size of coherently scattering domains, lattice strain, the type of defects or preferred orientation of crystallites. The specific case of characterizing a multi-phase multi-grain sample following <i>in situ</i> structural changes during mechanical treatment in a mill or on hydrostatic compression is also examined. We give examples of when revisiting old data adds a new knowledge and comment on the challenges of using deposited data for machine learning. Powder diffraction ; Diffraction ; Crystallography ; Materials science ; Computer science ; Physics ; Chemistry ; Optics X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies ; Polymer Synthesis and Characterization
Authors: T. Kanno | Tatsushi Ikeda | Akira Nakayama
Source: Journal of Chemical Theory and Computation (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Grand Canonical Monte Carlo Simulations for Hydrogen Adsorption on Metal Surfaces Using Neural Network Potentials Hydrogen adsorption on metal surfaces (Pt, Pd, and Ir) is investigated by the grand canonical Monte Carlo simulations using density functional theory-based neural network potentials. By introducing the modified cavity bias method, generalized hybrid Monte Carlo method, and replica exchange method, atomic configurations across various temperatures and pressures are efficiently sampled. To ensure accurate predictions of adsorption energy of hydrogen atoms in diverse configurations, the neural network potentials are refined using additional loss terms that capture energy differences between systems with and without hydrogen atoms. These combined techniques enable us to analyze hydrogen density profiles on metal surfaces with reliable accuracy. Our results highlight the influence of temperature and pressure on hydrogen atom configurations and their interactions with the surfaces. This approach not only elucidates the intricate interactions between hydrogen atoms under specific thermodynamic conditions, which are critical for optimizing catalytic and hydrogen storage materials, but also demonstrates the efficacy of neural network potentials in providing high-fidelity simulations that are computationally feasible for complex surface phenomena. Hydrogen ; Monte Carlo method ; Artificial neural network ; Adsorption ; Metallic hydrogen ; Hydrogen storage ; Grand canonical ensemble ; Metal ; Replica Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Xiaotian Wang | Wuliji Hasi | Shuang Lin | Xinxin Li | Siqingaowa Han | Guoqiang Fang | Xiang Lin
Source: Analytical Chemistry (article, 2025-12-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of Highly Sensitive Plasmonic Nanocavities and the Application in Trace Detection of Chromium (VI) Ion The development of plasmonic nanocavities with high structural complexity is essential for various applications, particularly for SERS sensing, where their powerful electromagnetic field confinement capabilities enable efficient optical signal conversion and enhancement. In this study, six morphologies of Au-Ag core-shell nanocuboids (NCs) were prepared using the seed growth method and electrolytic reduction reaction, and assembled on the surface of an Au film to form a plasma nanocavity SERS substrate. The SERS performance of different structural plasmonic nanocavities was investigated using the 4-MBA probe molecule. The results showed that the most prominent SERS performance appeared in Au film/Au-Ag Rough nanocuboids (Au film/Au-Ag RNCs), with an enhancement factor as high as 2.17 × 10<sup>9</sup>. Since the Au-Ag nanoparticles (NPs) on the surface of Au film/Au-Ag RNCs not only increase the specific surface area and provide more adsorption sites but act as a metal isolation layer, forming abundant plasmonic nanocavities between the NPs and the Au film as well as between the NPs themselves. Finally, with the help of machine learning algorithms, this highly sensitive plasmonic nanocavities SERS substrate was used for the quantitative detection of Cr<sup>6+</sup>, with a coefficient of determination between the predicted concentration and the actual concentration as high as 0.990, strongly indicating its advantages as a highly sensitive SERS nanoprobe. Plasmon ; Substrate (aquarium) ; Adsorption ; Nanoparticle ; Plasmonic nanoparticles ; Metal ; Metal ions in aqueous solution Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Laser-Ablation Synthesis of Nanoparticles
Authors: Alejandra Pita‐Milleiro | Magnus Strandgaard | Trond Linjordet | Hannes Kneiding | David Balcells
Source: ChemRxiv (preprint, 2025-07-07)
Field: Materials Science — Materials Chemistry

Evolving Light Harvesting Metal Complexes with AI-Made Ligands Light harvesting materials play a fundamental role in the development of photovoltaic technologies, including dye-sensitized solar cells. Transition metal complex (TMC) chromophores can push this field forward but their design is challenged by the need for optimizing multiple properties. An ideal chromophore would exhibit both intense and broad absorption in the visible range of the spectrum as well as, from a green chemistry perspective, high solubility in polar solvents including water. We hereby present a computational, data-driven approach to the discovery of novel TMC chromophores based on an evolutionary machine learning method combining elements of artificial intelligence (AI) and evolutionary computing (EC). In particular, AI-made bidentate ligands generated by a variational autoencoder were leveraged with an EC genetic algorithm (GA) for the multiobjective optimization of [RuL3]2+ chromophores. The fitness of the hits was consistent with intense, broad-spectrum absorption, and high solubility in polar solvents. The evolution of the absorption spectrum could be monitored step by step and easily interpreted by analyzing the frequency with which the ligands were selected by the GA. Based on the results, we suggest a set of experiments to the community doing wet lab research in this field. Metal ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Nanomaterials in Catalysis
Authors: Farah S. Elbitar | Faisal K. Algethami | Alaa Bedair | Fotouh R. Mansour
Source: TrAC Trends in Analytical Chemistry (article, 2025-10-24)
Field: Chemistry — Analytical Chemistry

