jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sergei Manzhos | Johann Lüder | Pavlo Golub | Manabu Ihara
Source: Machine Learning Science and Technology (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory Abstract Machine learning (ML) of kinetic energy functionals (KEF) for orbital-free density functional theory (DFT) holds the promise of addressing an important bottleneck in large-scale ab initio materials modeling where sufficiently accurate analytic KEFs are lacking. However, ML models are not as easily handled as analytic expressions; they need to be provided in the form of algorithms and associated data. Here, we bridge the two approaches and construct an analytic expression for a KEF guided by interpretative ML of crystal cell-averaged kinetic energy densities ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mrow> <mml:mover> <mml:mi>τ</mml:mi> <mml:mo stretchy="false">¯</mml:mo> </mml:mover> </mml:mrow> </mml:mrow> </mml:mrow> </mml:math> ) of several hundred materials. A previously published dataset including multiple phases of 433 unary, binary, and ternary compounds containing Li, Al, Mg, Si, As, Ga, Sb, Na, Sn, P, and In was used for training, including data at the equilibrium geometry as well as strained structures. A hybrid Gaussian process regression—neural network method was used to understand the type of functional dependence of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mover> <mml:mi>τ</mml:mi> <mml:mo accent="true">―</mml:mo> </mml:mover> </mml:mrow> </mml:math> on the features which contained cell-averaged terms of the 4th order gradient expansion and the product of the electron density and Kohn–Sham (KS) effective potential. Based on this analysis, an analytic model is constructed that can reproduce KS DFT energy–volume curves with sufficient accuracy (pronounced minima that are sufficiently close to the minima of the Kohn–Sham DFT-based curves and with sufficiently close curvatures) to enable structure optimizations and elastic response calculations. Orbital-free density functional theory ; Density functional theory ; Kinetic energy ; Computer science ; Energy (signal processing) ; Physics ; Hybrid functional ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science
Authors: Martin Vondrák | Karsten Reuter | Johannes T. Margraf
Source: npj Computational Materials (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Pushing charge equilibration-based machine learning potentials to their limits Abstract Machine learning (ML) has demonstrated its potential in atomistic simulations to bridge the gap between accurate first-principles methods and computationally efficient empirical potentials. This is achieved by learning mappings between a system’s structure and its physical properties. State-of-the-art models for potential energy surfaces typically represent chemical structures through (semi-)local atomic environments. However, this approach neglects long-range interactions (most notably electrostatics) and non-local phenomena such as charge transfer, leading to significant errors in the description of molecules or materials in polar anisotropic environments. To address these challenges, ML frameworks that predict self-consistent charge distributions in atomistic systems using the Charge Equilibration (QEq) method are currently popular. In this approach, atomic charges are derived from an electrostatic energy expression that incorporates environment-dependent atomic electronegativities. Herein, we explore the limits of this concept at the example of the previously reported Kernel Charge Equilibration (kQEq) approach, combined with local short-ranged potentials. To this end we consider prototypical systems with varying total charge states and applied electric fields. We find that charge equilibration-based models perform well in most situations. However, we also find that some pathologies of conventional QEq carry over to the ML variants in the form of spurious charge transfer and overpolarization in the presence of static electric fields. This indicates a need for new methodological developments. Spurious relationship ; Charge (physics) ; Charge conservation ; Kernel (algebra) ; Electric charge ; Partial charge ; Electrostatics ; Atomic charge ; Electrostatic induction Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Lijiao Wu | Wei Wang | Zhuangwei Shi | Haiyu Zhang | Lianbao Ke | Xiaohong Liang | Dongmei Tian | Zhang He-Ming | Hai Bi | Wangqiao Chen | Guofu Zhou
Source: Chemical Engineering Journal (article, 2025-04-15)
Field: Chemistry — Analytical Chemistry

Rapid identification of defects in doped organic crystalline films via machine learning-enhanced hyperspectral imaging Hyperspectral imaging ; Doping ; Identification (biology) ; Materials science ; Chemical imaging ; Nanotechnology ; Artificial intelligence ; Remote sensing ; Optoelectronics ; Computer science ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Mariana Rossi | Kevin Rossi | Alan M. Lewis | Mathieu Salanne | Andrea Grisafi
Source: The Journal of Physical Chemistry Letters (article, 2025-02-24)
Field: Materials Science — Materials Chemistry

