jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Irea Mosquera‐Lois | Johan Klarbring | Aron Walsh
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Point defect formation at finite temperatures with machine learning force fields Point defects dictate the properties of many functional materials. The standard approach to modelling the thermodynamics of defects relies on a static description, where the change in Gibbs free energy is approximated by the internal energy. This approach has a low computational cost, but ignores contributions from atomic vibrations and structural configurations that can be accessed at finite temperatures. We train a machine learning force field (MLFF) to explore dynamic defect behaviour using Te<sup>+1</sup> <sub>i</sub> and <i>V</i> <sup>+2</sup> <sub>Te</sub> in CdTe as exemplars. We consider the different entropic contributions (<i>e.g.</i>, electronic, spin, vibrational, orientational, and configurational) and compare methods to compute the defect free energies, ranging from a harmonic treatment to a fully anharmonic approach based on thermodynamic integration. We find that metastable configurations are populated at room temperature and thermal effects increase the predicted concentration of Te<sup>+1</sup> <sub><i>i</i></sub> by two orders of magnitude - and can thus significantly affect the predicted properties. Overall, our study underscores the importance of finite-temperature effects and the potential of MLFFs to model defect dynamics at both synthesis and device operating temperatures. Point (geometry) ; Computer science ; Theoretical physics ; Statistical physics ; Physics ; Classical mechanics ; Materials science ; Mathematics ; Geometry Machine Learning in Materials Science
Authors: Kazuma Ito | Tatsuya Yokoi | Katsutoshi Hyodo | Hideki Mori
Source: International Journal of Mechanical Sciences (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Transferability of machine-learning interatomic potential to α-Fe nanocrystalline deformation Transferability ; Nanocrystalline material ; Interatomic potential ; Deformation (meteorology) ; Materials science ; Metallurgy ; Computer science ; Composite material ; Machine learning ; Molecular dynamics ; Nanotechnology ; Chemistry ; Computational chemistry Microstructure and mechanical properties ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Debdipto Acharya | Omar Abou El Kheir | Simone Marcorini | Marco Bernasconi
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Simulation of the crystallization process of Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> nanoconfined in superlattice geometries for phase change memories Phase change materials are the most promising candidates for the realization of artificial synapses for neuromorphic computing. Different resistance levels corresponding to analogic values of the synapsis conductance can be achieved by modulating the size of an amorphous region embedded in its crystalline matrix. Recently, it has been proposed that a superlattice made of alternating layers of the phase change compound Sb<sub>2</sub>Te<sub>3</sub> and of the TiTe<sub>2</sub> confining material allows for a better control of multiple intermediate resistance states and for a lower drift with time of the electrical resistance of the amorphous phase. In this work, we consider the substitution of Sb<sub>2</sub>Te<sub>3</sub> with the Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> prototypical phase change compound that should feature better data retention. By exploiting molecular dynamics simulations with a machine learning interatomic potential, we have investigated the crystallization kinetics of Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> nanoconfined in geometries mimicking Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub>/TiTe<sub>2</sub> superlattices. It turns out that nanoconfinement induces a slight reduction in the crystal growth velocities with respect to the bulk, but also an enhancement of the nucleation rate due to heterogeneous nucleation. The results support the idea of investigating Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub>/TiTe<sub>2</sub> superlattices for applications in neuromorphic devices with improved data retention. The effect on the crystallization kinetics of the addition of van der Waals interaction to the interatomic potential is also discussed. Superlattice ; Crystallization ; Materials science ; Amorphous solid ; Germanium compounds ; Molecular dynamics ; Phase (matter) ; Slab ; Kinetics ; Crystallography ; Chemical physics ; Germanium ; Condensed matter physics ; Thermodynamics ; Optoelectronics ; Chemistry ; Physics ; Computational chemistry ; Silicon Phase-change materials and chalcogenides ; Nonlinear Optical Materials Studies ; Chalcogenide Semiconductor Thin Films
Authors: Benjie Zhu | Jialiang Liu | Lingyu Liu | Chen Liu | Fang Liu | Xiaoguang Duan
Source: Chemical Engineering Journal (article, 2025-03-11)
Field: Materials Science — Materials Chemistry

