jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sherif Abdulkader Tawfik | Sunil Gupta | Svetha Venkatesh
Source: The Journal of Physical Chemistry A (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

Predicting the Electron Density of Charged Systems Using Machine Learning The prediction of the electron density in molecules and crystals is a key pillar in the first-principles computation of their properties. Using machine learning to predict the electron density by using the atomic structure alone can save the computational cost of performing first-principles computations. While various machine learning approaches have been introduced for predicting the electron density, none of them predict the electron density for charged systems. This work extends a recent machine learning charge density model, DeepDFT, by including the charge of the structure as an input parameter into the model. We establish an input charge representation approach that successfully predicts the charged electron densities for several test cases, including charged defective perovskites, LiCoO<sub>2</sub> supercells with multiple Li vacancies, diamond-based defects, metal-organic frameworks, and molecular crystals. Electron density ; Computer science ; Materials science ; Electron ; Physics ; Nuclear physics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fuel Cells and Related Materials
Authors: Hiroto Okuyama | Yuuki Sugawara | Takeo Yamaguchi
Source: ACS Applied Polymer Materials (article, 2025-09-04)
Field: Materials Science — Surfaces, Coatings and Films

Quantitative Comparison of Protein Adsorption on Polyethylene Glycol and Zwitterionic Brushes: A Machine Learning Approach Endowing a material surface with antifouling property using hydrophilic polymer brushes has attracted considerable attention in the design of devices such as water treatment membranes and medical devices. To design an ideal antifouling surface, a comprehensive understanding of protein adsorption on the polymer brush is essential; however, research in this regard is limited because of the complexity of the fouling phenomenon. In this study, a machine learning (ML) approach was employed to evaluate the protein adsorption behavior on a nonionic polyethylene glycol (PEG) brush. The ML model was constructed including the polymer density and molecular weight characteristics of the brush, enabling the prediction of the desired PEG brush state in a specific aqueous environment. The obtained results were then compared with the predictions from the previously constructed data set for a zwitterionic brush, revealing the differences in adsorption properties between the ionic and nonionic brushes. This approach provides a platform for the selection of suitable polymer brushes for practical application in aqueous environments. Polyethylene glycol ; Adsorption ; Protein adsorption ; Chemical engineering ; Chemistry ; Materials science ; Chromatography ; Computer science ; Organic chemistry ; Engineering Polymer Surface Interaction Studies ; Membrane Separation Technologies ; Minerals Flotation and Separation Techniques
Authors: Junsoo Han | Kévin Ogle
Source: Research Square (preprint, 2025-04-28)
Field: Materials Science — Materials Chemistry