Learning the Electrostatic Response of the Electron Density through a Symmetry-Adapted Vector Field Model A current challenge in atomistic machine learning is that of efficiently predicting the response of the electron density under electric fields. We address this challenge with symmetry-adapted kernel functions that are specifically derived to account for the rotational symmetry of a three-dimensional vector field. We demonstrate the equivariance of the method on a set of rotated water molecules and show its high efficiency with respect to number of training configurations and features for liquid water and naphthalene crystals. We conclude showcasing applications for relaxed configurations of gold nanoparticles, reproducing the scaling law of the electronic polarizability with size, up to systems with more than 2000 atoms. By deriving a natural extension to equivariant learning models of the electron density, our method provides an accurate and inexpensive strategy to predict the electrostatic response of molecules and materials. Polarizability ; Symmetry (geometry) ; Scaling ; Electric field ; Equivariant map ; Kernel (algebra) ; Field (mathematics) ; Physics ; Molecule ; Chemical physics ; Materials science ; Mathematics ; Quantum mechanics ; Pure mathematics ; Geometry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods
Authors: Yu Shu | Naihua Miao | Siyu Han | Jian Zhou | Stephen R. Elliott | Zhimei Sun
Source: Journal of the American Chemical Society (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Ultralow Contact Resistance in Two-Dimensional Semiconductor Transistors Approaching the Quantum Limit The miniaturization of silicon-based integrated circuits is approaching its limits owing to strong quantum confinement and interfacial effects. Atomically thin, two-dimensional (2D) materials show great potential to break such a limit, but the realization of an ultralow contact resistance (<i>R</i><sub>C</sub>) remains a key challenge because of natural van der Waals gaps in 2D semiconductors and weak interlayer coupling at the metal-semiconductor junction (MSJ) interface. Herein, we develop efficient strategies to tune <i>R</i><sub>C</sub> and the Schottky barrier height (SBH) by combining high-throughput first-principles calculations and machine-learning techniques. Notably, we reveal that hydrogen-bonding interactions at the MSJ interface containing -OH functional groups significantly enhance metal/2D semiconductor coupling, leading to effective tunneling-barrier reduction and charge redistribution. Such interactions enable the formation of Ohmic contacts, yielding ultralow values of <i>R</i><sub>C</sub> approaching the quantum limit. Through symbolic regression, we establish robust physical models by linking <i>R</i><sub>C</sub> with critical properties, including SBH and the tunneling-specific resistivity. These findings provide critical insights and efficient approaches for designing next-generation transistor architectures with superior electrical-contact performance. Ohmic contact ; van der Waals force ; Semiconductor ; Schottky barrier ; Miniaturization ; Transistor ; Contact resistance ; Quantum ; Schottky diode 2D Materials and Applications ; Graphene research and applications ; Electronic and Structural Properties of Oxides
Authors: Shan Zhu | Junjie Qian | Yijia Dong | Fengxiang Sun | Kezhu Jiang | Shijian Zheng
Source: ACS Applied Nano Materials (article, 2025-02-28)
Field: Chemistry — Electrochemistry

Machine-Learning-Enabled Quantification of Metal-Based Nanoparticle Sizes Using Linear Sweep Voltammetry Metal-based nanoparticles serve as critical catalysts in reactions, such as hydrogen evolution and oxygen reduction, with their catalytic activity strongly influenced by particle size due to the higher density of active sites in smaller particles. Current techniques for nanoparticle size characterization often rely on costly and complex instrumentation, limiting their efficiency. Here, we present “LSV2NP”, an innovative machine learning approach leveraging the gradient boosting regression (GBR) model to predict nanoparticle sizes with the help of the selected overpotential data from a linear sweep voltammetry (LSV) test. Utilizing an extensive database of nanoparticle compositions and electrochemical properties, LSV2NP achieves accurate size predictions through data-driven insights. Validation with commercial Pt/C and PtCo/C catalysts confirms the model’s high predictive accuracy. Furthermore, we demonstrate the characterization capabilities of LSV2NP following a 15 h chronoamperometry test on Pt/C. A subsequent LSV measurement, analyzed via LSV2NP, provided particle size estimates consistent with transmission electron microscopy observations, revealing aggregated particle sizes. LSV2NP offers a powerful tool for catalyst development and the broader study of nanomaterials, streamlining nanoparticle size characterization. Linear sweep voltammetry ; Nanoparticle ; Voltammetry ; Materials science ; Cyclic voltammetry ; Nanotechnology ; Electrochemistry ; Chemistry ; Electrode Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Machine Learning and ELM
Authors: Wei Chen | Yuping Duan | Da Ma | Meng Wang | Shude Gu | Jiangyong Liu | Yupeng Shi | Yang Yang
Source: Materials Today Physics (article, 2025-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural network–enabled accelerated discovery of multifunctional metamaterials for adaptive multispectral stealth applications Multispectral image ; Materials science ; Metamaterial ; Artificial neural network ; Nanotechnology ; Remote sensing ; Systems engineering ; Artificial intelligence ; Optoelectronics ; Computer science ; Engineering Metamaterials and Metasurfaces Applications ; Optical and Acousto-Optic Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Tengdong Zhang | Chongxian Suo | Yanling Wu | Xiaodan Xu | Yong Liu | Dao‐Xin Yao | Jun Li