Orientational dipole interaction mediated by crystallites and defects in biomass derived carbon materials of heterogeneous catalytic ozonation process Crystallite ; Catalysis ; Dipole ; Carbon fibers ; Biomass (ecology) ; Process (computing) ; Chemical engineering ; Chemistry ; Materials science ; Chemical physics ; Photochemistry ; Organic chemistry ; Crystallography ; Composite material ; Computer science ; Engineering ; Geology Thermal and Kinetic Analysis ; Luminescence Properties of Advanced Materials ; Energetic Materials and Combustion
Authors: Abdulelah S. Alshehri | Michael T. Bergman | Fengqi You | Carol K. Hall
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Biophysics-guided uncertainty-aware deep learning uncovers high-affinity plastic-binding peptides Plastic pollution, particularly microplastics (MPs), poses a significant global threat to ecosystems and human health, necessitating innovative remediation strategies. Biocompatible and biodegradable plastic-binding peptides (PBPs) offer a potential solution through targeted adsorption and subsequent MP detection or removal from the environment. A challenge in discovering plastic-binding peptides is the vast combinatorial space of possible peptides (<i>i.e.</i>, over 10<sup>15</sup> for 12-mer peptides), which far exceeds the sample sizes typically reachable by experiments or biophysics-based computational methods. One step towards addressing this issue is to train deep learning models on experimental or biophysical datasets, permitting faster and cheaper evaluations of peptides. However, deep learning predictions are not always accurate, which could waste time and money due to synthesizing and evaluating false positives. Here, we resolve this issue by combining biophysical modeling data from Peptide Binder Design (PepBD) algorithm, the predictive power and uncertainty quantification of evidential deep learning, and metaheuristic search methods to identify high-affinity PBPs for several common plastics. Molecular dynamics simulations show that the discovered PBPs have greater median adsorption free energies for polyethylene (5%), polypropylene (18%), and polystyrene (34%) relative to PBPs previously designed by PepBD. The impact of including uncertainty quantification in peptide design is demonstrated by the increasing improvement in the median adsorption free energy with decreasing uncertainty. This robust framework accelerates peptide discovery, paving the way for effective, bio-inspired solutions to MP remediation. Computational biology ; Biophysics ; Nanotechnology ; Chemistry ; Computer science ; Biology ; Materials science Machine Learning in Materials Science
Authors: Lin Li | Rong Cao | Ling Zhao | Nan Liu | Huihui Sun | Zhaohui Zhang | Yong Sun
Source: Foods (article, 2025-04-08)
Field: Chemistry — Analytical Chemistry

Near-Infrared Spectroscopy Combined with Explainable Machine Learning for Storage Time Prediction of Frozen Antarctic Krill Antarctic krill (<i>Euphausia superba</i>) represents a promising sustainable protein source for human consumption. While a portion of the catch undergoes immediate onboard processing, the majority is preserved as frozen raw material, with storage duration significantly impacting product quality and safety. This study established a novel approach for rapid quality assessment through storage time prediction. Traditional chemical quality indicators of krill during a 12-month storage were first monitored and the correlation between the quality and storage time was verified. Coupled with four different regression machine learning algorithms, near-infrared spectroscopy (NIRS) was applied to develop models. Following optimal spectral preprocessing selection and hyperparameters optimization, the light gradient boosting machine (LightGBM) model yielded the best storage time prediction performance, with the R<sup>2</sup> of the test set being 0.9882 and the errors RMSE, MAE, and MAPE being 0.3724, 0.2018, and 0.0431, respectively. Subsequent model interpretation results revealed a strong correspondence between model-related NIR features and chemical indicators associated with quality changes during krill frozen storage, which further justified the model's predictive capability. The results proved that NIR spectroscopy combined with LightGBM could be used as a rapid and effective technique for the quality evaluation of frozen Antarctic krill, offering substantial potential for industrial implementation. Antarctic krill ; Hyperparameter ; Artificial intelligence ; Computer science ; Krill ; Machine learning ; Environmental science ; Biological system ; Fishery ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Luan Minh Nguyen | Yufeng Wang | Giao Thuy-Quynh Vu | Qui Thanh Hoai Ta | Dieu Linh Tran | Ngoc Hoi Nguyen | Thuan V. Tran | Chao Zhang | Dai Hai Nguyen
Source: Nanoscale Advances (review, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

The synergy of metal–organic frameworks and biomaterials for bone tissue engineering: recent advances, challenges, and future recommendations There has recently been a noticeable increase in the prevalence of bone-related conditions, including osteoarthritis, arthritis, fractures, bone cancer, and infections, thereby creating an urgent demand for advanced biomaterials in regenerative medicine. Among emerging candidates, metal-organic frameworks (MOFs), with their large surface area, tunable porosity, and inherent bioactivity, have demonstrated considerable potential in bone tissue engineering. Initially, research focused on pristine MOFs as bioactive scaffolds or drug delivery vehicles due to their capacity for controlled encapsulation and release of therapeutic agents. However, issues such as poor stability, potential toxicity, and limited mechanical strength have driven the development of MOF-based composites. By incorporating MOFs into hydrogels, electrospun fibers, biocements, and three-dimensional scaffolds, researchers have improved biocompatibility, enhanced structural integrity, and achieved synergistic effects on bone regeneration. Consequently, these composites offer multifunctional platforms that simultaneously provide mechanical support, local drug delivery, and osteoinductive cues. This review highlights recent advances in the field, analyzes key limitations, and emphasizes the need for systematic strategies in design, synthesis, and evaluation. Furthermore, the integration of computational modeling and machine learning is proposed as a promising direction for optimizing material performance and accelerating clinical translation. Ultimately, interdisciplinary collaboration will be essential to realize the full potential of next-generation MOF-based composites in bone repair and regenerative therapies. Osteoarthritis ; Medicine ; Engineering ; Pathology ; Alternative medicine Metal-Organic Frameworks: Synthesis and Applications ; Bone Tissue Engineering Materials ; Boron Compounds in Chemistry
Authors: Yasmin Lima Brasil | J.P. Cruz-Tirado | Douglas Fernandes Barbin
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-02-04)
Field: Chemistry — Analytical Chemistry

