jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yufei Deng | Haipan Xiang | Y. L. Chen | Dong Wang | Yi Zhao | Kun He | Huiqiu Deng
Source: Journal of Applied Physics (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Atomistic mechanisms of helium–dislocation loop interactions in molybdenum: Insights from a machine-learning moment tensor potential Molybdenum (Mo) is a key structural material for advanced nuclear systems, yet under irradiation, the formation of helium (He) bubbles and dislocation loops leads to severe degradation. Understanding He–dislocation loop interactions at the atomic scale is essential for predicting long-term performance. In this work, we develop a machine-learning Moment Tensor Potential and perform molecular dynamics simulations to systematically study the interactions between He atoms and the primary interstitial-type dislocation loops (1/2 ⟨111⟩ and ⟨100⟩) in BCC Mo. Our results show that dislocation loops act as strong trapping sites for He, modifying its spatial distribution and promoting heterogeneous nucleation of He clusters, with the ⟨100⟩ loop having a stronger effect. Conversely, He enhances loop growth by absorbing self-interstitial atoms, a process that intensifies with He concentration. A pronounced pinning effect of He on loop glide is observed, effectively immobilizing loops at 600 and 1200 K, though thermal activation at 1800 K partially releases this pinning. This study elucidates the mutual enhancement mechanism between He and dislocation loops, offering fundamental insights for designing radiation-resistant Mo alloys. Loop (graph theory) ; Nucleation ; Dislocation ; Molecular dynamics ; Moment (physics) ; Tensor (intrinsic definition) ; Trapping Fusion materials and technologies ; Nuclear Materials and Properties ; Hydrogen Storage and Materials
Authors: Pratiman De | Subhankar Mishra | Arindam Ghosh
Source: ChemRxiv (article, 2026-01-09)
Field: Chemistry — Spectroscopy

Overcoming Sensitivity Barrier in 13C NMR Using Frequency–Time Dual-Domain GANs: Quantitative Reaction Monitoring at Natural Abundance Low sensitivity has long limited the use of heteronuclei such as 13C and 15N NMR for quantitative chemical measurements, confining reaction monitoring largely to proton-detected experiments. Although highly sensitive, proton NMR often becomes unreliable due to severe signal overlap. Here we show that machine-learning-enabled heteronuclear NMR overcomes this limitation, enabling accurate, time-resolved reaction monitoring using natural-abundance 13C nuclei without isotopic enrichment or hardware modification. We introduce a frequency–time dual-domain generative adversarial network that enforces physical consistency between the free-induction decay and its frequency-domain spectrum. This dual-domain constraint suppresses noise while preserving peak intensities and lineshapes essential for quantitative analysis. We demonstrate that baseline-based signal-to-noise ratios become unreliable after ML-assisted denoising due to non-uniform noise floors, and therefore adopt a true signal-to-noise ratio based on peak-area variance. Using this metric, our approach achieves a two- to three-fold improvement in quantitative sensitivity, comparable to detection using cryogenically cooled probe-heads. Applied to the acid-catalysed inversion of sucrose, the method enables reliable 13C-based kinetic analysis with sub-minute acquisition times per spectrum, establishing a general framework for extending quantitative reaction monitoring beyond proton NMR. Heteronuclear molecule ; Sensitivity (control systems) ; Proton ; Analyte ; Analytical Chemistry (journal) ; Signal-to-noise ratio (imaging) ; Noise (video) ; Carbon-13 NMR ; SIGNAL (programming language) Advanced NMR Techniques and Applications ; Protein Structure and Dynamics ; Machine Learning in Materials Science
Authors: Tingfa Zhou | Chao Hu
Source: Environmental Research (article, 2026-02-08)
Field: Materials Science — Materials Chemistry

Commercialization path of data-driven green catalytic technology: the application of machine learning in technology lifecycle management Commercialization ; Context (archaeology) ; Process (computing) ; Big data ; Key (lock) ; Path (computing) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Ruhi Sayana | Kate M. Callon | Jennifer Xu | Jonathan Deutsch | Steven Chu | James Zou | John Janetzko | Rabindra V. Shivnaraine | Kyle Swanson
Source: arXiv (Cornell University) (preprint, 2026-01-12)
Field: Materials Science — Materials Chemistry

