jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Johannes Wasmer | Philipp Rüssmann | Stefano Sanvito | Attila Cangi | Ira Assent
Source: JuSER (Forschungszentrum Jülich) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Towards all-electron treatment in electronic structure machine learning Electronic structure calculations with density functional theory (DFT) are today the most widely used technique for computational materials design. As materials design enters the artificial intelligence (AI) age, large databases of these calculations, such as the Materials Project, form the basis for creating foundation models. This can be seen in leaderboards such as Matbench Discovery [1]. These AI foundation models mark a paradigm shift in computational materials design since they accelerate the time to solution by orders of magnitude. However, the current DFT databases and AI surrogate models are not accurate enough to design quantum materials. This problem can be solved by employing all-electron, full-potential, fully relativistic DFT, that can reach the required calculation precision. The JuDFT.de codes are among the few established implementations of such methods [2]. They are the only all-electron DFT codes supporting high-throughput data generation that implement the FAIR data guidelines via their plugin integration with the AiiDA.net workflow engine.We present our progress on the open problem of surrogate AI models for all-electron DFT data for the JuDFT codes. We focus on the JuKKR code, and materials systems with defects, such as magnetically doped topological insulators [3]. We predict the converged electron potential for accelerated DFT, and first-principles exchange interactions for accelerated spin dynamics.[1] Riebesell, J. et al. Matbench Discovery – A framework to evaluate machine learning crystal stability predictions. Preprint at https://doi.org/10.48550/arXiv.2308.14920 (2024).[2] Bosoni, E. et al. How to verify the precision of density-functional-theory implementations via reproducible and universal workflows. Nat Rev Phys 6, 45–58 (2024).[3] Mozumder, R., Wasmer, J., Antognini Silva, D., Blügel, S. &amp; Rüßmann, P. High-throughput magnetic co-doping and design of exchange interactions in topological insulators. Phys. Rev. Mater. 8, 104201 (2024). Workflow ; Implementation ; Stability (learning theory) ; Preprint ; Electronic structure ; Density functional theory ; Computational learning theory ; Computational model Machine Learning in Materials Science ; Topological Materials and Phenomena ; Quantum many-body systems
Authors: Heng Ni | Hegui Liu | Yijun Zhang | Xiaodong Yang | Yang Shen
Source: Applied Physics Letters (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

High-throughput discovery of surface-functionalized MXenes for photocatalytic water splitting via integrated machine learning and DFT screening MXenes, as highly promising two-dimensional (2D) materials, have attracted significant attention in the field of photocatalytic water splitting. However, the enormous diversity of material structures and the substantial costs associated with experimental validation have hindered the exploration of MXenes' potential. In this work, we obtained property data for 213 different MXene structures through density functional theory (DFT) calculations and subsequently developed an atomic descriptor suitable for MXenes. Building upon this foundation, we established two machine learning (ML) prediction models, used the predicted data to perform high-throughput screening of numerous MXene structures, and finally conducted theoretical validation using the Heyd–Scuseria–Ernzerhof (HSE06) method and ab initio molecular dynamics simulations. The results show that the coefficient of determination (R2) of the two optimal prediction models reached 95% and 93%, respectively. Through high-throughput screening, 21 photocatalyst candidates were identified from 23 857 MXene structures, among which YTaC(OCl)(NCS) and Y2CFH exhibit high optical absorption coefficients (&amp;gt;1 × 105 cm−1), solar-to-hydrogen (STH) efficiencies (&amp;gt;14%), and thermodynamic stability. This work establishes an integrated strategy combining ML, DFT, and high-throughput screening, providing an effective approach for discovering and investigating potential MXene photocatalysts. MXenes ; Photocatalysis ; Ab initio ; Density functional theory ; Field (mathematics) ; Property (philosophy) ; Work (physics) MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Matthew Niichel | R. P. Madden | Hannah Pike | True Miller | Brian Jowers | Stylianos Chatzidakis | Vasileios Theos
Source: Preprints.org (preprint, 2025-04-12)
Field: Materials Science — Materials Chemistry

