jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Piyush Paliwal | Aftab Alam
Source: ArXiv.org (preprint, 2025-11-17)
Field: Materials Science — Materials Chemistry

Accelerated Prediction of Temperature-Dependent Lattice Thermal Conductivity via Ensembled Machine Learning Models Lattice thermal conductivity ($κ_L$) is a key physical property governing heat transport in solids, with direct relevance to thermoelectrics, thermal barrier coatings, and heat management applications. However, while experimental determination of $κ_L$ is challenging, its theoretical calculation via ab initio methods particularly using density functional theory (DFT) is computationally intensive, often more demanding than electronic transport calculations by an order of magnitude. In this work, we present a machine learning (ML) approach to predict $κ_L$ with DFT-level accuracy over a wide temperature range (100-1000 K). Among various models trained on DFT-calculated data obtained from literature, the Extra Trees Regressor (ETR) yielded the best performance on log-scaled $κ_L$, achieving an average $R^2$ of 0.9994 and a root mean square error (RMSE) of 0.0466 $W\,m^{-1}\,K^{-1}$. The ETR model also generalized well to twelve previously unseen (randomly chosen) low and high $κ_L$ compounds with diverse space group symmetries, reaching an $R^2$ of 0.961 against DFT benchmarks. Notably, the model excels in predicting $κ_L$ for both low- and high-symmetry compounds, enabling efficient high-throughput screening. We also demonstrate this capability by screening ultralow and ultrahigh $κ_L$ candidates among 960 half-Heusler compounds and 60,000 ICSD compounds from the AFLOW database. This result shows reliability of model developed for screening of potential thermoelectric materials. At the end, we have tested model's prediction ability on systems that have experimental $κ_L$ available that shows model's ability to search material that has desirable experimental $κ_L$ for thermoelectric applications. Thermal conductivity ; Root mean square ; Thermoelectric effect ; Lattice (music) ; Ab initio ; Mean squared error ; Reliability (semiconductor) ; Experimental data ; Density functional theory Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Kimihiro Yamazaki | Mutsuyo Wada | Yuichiro Wada | Atsushi Tokuhisa | Kento Ohga | Akira Nakagawa | Yoshinobu Akinaga | Yoko Sasakura | Takashi Katoh | Shigeyuki Matsumoto | Yasushi Okuno
Source: ChemRxiv (preprint, 2025-03-04)
Field: Materials Science — Materials Chemistry

Generating Free-energy Landscapes and Mapping Conformational Transition Pathways from cryo-EM Images using a Deep Isometric Autoencoder Biological functions of macromolecules are regulated by their three-dimensional structures and dynamic behaviors. Cryo-electron microscopy (cryo-EM) is increasingly gaining attention for determining novel static structures with large molecular sizes and complex structural assemblies. However, investigating the dynamic behaviors of these structures on free-energy landscapes is substantially difficult. Herein, we developed a deep-learning-based method, PaStEL with cryoTWIN, to uncover thermodynamic behaviors recorded in cryo-EM data. CryoTWIN trained with cryo-EM particle images builds an isometric latent space for a conformational space of the free-energy landscape, allowing PaStEL to generate the energy-based conformational transition pathways among the heterogeneous structural states. The theoretical discoveries and the simulation-based experiments guaranteed our approach. Furthermore, using experimental data, parallel assembly pathways in 50S-ribosome and the thermodynamic basis of the enhanced infectivity of SARS-CoV-2 spike protein were elucidated without prior biological knowledge, thereby highlighting the impact of our method in structural biology researches. Thus, PaStEL with cryoTWIN had a potential to advance biological foundation by depicting whole pictures of the dynamic behaviors. Autoencoder ; Isometric exercise ; Energy landscape ; Energy (signal processing) ; Artificial intelligence ; Computer science ; Computer vision ; Deep learning ; Chemistry ; Physics ; Biology ; Biochemistry Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Ivan V. Grenev | Matvey E. Bobkov | A. Ivanov | Anastasiia I. Uliankina | Aleksandr A. Shubin
Source: Materials Chemistry and Physics (article, 2025-11-29)
Field: Chemistry — Inorganic Chemistry

Machine learning-based predicting of the equilibrium cation distribution in faujasite-type zeolites Monte Carlo method ; Zeolite ; Cationic polymerization ; Force field (fiction) ; Field (mathematics) ; Reference data ; Range (aeronautics) ; Set (abstract data type) Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis and Oxidation Reactions
Authors: Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono | Hideaki Kasai | Moses M. Solomon
Source: Brazilian Journal of Chemical Engineering (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Investigation of corrosion Inhibition capability of ionic liquid: a machine learning approach Ionic liquid ; Corrosion ; Chemistry ; Biochemical engineering ; Computer science ; Materials science ; Engineering ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Zhen Qiu | Xintong Chen | Jiajun Guo | Ningbo Kang | Guishan Liu
Source: Journal of Food Composition and Analysis (article, 2025-11-29)
Field: Chemistry — Analytical Chemistry