Generating readily synthesizable small molecule fluorophore scaffolds with reinforcement learning Developing new fluorophores for advanced imaging techniques requires exploring new chemical space. While generative AI approaches have shown promise in designing novel dye scaffolds, prior efforts often produced synthetically intractable candidates due to a lack of reaction constraints. Here, we developed SyntheFluor-RL, a generative AI model that employs known reaction libraries and molecular building blocks to create readily synthesizable fluorescent molecule scaffolds via reinforcement learning. To guide the generation of fluorophores, SyntheFluor-RL employs a scoring function built on multiple graph neural networks (GNNs) that predict key photophysical properties, including photoluminescence quantum yield, absorption, and emission wavelengths. These outputs are dynamically weighted and combined with a computed pi-conjugation score to prioritize candidates with desirable optical characteristics and synthetic feasibility. SyntheFluor-RL generated 11,590 candidate molecules, which were filtered to 19 structures predicted to possess dye-like properties. Of the 19 molecules, 14 were synthesized and 13 were experimentally confirmed. The top three were characterized, with the lead compound featuring a benzothiadiazole chromophore and exhibiting strong fluorescence (PLQY = 0.62), a large Stokes shift (97 nm), and a long excited-state lifetime (11.5 ns). These results demonstrate the effectiveness of SyntheFluor-RL in the identification of synthetically accessible fluorophores for further development. Fluorophore ; Chromophore ; Reinforcement learning ; Fluorescence ; Small molecule ; Molecule ; Function (biology) Luminescence and Fluorescent Materials ; Advanced Fluorescence Microscopy Techniques ; Molecular Sensors and Ion Detection
Authors: Baoming Shi | Dawei Wu | Lei Zhang | PingWen Zhang
Source: Open MIND (preprint, 2026-02-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From molecular model to tensor model of nematic liquid crystals through entropy decomposition In the mathematical modeling of nematic liquid crystals, a practical and physically reliable $\mathbf{Q}$-tensor model can be derived from Onsager's molecular model with the Bingham closure. However, this procedure leads to a singular entropy term that implicitly depends on $\mathbf{Q}$, creating both computational and theoretical difficulties. In this paper, we characterize this entropy contribution by splitting it into a singular but explicit leading term and an implicit but regular correction term, the latter of which is proven to be sufficiently regular to be accurately approximated numerically, for example, by neural networks. This yields a computationally convenient free energy that can be used for the computation of nematic liquid crystals. Our numerical experiments demonstrate that the resulting free energy can capture the isotropic-nematic phase transition as well as the free-boundary droplet configurations. Liquid crystal ; Computation ; Entropy (arrow of time) ; Tensor (intrinsic definition) ; Configuration entropy ; Phase transition ; Phase (matter) ; Physical system Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Nonlinear Waves and Solitons
Authors: Zhixing Wang | Xiangyue Chen | Dongqing Zhang | Ge Ping Wu | Yan Ma | Xiaoqin Zeng | Ziyuan Rao | Chang Liu | Evan Ma
Source: Proceedings of the National Academy of Sciences (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Active learning design of bcc solid solution alloys with gigapascal strength and elemental metal–level ductility Body-centered cubic (bcc) alloys can achieve gigapascal-level yield strengths but typically are limited in tensile ductility (<20%), contrasting sharply with elemental metals (the largest elongation of ~50%). Multi-principal-element alloys offer vast compositional space to reach synergistic strength-ductility combinations. However, combinatorial trial-and-error exploration is prohibitively costly, while machine learning (ML) approaches are hindered by data scarcity. Here, we develop an ML-guided framework integrating active learning with physics-informed Bayesian optimization to rapidly converge on optimal compositions. The resulting Ti<sub>36</sub>V<sub>14</sub>Nb<sub>22</sub>Hf<sub>22</sub>Zr<sub>1</sub>Al<sub>5</sub> alloy achieves a yield strength of 953 MPa and a large tensile ductility of 42%. The high strength arises from the substantial lattice distortion, as well as the ~1-nm-sized local chemical fluctuations (LCFs) inherent to the highly concentrated bcc solid solution. The ubiquitous LCFs also substantially promote dislocation multiplication and strain hardening, enabling a large tensile ductility. Our approach demonstrates ML's efficacy in accelerating the finding of high-performance alloys. Ultimate tensile strength ; Bayesian optimization ; Ductility (Earth science) ; Alloy ; Solid solution ; Yield (engineering) ; Elongation ; Active learning (machine learning) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Masato Sumita | Shoichi Ishida | Kazuki Yoshizoe | Ryo Tamura | Kei Terayama | Koji Tsuda