&lt;span style="mso-bidi-font-size: 18.0pt; border: none windowtext 1.0pt; mso-border-alt: none windowtext 0cm; padding: 0cm;"&gt;Design and Characterization of the Modified Purdue Subcritical Pile for Nuclear Research Applications&lt;span style="mso-fareast-language: EN-US;"&gt; First demonstrated in 1942, subcritical and zero-power critical assemblies, also known as piles, are a fundamental tool for research and education at universities. Traditionally, their role has been primarily instructional and for measuring fundamental properties of neutron diffusion and transport. However, these assemblies could hold potential for modern applications and nuclear research. The Purdue University subcritical pile previously lacked a substantial testing volume, limiting its utility to simple neutron activation experiments for the purpose of undergraduate education. Following the design and addition of a mechanical and electrical testbed, this paper aims to provide an overview of the testbed design and characterize its neutron and gamma flux of the rearranged Purdue subcritical pile, justifying its use as a modern scientific instrument. The newly installed 1.5*10^5 cubic-centimeter volume testbed enables a systematic investigation of neutron and gamma effects on materials and the generation of a comprehensive dataset with the potential for machine learning applications. The neutron flux throughout the pile is measured using gold-197 and indium-115 foil activation alongside cadmium-covered foils for two-group neutron energy classification. The neutron flux measurements are then used to benchmark a detailed geometrically and materialistic accurate Monte-Carlo model using OpenMC and MCNP6.3. The experimental measurements reveal the testbed has a neutron environment with a total neutron flux approaching 9.5*10^3 n/cm^2*s and a thermal flux of 6.5*10^3 n/cm^2*s. This work establishes the modified Purdue subcritical pile can provide fair neutron and gamma fluxes within a large volume to enable radiation testing of integral electronic components and as a versatile research instrument with the potential to support material testing and limited isotope activation, while generating valuable training datasets for machine learning algorithms in nuclear applications. Padding ; Pile ; Span (engineering) ; Characterization (materials science) ; Structural engineering ; Mathematics ; Engineering ; Materials science ; Statistics ; Nanotechnology Graphite, nuclear technology, radiation studies ; Nuclear Physics and Applications ; Nuclear reactor physics and engineering
Authors: Murad  A. Yaghi | M.  Kh Hamad | M.I. Sayyed | M.H.A. Mhareb
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Modeling and Simulation of Linear Attenuation Coefficient Using Machine Learning: A Cross-Methodological Comparison Monte Carlo method ; Mean squared error ; Robustness (evolution) ; Decision tree ; Random forest ; Tree (set theory) Glass properties and applications ; Epoxy Resin Curing Processes ; Machine Learning in Materials Science
Authors: Valeriia Hritskova | Oleh Semenenko | Mariya Shapovalova | Олексій Водка
Source:  (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

