jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wanhui Wen | Fanda Zeng | Ben Xu | Ke Bi | Zhipeng Xing | Hao‐Cheng Thong | Ke Wang
Source: npj Computational Materials (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Machine learning-enabled atomistic insights into phase boundary engineering of solid-solution ferroelectrics Atomistic control of phase boundaries is crucial for optimizing the functional properties of solid-solution ferroelectrics, yet their microstructural mechanisms remain elusive. Here, we harness machine-learning-driven molecular dynamics to resolve the phase boundary behavior in the KNbO3–KTaO3 (KNTO) system. Our simulations reveal that chemical composition and ordering enable precise modulation of polymorphic phase boundaries (PPBs), offering a versatile pathway for materials engineering. Diffused PPBs and polar nano regions, predicted by our model, highly match with experiments, underscoring the fidelity of the machine-learning atomistic simulation. Crucially, we identify elastic and electrostatic mismatches between ferroelectric KNbO3 and paraelectric KTaO3 as the driving forces behind complex microstructural evolution. This work not only resolves the longstanding microstructural debate but also establishes a generalizable framework for phase boundary engineering toward next-generation high-performance ferroelectrics. Molecular dynamics ; Phase (matter) ; Boundary (topology) ; Work (physics) ; Phase boundary ; Ferroelectricity ; Dielectric ; Polar Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Jianli Cheng | James Stevenson | Garvit Agarwal | John L. Weber | Leif D. Jacobson | Karl Leswing
Source: ChemRxiv (preprint, 2025-01-08)
Field: Materials Science — Materials Chemistry

Large-scale Atomistic Simulations of Lithium Diffusion in a Graphite Anode with a Machine Learning Force Field Graphite is the anode material in the vast majority of current commercial lithium-ion batteries (LIBs) due to its excellent electrochemical performance and abundant reserves. However, the development of high-performance LIBs still faces technical bottlenecks including limited rate capability, partly because of the slow Li diffusion in the graphite anode. Experimental measurements of Li diffusivity versus Li contents in graphite show both monotonic and non-monotonic trends with reported Li diffusivity spanning orders of magnitude. Comprehensive insights of the Li diffusion process and its bottleneck in graphite are essential to develop the next-generation LIBs. In this work, we developed a machine learning force field (MLFF) to investigate the Li diffusion in the LixC6 system. We benchmark density functional theory (DFT) functionals and dispersion corrections versus experiment, finding nonlocal van der Waals functional rVV10 shows a good agreement with experimental results in terms of the structural, energetic, and electrochemical properties of the LixC6 system. We then train a MLFF based on our recently developed charge recursive neural network (QRNN) architecture to simulate Li diffusion in graphite at different Li contents, stage structures and temperatures. Our Li diffusion analysis demonstrates a phase-transition dependent Li diffusion in the LixC6 system, which supports the experimental measurements of a non-monotonic relation between Li diffusivity and Li content. This work demonstrates the capability of our QRNN model in carrying out large scale molecular dynamics simulations to identify the Li diffusion bottleneck in a graphite anode. Anode ; Lithium (medication) ; Graphite ; Diffusion ; Scale (ratio) ; Force field (fiction) ; Field (mathematics) ; Materials science ; Chemical physics ; Statistical physics ; Computer science ; Nanotechnology ; Artificial intelligence ; Physics ; Thermodynamics ; Psychology ; Composite material ; Chemistry ; Physical chemistry ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fuel Cells and Related Materials
Authors: Etinosa Osaro | Yamil J. Colón
Source: Chemical Physics Reviews (article, 2025-12-01)
Field: Chemistry — Inorganic Chemistry