Source: Scientific Data (article, 2025-05-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SuperBand: an Electronic-band and Fermi surface structure database of superconductors In comparison to simpler data such as chemical formulas and lattice structures, electronic band structure data provide a more fundamental and intuitive insight into superconducting phenomena. In this work, we generate superconductor's lattice structure files optimized for density functional theory (DFT) calculations. Through DFT, we obtain electronic band for superconductors, including band structures, density of states (DOS), and Fermi surface data. Additionally, we outline efficient methodologies for acquiring structure data, establish high-throughput DFT computational protocols, and introduce tools for extracting this data from large-scale DFT calculations. As an example, we have curated a dataset containing information on 1,362 superconductors along with their experimentally determined superconducting transition temperatures (T<sub>c</sub>) as well as 1,112 experimentally verified non-superconducting materials, which is well-suited for machine learning applications. This dataset is constructed with a focus on data quality, accessibility, and usability for machine learning models aimed at predicting superconducting properties. Fermi surface ; Superconductivity ; Condensed matter physics ; Electronic structure ; Fermi level ; Electronic database ; Materials science ; Database ; Physics ; Computer science ; Nuclear physics ; Electron Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Superconductivity in MgB2 and Alloys
Authors: Weirong Miao | Qiuling Jiang | Xiuli Hou | Cheng Lin | Shize Liu | Ying Cheng | Ying Wang
Source: Journal of Colloid and Interface Science (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Tailoring metals, axial ligands, and carbon substrates in TMN4–graphene for direct seawater electrolysis: A synergistic approach using density functional theory and machine learning algorithms Seawater ; Electrolysis ; Graphene ; Density functional theory ; Carbon fibers ; Materials science ; Algorithm ; Chemistry ; Chemical engineering ; Computer science ; Nanotechnology ; Computational chemistry ; Composite number ; Engineering ; Oceanography ; Physical chemistry ; Electrode ; Geology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Shuting Jin | Xiangrong Liu | Junlin Xu | Sisi Yuan | Hao Xiang | Lian Shen | Chunyan Li | Zhangming Niu | Yinhui Jiang
Source: Information Fusion (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Adaptive symmetry-based adversarial perturbation augmentation for molecular graph representations with dual-fusion attention information Adversarial system ; Computer science ; Dual (grammatical number) ; Perturbation (astronomy) ; Graph ; Theoretical computer science ; Information fusion ; Artificial intelligence ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Adversarial Robustness in Machine Learning
Authors: Jinni Yang | Runtong Pan | Jikai Sun | Jianzhong Wu
Source: Journal of Chemical Theory and Computation (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

High-Dimensional Operator Learning for Molecular Density Functional Theory Classical density functional theory (cDFT) provides a systematic framework to predict the structure and thermodynamic properties of chemical systems through molecular density profiles. Whereas the statistical-mechanical framework is theoretically rigorous, its applications are often constrained by challenges in formulating a reliable free-energy functional and the complexity of solving multidimensional integro-differential equations. In this work, we established a convolutional operator learning method that effectively separates the high-dimensional molecular density profile into lower-dimensional components, thereby exponentially reducing the vast input space. The operator learning network demonstrates exceptional learning capabilities, accurately mapping the relationship between the molecular density profile and its one-body direct correlation function for an atomistic polarizable model of carbon dioxide. The machine-learning procedure can be generalized to more complex molecular systems, offering high-precision operator-cDFT calculations at a low computational cost. Operator (biology) ; Computer science ; Density functional theory ; Polarizability ; Function (biology) ; Statistical physics ; Work (physics) ; Physics ; Chemistry ; Computational chemistry ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Phase Equilibria and Thermodynamics
Authors: Chaeseong Na | Sei‐Moon Shin | Dong Hun Lee | Yeomyung Yoon | Suk‐kyun Ahn | Hyosung An | Jaegeun Lee | Chae Bin Kim
Source: Composites Science and Technology (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Data-driven engineering and analysis of polymer composites with high thermal conductivity Thermal conductivity ; Composite number ; Thermal ; Micromechanics ; Polydimethylsiloxane ; Polymer ; Particle (ecology) ; Thermal grease ; Thermal stability Thermal properties of materials ; Composite Material Mechanics ; Machine Learning in Materials Science
Authors: Rui Shu | Sijie Liu | Jianlong Wang | Daohong Zhang
Source: Trends in Food Science & Technology (article, 2025-02-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Anisotropic and isotropic gold nanomaterials continue to heat up: From properties to food safety monitoring Nanomaterials ; Isotropy ; Anisotropy ; Materials science ; Nanotechnology ; Food safety ; Physics ; Chemistry ; Food science ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis
Authors: Jinwoo Kim | Jaewan Baek | Mingi Choi
Source: Array (article, 2025-05-04)
Field: Materials Science — Materials Chemistry