Element-resolved electrochemical database: AESEC polarization curves of ZnAlMg alloy coating constituents Polarization (electrochemistry) ; Materials science ; Coating ; Alloy ; Electrochemistry ; Metallurgy ; Composite material ; Chemistry ; Electrode ; Physical chemistry Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Zige Liu | Siqin Cao | Bojun Liu | Xuhui Huang
Source: Aggregate (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Revealing Competing Kinetic Pathways in Amphiphilic Pt(II) Complex Self‐Assembly via Deep Learning with Graph Neural Networks ABSTRACT Supramolecular assembly is a versatile bottom‐up strategy for creating advanced functional materials. Metallic platinum–platinum (Pt···Pt) interactions provide a distinctive driving force for supramolecular assembly due to their strong, directional, and long‐range nature. Despite their importance, the microscopic dynamics underlying the self‐assembly of Pt(II) complexes remain challenging to probe experimentally. Molecular dynamics (MD) simulations can capture these processes at atomic resolution, but extracting kinetic pathways is complicated by the indistinguishability and permutation of identical monomers within self‐assembled structures. In this study, we employ GraphVAMPnet, a deep learning framework based on graph neural networks (GNN), on extensive MD simulations of amphiphilic PtB complexes during the early stage of self‐assembly. GraphVAMPnet inherently accounts for permutational, rotational, and translational invariance, making it well‐suited for analyzing self‐assembly dynamics. Our analysis reveals three slow collective variables (CVs) that govern PtB self‐assembly. The slowest mode (CV1) separates two distinct kinetic growth routes: an incremental growth mechanism, in which single monomers join existing aggregates with predominantly antiparallel packing between two adjacent PtB complexes (CV3), and a hopping growth mechanism, in which clusters of smaller size merge via heterogeneous collisions, yielding a mix of antiparallel and parallel packing arrangements (CV2). Further energetic analysis indicates that incremental growth is favored, potentially leading to the well‐ordered nanosheet morphologies observed experimentally. Our findings provide molecular‐level insight into PtB self‐assembly pathways and showcase the capability of GraphVAMPnet in dissecting the complex dynamics of supramolecular assembly. Antiparallel (mathematics) ; Supramolecular chemistry ; Monomer ; Merge (version control) ; Deep learning ; Artificial neural network ; Kinetic energy Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Supramolecular Chemistry and Complexes
Authors: Yoyo Hinuma
Source: Acta Crystallographica Section A Foundations and Advances (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Direct derivation of anisotropic atomic displacement parameters from molecular dynamics simulations in extended solids with substitutional disorder using a neural network potential Atomic displacement parameters (ADPs) are crystallographic information describing the statistical distribution of atoms around an atom site. Anisotropic ADPs by atom were directly derived from classical molecular dynamics (MD) simulations using a universal machine-learned potential. The (co)valences of atom positions were taken over recordings at different time steps in a single MD simulation. The procedure is demonstrated on extended solids, namely rocksalt structure MgO and three thermoelectric materials, Ag<sub>8</sub>SnSe<sub>6</sub>, Na<sub>2</sub>In<sub>2</sub>Sn<sub>4</sub> and BaCu<sub>1.14</sub>In<sub>0.86</sub>P<sub>2</sub>. Unlike the very frequently used lattice dynamics approach, the MD approach can obtain ADPs in crystals with substitutional disorder and explicitly at finite temperature, but not under conditions where atoms migrate in the crystal. The calculated ADP approaches 0 when the temperature approaches 0, and the ADP is proportional to the temperature when the atom is in a harmonic potential and the sole contribution to the actual non-zero ADP is from the zero-point motion. The zero-point motion contribution can be estimated from the proportionality constant assuming this Einstein model. ADPs from MD simulations could act as a tool complementing experimental efforts to understand the crystal structure including the distribution of atoms around atom sites. Anisotropy ; Molecular dynamics ; Displacement (psychology) ; Artificial neural network ; Materials science ; Statistical physics ; Dynamics (music) ; Physics ; Classical mechanics ; Condensed matter physics ; Computer science ; Quantum mechanics ; Artificial intelligence ; Psychology X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Surface and Thin Film Phenomena
Authors: Igor Vorotnikov | Fedor Romashov | Nikita Rybin | Maxim Rakhuba | Ivan S. Novikov
Source: The Journal of Chemical Physics (article, 2025-12-28)
Field: Materials Science — Materials Chemistry

Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials Machine-learning interatomic potentials (MLIPs) have become a mainstay in computationally guided materials science, surpassing traditional force fields due to their flexible functional form and superior accuracy in reproducing physical properties of materials. This flexibility is achieved through mathematically rigorous basis sets that describe interatomic interactions within a local atomic environment. The number of parameters in these basis sets influences both the size of the training dataset required and the computational speed of the MLIP. Consequently, compressing MLIPs by reducing the number of parameters is a promising route to more efficient simulations. In this work, we use low-rank matrix and tensor factorizations under fixed-rank constraints to achieve this compression. In addition, we demonstrate that an algorithm with automatic rank augmentation helps to find a deeper local minimum of the fitted potential. The methodology is mainly verified using the Moment Tensor Potential (MTP) model and benchmarked on multi-component systems: a Mo-Nb-Ta-W medium-entropy alloy, molten LiF-NaF-KF, and a glycine molecular crystal. The proposed approach achieves up to 50% compression without any loss of MTP accuracy. We also demonstrate that the developed methodology is universal and can be applied to compress other MLIPs on the example of atomic cluster expansion. Tensor (intrinsic definition) ; Interatomic potential ; Basis (linear algebra) ; Matrix (chemical analysis) ; Compression (physics) ; Rank (graph theory) ; Flexibility (engineering) ; A priori and a posteriori Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Ahmet Gürkan Yüksek
Source: Scientia Iranica (article, 2025-01-20)
Field: Materials Science — Materials Chemistry