Source: Chemical Reviews (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules Progress in chemistry has been driven by the streamlining of inverse problem-solving methods. In the history of chemistry, several revolutionary technologies have led to leaps forward: the establishment of atomistic theory in the 19th century, structural analysis by spectroscopy in the 20th century, and the development of simulation by theoretical chemistry. Currently, chemistry is about to make a significant leap forward by integrating generative artificial intelligence (AI). In 2016, deep learning techniques were introduced in this domain, leading to explosive development. This paper reviews the development path, including traditional models such as variational autoencoders and more up-to-date models such as large language models and diffusion models. We also discuss how AI can have a real impact on chemistry, including the possibilities and problems associated with synthesizing AI-generated molecules. LEAPS ; Generative grammar ; Small molecule ; Deep learning ; Development (topology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Ze Sheng Lu | Jianli Zhou | Jiahao Li | Ao Liu | Yuefei Zhang | Ze Zhang
Source: Journal of Materials Research and Technology (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

DT-MIM: A network suitable for alloy microstructure evolution prediction and characterization The evolution of microstructure is essential in determining the physical properties of materials. Accurate modeling and prediction of its dynamics are critical for unraveling the complex interrelationships between processing, structure, and performance. However, the inherent nonlinearity and non-stationarity of microstructural evolution, compounded by limited, variable, and uncontrollable experimental datasets, present major challenges to precise dynamic characterization. Here we address these challenges by focusing on critical deformation phenomena in Zr-4 alloys, including grain deformation, orientation transitions, and twin nucleation and growth. We introduce the Difference-aware Temporal Attention Memory In Memory (DT-MIM) network, an improved predictive recurrent neural network tailored to capture and represent the spatiotemporal dynamics of microstructural evolution. The model was trained and evaluated using data acquired with a custom scanning electron microscopy (SEM)-integrated system. The proposed DT-MIM model offers a novel framework for predicting microstructural evolution, employing a temporal attention mechanism on hidden-state differential information that effectively identifies highly dynamic and non-stationary transitions. Furthermore, the model demonstrates strong generalizability and can be extended to various alloy systems, providing a powerful new tool for material design and performance improvement. Microstructure ; Characterization (materials science) ; Nucleation ; Shape-memory alloy ; Alloy ; Artificial neural network Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Nuclear Materials and Properties
Authors: Dong-Hui Xu | Shuangli Yue | Li Zhang | Gang Jiang | Y. Lu | Mingli Yang | Xian Wang
Source: Nanoscale (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Intercalation-mediated activation and enhancement of ferroelectricity in transition metal dichalcogenide heterobilayers Ferroelectricity (FE) in two-dimensional (2D) materials holds considerable promise for ultrathin, low-power memory and logic applications, but its advancement remains limited by the scarcity of high-performance candidates and the generally weak out-of-plane polarization (OOP). Here, we demonstrate that metal intercalation provides a general and experimentally feasible strategy to activate and enhance sliding FE in transition metal dichalcogenide (TMD) heterobilayers. Through high-throughput calculations on 5264 pristine and intercalated configurations, systematically derived from accessible TMD monolayers and nine representative non-magnetic metal intercalants (Cu, Ag, Au, Pt, Zn, Cd, Hg, Ga, and In), we identify 234 intercalated systems with switchable OOP, including 190 that exceed the