Machine-learning-driven feature importance analysis for guiding the protonic ceramic fuel cell manufacturing The protonic ceramic fuel cell (PCFC) is currently attracting attention as a promising energy-conversion device capable of generating electricity from hydrogen with high efficiency. However, when developing high-performance PCFCs, a wide range of material properties and manufacturing processes must be optimized, necessitating tremendous time and manpower investments as well as a high cost. To address these issues, this study proposes a method by which to analyze the effects of certain materials and manufacturing processes on the fabrication of PCFCs, assisted by machine learning (ML). Based on data from earlier work, we first evaluate the performance-predicting capabilities of 6 ML models, showing the best-predicting performance with XGBoost model. Based on the selected model of XGBoost, we also conduct the feature analysis using Shapley additive explanations, which successfully determine the factors contributing most to the PCFC performance in terms of the materials and manufacturing processes for the anode, cathode, and electrolyte in each case. These results can give us guidelines for the efficient manufacturing of the PCFC. • ML guided the optimization of material and manufacturing processes for PCFCs. • XGBoost model exhibits the best PCFC performance prediction. • Feature-importance analysis provides the importance of the manufacturing process. • SHAP was used to analyze variable effects on performance and identify correlations. Feature (linguistics) ; Ceramic ; Fuel cells ; Manufacturing engineering ; Artificial intelligence ; Computer science ; Materials science ; Engineering ; Chemical engineering ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electronic and Structural Properties of Oxides
Authors: Zelin Zhang | Chunlei Shang | Hong‐Hui Wu | Chaolei Zhang | Faguo Hou | Haoliang Wang | Shuize Wang | Junheng Gao | Haitao Zhao | Xinping Mao
Source: Rare Metals (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Recent developments in low‐expansion alloys for high‐performance applications Abstract The growing demand for low‐expansion alloys in high‐tech industries such as aerospace, electronics, communications, and healthcare underscores the necessity of enhancing their performance under extreme operating conditions. This review explores the influence of alloy composition and processing techniques on the key properties of low‐expansion alloys, including strength, operating temperature range, magnetic properties, corrosion resistance, and thermal conductivity. The role of microalloying and the optimization of processing parameters in improving these properties are discussed, with an emphasis on the underlying mechanisms and the intricate relationships between composition, processing, and properties. Future breakthroughs in studying low‐expansion alloys are anticipated through the use of multi‐functional databases, high‐throughput experiments or calculations, and machine learning for multi‐objective optimization. This work provides insightful perspectives and practical guidance for advancing low‐expansion alloys in both academic research and industrial applications. Materials science ; Engineering physics ; Nanotechnology ; Engineering Thermal Expansion and Ionic Conductivity ; High Entropy Alloys Studies ; Magnetic Properties of Alloys
Authors: Josef M. Gallmetzer | Jakob Gamper | Stefanie Kröll | Thomas S. Hofer
Source: The Journal of Physical Chemistry C (article, 2025-03-04)
Field: Chemistry — Inorganic Chemistry

Comparative Study of UMCM-9 Polymorphs: Structural, Dynamic, and Hydrogen Storage Properties via Atomistic Simulations The structural and dynamic properties of two polymorphs of the metal-organic framework UMCM-9 (UMCM-9-α and -β) have been studied via molecular dynamics (MD) simulations in conjunction with density functional tight binding (DFTB) as well as the newly developed MACE-MP neural network potential (NNP). Based on these calculations, a novel UMCM-9-β polymorph is proposed that exhibits reduced linker strain and increased flexibility compared to UMCM-9-α, which is shown to be energetically less stable. UMCM-9-β exhibits enhanced diffusion of molecular hydrogen due to weaker host-guest interactions, whereas UMCM-9-α exhibits stronger interactions, leading to improved hydrogen adsorption. The results suggest that synthesis conditions may control the formation of both polymorphs: UMCM-9-β is likely to be the thermodynamic product, forming under stable conditions, while UMCM-9-α may be the kinetic product, forming under accelerated synthesis conditions. This study highlights the potential for optimizing MOFs for specific gas storage applications to achieve the desired structural and associated gas storage properties. Hydrogen storage ; Molecular dynamics ; Materials science ; Diffusion ; Chemical physics ; Hydrogen ; Chemistry ; Thermodynamics ; Computational chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Crystallography and molecular interactions
Authors: Shuwei Tang | Yunzhuo Zhang | Ai Peng | Da Wan | T. F. Zheng | Pengfei Zhang | Zhiwei Zhang | Yujie Bao | Shu‐Lin Bai
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-driven approach to the performance of SnSe-based thermoelectric materials An interpretable machine learning approach is used to optimize SnSe-based thermoelectric materials, identifying key doping strategies and offering data-driven insights for future material design. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Nanotechnology ; Engineering physics ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science