Intelligent screening of porous materials: A review of active-learning approaches in MOF research The discovery and optimization of porous materials, particularly metal–organic frameworks (MOFs), are critical for advancing a range of applications, including gas storage, separation, catalysis, and energy technologies. Traditional molecular modeling methods such as Monte Carlo simulations, molecular dynamics (MD), and quantum based method such as density functional theory (DFT), has long provided valuable insights into material behavior but is often limited by high computational costs, scalability challenges, and the vast complexity of material design spaces. Machine learning has addressed some of these limitations but often requires extensive datasets, which introduce new challenges in computational efficiency. Active learning (AL) has emerged as a promising approach, offering a data-efficient framework to address these limitations. AL minimizes computational demands while maintaining high predictive accuracy by iteratively refining surrogate models and prioritizing the acquisition of the most informative data points. This review presents AL across the major tasks in MOF research: single- and multicomponent adsorption (including universal, cross-adsorbate surrogates built via alchemical-to-real transfer), diffusion and transport, electronic-structure/property prediction, experiment-in-the-loop optimization, and the training of machine-learned interatomic potentials (MLIPs). Case studies show AL recovering full isotherms and mixture landscapes with a fraction of grand canonical Monte Carlo labels, cutting MD trajectories for diffusivity, curating balanced sets for band gaps and adsorption targets, and enabling near-DFT MLIPs that capture rare events and phase changes through enhanced-sampling or uncertainty-biased data acquisition. Looking forward, we outline a path to end-to-end discovery that couples AL with generative MOF models, graph neural networks, foundational MLIPs, and that integrates experimental feedback. Together, these advances move AL beyond label efficiency toward reliable, scalable discovery workflows for gas storage, separations, catalysis, and stability screening in MOFs. Scalability ; Monte Carlo method ; Range (aeronautics) ; Computational model ; Path (computing) ; Graph ; Computational complexity theory Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Jie Hao | Jiarui Cui | Sijia Liu | Yu Lv | Zhongxiong Zhang | Songlei Wang
Source: Food Chemistry (article, 2025-07-26)
Field: Chemistry — Analytical Chemistry

Machine learning for polycyclic aromatic hydrocarbons analysis in roasted lamb: new insights from spectral and chemical data Chemistry ; Environmental chemistry Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Mineral Processing and Grinding
Authors: Shanglin Wu | Shisheng Zheng | Wentao Zhang | Mingzheng Zhang | Shunning Li | Feng Pan
Source: Journal of Materials Informatics (article, 2025-02-26)
Field: Materials Science — Materials Chemistry

Machine-learning prediction of facet-dependent CO coverage on Cu electrocatalysts Copper-based electrocatalysts, which hold great promise in selectively reducing CO2 into multicarbon products, have attracted significant recent interest, both experimentally and theoretically. While many studies have suggested a strong dependence of catalytic selectivity on the concentration of the *CO reaction intermediate on the Cu surface, it remains challenging for a direct experimental probe of the CO coverage. This necessitates a reliable computational method that can accurately establish the theoretical coverage-dependent phase diagram of CO adsorbates on the catalyst. Here we propose a scheme composed of density functional theory calculations, machine-learning force fields and graph neural networks as a solution. This method enables a fast screening of 7 million adsorption configurations based on a small set of density functional theory data, with a balance between accuracy and efficiency tuned by the combinatorial use of machine-learning force field and graph neural network models. We have investigated eight different Cu facets and discovered that the high-index facets such as (310), (210) and (322) exhibit a much higher CO coverage than the low-index counterparts such as (111), leading to an increased opportunity for C-C coupling for the former. Our results can provide a new perspective for the understanding of the fundamental role of CO coverage on the Cu surface for electrochemical CO2 reduction. Facet (psychology) ; Materials science ; Computer science ; Environmental science ; Psychology ; Social psychology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Nutth Tuchinda | Changle Li | Christopher A. Schuh
Source: Scripta Materialia (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