Performance Evaluation of ANN and Ensemble Learning Methods in Predicting Wear Properties of Porcelain Ceramic Composites This study extensively investigated the fabrication and wear properties of aluminum titanate (Al₂TiO₅) and mullite (3Al₂O₃-2SiO₂) doped porcelain ceramic-composites produced by the powder metallurgy method. The porcelain ceramics were prepared by powder metallurgy and the wear resistance and other mechanical properties were evaluated based on the data obtained. The experimental wear results were modeled and analyzed using ensemble learning(EL) methods and Artificial Neural Networks (ANN). Boosting and random forest(RF) algorithms were employed among the ensemble methods. Basic statistical measures such as R², RMSE, MAE, and MAPE were utilized to evaluate model performance. Boosting and RF methods also produced successful results, but ANN was found to be superior in terms of accuracy and overall performance. In the study, pure porcelain (P), mullite doped porcelain (PM), aluminum titanate doped porcelain (PAT) and aluminum titanate-mullite doped (PMAT) porcelain models were investigated and compared separately. The findings provide valuable contributions to the development of high-performance ceramic-composites in materials engineering and optimization of the wear behavior of these materials. In this context, the applicability of advanced machine learning methods in materials science and the advantages of these methods are discussed in detail. Composite material ; Materials science ; Ceramic ; Ensemble learning ; Artificial intelligence ; Computer science Metal Alloys Wear and Properties ; Industrial Vision Systems and Defect Detection ; Tribology and Wear Analysis
Authors: Hanhwi Jang | Wooseok Lee | Hwa‐Jung Kim | S.H. Cha | Hosun Shin | Won Bo Lee | Min‐Wook Oh | Yeon Sik Jung | YongJoo Kim
Source: Advanced Materials (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics High-entropy alloys are emerging as highly efficient thermoelectrics, but their vast compositional spaces hinder efficient material discovery using conventional heuristics-based and advanced machine learning approaches. Here, this fundamental challenge is addressed by demonstrating an active learning framework that leverages sparse experimental data (80 out of 16206) to efficiently identify three new high-entropy chalcogenides (HECs) with remarkable thermoelectric performance (zT >2). By integrating physics-informed descriptors with uncertainty-aware sampling, this model efficiently assimilates latent structure-property relationships. This allows for systematic exclusion of unfavorable chemistries, enabling even non-experts in thermoelectrics to design unexplored systems with arbitrary components. Furthermore, novel atomic arrangements and distinctive electron and phonon transport properties are uncovered, which are responsible for the superior performance in HECs, advancing the understanding of physical phenomena in disorder-rich systems. Thermoelectric materials ; Thermoelectric effect ; Phonon ; Public records ; Germanium compounds Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Dongyang Kuang | Shiwei Li | Buxuan Wang | Chao Xiong | Shi-Chang Zhang | Yanyao Zhang
Source: Scientific Reports (article, 2025-07-25)
Field: Materials Science — Materials Chemistry

Joint learning equation of state surfaces with uncertainty-aware physically regularized neural networks The equation of state (EOS) is essential for understanding material behavior under different pressure-temperature-volume (P-T-V) conditions across various disciplines. Traditional models, such as the Mie-Gr[Formula: see text]neisen-Debye equation, rely on thermodynamic assumptions and expert knowledge, while classical Gaussian process based machine learning approaches can be sensitive to choice of kernels and are limited by scalability and extrapolability. To overcome these limitations, we propose EOSNN, a neural network based physics informed deep learning method that jointly learns multiple EOS surfaces from diverse data sources, including static and dynamic compression and ab initio calculations. Additionally, a probabilistic model is developed to account for both aleatoric and epistemic uncertainties. Our numerical experiments show that EOSNN outperforms traditional and Gaussian process methods in several aspects including accuracy, flexibility under different constraints, and extensibility for various tasks and constraints. Particularly on the challenging partially supervised task where energy information is limited on part of the Hugoniot curve, our method's prediction for the energy off-Hugoniot can still reach a [Formula: see text] score as high as 0.83 and RMSE as low as 0.52 eV/atom with proper selection of regularization for physical consistency. This result shows a significant improvement over the case without regularization, with an increase of 0.2 in [Formula: see text] and a reduction of 0.26 eV/atom in energy prediction error, and is even slightly better than the fully supervised case for traditional regression method based on the Mie-Gr[Formula: see text]neisen equation. These benefits can be further enhanced with physics-informed regularizations on quantities such as heat capacity ([Formula: see text]), Gr[Formula: see text]neisen parameter (γ) or bulk modulus ([Formula: see text]). Computer science ; Regularization (linguistics) ; Gaussian process ; Artificial neural network ; Leverage (statistics) ; Kriging ; Gaussian ; Equation of state ; Probabilistic logic ; Machine learning ; Artificial intelligence ; Algorithm ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Advanced Chemical Physics Studies
Authors: Jinhan Xu | Xuan Chen | Xiaodan Bai | Chengyu Ding | Guisen Liu | Yuanjun Sun | Hongxiang Zong | Xiangdong Ding
Source: Advanced Science (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Mechanism‐Aware Digital Twin for High‐Temperature Creep Prediction in Mo–Re Alloys Predicting the long-term deformation of structural materials under extreme conditions remains a grand challenge in materials science, especially for refractory alloys, where high-temperature creep limits performance and service life. Here, a physics-informed digital twin framework is developed that integrates a viscoplastic self-consistent (VPSC) model, real-time high-temperature creep experiments, and a calibration neural network to predict and elucidate the creep behavior of Mo-14Re alloys. The digital twin accurately reproduces creep curves across 1000-1200 °C and 60-150 MPa, achieving <5% deviation from experiments. Crucially, the learned parameter trajectories uncover a previously unrecognized mechanism: Re solute atoms are dragged by gliding dislocations ("solute-drag" effect), leading to rhenium segregation at grain boundaries and compromised creep strength. This is corroborated by post-mortem TEM and molecular dynamics simulations. Furthermore, the model-guided strategy reveals that nanoscale La<sub>2</sub>O<sub>3</sub> precipitates can pin dislocations and suppress Re segregation, significantly improving creep resistance. This work advances the mechanistic understanding of refractory alloy creep and demonstrates a transferable AI-enabled digital twin approach for materials design under extreme environments. Creep ; Viscoplasticity ; Refractory (planetary science) ; Grain boundary ; Work (physics) ; Alloy ; Deformation (meteorology) ; Structural material Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; MXene and MAX Phase Materials
Authors: Konstantin Ushenin | Kuzma Khrabrov | Artem Tsypin | Anton Ber | Egor Rumiantsev | Artur Kadurin
Source: Journal of Cheminformatics (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