MatViz3D Software Tool for Simulation of Polycrystalline and Hybrid Materials This study presents a significant extension of the MatViz3D software environment, initially designed to generate polycrystalline microstructures, to support a broader range of material systems. In response to the growing demand for versatile computational tools in materials science, the enhanced MatViz3D platform now enables the modeling of diverse microstructure types, including mesoporous architectures, composite materials, and thin films. These extensions are achieved by integrating new generation algorithms and configurable parameter sets seamlessly into the existing modular framework. The improved system allows for precise geometric control, supports high-resolution voxel-based representations, and facilitates export to external simulation platforms for mechanical analysis. Comprehensive testing confirms the generated structures' stability, scalability, and reproducibility. As a result, MatViz3D emerges as a flexible software tool suitable for computational research, digital material design, and data generation for machine learning workflows in materials engineering. Modular design ; Workflow ; Software ; Software tool ; Hybrid system ; Range (aeronautics) Machine Learning in Materials Science ; Composite Material Mechanics ; Block Copolymer Self-Assembly
Authors: Yuyang Cai | Zhiwei Zheng | Zhineng Zhong | Yuhan Zhang | Tengyan Huang | Yucheng Cao | Dawei Zheng | Yen‐Hung Lin | Fion Sze Yan Yeung | Kuan‐Chang Chang | Jie Chen | Hoi Sing Kwok | Lei Lü | Yufeng Jin
Source: Advanced Science (article, 2025-12-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Heterointerface‐Functionalized Photoelectric Response of Metal‐Oxide Schottky Photodiode for Intelligent Fire Detection Ultraviolet photodetectors (UV-PDs) based on wide-bandgap oxide semiconductors (OSs) hold broad prospects in scientific, civil, and especially fire alarm applications. However, the hotly pursued flexible and hetero-integration capabilities are hindered by the high processing temperature of mainstream Ga<sub>2</sub>O<sub>3</sub> UV-PDs, while the selective UV detection capability of low-temperature OS (LT-OS) is undermined by the pronounced contradictory effects of abundant native defects on electric and optoelectrical properties. Herein, a heterointerface-functionalized Schottky photodiode (HF-SPD) is proposed to realize high-performance LT-OS UV-PD. Due to the sophistically modulated Schottky contact and defect distribution, ultralow dark current and remarkable photoresponse are simultaneously achieved in In<sub>2</sub>O<sub>3</sub> HF-SPDs. Under the typical 360-nm UV illumination, the responsivity (R) and detectivity (D<sup>*</sup>) respectively reach 27.75 A W<sup>-1</sup> and 2.036 × 10<sup>13</sup> Jones, enabling the early-fire-warning capability. Moreover, the HF-SPD possesses unique wavelength selectivity and bias-tunable photoelectrical characteristics. By feeding such multidimensional feature data into the multi-layer perceptron (MLP) neural network, the pattern recognition of the spectrum delivers a remarkable accuracy of 99.94%, ensuring the precise identification of combustion materials. Such an intelligent fire warning system demonstrates the prospects of algorithm-boosted LT-OS HF-SPDs in ubiquitous intelligent systems, such as wearable photodetection and in-sensor computing (ISC) devices. Responsivity ; Photodiode ; Photodetector ; Schottky diode ; Detector ; Ultraviolet ; Photodetection ; Photoelectric effect Ga2O3 and related materials ; Gas Sensing Nanomaterials and Sensors ; Transition Metal Oxide Nanomaterials
Authors: Kairi Masuda | Yu Kumagai
Source: ArXiv.org (preprint, 2025-09-16)
Field: Materials Science — Materials Chemistry

Atomic-scale phase-field modeling with universal machine learning potentials Atomic-scale phase-field modeling formulates the probability densities of atomic vibrations as Gaussian distributions and derives a free energy functional using variational Gaussian theory and interatomic potentials. This framework permits per-Gaussian decomposition of the free energy, providing a description of local thermodynamic states with atomic resolution. However, existing formulations are limited to classical pairwise interatomic potentials, restricting their applicability to specific materials and compromising quantitative accuracy. In this work, we extend the atomic-scale phase-field methodology by incorporating universal machine learning interatomic potentials, thereby generalizing the free energy functional to many-body systems. This extension enhances both the accuracy and transferability of the approach. We demonstrate the method by applying it to bulk copper under NVT and NPT ensembles, where the predicted pressures and equilibrium lattice constants show excellent agreement with molecular dynamics simulations, validating the theoretical framework. Furthermore, we apply the method to Σ5(310)[001] grain boundaries in copper, enabling the visualization of local free energy distributions with atomic-scale resolution. The results reveal a pronounced free energy concentration at the grain boundary core, capturing the thermodynamic signature of the interface. This study establishes a versatile and accurate framework for atomic-scale thermodynamic modeling, significantly broadening the scope of phase-field approaches to include complex materials and defect structures. Gaussian ; Interatomic potential ; Pairwise comparison ; Molecular dynamics ; Lattice (music) ; Energy (signal processing) ; Transferability ; Visualization Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nuclear Materials and Properties
Authors: Yan Yuan (20767) | Yilin Zhao (487866) | Linling Lu (21518848) | Junjie Wang (172690) | Jingbo Chen (574291) | Shubin Liu (1262169) | Paul W. Ayers (1487719) | Dongbo Zhao (577120)
Source: Figshare (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Multiproperty DeepLearning of the Correlation Energyof Electrons and the Physicochemical Properties of Molecules The density-based descriptors from the information-theoretic approach (ITA) are used as features for multiproperty deep learning (DL), predicting the correlation energy and physicochemical properties of molecules. In addition to response properties (molecular polarizability α<sub>iso</sub> and NMR shielding constant σ<sub>iso</sub>) where ITA has been shown to work well before, we consider four conceptually distinct but practically related concepts: electron correlation, redox potential, octanol–water partition coefficient (log<i>K</i><sub>ow</sub>), and the wavelength of maximum absorption (λ<sub>max</sub>). The DL-predicted results are in good agreement with either the calculated or experimental counterparts, indicative of the model’s robustness. We verified the transferability of redox potentials of phenazine derivatives. Generalizability is observed for the λ<sub>max</sub> data: small chromophores are used for training/validation but the test set has sizable molecules. The trained DL model outperforms the conventional TD-DFT method in terms of accuracy and efficiency. We also showcase that the isotropic quadrupole moment (Θ<sub>iso</sub>) is a good predictor of log<i>K</i><sub>ow</sub>. This establishes that versatile density-based ITA quantities can be used to make accurate, low-cost predictions of both extensive and intensive properties, suggesting that this ITA-DL protocol has the potential for closed-loop chemistry automation. Implication of this work is straightforward, that a universal framework should be possible based on the ITA-based DL models. Polarizability ; Dipole ; Electron ; Work (physics) ; Moment (physics) ; Test set ; Chromophore ; Transferability Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Chang Zhang | Li Zhang | Dezhi Yang | Henry H. Y. Tong | Zhongqiu Lu | Zhengzheng Zhou
Source: Chinese Chemical Letters (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

