jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Logan
M. Brabson (22209492) | Andrew J. Medford (2256706) | David S. Sholl (1270002)
Source: Figshare (article, 2025-09-08)
Field: Chemistry — Inorganic Chemistry

Comparing Classicaland Machine Learning Force Fieldsfor Modeling Deformation of Metal–Organic Frameworks Relevantfor Direct Air Capture Deformation of metal–organic frameworks (MOFs) induced by adsorbate molecules can affect adsorption properties such as capacity and selectivity, but most computational studies of MOFs assume framework rigidity to simplify calculations. Although flexible force fields (FFs) for MOFs have been parametrized for specific materials, the generality of FFs for reliably modeling adsorbate-induced deformation to accuracy nearing that of density functional theory (DFT) has not been established. This work confirms using DFT calculations that adsorbate-induced deformation can affect CO<sub>2</sub> and H<sub>2</sub>O adsorption energies in a considerable fraction of MOFs promising for direct air capture (DAC). We then benchmark the efficacy of several general-purpose FFs in describing adsorbate-induced deformation for DAC against DFT. Our results show that current classical FFs are insufficient for describing MOF deformation, especially in cases of interest for DAC where strong interactions exist between adsorbed molecules and MOF frameworks. Some emerging machine learning force fields (MLFFs) we tested, particularly CHGNet, MACE-MP-0, and Equiformer V2, appear to be more promising than the classical FF for emulating the deformation behavior described by DFT. The best performing FF (CHGNet), however, fails to achieve the accuracy required for practical predictions with a mean absolute adsorption energy error of 0.124 eV. Deformation (meteorology) ; Rigidity (electromagnetism) ; Benchmark (surveying) ; Work (physics) ; Density functional theory ; Adsorption ; Force field (fiction) ; Generality Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Phase Equilibria and Thermodynamics
Authors: A. W. Neitzel | T. L. Campante | D. Bossini | A. Miglio
Source: ArXiv.org (preprint, 2025-01-27)
Field: Chemistry — Analytical Chemistry

Dissecting stellar populations with manifold learning I. Validation of the method on a synthetic Milky Way-like galaxy Different stellar populations may be identified through differences in chemical, kinematic, and chronological properties, suggesting the interplay of various physical mechanisms that led to their origin and subsequent evolution. As such, the identification of stellar populations is key for gaining insight into the evolutionary history of the Milky Way galaxy. This task is complicated by the fact that stellar populations share significant overlap in their chrono-chemo-kinematic properties, hindering efforts to identify and define stellar populations. Our goal is to offer a novel and effective methodology that can provide deeper insight into the nonlinear and nonparametric properties of the multidimensional physical parameters that define stellar populations. For this purpose we explore the ability of manifold learning to differentiate stellar populations with minimal assumptions about their number and nature. Manifold learning is an unsupervised machine learning technique that seeks to intelligently identify and disentangle manifolds hidden within the input data. To test this method, we make use of Gaia DR3-like synthetic stellar samples generated from the FIRE-2 cosmological simulations. These represent red-giant stars constrained by asteroseismic data from TESS. We reduce the 5-dimensional input chrono-chemo-kinematic parameter space into 2-dimensional latent space embeddings generated by manifold learning. We then study these embeddings to assess how accurately they represent the original data and whether they contain meaningful information that can be used to discern stellar populations. We conclude that manifold learning possesses promising abilities to differentiate stellar populations when considering realistic observational constraints. Milky Way ; Galaxy ; Manifold (fluid mechanics) ; Astrophysics ; Physics ; Astronomy ; Artificial intelligence ; Computer science ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Astronomical Observations and Instrumentation
Authors: Linpo Yang | Jia Wei | Yinglin Song