Analysis of biodegradable films made from cassava starch and oregano essential oil using hyperspectral imaging and portable NIR spectroscopy Chemistry ; Hyperspectral imaging ; Starch ; Spectroscopy ; Essential oil ; Food science ; Remote sensing Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Essential Oils and Antimicrobial Activity
Authors: Luhao Qiu | Weifeng Peng | Bingyu Zou | Bingxi He | Feng Bao | Huanyu Lei | Mingjun Huang
Source: Polymer (article, 2025-06-26)
Field: Materials Science — Polymers and Plastics

Comprehensive advantages of colorless, transparent, and thermally stable aromatic polyimides via fluorinated terphenyl dianhydride Terphenyl ; Materials science ; Thermal stability ; Polymer chemistry ; Organic chemistry ; Chemistry Synthesis and properties of polymers ; Epoxy Resin Curing Processes ; Tribology and Wear Analysis
Authors: A. Simon | Luc Belloni | Daniel Borgis | Martin Oettel
Source: The Journal of Chemical Physics (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

The orientational structure of a model patchy particle fluid: Simulations, integral equations, density functional theory, and machine learning We investigate the orientational properties of a homogeneous and inhomogeneous tetrahedral four-patch fluid (Bol-Kern-Frenkel model). Using integral equations, either (i) HNC or (ii) a modified HNC scheme with a simulation input, the full orientational dependence of pair and direct correlation functions is determined. Density functionals for the inhomogeneous problem are constructed via two different methods. The first, molecular density functional theory, utilizes the full direct correlation function and an isotropic hard-sphere bridge functional. The second method, a machine learning approach, uses a decomposition of the functional into an isotropic reference part and a mean-field orientational part, where both parts are improved by machine learning techniques. A comparison with the simulation data at hard walls and around hard tracers shows a similar performance of the two functionals. Machine learning strategies are discussed to eliminate residual differences, with the goal of obtaining machine-learning enhanced functionals for the general anisotropic fluid. Particle (ecology) ; Integral equation ; Classical mechanics ; Functional integration ; Statistical physics ; Density functional theory ; Physics ; Computer science ; Mathematics ; Mathematical analysis ; Geology ; Quantum mechanics Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Phase Equilibria and Thermodynamics
Authors: Zhiqing Zhang | He Zhang | Yuguo Wang | Feng Jiang | Weihuang Zhong | Bin Zhang | Yanshuai Ye | Zibin Yin | Dongli Tan
Source: Fuel Processing Technology (article, 2025-01-22)
Field: Materials Science — Materials Chemistry

An artificial intelligence optimization of NOx conversion efficiency under dual catalytic mechanism reaction based on multi-objective gray wolf algorithm In the era of industry 4.0, artificial intelligence (AI) offers new perspectives for researching the complex sustainable chemical reactions in selective catalytic reduction (SCR). This aims to further improve the utilization and efficiency of SCR. In this study, a fuzzy gray relational analysis coupled with random forest (RF) and back propagation artificial neural network (BP-ANN) model was developed. This model was trained based on the Langmuir-Hinshelwood and Eley-Rideal coupled mechanism for SCR reaction mechanism, and had good fitting effect on the heat transfer rate, catalytic efficiency and ammonia (NH 3 ) slip rate of the catalytic reaction under loading conditions. And this was used as a guiding method to direct the multi-objective gray wolf optimization algorithm to optimize the basic parameters. The optimization results showed that the NH 3 slip rate of the SCR was slightly improved and the denitrification efficiency was increased up to 28 % under different loads, which had guiding significance for the lightweighting and thermal control of industrial equipment. • AI model is employed to investigate complex NO x conversiton for multi-objective optimization. • Langmuir-Hinshelwood and Eley-Rideal are coupled for SCR reaction mechanism. • The multi-objective gray wolf optimization algorithm is developed and optimized. NOx ; Dual (grammatical number) ; Mechanism (biology) ; Catalysis ; Computer science ; Optimization algorithm ; Chemistry ; Process engineering ; Mathematical optimization ; Mathematics ; Engineering ; Physics ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Industrial Gas Emission Control
Authors: A. S. Athul | Aswin V. Muthachikavil | Venkata Sudheendra Buddhiraju | Karundev Premraj | Venkataramana Runkana
Source: Energy Storage (article, 2025-01-06)
Field: Materials Science — Materials Chemistry