A review of non-destructive spectroscopy techniques for the identification and analysis of frozen-thawed muscle foods Hyperspectral imaging ; Chemometrics ; Identification (biology) ; Quality (philosophy) ; Muscle tissue ; Spectroscopy ; Meat tenderness ; Spectral analysis Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jocelyn Ochoa | Alauna C. Wheeler | Nicholas Javier C. Licauco | Jasmine Vu | J. Emmanuel Flores-Calderón | Timothy J. Atherton | Benjamin J. Stokes | Linda S. Hirst
Source: Liquid Crystals (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Combining fluorescence microscopy and machine learning for a powerful method to analyse nematic domain growth in quantum dot nanocomposites Nanoparticle transport by a moving liquid crystal phase boundary was recently shown to facilitate the formation of nanoparticle-based porous micro-assemblies (capsules, foams, etc). A key challenge is that it can be very difficult to accurately image this process and thus to gain better insight into the mechanisms that control particle transport and assembly. The first stage in micro-assembly formation involves nematic domain nucleation and growth from an isotropic phase that includes dispersed nanoparticles. Here, we report a fluorescence method to quantify domain growth using luminescent nanoparticles (surface-modified quantum dots) to visualise domain growth following a controlled temperature quench. This technique provides enhanced resolution of domain edges when compared with birefringence imaging, especially for very small domains (&lt;2 µm). We compare manual and automated measures of domain growth using ilastik, a machine-learning-enabled segmentation tool. This high-throughput method allows us to measure the growth rate of thousands of domains simultaneously with a high degree of confidence. From these measurements, we report time dependent nematic domain size distributions and growth exponents at two different quantum dot concentrations and cooling rates. The results match well with theoretical predictions and show that under our experimental conditions, the nanoparticles do not significantly impact domain growth. Quantum dot ; Liquid crystal ; Domain (mathematical analysis) ; Fluorescence ; Microscopy ; Nanocomposite Quantum Dots Synthesis And Properties ; Chemical and Physical Properties of Materials ; Perovskite Materials and Applications
Authors: Shuqi Xie (2243074) | Kun Ni (2880800) | Yanwu Zhu (1269969)
Source: Figshare (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Nanotube DerivedOrdered Carbons Predicted by NeuralNetwork Potential Searching for novel carbon allotropes with excellent mechanical and interesting electronic properties is valuable, but such a large structural search remains a challenge if purely based on the traditional density functional theory (DFT) combined with Monte-Carlo (MC) methods. Herein, the neural network potential is utilized to accelerate the sampling of the stochastic surface walking algorithm for a global structural search of ordered carbons from carbon nanotubes (CNTs) under pressure. A variety of unreported ordered carbons are obtained, among which CNTs with diameters smaller than 0.7 nm are more sensitive to pressure than bigger tubes. Most ordered carbons obtained show great thermodynamical and kinetic stability, verified by <i>ab initio</i> molecular dynamics simulations and phonon spectra. The ordered carbons demonstrate direct or indirect band gaps in the range of 0 to 4.4 eV, including 13 superhard (H<sub>v</sub> > 40 GPa) structures and 1 ductile (Pugh’s Ratio G/B < 0.57) structure, in which the modulus of ordered carbons exhibits a linear correlation with the density. Our study provides a pathway to create new carbons from nanotubes and a deeper understanding of the structural evolution of CNTs under pressure. Carbon nanotube ; Carbon fibers ; Molecular dynamics ; Range (aeronautics) ; Density functional theory ; Phonon ; Modulus Boron and Carbon Nanomaterials Research ; Carbon Nanotubes in Composites ; Machine Learning in Materials Science
Authors: Amirsalar Mansouri | Nipun Babu Varukattu | Brennan J. Curole | Omeed Moaven | Jiří Adamec
Source: Analytica Chimica Acta (article, 2025-11-24)
Field: Chemistry — Spectroscopy

Mass spectrometry imaging tutorial: From cancer biomarker discovery to clinical applications Mass spectrometry ; Mass spectrometry imaging ; Analyte ; Desorption electrospray ionization ; Biomarker discovery ; Electrospray ionization ; Profiling (computer programming) ; Molecular imaging ; Biomarker Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Anjali Gaur (9437755) | Nikhil V. S. Avula (9437758) | Sundaram Balasubramanian (810870)
Source: Figshare (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