experimentally reported value for the MoS<sub>2</sub>/WS<sub>2</sub> system, with some achieving OOP values up to 37× higher. This represents a 13-fold increase over pristine counterparts. Notably, Pt intercalation exhibits the most pronounced effect, delivering the strongest enhancement of OOP. To enable efficient exploration of this vast configuration space, we further introduce a crystal equivariant graph neural network that accurately predicts OOP directly from atomic structures (<i>R</i><sup>2</sup> = 0.98), including both its magnitude and reversible directionality, thereby bypassing the need for computationally intensive DFT calculations. Together, these results elucidate the mechanistic role of interfacial intercalation in tuning symmetry breaking and interlayer coupling, and establish a scalable, machine learning-accelerated framework for the discovery of next-generation 2D sliding ferroelectrics with enhanced functional performance and broad technological relevance. Intercalation (chemistry) ; Ferroelectricity ; Transition metal ; Polarization (electrochemistry) ; Monolayer ; Symmetry breaking ; Point reflection 2D Materials and Applications ; Ferrocene Chemistry and Applications ; Photochromic and Fluorescence Chemistry
Authors: D. S. Kim | Bingqing Cheng
Source: The Journal of Chemical Physics (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Long-range electrostatics for machine learning interatomic potentials is easier than we thought The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer reactions, polar and ionic materials, and biomolecules. In this Perspective, we distill two design principles behind the Latent Ewald Summation framework, which can capture long-range interactions, charges, and electrical response just by learning from standard energy and force training data: (i) use a Coulomb functional form with environment-dependent charges to capture electrostatic interactions, and (ii) avoid explicit training on ambiguous density functional theory partial charges. When both principles are satisfied, substantial flexibility remains: essentially any short-range MLIP can be augmented; charge equilibration schemes can be added when desired; dipoles and Born effective charges can be inferred or fine-tuned; and charge/spin-state embeddings or tensorial targets can be further incorporated. We also discuss current limitations and open challenges. Together, these minimal, physics-guided design rules suggest that incorporating long-range electrostatics into MLIPs is simpler and perhaps more broadly applicable than is commonly assumed. Electrostatics ; Flexibility (engineering) ; Coulomb ; Charge (physics) ; Dipole ; Electric potential energy Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Ruhi Sayana | Kate M. Callon | Jennifer Xu | Jonathan Deutsch | Steven Chu | James Zou | John Janetzko | Rabindra V. Shivnaraine | Kyle Swanson
Source: ArXiv.org (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Generating readily synthesizable small molecule fluorophore scaffolds with reinforcement learning Developing new fluorophores for advanced imaging techniques requires exploring new chemical space. While generative AI approaches have shown promise in designing novel dye scaffolds, prior efforts often produced synthetically intractable candidates due to a lack of reaction constraints. Here, we developed SyntheFluor-RL, a generative AI model that employs known reaction libraries and molecular building blocks to create readily synthesizable fluorescent molecule scaffolds via reinforcement learning. To guide the generation of fluorophores, SyntheFluor-RL employs a scoring function built on multiple graph neural networks (GNNs) that predict key photophysical properties, including photoluminescence quantum yield, absorption, and emission wavelengths. These outputs are dynamically weighted and combined with a computed pi-conjugation score to prioritize candidates with desirable optical characteristics and synthetic feasibility. SyntheFluor-RL generated 11,590 candidate molecules, which were filtered to 19 structures predicted to possess dye-like properties. Of the 19 molecules, 14 were synthesized and 13 were experimentally confirmed. The top three were characterized, with the lead compound featuring a benzothiadiazole chromophore and exhibiting strong fluorescence (PLQY = 0.62), a large Stokes shift (97 nm), and a long excited-state lifetime (11.5 ns). These results demonstrate the effectiveness of SyntheFluor-RL in the identification of synthetically accessible fluorophores for further development. Fluorophore ; Chromophore ; Reinforcement learning ; Fluorescence ; Small molecule ; Molecule ; Function (biology) Luminescence and Fluorescent Materials ; Advanced Fluorescence Microscopy Techniques ; Molecular Sensors and Ion Detection