Source: Physical Chemistry Chemical Physics (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Insight into the temperature-dependent lattice thermal properties of CeO <sub>2</sub> stabilized ZrO <sub>2</sub> by machine learning force fields The lattice thermal conductivity (LTC) and related properties of the ZrO<sub>2</sub>-CeO<sub>2</sub> system are critically important for industrial applications, particularly in thermal protection. However, elucidating the complex atomic-scale processes governing these phenomena remains challenging. In this work, the farthest point sampling (FPS) method was employed to sample the dataset, and a machine learning (ML) approach was applied to fit the sampled data, successfully deriving the interatomic potential. This methodology overcomes the limitations related to size and temperature effects inherent in density functional theory. Thermodynamic processes (including phase transitions and lattice thermal conductivity) in a large ZrO<sub>2</sub>-CeO<sub>2</sub> superlattice system containing 20 736 atoms were investigated using the neuroevolution potential (NEP) framework within molecular dynamics simulations. Significant first-order and second-order phase transitions were observed during heating. The homogeneous non-equilibrium molecular dynamics (HNEMD) method was applied to examine variations in thermal conductivity due to lattice size effects, while NEP-based MD simulations were used to predict the lattice thermal conductivity at different doping concentrations. At room temperature, the system exhibits relatively low thermal conductivity. To understand the underlying mechanisms of these thermal transport properties, spectral decomposition analysis was further conducted. Thermal conductivity ; Lattice (music) ; Molecular dynamics ; Thermal ; Interatomic potential ; Phase transition Thermal properties of materials ; Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity
Authors: Alexandra Foetisch | Collin J. Weber | Kerstin Stricker | Moritz Bigalke
Source:  (preprint, 2025-03-15)
Field: Materials Science — Materials Chemistry

&amp;#160;Overcome the obstacle of NP analysis &amp;#8211; a concept of chemical/microscopic methods combined with artificial intelligence Numerous studies have shown the potential risk that nanoplastic (NP) represents for the living organisms in the different ecosystems. However, the amount and characteristics of NP present in the environment are still unknown in its full extent. Even if several methods have already managed to quantify or characterize environmental NP, none, to our best knowledge, could yet provide a single particle complete characterisation over the full nanoscale range combined with a high sample throughput.The present work tackles the challenge of NP full characterisation in soil by testing an innovative combination and alignment of &amp;#181; Raman spectroscopy (RS), scanning electron microscopy coupled with energy dispersive x-ray spectroscopy (SEM/EDX), pyrolysis gas chromatography mass spectrometry (Py-GC/MS) and artificial intelligence (AI). The aim is to use the RS data to train an AI model that can automatically recognise NP in environmental samples using SEM/EDX data. The SEM data used to classify the particles include textural features extracted from the 2D images, elemental composition given by the EDX spectrum and the particle behaviour under the electron beam. Particles shape/size transformation when being exposed to high voltage has already been used for microplastic identification but still need to be tested for NP.First, NP down to 500 nm are identified using RS in samples of increasing complexity, starting with pure NP, mixed NP, spiked media and, finally, environmental samples. Secondly, the suitability of NP behaviour under electron beam to identify plastic material in complex matrices with SEM is tested on the identified NP. Then, the dataset acquired with RS and SEM/EDX on NP is divided into a training and testing set to build a convolutional neural network (CNN) allowing the differentiation between NP and non-NP particles present in a sample. Finally, textural features, elemental composition and behaviour under the beam data are acquired for all particles down to 50 nm in the different samples. The total mass of each polymer present in the sample is extrapolated and then cross-validated by performing a Py-GC/MS analysis on the same sample. Monte Carlo simulations are then used to model the error of the extrapolation based on data provided by the RS and SEM data. The aim of this model is then to allow the identification and characterisation of Obstacle ; Artificial intelligence ; Computer science ; Chemistry ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Robinson Musembi | Mwende Mbilo