Decoding Temperature-DependentConformer Populationsand Dipole Moments in Liquid Ethylene Glycol with Classical and Machine-LearnedPotentials Delineating the conformer populations in neat liquid ethylene glycol (EG) has been challenging as experiments lack sufficient resolution, while simulations suffer from low accuracy and/or high computational demand. Ab initio MD (AIMD) simulations of liquid EG at ambient conditions have reported that about 20% of the molecules have their central (OCCO) dihedral in the <i>trans</i> conformation. Yet, adequate conformational sampling is an issue with AIMD simulations, given the limited duration of the trajectory. We describe the development of deep neural network-based potentials (DP) fitted to density functional theory data and confirm the <i>trans</i> population to be 22% at ambient conditions. The DP models provide well-converged structural properties and conformer populations over the entire temperature range of liquid EG. An increase in the population of <i>trans</i> conformers was observed with increasing temperature and is rationalized on two grounds: the increasingly similar coordination environments of <i>trans</i> and <i>gauche</i> conformers, and a decrease in the polarity of the liquid with temperature. Despite the latter, the <i>trans</i> population saturates to a value of 24% at 400 K as rising intermolecular distances favor the <i>gauche</i> conformation with intramolecular hydrogen bonding. Wannier Centroid analysis carried out using a Deep Wannier model reveals a mean molecular dipole moment in liquid EG of 3.3 D at ambient conditions, which decreases to a value of 3.0 D at 463 K. Conformational isomerism ; Dipole ; Intramolecular force ; Ab initio ; Population ; Ab initio quantum chemistry methods ; Density functional theory ; Intermolecular force Material Dynamics and Properties ; Machine Learning in Materials Science ; Molecular spectroscopy and chirality
Authors: Onurcan Kaya | Hyeongjoon Kim | Byeongkyu Kim | Thomas Galvani | Luigi Colombo | Mario Lanza | Hyeon‐Jin Shin | Ivan Cole | Hyeon Suk Shin | Stephan Roche
Source: 2D Materials (article, 2025-11-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Amorphous boron nitride as an ultrathin copper diffusion barrier for advanced interconnects Abstract This study focuses on amorphous boron nitride ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mi>α</mml:mi> </mml:mrow> </mml:mrow> </mml:math> -BN) as a novel diffusion barrier for advanced semiconductor technology, particularly addressing the critical challenge of copper diffusion in back-end-of-line (BEOL) interconnects. Owing to its ultralow dielectric constant and robust barrier properties, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mi>α</mml:mi> </mml:mrow> </mml:mrow> </mml:math> -BN is examined as an alternative to conventional low-k dielectrics. The investigation primarily employs theoretical modelling, using a Gaussian approximation potential, to simulate and understand the atomic-level interactions. This machine-learning-based potential enables realistic simulations of amorphous <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mi>α</mml:mi> </mml:mrow> </mml:mrow> </mml:math> -BN structures and allows us to examine how different film morphologies affect barrier performance. Furthermore, we studied the electronic and optical properties of the films using a simple Tight-Binding model. In addition to the theoretical work, we performed copper diffusion experiments through PECVD-grown <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mi>α</mml:mi> </mml:mrow> </mml:mrow> </mml:math> -BN on Si substrates. Theoretical and experimental results indicate that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mi>α</mml:mi> </mml:mrow> </mml:mrow> </mml:math> -BN films can suppress Cu diffusion at nanometre thicknesses. Together, molecular dynamics simulations based on a machine-learned interatomic potential and PECVD experiments support the use of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mi>α</mml:mi> </mml:mrow> </mml:mrow> </mml:math> -BN as a Cu diffusion barrier for BEOL interconnects. Amorphous solid ; Boron nitride ; Diffusion barrier ; Copper ; Diffusion ; Nanometre ; Dielectric ; Molecular dynamics Copper Interconnects and Reliability ; Semiconductor materials and devices ; Semiconductor materials and interfaces
Authors: Tengxin Huang (17431548) | Zhijiang Yang (16869027) | Mingchi Gao (22464320) | Li Pan (49390) | Liangliang Wang (580395) | Junjie Ding (4849099)
Source: Figshare (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Multi-ConformationEnhanced Equivariant Graph NeuralNetwork: Advancing Melting Point Prediction Accuracy for Organic SmallMolecules The melting points of organic small molecules are governed by their three-dimensional (3D) conformations. We developed a novel framework termed 3D<sup><i>x</i></sup>G-MP (where <i>x</i> denotes the number of molecular conformations introduced into the model) to address the lack of 3D conformational information in existing melting point prediction approaches. This framework integrates a 3D equivariant graph neural network (EGNN) with 3D structural information and conformational data, and was trained on an extensive data set of 237,406 melting points. The 3D<sup>3</sup>G-MP model, which was augmented with three low-energy conformations per molecule, outperformed traditional methods (XGBoost, long short-term memory, two-dimensional (2D) graph attention networks) in melting point prediction, with mean absolute errors (MAEs) of 21.719, 23.014, and 22.956 °C for the training, validation, and test sets, respectively. Notably, the MAE was 10.04% lower than that of XGBoost for the melting point prediction of flexible molecules with ≥7 rotatable bonds. External validation with 88 independent molecules confirmed the reliability of the model, yielding a MAE of 22.471 °C. This study demonstrates that combining 3D structural information with conformational data significantly improves melting point prediction accuracy. The 3D<sup><i>x</i></sup>G-MP framework is promising for predicting the melting points of organic small molecules while offering extensibility to the prediction of other physicochemical properties. Melting point ; Graph ; Small molecule ; Equivariant map ; Artificial neural network ; Set (abstract data type) ; Molecule ; Point (geometry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Gourav Chakraborty | Niladri Patra
Source: Journal of Chemical Information and Modeling (article, 2025-12-13)
Field: Materials Science — Materials Chemistry