The augmented potential method: Multiscale modeling toward a spectral defect genome Multiscale modeling ; Computation ; Flexibility (engineering) ; Binary number ; Grain boundary ; Granularity ; Boundary (topology) ; Computational model Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Electron Microscopy Techniques and Applications
Authors: Takuya Naruse | Atsuto Seko | Isao Tanaka
Source: Journal of the Physical Society of Japan (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

A Systematic Approach to Crystal Structure Prediction Following Imaginary Phonon Modes Combined With Polynomial Machine Learning Potentials Phonon ; The Imaginary ; Polynomial ; Crystal structure ; Crystal (programming language) ; Condensed matter physics ; Computer science ; Physics ; Statistical physics ; Theoretical physics ; Mathematical analysis ; Mathematics ; Crystallography ; Psychology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: J.A. Lenis | Julián Arias Velandia | Robinson Aguirre Ocampo | Alejandro Zuleta | Sindy Bello | Esteban Correa | Carlos E. Arrieta | F.J. Bolívar | Félix Echeverría
Source: Journal of Power Sources (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

Challenges and potential future trends on high entropy alloy for solid hydrogen storage: Systematic review This work consists of a systematic review showing recent progress and trends in the development of high entropy alloys (HEA) for solid-state hydrogen storage. The information was compiled from academic papers from the following databases: Google Scholar, ScienceDirect, Springer, SCOPUS, American Chemical Society, MDPI; as well as the patent banks United States Patent and Trademark Office, Google Patent and lens.org. This article discusses key aspects such as HEA design (elements used, thermodynamic and geometric characteristics, thermodynamic simulations and synthesis methods); HEA evaluation focusing on crystallinity, thermal behavior and hydrogen storage; HEA-related trends including MgH 2 modification, the advancement of lightweight alloys and the use of machine learning. • Systematic review on the development of HEA for hydrogen storage. • Relationship between thermodynamic and geometric parameters on storage capacity. • Role of crystal structure (BCC, FCC, C14 Laves) on hydrogen storage capacity. • Identification of HEA and MEA modified MgH 2 as a current trend in research. • The highest reported storage capacity of 3.7 wt % H 2 falls short of DOE targets. Hydrogen storage ; Alloy ; High entropy alloys ; Materials science ; Computer science ; Metallurgy Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Inorganic Chemistry and Materials
Authors: Yin Qingfeng | Yuan Wu | Hong‐Hui Wu | Xiaobin Zhang | Suihe Jiang | Hui Wang | Xiongjun Liu | Zhaoping Lu
Source: npj Computational Materials (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

A multi-objective synergistic design for low modulus and high yield strength in complex concentrated alloys Low Young’s modulus and high yield strength are concurrently needed to meet the performance requirements of metallic implant materials. The single-objective performance-oriented alloy design strategies face challenges in effectively addressing the inherent conflict between Young’s modulus and yield strength. In this study, we developed a machine learning model for multi-objective synergistic optimization of modulus and yield strength, successfully enabling simultaneous prediction of Young’s modulus and yield strength in the Ti-Zr-Hf-Nb-Ta-Mo-Sn alloy system. The critical features influencing the modulus and strength of the alloys were systematically analyzed and identified. Moreover, a series of complex concentrated alloy (CCAs) with low Young’s modulus and high yield strength were successfully prepared based on this model. The newly developed alloys exhibited a stable single-phase BCC (body-centered-cubic) structure with Young’s modulus in the range of 40–50 GPa, yield strength of 600–915 MPa, and elastic admissible strain of approximately 1.5%. The multi-objective machine learning model developed in this study can synergistically optimize low Young’s modulus and high yield strength in complex alloys, providing a novel approach for the design of advanced biomedical alloys. Yield (engineering) ; Modulus ; Materials science ; Composite material Titanium Alloys Microstructure and Properties ; High Entropy Alloys Studies ; Intermetallics and Advanced Alloy Properties
Authors: Tim Bechtel | Daniel T. Speckhard | Jonathan Godwin | Claudia Draxl
Source: Chemistry of Materials (article, 2025-02-12)
Field: Materials Science — Materials Chemistry