Source: Physica B Condensed Matter (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Optoelectronic and thermoelectric performance of Tl2SiSe3 ternary chalcogenide compound: A first-principles study Thermoelectric effect ; Band gap ; Ternary operation ; Chalcogenide ; Triclinic crystal system ; Direct and indirect band gaps ; Lattice constant ; Seebeck coefficient ; Figure of merit Advanced Thermoelectric Materials and Devices ; Solid-state spectroscopy and crystallography ; Phase-change materials and chalcogenides
Authors: Peng Zheng (187813) | Lintong Wu (8563140) | Michael Ka Ho Lee (20993268) | Andy Nelson (7060424) | Michael Betenbaugh (2029237) | Ishan Barman (330976)
Source: Figshare (article, 2025-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning-PoweredColloidal Digital SERS for PreciseMonitoring of Cell Culture Media Maintaining consistent quality in biomanufacturing is essential for producing high-quality complex biologics. Yet, current process analytical technologies (PAT) often fall short in achieving rapid and accurate monitoring of small-molecule critical process parameters and critical quality attributes. Surface-enhanced Raman spectroscopy (SERS) holds great promise but faces challenges like intensity fluctuations, compromising reproducibility. Herein, we propose a deep learning-powered colloidal digital SERS platform. This innovation converts SERS spectra into binary “ON/OFF” signals based on defined intensity thresholds, which allows single-molecule event visualization and reduces false positives. Through integration with deep learning, this platform enables detection of a broad range of analytes, unlimited by the lack of characteristic SERS peaks. Furthermore, we demonstrate its accuracy and reproducibility for studying AMBIC 1.1 mammalian cell culture media. These results highlight its rapidity, accuracy, and precision, paving the way for widespread adoption and scale-up as a novel PAT tool in biomanufacturing and diagnostics. Biomanufacturing ; Process (computing) ; Process analytical technology ; Raman spectroscopy ; Event (particle physics) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Wenying Li (842753) | Jingchun Feng (20665700) | Kexuan Wu (20665703) | Si Zhang (111568) | Yuelu Jiang (347696) | Guozhong Wu (681970)
Source: Figshare (article, 2025-02-04)
Field: Chemistry — Analytical Chemistry

Equilibriumand Temporal Evolution of Asphaltene Nanoaggregatesduring Dynamic Heteroaggregation with Polysaccharides Asphaltene blockage and microbiologically induced corrosion (MIC) in oil pipes pose substantial flow assurance challenges, but their interrelationship remains poorly understood despite extensive individual research efforts. This study utilized molecular dynamics simulation and machine learning techniques to elucidate the effects of alginate, a representative biofilm polysaccharide associated with MIC, on asphaltene aggregation. Results underscored the ability of alginates to disperse large asphaltene nanoaggregates into smaller ones with more pronounced dispersion effects at higher concentrations. This was achieved through forming asphaltene–alginate heteroaggregates wherein asphaltene nanoaggregates were tightly wrapped by cross-linked alginate chains stabilized by Na<sup>+</sup> bridging. The presence of alginates significantly increased the solvent accessible surface area (SASA) of asphaltenes, while the heteroaggregation process resulted in abundant hydroxyl and carboxyl groups coating the surface of the asphaltene nanoaggregates. Furthermore, this study showed that the time-varying asphaltene aggregation parameters such as nanoaggregate number, intermolecular contact number, and SASA could be accurately predicted by alginate structural properties using optimized random forest models built from 3636 data sets extracted from molecular trajectories (<i>R</i><sup>2</sup> = 0.9628–0.9827). This enabled fast prediction of asphaltene aggregation when the polysaccharide composition and concentration changed during the MIC process. These findings provided theoretical support for developing strategies to address asphaltene-related issues in the presence of a biofilm polysaccharide. Asphaltene ; Flow assurance ; Polysaccharide ; Dispersion (optics) ; Adsorption ; Solvent ; Molecular dynamics ; Coating Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Corrosion Behavior and Inhibition