From traditional screening to machine learning facilitated development of pharmaceutical cocrystals Cocrystal ; Transformative learning ; Process (computing) ; Key (lock) ; Property (philosophy) Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: A. Charan Kumari | Gadha Ramesh | Debasish Koner | Rakesh S. Singh | Mantu Santra
Source: ArXiv.org (preprint, 2025-06-17)
Field: Chemistry — Spectroscopy

Compositional fluctuations and polymorph selection in crystallization of model soft colloids Understanding polymorph selection in atomic and molecular systems and its control through thermodynamic conditions and external factors (such as seed characteristics) is fundamental to the design of targeted materials and holds great significance in materials sciences. In this work, using Monte Carlo simulations on the Gaussian Core Model and Hard-Core Yukawa colloidal systems, we investigated the control of polymorph selection and explored the underlying mechanisms by tuning thermodynamic parameters. We demonstrate that by carefully modifying the free energy landscape to render the globally stable face-centered cubic (FCC) phase metastable with respect to the body-centered cubic (BCC) phase, the polymorphic identity of particles transitions from FCC-dominated to BCC-dominated via an intermediate regime where both phases nucleate -- either selectively or competitively -- giving rise to a critical-like composition fluctuation of the growing solid-like cluster during the nucleation process. We further probed the critical solid-like cluster compositions, especially in the vicinity of the triple point where the three phases coexist, and observed an interpenetrating arrangement of FCC- and BCC-like particles rather than a commonly observed non-classical core-shell-like two-step nucleation scenario. In addition, we investigated the polymorph selection signatures encoded in local structural fluctuations of the metastable fluid using a machine learning approach based on structural descriptors derived from persistent homology, a topological data analysis method. We believe that the insights gained from this work have the potential to add to the ongoing efforts to control crystallization pathways to obtain the desired functional material. Nucleation ; Metastability ; Crystallization ; Cluster (spacecraft) ; Yukawa potential ; Molecular dynamics ; Monte Carlo method ; Work (physics) ; Phase (matter) Adsorption, diffusion, and thermodynamic properties of materials ; Crystallization and Solubility Studies
Authors: Keisuke Mukai | Minoru Kusaba | Taehyun Hwang | Jae-Hwan Kim
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accident-Tolerant Hybrid Ceramics for Fusion Breeding Blanket Blanket ; Ceramic ; Fusion ; Nuclear engineering ; Materials science ; Engineering ; Composite material Nuclear Materials and Properties ; Nuclear materials and radiation effects
Authors: Jaroslav Vacek | Dávid Vrška | Debashree Manna | Rabindranath Lo | Pavel Hobza
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Solvation strategies for free-energy calculations in a halogen-bonded complex: implicit, explicit, and machine learning approaches In pursuit of an efficient solvation approach for the halogen bonded complex between molecular iodine and tetramethylthiourea that reliably reproduces experimental trends, we investigated a range of solvent models, from implicit representations to periodic metadynamics simulations alongside micro-solvation and ONIOM-based methods as robust alternatives. Implicit solvent models fail to describe halogen-bonded complexes in high-polar solvents but provide surprisingly accurate estimates of binding free energies in all low to moderately polar solvents. For accurate and reliable modeling, especially in polar media, explicit solvent representations are essential. Periodic metadynamics simulations typically provide enhanced accuracy in calculating free energy differences, particularly for systems with complex solvation behavior. However, they are computationally demanding and restricted to generalized gradient approximation functionals (GGA). To overcome this limitation and improve accuracy, we employed the machine learning perturbation theory technique, enabling the estimation of free energies at levels of theory beyond the GGA. Metadynamics ; Solvation ; Polar ; Perturbation theory (quantum mechanics) ; Implicit solvation ; Complex system ; Range (aeronautics) ; Molecular dynamics ; Free energy perturbation Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Mateusz Praski
Source:  (article, 2025-11-08)
Field: Materials Science — Materials Chemistry