A Hybrid OPES-eABF Framework for Efficient Exploration and Data-Driven Collective Variable Discovery in Complex Free-Energy Landscapes Molecular dynamics (MD) simulations are powerful tools for studying biomolecular systems, but they are fundamentally limited by accessible time scales, making the study of rare events such as protein folding or ligand unbinding computationally challenging. While several enhanced-sampling methods exist to accelerate such processes, their efficacy depends critically on the choice of collective variables (CVs), which are typically intuition-driven and must be defined a priori. To address this, we introduced a unified framework that couples the recently developed OPES-eABF hybrid scheme with a data-driven machine learning workflow for automated CV discovery. We first extrapolated the applicability of OPES-eABF across systems of increasing complexity─alanine dipeptide in explicit water, chignolin folding-unfolding, and benzene unbinding from T4 (L99A) lysozyme. In all cases, the hybrid method achieved faster sampling than either of OPES or eABF alone. The efficiency of the framework was found to depend strongly on the FULLSAMPLES/PACE ratio, which regulates the balance between eABF force estimations and OPES bias depositions, wherein a ratio < 1 yielded the most reversible and ergodic sampling behavior. To improve reaction-coordinate quality, the OPES-eABF framework was integrated with a Koopman-reweighted DeepTICA-LASSO pipeline for interpretable CV construction. The machine-learned CVs successfully captured the slow folding and unbinding modes across systems, producing physically meaningful free-energy surfaces for chignolin. In the T4 lysozyme-benzene complex, the Koopman-LASSO CV yielded an unbinding free energy of 5.78 ± 0.04 kcal/mol, in close agreement with the experimental value (-5.19 ± 0.16 kcal/mol), while committor and transition-path analyses confirmed its mechanistic relevance. Moreover, accounting for the volume corrections further improved the agreement with the experimental binding affinity, resulting in a final value of -5.35 kcal/mol. Overall, this study demonstrates that the integration of OPES-eABF with data-driven CV optimization provides a robust, transferable, and physically interpretable approach for probing complex rare-event kinetics and thermodynamics in biomolecular systems. Pipeline (software) ; Molecular dynamics ; Workflow ; Folding (DSP implementation) ; Sampling (signal processing) ; Energy landscape ; Energy (signal processing) ; Variable (mathematics) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: M. Zhang | Pierre Richard Dahoo | Azzedine Lakhlifi | David Bassir
Source:  (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Neural network algorithm to analyze IR Spectroscopic data: Application to CO <sub>2</sub> The $\mathbf{C O}_{\mathbf{2}}$ molecule, a greenhouse gas, is characterized by three vibrational modes: $i) v_{1}$, a symmetric stretching vibration, ii) $v_{2}$, a double degenerate symmetric bending mode and iii) $v_{3}$, an anti-symmetric stretching vibration, with absorption frequencies in the mid infrared range. In a recent publication, it was shown that at the harmonic level, the CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> molecule preserves its vibrational symmetry when trapped in nanocages of Clathrate or rare gas matrices with a degeneracy lifting of the bending mode. The symmetry of the molecule is preserved even for the fermi levels that originate from the interaction between fundamental $v_{1}$ and $2 v_{2}$ states when their frequency values coincide. This work, is an attempt to analyze the vibrational degrees of freedom of $\mathbf{C O}_{\mathbf{2}}$ from a numerical algorithm based on a neural network algorithm used as a tool to sample the electronic environment from the vibrational motion of the nuclei clamped to form the molecule either when it moves freely as in gas phase or under an electro-magnetic constraint when trapped in a nanocage. To achieve this goal, the molecule is characterized by its force constant as determined in the previous work rather than by its harmonic vibrational frequencies. Different recognition strategies will be shown through the application of standard neural network with back-propagation algorithm. Molecular vibration ; Artificial neural network ; Degenerate energy levels ; Molecule ; Symmetry (geometry) ; Degeneracy (biology) ; Vibration ; Degrees of freedom (physics and chemistry) ; Normal mode Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Spectroscopy and Quantum Chemical Studies