Authors: Alexander Urban
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> First-Principles Computational Tools for Electrochemical Metal Extraction The transition to a sustainable clean-energy economy demands a dramatic increase in the production of critical metals such as copper, nickel, cobalt, lithium, and rare earth elements. However, conventional extraction processes are energy-intensive and largely powered by fossil fuels. Developing cleaner, more sustainable alternatives, including pathways that enable metal recycling, is therefore a pressing challenge. I will present recent progress in developing first-principles computational tools to support and accelerate the design of clean metal-extraction technologies. For redox-mediated leaching in aqueous systems, we introduced an empirically corrected density-functional theory (DFT) method for accurate predictions of mineral redox potentials and Pourbaix diagrams [1]. For electrolytic extraction in molten salts, i.e. , Hall–Héroult-type processes, we combined DFT and machine learning to predict salt melting and oxide reduction temperatures [2,3]. We also demonstrate how DFT-driven models and machine-learned interatomic potentials can aid the discovery of novel ligands for selective metal separation. [1] B. Donovan, A. C. West, and A. Urban, in revision (2025). [2] V. Gharakhanyan et al., J. Phys. Chem. 160 (2024) 204112. [3] J. A. Garrido Torres et al., Nat. Commun. 12 (2021) 1-9. Pourbaix diagram ; Extraction (chemistry) ; Oxide ; Leaching (pedology) ; Dissolution ; Electrochemistry ; Electrolyte ; Metal Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; Advancements in Battery Materials
Authors: Yong Quan Zeng | Da Gong | Yutong Zu | Qiong Zhang
Source: Electric Power Systems Research (article, 2025-12-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Material-adaptive excitation analysis: Benchmarking dimensionality reduction strategies for high-accuracy waveform classification Waveform ; Dimensionality reduction ; Pattern recognition (psychology) ; Feature extraction ; Feature (linguistics) ; Curse of dimensionality ; Decision tree ; Principal component analysis ; Discriminative model ; Preprocessor Magnetic Properties and Applications ; Machine Learning in Materials Science ; Advanced DC-DC Converters
Authors: Yulian T. Manchev | Paul L. A. Popelier
Source: Journal of Chemical Theory and Computation (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Impact of Derivative Observations on Gaussian Process Machine Learning Potentials: A Direct Comparison of Three Modeling Approaches Machine learning (ML) potentials have become a well-established tool for providing inexpensive, yet quantum-mechanically accurate, atomistic simulations. Here, we extend our current modeling procedure, based on Gaussian process regression, to include derivative observations into the ML models. We directly compare three system-energy modeling approaches based on quantum mechanically derived quantities: (i) atomic energies, (ii) total system energy, and (iii) total system energy with derivative observations. We find that modeling the total energy with derivative observations has the best performance across the board, achieving chemical accuracy with fewer training data. In addition, both energy and force errors are around an order of magnitude lower when derivative observations are added to the models in some cases. We follow up with a discussion on the multiple advantages the proposed method of modeling brings, such as improved data set availability and the ability to easily include dispersion interactions. Additionally, we discuss the use cases of the new modeling approach in the ML force field FFLUX. Gaussian process ; Computer science ; Process (computing) ; Derivative (finance) ; Machine learning ; Artificial intelligence ; Gaussian ; Statistical physics ; Data mining ; Pattern recognition (psychology) ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Reservoir Engineering and Simulation Methods
Authors: Adewale K. Ipadeola | Mostafa H. Sliem | Dana Abdeen | Nicholas Laycock | Ashwin RajKumar | Phaneendra K. Yalavarthy | Aboubakr M. Abdullah
Source: Petroleum Research (article, 2025-10-01)
Field: Materials Science — Metals and Alloys