The Landscape of Foundation Models for Molecular Chemistry Pre-trained neural networks have recently emerged as powerful tools for molecular data mining, offering an alternative to classical approaches. However, these models are often evaluated on limited datasets with narrow baselines, leaving their benefits unclear. We present the first large-scale benchmark comparing pre-trained molecular embedding models across 20 public datasets spanning classification and regression tasks. Our evaluation covers text-based, graph-based, and multimodal architectures, all tested under a unified methodology. The results show that the classical fingerprint-based models remain highly competitive. Only a few models consistently exceeded the baseline. We also highlight key factors influencing model performance, offering practical guidance for model selection and future improvements in molecular embeddings. Benchmark (surveying) ; Foundation (evidence) ; Selection (genetic algorithm) ; Key (lock) ; Model selection ; Molecular model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Sahil Rana | Uday Sankar Manoj | Upakarasamy Lourderaj | N. Sathyamurthy
Source: Journal of Computational Chemistry (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

Artificial Neural Networks Fitting of Potential Energy Curves and Surfaces: The 1/R Conundrum Within the Born-Oppenheimer approximation, the potential energy of a molecular system is written as a sum of electronic energy and nuclear-nuclear repulsion energy terms. The potential energy surface (PES), computed ab initio, as a function of bond distances and bond angles, has traditionally been represented using analytic functions and/or interpolation methods. We show here that the ab initio computed electronic energy values of a molecular system can be fitted more accurately than the corresponding potential energy values using the artificial neural network methodology. The exact Coulombic internuclear repulsion energy can be added subsequently to the fitted electronic energy to obtain an accurate PES. Potential energy ; Energy (signal processing) ; Artificial neural network ; Interpolation (computer graphics) ; Function (biology) ; Ab initio ; Potential energy surface ; Electronic structure Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: C.P. Lee | Jun-Kai Wang | Shao-Chi Wu | J.C. Ke | Chia-Wei Chang | Chia‐Yuan Chen | Hui‐Hsu Gavin Tsai
Source: Corrosion Science (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

RETRACTED: Machine Learning-Driven Prediction of Corrosion Inhibition Efficiency on Copper: Incorporating Tautomer Stability and Virtual Screening for Inhibitor Repositioning Corrosion ; Corrosion inhibitor ; Copper ; Virtual screening ; Tautomer ; Stability (learning theory) ; Erosion corrosion of copper water tubes ; Materials science ; Metallurgy ; Chemistry ; Computer science ; Machine learning ; Molecular dynamics ; Computational chemistry ; Stereochemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science