LAGNet: better electron density prediction for LCAO-based data and drug-like substances The electron density is an important object in quantum chemistry that is crucial for many downstream tasks in drug design. Recent deep learning approaches predict the electron density around a molecule from atom types and atom positions. Most of these methods use the plane wave (PW) numerical method as a source of ground-truth training data. However, the drug design field mostly uses the Linear Combination of Atomic Orbitals (LCAO) for computation of quantum properties. In this study, we focus on prediction of the electron density for drug-like substances and training neural networks with LCAO-based datasets. Our experiments show that proper handling of large amplitudes of core orbitals is crucial for training on LCAO-based data. We propose to store the electron density with the standard grids instead of the uniform grid. This allowed us to reduce the number of probing points per molecule by 43 times and reduce storage space requirements by 8 times. Finally, we propose a novel architecture based on the DeepDFT model that we name LAGNet. It is specifically designed and tuned for drug-like substances and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>∇</mml:mi> <mml:mn>2</mml:mn></mml:msup> </mml:math> DFT dataset. Computer science ; Data mining ; Drug ; Linear combination of atomic orbitals ; Data science ; Chemistry ; Computational chemistry ; Pharmacology ; Density functional theory ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Wenjie Zhou | Shi Liu
Source: ArXiv.org (preprint, 2025-01-17)
Field: Materials Science — Materials Chemistry

Two-dimensional ferroelectric crystal with temperature-invariant ultralow thermal conductivity We report the discovery of temperature-invariant ultralow thermal conductivity ($κ$) in monolayer $β'$-In$_2$Se$_3$, a two-dimensional ferroelectric crystal with in-plane polarization. Using a combination of generalized Wigner transport equation theory and machine-learning-assisted molecular dynamics simulations, we reveal that the balance between particle-like phonon propagating and wave-like tunneling transport mechanisms results in a propagating-tunneling-invariant (PTI) ultralow thermal conductivity of approximately 0.6 W/mK (comparable to that of glass) over a broad temperature range ($150 Ferroelectricity ; Thermal conductivity ; Invariant (physics) ; Materials science ; Condensed matter physics ; Thermal ; Crystal (programming language) ; Thermodynamics ; Physics ; Mathematical physics ; Composite material ; Optoelectronics ; Computer science ; Dielectric Material Dynamics and Properties ; Material Properties and Applications
Authors: Hui Li | Pan Liu | Shijing Liu | Bin Yang
Source: Scientific Reports (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Deep learning-assisted attribute prediction of chalcogenide glasses based on graph classification Chalcogenide glasses, renowned for their exceptional optoelectronic properties, have become the material of choice for numerous microelectronic and optical devices. With the rapid development of artificial intelligence technologies in the field of materials science, researchers have increasingly incorporated machine learning (ML) methods to accelerate the exploration of composition-structure-property relationships in chalcogenide glasses. However, traditional ML methods, which predominantly rely on single-property modelling, are often inadequate for addressing the practical demand for multiproperty collaborative optimization. To overcome this limitation, this study proposes a graph-based deep learning approach and develops a novel model for the efficient prediction of key properties of chalcogenide glasses. Furthermore, relevant data were collected from the publicly available SciGlass database to construct an experimental dataset, and the performance of the developed model was systematically evaluated. The experimental results demonstrate the model's remarkable stability and predictive performance, highlighting its potential application value in the design and development of chalcogenide glasses. Computer science ; Graph ; Artificial intelligence ; Chalcogenide ; Machine learning ; Materials science ; Theoretical computer science Phase-change materials and chalcogenides