Band-Gap Regression with Architecture-Optimized Message-Passing Neural Networks Graph-based neural networks and, specifically, message-passing neural networks (MPNNs) have shown great potential in predicting physical properties of solids. In this work, we train an MPNN to first classify materials through density functional theory data from the AFLOW database as being metallic or semiconducting/insulating. We then perform a neural-architecture search to explore the model architecture and hyperparameter space of MPNNs to predict the band gaps of the materials identified as nonmetals. The top-performing models from the search are pooled into an ensemble that significantly outperforms the best single model. Uncertainty quantification is evaluated with Monte Carlo dropout and ensembling, with the ensemble method proving superior. The domain of applicability of the ensemble model is analyzed with respect to the crystal systems, the inclusion of a Hubbard parameter in the density-functional-theory calculations, and the atomic species building up the materials. Message passing ; Regression ; Architecture ; Artificial neural network ; Computer science ; Message broker ; Regression analysis ; Artificial intelligence ; Computer architecture ; Computer network ; Distributed computing ; Machine learning ; Mathematics ; Statistics ; Geography Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Advancements in Semiconductor Devices and Circuit Design
Authors: Jamie Holber | Krishna Garikipati
Source: Computer Methods in Applied Mechanics and Engineering (article, 2025-10-04)
Field: Materials Science — Materials Chemistry

Physics- and data-driven active learning of neural network representations for free energy density functions of materials from statistical mechanics Artificial neural network ; Statistical mechanics ; Statistical learning ; Energy (signal processing) ; Active learning (machine learning) ; Probability density function ; Statistical analysis Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Probabilistic and Robust Engineering Design
Authors: Zheng Cao | Lei Wang
Source: ArXiv.org (preprint, 2025-04-03)
Field: Materials Science — Ceramics and Composites

Reinforcement Fine-Tuning for Materials Design Reinforcement fine-tuning played an instrumental role in enhancing the instruction-following and reasoning abilities of large language models. In this work, we employ reinforcement fine-tuning for materials design, in which discriminative machine learning models are used to provide rewards to the autoregressive transformer-based materials generative model CrystalFormer. By optimizing the reward signals-such as energy above the convex hull and material properties figures of merit-reinforcement fine-tuning infuses knowledge from discriminative models into generative models. The resulting model, CrystalFormer-RL, shows enhanced stability in generated crystals and successfully discovers crystals with desirable yet conflicting material properties, such as substantial dielectric constant and band gap simultaneously. Notably, we observe that reinforcement fine-tuning not only enables the property-guided material design but also unlocks property-based material retrieval behavior of pretrained generative model. The present framework opens an exciting gateway to the synergies of the machine learning ecosystem for materials design. Reinforcement ; Reinforcement learning ; Computer science ; Materials science ; Composite material ; Artificial intelligence Advanced ceramic materials synthesis ; Material Properties and Applications ; Material Selection and Properties
Authors: Aliaksei V. Krautsou | Innokentiy S. Humonen | Vladimir D. Lazarev | Roman A. Eremin | Semen Budennyy
Source: Scientific Reports (article, 2025-03-14)
Field: Materials Science — Materials Chemistry