Integrated electrochemical and machine learning framework for SiO2/CaCO3 under-deposits driven welded X65 carbon steel corrosion mitigation in sour service conditions The integrity of welded X65 carbon steel (CS) pipelines in oil and gas systems is significantly compromised by preferential weldment corrosion (PWC) and pitting in sour conditions. This study clarifies that the effect of pre-corrosion conditioning and inorganic deposits (SiO 2 or CaCO 3 ) on welded CS pipelines, with SiO 2 deposits leading to the most substantial damage at weld metal (WM) and heat-affected zone (HAZ) relative to parent metal (PM). Due to localized acidification and differences in potential, corrosion rates (CRs) hierarchies are SiO 2 -deposited > CaCO 3 -deposited > non-deposited. Microstructural analysis reveals that deposited CS-WM has carbide redistribution and increase residual stress from the welding thermal process, with increased aggressive species and stabilized iron oxides, leading to intensified PWC. However, amine-based inhibitor CRW11 suppresses the CRs below 0.1 mmpy through chemisorbed films that disrupt microgalvanic coupling and protective corrosion product layers with inhibition efficiency (IE > 85%). CS-PM-CaCO 3 has a high pit depth (68.3 ± 2.7 μm) and propagation rate (1.2 ± 0.2 mmpy), making it the most vulnerable region to pitting relative to CS-WM-CaCO 3 (52.5 ± 1.1 μm; 0.9 ± 0.1 mmpy) and CS-HAZ-CaCO 3 (29.3 ± 0.6 μm; 0.5 ± 0.1 mmpy). This proves that CaCO 3 deposit on welded CS creates a physical barrier for the surface nucleation effect and improves anodic dissolution. Machine learning models (Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost)) accurately predict IE ( R 2 = 0.99). However, RF performs best for data without deposits (RMSE = 0.9), while DT is most appropriate model for those with SiO 2 and CaCO 3 deposits (lowest RMSE = 0.5 and 0.2). These findings provide coupled electrochemical, microstructural, and machine learning frameworks for PWC mitigation, allowing for adaptive corrosion management methods in sour service pipes and material designs optimized for topology. Welding ; Corrosion ; Pitting corrosion ; Carbide ; Pipeline transport ; Residual stress ; Coating ; Corrosion fatigue Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Corrosion Behavior and Inhibition
Authors: Yulian
T. Manchev (19838767) | Paul L. A. Popelier (11466731)
Source: Figshare (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Impact of DerivativeObservations on Gaussian ProcessMachine Learning Potentials: A Direct Comparison of Three ModelingApproaches Machine learning (ML) potentials have become a well-established tool for providing inexpensive, yet quantum-mechanically accurate, atomistic simulations. Here, we extend our current modeling procedure, based on Gaussian process regression, to include derivative observations into the ML models. We directly compare three system-energy modeling approaches based on quantum mechanically derived quantities: (i) atomic energies, (ii) total system energy, and (iii) total system energy with derivative observations. We find that modeling the total energy with derivative observations has the best performance across the board, achieving chemical accuracy with fewer training data. In addition, both energy and force errors are around an order of magnitude lower when derivative observations are added to the models in some cases. We follow up with a discussion on the multiple advantages the proposed method of modeling brings, such as improved data set availability and the ability to easily include dispersion interactions. Additionally, we discuss the use cases of the new modeling approach in the ML force field FFLUX. Derivative (finance) ; Gaussian process ; Process (computing) ; Energy (signal processing) ; Second derivative ; Gaussian ; Set (abstract data type) ; Dispersion (optics) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Protein Structure and Dynamics
Authors: Reyhaneh Sadat Motevallian | Parviz Parvin | Seyedeh Zahra Mortazavi | Ali Reyhani | Nafiseh Sadat Kalantari | Negin Nezamlou | Amir Jafargholi | Mohammadreza Aghaei | Mahdi Ebrahimi | Mehdi Sohrabi
Source: Surfaces and Interfaces (article, 2025-10-01)
Field: Materials Science — Polymers and Plastics

Alpha particle fluence-dependent physico-chemical properties of polycarbonate (Makrofol) surface after electrochemical etching Polycarbonate ; Analytical Chemistry (journal) ; Scanning electron microscope ; X-ray photoelectron spectroscopy ; Irradiation ; Etching (microfabrication) ; Contact angle ; Fluence ; Wetting Polymer Nanocomposite Synthesis and Irradiation ; Ion-surface interactions and analysis ; Graphite, nuclear technology, radiation studies