Authors: Baoming Shi | Dawei Wu | Lei Zhang | PingWen Zhang
Source: arXiv (Cornell University) (article, 2026-02-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From molecular model to tensor model of nematic liquid crystals through entropy decomposition In the mathematical modeling of nematic liquid crystals, a practical and physically reliable $\mathbf{Q}$-tensor model can be derived from Onsager's molecular model with the Bingham closure. However, this procedure leads to a singular entropy term that implicitly depends on $\mathbf{Q}$, creating both computational and theoretical difficulties. In this paper, we characterize this entropy contribution by splitting it into a singular but explicit leading term and an implicit but regular correction term, the latter of which is proven to be sufficiently regular to be accurately approximated numerically, for example, by neural networks. This yields a computationally convenient free energy that can be used for the computation of nematic liquid crystals. Our numerical experiments demonstrate that the resulting free energy can capture the isotropic-nematic phase transition as well as the free-boundary droplet configurations. Liquid crystal ; Computation ; Entropy (arrow of time) ; Tensor (intrinsic definition) ; Configuration entropy ; Phase transition ; Phase (matter) ; Physical system Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Nonlinear Waves and Solitons
Authors: Wei-Feng Kao | Pei-Hsuan Hsieh | Yueh-Ting Shih
Source: Journal of Non-Crystalline Solids (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Interpretable convolutional neural network prediction of heat treatment conditions from lithium disilicate glass-ceramic microstructures Interpretability ; Microstructure ; Convolutional neural network ; Lithium disilicate ; Crystallinity ; Lithium (medication) ; Crystallization Machine Learning in Materials Science ; Glass properties and applications ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Shihao Zhang | Yan Li | Eita TOCHIGI | Kimitaka Higuchi | Yin Zhang | Shigeo Arai | Ting Zhu | Shigenobu Ogata | Atsutomo Nakamura
Source: Journal of Material Science and Technology (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Formation of a one-dimensional dislocation loop array in wurtzite zinc oxide at ambient temperature • First demonstration of ordered prismatic dislocation loop array in brittle ceramics • Stable PDL arrays experimentally observed in single-crystal ZnO at room temperature • Loops verified by 3D TEM and machine-learning-potential atomic simulations • Force-balance model proposed for pile-up of dislocation loop array in brittle solid Introducing highly ordered dislocation configurations into brittle solids remains a significant challenge because of their inherently limited dislocation mobility. A prototypical example is the prismatic dislocation loop (PDL) array, and whether it can form and remain stable in brittle solids, as it does in metals, remains an open question. In this work, we show the formation of a long, one-dimensional array of PDLs in wurtzite zinc oxide ceramics using nanoindentation, confirmed by transmission electron microscope observations of their three-dimensional configurations and atomistic simulation based on our state-of-the-art neural network potential. Each PDL consisted of alternating basal and prismatic-I edge dislocation segments with a Burgers vector b = 1/3[ 11 2 ¯ 0 ], forming a closed dislocation loop. Nearly identical PDLs were continuously punched into the sample by the indenter and piled up along the [ 11 2 ¯ 0 ] direction. As a result, a one-dimensional dislocation loop array formed, while the surrounding region remained dislocation-free. We demonstrate that the loop spacing can be predicted within a theoretical framework based on the force-balance condition of dislocation loops, in agreement with experimental observations. This research not only advances the understanding of dislocation-based plasticity but also provides a strategy for the controlled formation of highly ordered PDL arrays in brittle technologically critical materials. Dislocation ; Brittleness ; Burgers vector ; Wurtzite crystal structure ; Loop (graph theory) ; Transmission electron microscopy ; Oxide Microstructure and mechanical properties ; Ion-surface interactions and analysis ; Advanced Electron Microscopy Techniques and Applications