Impact of crystal structure symmetry in training datasets on GNN-based energy assessments for chemically disordered CsPbI3 Robust solutions combining computational chemistry and data-driven approaches are in high demand in various areas of materials science. For instance, such methods can use machine learning models trained on a limited dataset to make structure-to-property predictions over large search spaces. This paper examines the impact of data selection mechanisms on thermodynamic property assessments for chemically modified lead halide perovskite γ-CsPbI<sub>3</sub> and non-perovskite δ-CsPbI<sub>3</sub>. For disordered states of these phases, complete composition/configuration spaces are built by adding Cd or Zn substitutions of Pb and Br substitutions of I and comprise 2,946,709 and 2,995,462 inequivalent spatial arrangements of substituents, respectively. Using the properties of 1162 entries of the built spaces evaluated by means of density functional theory, we implement independent procedures for training graph neural networks (GNNs). In each of them, a training dataset is constructed depending on the defect contents and presence of low- and high-symmetry structures. The results show that symmetries of training structures can significantly influence quality of the subsequent GNNs' predictions and can result in twofold increase in errors due to the preferential selection of high-symmetry structures. Symmetry (geometry) ; Energy (signal processing) ; Crystal (programming language) ; Computer science ; Physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Inorganic Chemistry and Materials
Authors: Harender S. Dhattarwal | Richard C. Remsing
Source: ChemPhysChem (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

Electronic Paddlewheels Impact the Dynamics of Superionic Conduction in AgI Solid-state ion conductors hold promise as next-generation battery materials. To realize their full potential, an understanding of atomic-scale ion conduction mechanisms is needed, including ionic and electronic degrees of freedom. Molecular simulations can create such an understanding; however, including a description of electronic structure necessitates computationally expensive methods that limit their application to small scales. We examine an alternative approach in which neural network models are used to efficiently sample ionic configurations and dynamics at ab initio accuracy. Then, these configurations are used to determine electronic properties in a postprocessing step. We demonstrate this approach by modeling the superionic phase of AgI, in which cation diffusion is coupled to rotational motion of local electron density on the surrounding iodide ions, termed electronic paddlewheels. The neural network potential can capture the many-body effects of electronic paddlewheels on ionic dynamics, but classical force field models cannot. Through an analysis rooted in the generalized Langevin equation framework, we find that electronic paddlewheels have a significant impact on the time-dependent friction experienced by a mobile cation. Our approach will enable investigations of electronic fluctuations in materials on large length and time scales, and ultimately the control of ion dynamics through electronic paddlewheels. Electronic structure ; Ionic bonding ; Ion ; Fast ion conductor ; Ionic conductivity ; Molecular dynamics ; Ab initio ; Field (mathematics) ; Langevin dynamics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Spectroscopy and Quantum Chemical Studies
Authors: Shahrier Hasan | Hadia Bayat | Wibe A. de Jong | Wenwu Xu
Source: Materials & Design (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Machine learning-enabled multiscale modeling of mechanical deformation of aluminum and Al-SiC nanocomposites • Machine learning bridges atomistic to continuum scales in Al and Al-SiC np. • Neural networks capture interface-driven deformation beyond traditional methods. • Multiscale model predictions match closely with experimental SEM tensile tests. A machine learning-enabled multiscale framework is developed for modeling the mechanical response of both pure metal and nanoparticle-reinforced metal matrix nanocomposites (MMNCs). Using aluminum–silicon carbide (Al-SiC) as an example MMNC, atomistic simulations reveal three distinct deformation mechanisms (i.e., defect-free, dislocation-based, and interface separation) governed by the interfaces between the Al matrix and SiC nanoparticles. As compared with single crystal Al, the lattice undergoes a more abrupt failure once the dislocation network becomes extensive and void nucleation initiates, whereas in Al-SiC, nanoparticle interfaces enable a more gradual progression of damage. These mechanisms are captured through a combined classification-regression neural network surrogate model that bridges atomic-scale insights with continuum-scale finite element analysis. Machine learning-enabled multiscale modeling of pure Al accurately predicted strain localization and confirmed by in-situ scanning electron microscopic tensile testing on perforated Al specimens. This study underscores the promise of integrating physics-informed machine learning with hierarchical modeling to capture the interface dominated phenomena and guide the design of advanced MMNCs. Multiscale modeling ; Finite element method ; Nucleation ; Ultimate tensile strength ; Nanocomposite ; Void (composites) ; Artificial neural network ; Deformation (meteorology) ; Carbide ; Metal matrix composite Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Machine Learning in Materials Science