Analytical challenges and future directions for the holistic assessment of emerging contaminants in soils Environmental remediation ; Emerging technologies ; Soil contamination ; Human health ; Soil water ; Risk assessment ; Work (physics) ; Soil health Analytical chemistry methods development ; Pharmaceutical and Antibiotic Environmental Impacts ; Electrochemical Analysis and Applications
Authors: Hui‐Hsu Gavin Tsai | C.K. Lee | Jingfei Wang | Shao-Chi Wu | J.C. Ke | Chia-Wei Chang | Chia‐Yuan Chen
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Prediction of Corrosion Inhibition Efficiency on Copper: Incorporating Tautomer Stability and Virtual Screening for Inhibitor Repositioning Copper ; Tautomer ; Corrosion ; Corrosion inhibitor ; Stability (learning theory) ; Virtual screening ; Computer science ; Chemistry ; Artificial intelligence ; Machine learning ; Materials science ; Metallurgy ; Computational chemistry ; Stereochemistry ; Molecular dynamics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: C. Liu | Tong Li | Yuandong Guo | Guiping Lin
Source: Energy and AI (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Thermal conductivity prediction of BN composites based on Enhanced Co-ANN combined with physical attention mechanisms This study proposes an enhanced co-training artificial neural network (Enhanced Co-ANN) guided by the physical attention mechanisms for the effective thermal conductivity prediction of high filler volume fraction polymer/BN composites. The thermal conductivity of BN composites is influenced by multiple factors, including the morphology of the fillers, interface thermal resistance, and experimental noise. This model tackles complex physical processes by integrating a customized multi-head physical attention layer to emphasize key features, along with a physics-constrained loss function to ensure prediction consistency. A collaborative training strategy based on curriculum learning and consistency discrimination is adopted. The model is optimized using 3174 labeled experimental datasets and 50,000 unlabeled data generated from physical models. Weight distribution is systematically designed across three core levels: model architecture, loss function, and training strategy. This approach differs from traditional parameter weight adjustments, as it emphasizes key features, especially volume fraction (vf), and balances different learning objectives through a physically guided mechanism and dynamic training strategies. Attention visualization indicates that the model adaptively focuses on the volume fraction of the packing material and the interface effect, verifying the effectiveness of the physically guided design. Six groups of samples with different packing volume fractions were made for testing and validation. This model has high accuracy (R² = 0.982; MAE = 0.045 W/m K) and is extremely consistent with physical laws. This network framework provides a method with broad application prospects for the rapid calculation, screening, and efficient design of high-performance polymer/BN thermal conductive materials. Volume fraction ; Thermal conductivity ; Consistency (knowledge bases) ; Artificial neural network ; Interface (matter) ; Electrical conductor ; Physical property ; Thermal ; Volume (thermodynamics) Thermal properties of materials ; Machine Learning in Materials Science ; Composite Material Mechanics
Authors: Yan Niu | Haoxuan Li | Minghui Yao | Qiliang Wu | S. W. Yang
Source: Journal of Vibration Engineering & Technologies (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Prediction of Composite Stress Field Using UNet with Attention Mechanisms Generalization ; Stress (linguistics) ; Composite number ; Field (mathematics) ; Block (permutation group theory) ; Convolutional neural network ; Transfer function Machine Learning in Materials Science ; Composite Material Mechanics ; Mechanical Behavior of Composites
Authors: Ryosuke Akashi | Mihira Sogal | Kieron Burke
Source: ArXiv.org (preprint, 2025-03-03)
Field: Materials Science — Materials Chemistry

Can machines learn density functionals? Past, present, and future of ML in DFT Density functional theory has become the world's favorite electronic structure method, and is routinely applied to both materials and molecules. Here, we review recent attempts to use modern machine-learning to improve density functional approximations. Many different researchers have tried many different approaches, but some common themes and lessons have emerged. We discuss these trends and where they might bring us in the future. Density functional theory ; Key (lock) ; Time-dependent density functional theory Machine Learning in Materials Science
Authors: Karim Elgammal | Marc Maußner
Source:  (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Quantum-Accelerated Supercomputing Atomistic Simulations for Corrosion Inhibition This work demonstrated a proof-of-concept use case for the emerging quantum-centric supercomputing approaches combining HPC resources with quantum computers. We presented a systematic implementation of hybrid quantum-classical computational method for accelerating atomistic simulations studying corrosion inhibition. We used aluminium surface with screened inhibitor molecule on top. We combined density functional theory (DFT) with quantum algorithm through an active space embedding scheme. We chosen inhibitor molecules of 1,2,4-Triazole and 1,2,4-Triazole-3-thiol. Our implementation leveraged the ADAPT-VQE algorithm with benchmarking against classical DFT calculations, achieving binding energies of -0.386 eV and -1.279 eV for 1,2,4-Triazole and 1,2,4-Triazole-3-thiol, respectively. The binding energy of the thiol derivative aligned with experimental observations regarding sulfur-functionalized inhibitors' which could improve corrosion protection. The methodology employed the orb-d3-v2 machine learning potential for rapid geometry optimizations, followed by accurate DFT calculations using CP2K with PBE functional and Grimme's D3 dispersion corrections. CP2K is a robust DFT package that can scale on HPC classical resources of CPUs and GPUs efficiently. We also benchmarked against smaller systems and revealed that StatefulAdaptVQE implementation achieves a 5-6×computational speedup while maintaining accuracy. This work contributes to the literature studying quantum-accelerated materials science applied to periodic systems, demonstrating the viability of hybrid quantum-classical approaches for studying surface-adsorbate interactions in corrosion inhibition applications. In which, can be transferable to other applications such as carbon capture on metal oxide frameworks and solid-state battery materials studies. Supercomputer ; Density functional theory ; Speedup ; Corrosion ; Embedding ; Molecular dynamics ; Work (physics) ; Corrosion inhibitor ; Chemical space Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Boron and Carbon Nanomaterials Research