Identification of Stable Intermetallic Compounds for Hydrogen Storage via Machine Learning ABSTRACT Hydrogen is one of the most promising alternatives to fossil fuels for energy as it is abundant, clean and efficient. Storage and transportation of hydrogen are two key challenges faced in utilizing it as a fuel. Storing H 2 in the form of metal hydrides is safe and cost effective when compared to its compression and liquefaction. Metal hydrides leverage the ability of metals to absorb H 2 and the stored H 2 can be released from the hydride by applying heat when needed. A multi‐step methodology is proposed to identify intermetallic compounds that are thermodynamically stable and have high hydrogen storage capacity (HSC). It combines compound generation, thermodynamic stability analysis, prediction of properties of the metal hydride and ranking of discovered materials based on predicted properties. The US Department of Energy (DoE) Hydrogen Storage Materials Database and the Open Quantum Materials Database (OQMD) are utilized for building and testing machine learning (ML) models for enthalpy of formation of the intermetallic compounds, stability analysis, and enthalpy of formation, equilibrium pressure and HSC of metal hydrides. The models proposed here require only attributes of elements involved and compositional information as inputs and do no need any experimental data. Random forest algorithm was found to be the most accurate amongst the ML algorithms explored for predicting all the properties of interest. A total of 349 772 hypothetical intermetallic compounds were generated initially, out of which, only 8568 compounds were found to be stable. The highest predicted HSC of these stable compounds was found to be 3.6. Magnesium, Lithium and Germanium constitute majority of the high HSC compounds. The predictions of HSC using the present models for metal hydrides that are not in the DoE database were reasonably close to the experimental data published recently but there is scope for improvement in prediction accuracy for metal hydrides with high HSC. The findings of this study will be useful in reducing the time required for development and discovery of new hydrogen storage materials and can be used to check the practical applicability of the hydride compound using the predicted properties. Intermetallic ; Hydrogen storage ; Enthalpy ; Hydride ; Hydrogen ; Leverage (statistics) ; Materials science ; Metal ; Thermodynamics ; Chemistry ; Computer science ; Alloy ; Metallurgy ; Organic chemistry ; Machine learning ; Physics Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Hybrid Renewable Energy Systems
Authors: Zheng Luo | Ming Feng | Zijian Gao | Jinyang Yu | Cheng Chen | Hu Liang | Tao Wang | Shaohua Xue | Shen Zhou | Fangping Ouyang | Dawei Feng | Huaimin Wang | Kele Xu | Jin Zhang | Shanshan Wang
Source: Advanced Materials (article, 2025-02-23)
Field: Materials Science — Materials Chemistry

Exploring Structure Diversity in Atomic Resolution Microscopy With Graph The emergence of deep learning (DL) has provided great opportunities for the high-throughput analysis of atomic-resolution micrographs. However, the DL models trained by image patches in fixed size generally lack efficiency and flexibility when processing micrographs containing diversified atomic configurations. Herein, inspired by the similarity between the atomic structures and graphs, a few-shot learning framework based on an equivariant graph neural network (EGNN) to analyze a library of atomic structures (e.g., vacancies, phases, grain boundaries, doping, etc.) is described, showing significantly promoted robustness and three orders of magnitude reduced computing parameters compared to the image-driven DL models, which is especially evident for those aggregated vacancy lines with flexible lattice distortion. Besides, the intuitiveness of graphs enables quantitative and straightforward extraction of the atomic-scale structural features in batches, thus statistically unveiling the self-assembly dynamics of vacancy lines under electron beam irradiation. A versatile model toolkit is established by integrating EGNN sub-models for single structure recognition to process images involving varied configurations in the form of a task chain, leading to the discovery of novel doping configurations with superior electrocatalytic properties for hydrogen evolution reactions. This work provides a powerful tool to explore structure diversity in a fast, accurate, and intelligent manner. Materials science ; Vacancy defect ; Atomic units ; Artificial neural network ; Computer science ; Artificial intelligence ; Nanotechnology ; Biological system ; Chemical physics ; Crystallography ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Logan M. Brabson | Andrew J. Medford | David S. Sholl
Source: The Journal of Physical Chemistry C (article, 2025-09-08)
Field: Chemistry — Inorganic Chemistry

Comparing Classical and Machine Learning Force Fields for Modeling Deformation of Metal–Organic Frameworks Relevant for 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 ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques