jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Jalili Zarabadi | Yi Xie | Farid Attar | Julie Tournet | Aniruddha Kulkarni | Siva Krishna Karuturi
Source: Process Safety and Environmental Protection (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Ammonia-fed proton- and oxygen-ion conducting solid oxide fuel cells: Performance, technoeconomic analysis and multi-objective optimisation Ammonia is a promising hydrogen carrier with significant advantages in storage and transport, offering an attractive pathway for zero-emission power generation. This study evaluates the performance, thermal behaviour, and economic feasibility of ammonia-fed solid oxide fuel cells (SOFCs) employing proton-conducting electrolytes (SOFC-H) and oxygen-ion-conducting electrolytes (SOFC-O) under co-flow (fuel and air flowing in the same direction) and counter-flow (opposing flow directions) configurations. A comprehensive model integrating electrochemical, thermal, and techno-economic analyses was developed. To reduce computational cost, Latin Hypercube Sampling (LHS) and Artificial Neural Network (ANN)-based surrogate models (R² > 0.9999) were combined with NSGA-II for multi-objective optimisation of the Levelised Cost of Electricity (LCOE) and maximum temperature gradient. SOFC-H in counter-flow achieved the best performance, reaching a peak current density of 10,966 A m - ² and a minimum LCOE of 0.28 $ kWh -1 . Optimised configurations selected via the TOPSIS method yielded a balanced trade-off: an LCOE of 0.32 $ kWh -1 with a 72% reduction in temperature gradient (2.65 °C cm -1 ). In comparison, SOFC-O configurations remained 30–33% more expensive than Australia’s average electricity cost (0.27 $ kWh -1 ). The sensitivity analysis identified ammonia price as the dominant cost factor, with SOFC-H systems showing greater economic resilience. This study demonstrates that combining advanced modelling with surrogate-assisted multi-objective optimisation enables the design of efficient and cost-effective ammonia-fed SOFC systems, offering a viable pathway for clean energy transition globally. Oxide ; Solid oxide fuel cell ; Fuel cells ; Solid fuel Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Thermal Expansion and Ionic Conductivity
Authors: Lei Wang | Dongle Cheng | Chen Wang | Xiaoqing Liu | Hailong Liu | Zhijie Chen | Xiaoqiang Cao | Longlong Zhang | Yanlong Wang | Jian Zhang
Source: Advances in Colloid and Interface Science (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Mechanism-guided engineering of phosphatase-like nanozymes for sensing and remediation of organophosphate esters Environmental remediation ; Paraoxon ; Catalysis ; Organophosphate ; Rational design ; Benchmarking Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Yu Pang | Yue Gu | Runsheng Zhong | Liyang Zou | Xiaobin Chen | Xiaolong Zou | Wenhui Duan
Source: arXiv (Cornell University) (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Symmetry-guided and AI-accelerated design of intercalated transition metal dichalcogenides for antiferromagnetic spintronics The advancement of antiferromagnetic spintronics depends on quantum materials with target symmetry-dictated functionalities, however, their systematic discovery is hindered by the immense configurational complexity of the available material space. Here, we introduce a symmetry-guided, AI-accelerated framework incorporating graph neural networks with high generalization ability to overcome this bottleneck. Based on fully intercalated transition metal dichalcogenides (iTMDs) and using only 200 relaxed partially intercalated structures for transfer learning, our model effectively explores more than 100,000 partially intercalated configurations and identifies 35 altermagnetic and 20 $Tτ$-antiferromagnetic ground-state candidates. Interestingly, we show that tuning spin-group symmetry through intercalant arrangement or magnetic ordering realizes a series of d-wave altermagnets in these hexagonal systems with high spin-charge conversion efficiency. Furthermore, we reveal plentiful $Tτ$-antiferromagnets enabling efficient Néel spin-orbit torque switching, driven by giant $T$-odd spin Edelstein susceptibilities. These results establish iTMDs as a versatile platform for spintronics and provide a general strategy for the accelerated design of symmetry-enforced quantum materials. Spintronics ; Antiferromagnetism ; Transition metal ; Quantum ; Quantum computer ; Ferromagnetism ; Spin (aerodynamics) ; Symmetry (geometry) 2D Materials and Applications ; Advanced Condensed Matter Physics ; Multiferroics and related materials
Authors: Jiasi Zuo | Zhipeng Zhang | Xiaoxing Li | Chuan Long | Yao Yao | Yujun Cheng | Qi Liu | Xiaoqing Chen
Source: Analytical Chemistry (article, 2026-03-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Supramolecular Plasmonic Nanogap Engineering for Machine Learning Decoded Multiplex Chiral Amino Acid SERS Analysis Chiral recognition sensing is pivotal in pharmaceutical synthesis and biomedical applications, yet three key challenges persist: the inability to simultaneously achieve high-resolution identification and characterization of enantiomers, insufficient understanding of the molecular mechanisms governing chiral recognition, and limited efficacy in analyzing complex mixtures of chiral compounds. Here, we present a supramolecular plasmonic nanoparticle-on-mirror (NPoM) platform for surface-enhanced Raman scattering (SERS) chiral sensing. By employing sulfhydryl-functionalized β-cyclodextrin (β-CD) as a supramolecular core for selective chiral amino acid capture, the platform facilitates the precise formation of a ∼1.2 nm plasmonic nanogap hotspot between gold nanoparticles (AuNPs) and a gold film (AuF). This configuration significantly enhances enantiomer-specific Raman signals, thereby enabling high-resolution identification and enantioselective discrimination of aromatic amino acid enantiomers. Density functional theory (DFT) calculations reveal the chiral recognition mechanism: β-CD forms diastereomeric complexes with d- and l-enantiomers through distinct spatial configurations and differential binding affinities, resulting in characteristic spectral differences that enable precise enantioselective identification. Furthermore, integration with machine learning (ML) algorithms facilitates rapid, highly accurate enantiomer classification and robust blind prediction of unknown samples. Notably, the proposed platform demonstrates exceptional performance in the simultaneous discrimination of complex enantiomeric mixtures comprising multiple chiral aromatic amino acids, providing a powerful strategy for advanced chiral analysis. Supramolecular chemistry ; Plasmon ; Diastereomer ; Molecular recognition ; Enantiomer ; Enantioselective synthesis ; Amino acid ; Aromatic amino acids Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Nanocluster Synthesis and Applications
Authors: Yu Pang | Yue Gu | Runsheng Zhong | Liyang Zou | Xiaobin Chen | Xiaolong Zou | Wenhui Duan
Source: arXiv (Cornell University) (preprint, 2026-04-09)
Field: Materials Science — Materials Chemistry

Symmetry-guided and AI-accelerated design of intercalated transition metal dichalcogenides for antiferromagnetic spintronics The advancement of antiferromagnetic spintronics depends on quantum materials with target symmetry-dictated functionalities, however, their systematic discovery is hindered by the immense configurational complexity of the available material space. Here, we introduce a symmetry-guided, AI-accelerated framework incorporating graph neural networks with high generalization ability to overcome this bottleneck. Based on fully intercalated transition metal dichalcogenides (iTMDs) and using only 200 relaxed partially intercalated structures for transfer learning, our model effectively explores more than 100,000 partially intercalated configurations and identifies 35 altermagnetic and 20 $Tτ$-antiferromagnetic ground-state candidates. Interestingly, we show that tuning spin-group symmetry through intercalant arrangement or magnetic ordering realizes a series of d-wave altermagnets in these hexagonal systems with high spin-charge conversion efficiency. Furthermore, we reveal plentiful $Tτ$-antiferromagnets enabling efficient Néel spin-orbit torque switching, driven by giant $T$-odd spin Edelstein susceptibilities. These results establish iTMDs as a versatile platform for spintronics and provide a general strategy for the accelerated design of symmetry-enforced quantum materials. Spintronics ; Antiferromagnetism ; Transition metal ; Quantum ; Quantum computer ; Ferromagnetism ; Spin (aerodynamics) ; Symmetry (geometry) 2D Materials and Applications ; Advanced Condensed Matter Physics ; Multiferroics and related materials
Authors: Kathrin Rübartsch | Leeroy Hendrickx | Ettore Bartalucci | М. С. Плеханов | Mirijam Zobel | Professor Dr. C. Bolm | Maria Fyta | Thomas Wiegand
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Chemistry — Spectroscopy

The accuracy of carbon-13 NMR magnetic-shielding tensors calculated using periodic DFT: a case study on the distinction of crystalline serine phases Carbon-13 nuclear magnetic resonance (NMR) spectroscopic fingerprints enable the unambiguous identification of a chemical compound. <i>Ab initio</i> calculations of NMR magnetic-shielding tensors are crucial for facilitating spectral resonance assignment, but require an accuracy on the order of one ppm or even less for carbon-13 for distinguishing structurally rather similar compounds, such as structural isomers and polymorphs. Using quantum-mechanical calculations within the density functional theory (DFT) approach and solid-state NMR spectroscopy under magic-angle spinning (MAS) conditions, we herein explore whether the accuracy of current DFT levels is sufficient to distinguish between three distinct solid phases of the amino acid serine using computer simulations. In that vein, enantiopure L-serine, L-serine monohydrate, and racemic DL-serine have been studied. Using solid-state calculations employing periodic boundary conditions, we computed NMR observables, such as <sup>13</sup>C isotropic magnetic-shielding values and <sup>13</sup>C magnetic-shielding anisotropy parameters of these phases. Different levels of DFT theory utilizing distinct exchange correlation functionals were tested in optimizing the hydrogen-atom positions prior to NMR observable calculations, which significantly improved the agreement with the experimental values. The comparison of the measured and the calculated observables confirmed the distinction between the serine phases not only experimentally but also by DFT calculations. We also compare the DFT-calculated NMR observables with predictions from a recently proposed machine learning (ML) approach. Our analysis clearly revealed that a rather low-cost exchange correlation functional in periodic solid-state DFT calculations reaches the desired accuracy of <sup>13</sup>C magnetic-shielding tensor calculations, provided that highly accurate initial structures are applied. These results underline that complementary experimental and computational studies can provide key insights into molecular systems and the interactions therein, enabling the distinction of structurally similar compounds (often polymorphs), which is of particular importance in pharmaceutics. Observable ; Density functional theory ; Tensor (intrinsic definition) ; Enantiopure drug ; Isotropy ; Anisotropy ; Nuclear magnetic resonance spectroscopy ; Resonance (particle physics) ; Periodic boundary conditions Advanced NMR Techniques and Applications ; Protein Structure and Dynamics ; Synthesis and Properties of Aromatic Compounds
Authors: Yaohui Gu | Binbo Li | Lingyang Jiang | Yuhui Hu | Wenqiang Liu | Lijun Xu | Pengfei Zhai | Jie Liu | JINGLAI DUAN
Source: arXiv (Cornell University) (preprint, 2026-01-15)
Field: Materials Science — Materials Chemistry

A Neuroevolution Potential for Gallium Oxide: Accurate and Efficient Modeling of Polymorphism and Swift Heavy-Ion Irradiation Gallium oxide (Ga2O3) is a wide-bandgap semiconductor with promising applications in high-power and high-frequency electronics. However, its complex polymorphic nature poses substantial challenges for fundamental studies, particularly in understanding phase-transformation behaviors under nonequilibrium conditions. Here, we develop a robust, accurate, and computationally efficient machine-learning interatomic potential (MLIP) for Ga2O3 based on the neuroevolution potential (NEP) framework combined with an energy-dependent weighting strategy. The resulting NEP potential demonstrates clear advantages over the state-of-the-art tabGAP potential with respect to both accuracy and computational efficiency. Furthermore, we introduce a physically process-oriented sampling strategy to systematically augment the training dataset, thereby enhancing the MLIP performance for targeted physical phenomena. As a representative application, a dedicated NEP potential is constructed for swift heavy-ion (SHI) irradiation simulations of \b{eta}-Ga2O3. The simulated results are in quantitative agreement with experimental observations and provide a consistent physical explanation for the reported experimental discrepancies regarding phase transformations in the ion track of \b{eta}-Ga2O3. Gallium ; Gallium ; Neuroevolution ; Neuroevolution ; Weighting ; Weighting ; Observable ; Observable ; Molecular dynamics ; Molecular dynamics Machine Learning in Materials Science ; Ga2O3 and related materials ; GaN-based semiconductor devices and materials
Authors: Haifang Cong | Siyu Chen | Yang Yang | Tianyun Luan | Chao Yang
Source: PLoS ONE (article, 2026-01-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A physics-inspired memory-augmented deep learning framework for magnetic core loss prediction Accurate prediction of magnetic core loss is a key challenge for improving the efficiency and reliability of power electronic systems. Traditional empirical models such as the Steinmetz equation are only applicable to sinusoidal steady-state conditions and struggle with the complex non-sinusoidal waveforms and variable operating conditions in modern power electronics. While existing deep learning methods have shown improvements, they still face fundamental limitations in handling the nonlinear mismatch between B(t) and H(t) waveforms, coupling of multi-scale loss mechanisms, and generalization under extreme operating conditions. This paper proposes an Enhanced Memory Augmented Mamba (EMA-Mamba) model that achieves breakthrough progress in magnetic core loss prediction. It utilizes a state-space memory augmentation mechanism that stores and retrieves typical magnetization patterns through a trainable external memory matrix, endowing the model with a capability similar to the "magnetic memory" of magnetic materials, effectively solving the gradient vanishing problem in long sequence modeling. Combined with an attention-guided intelligent feature selection mechanism, it adaptively identifies critical turning points in hysteresis curves through a Top-K strategy, fundamentally solving the temporal mismatch problem between B(t) and H(t) waveforms. Finally, through a physics-constrained multi-objective optimization framework, it achieves decoupled modeling of hysteresis loss, eddy current loss, and residual loss through loss function combination, overcoming the optimization difficulties caused by data spanning six orders of magnitude. Experiments on the MagNet dataset containing 10 materials and over 150,000 data points show that EMA-Mamba achieves an average prediction error of 4.50% and a coefficient of determination of 99.9947%, reducing error by 34.2% compared to state-of-the-art baseline methods, with a 36.2% reduction in 95th percentile error under extreme conditions. The model demonstrates excellent temperature robustness and cross-material generalization capability, providing a reliable theoretical tool for intelligent design and optimization of magnetic components. Hysteresis ; Residual ; Eddy current ; Feature (linguistics) ; Magnet ; Deep learning ; Power (physics) ; Nonlinear system ; Reliability (semiconductor) ; Magnetic hysteresis Magnetic Properties and Applications ; Silicon Carbide Semiconductor Technologies ; Advanced DC-DC Converters
Authors: Yongjian Zhou | Haoran Cui | Zefang Ye | Jung-Fu Lin | Yan Wang | Yaguo Wang
Source: ArXiv.org (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Stacking-Engineered Thermal Transport and Phonon Filtering in Rhenium Disulfide Cross-plane heat transport is a critical bottleneck for van der Waals (vdW) electronics, yet its microscopic governing principles remain elusive. We demonstrate that stacking order is an effective control knob for cross-plane phonon transport in multilayer Rhenium Disulfide (ReS2). Thickness-dependent thermal conductivity measurements reveal remarkably long cross-plane phonon mean free paths (MFPs) (>= 200-300 nm) and provide a direct experimental observation of the transition from quasi-ballistic transport to a thickness-independent ballistic limit. AA stacking exhibits nearly double the cross-plane thermal conductivity of AB stacking, driven by longer acoustic phonon lifetimes from a more "coherent" interlayer registry. Integrated deep neural-network molecular dynamics reveals that phonon filtering in ReS2 is fundamentally frequency-selective: weak vdW coupling acts as a low-pass filter, whereas stronger coupling broadens the transmission passband. These results establish ReS2 as a model system where stacking order and interlayer coupling can be engineered to tune heat conduction across diffusive, quasi-ballistic, and ballistic regimes, offering a new framework for thermal management in 2D electronics. Stacking ; Phonon ; Thermal conductivity ; Thermal conduction ; Rhenium ; van der Waals force ; Coupling (piping) Thermal properties of materials ; 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Galymzhan Moldagulov | Kisung Lee | Sanzhar Nurgaliyev | Assanali Salem | Anatolii I. Kuznietsov | Bartosz A. Grzybowski
Source: Angewandte Chemie (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Hybrid Computational Strategy for Predicting Complex Ligand–Metal Architectures ABSTRACT Understanding how metals coordinate to organic ligands is a precondition for the rational design of metal complexes and catalysts. Whereas certain types of ligands are capable of just one easy‐to‐predict coordination modality, others may present tens and sometimes even hundreds of coordination options (mono‐, bi‐, or polydentate), and predicting the correct one may be a challenge even to seasoned chemists. The current paper describes a “hybrid” computational approach in which a Machine Learning, ML, algorithm learns to predict complex coordination patterns using knowledge‐based “rules” derived from the Cambridge Structural Database, CSD. This model is applicable to a broad scope of ligands (including hemilabile and haptic ones as well as those with denticity &gt; 6) and different metals at different oxidation states. The algorithm's code is disclosed and can be readily deployed in RDKit via our RDMetallics python‐wrapper. It is also deployed as a publicly accessible web portal for demonstration and use. Scope (computer science) ; Denticity ; Precondition ; Code (set theory) ; Rational design ; Computational model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Anik Shrivastava
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-17)
Field: Materials Science — Materials Chemistry

Sustainable Future through the Convergence of Artificial Intelligence and Physics: A Comprehensive Review Sustainability has emerged as the defining global challenge of the twenty-first century, driven by climate change, rapid industrialization, and the depletion of natural resources. Addressing these complex, interconnected issues requires a shift from traditional scientific silos toward a "Third Paradigm" of science: the convergence of Artificial Intelligence (AI) and Physics. While physics provides the foundational laws governing energy, matter, and natural processes, AI enables the analysis, prediction, and optimization of high-dimensional systems. This review examines recent developments at this intersection, with a specific focus on Physics-Informed Neural Networks (PINNs) and their applications in renewable energy, climate modeling, materials discovery, and smart urban infrastructure. We argue that embedding physical constraints into AI architectures is essential for creating reliable, interpretable, and data-efficient solutions for long-term environmental and social sustainability. Sustainability ; Convergence (economics) ; Applications of artificial intelligence ; Artificial neural network ; Focus (optics) ; Natural (archaeology) ; Climate change Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Computational Physics and Python Applications
Authors: Junyu Yan | Chao Liu | Yujia Sun | Dong Liu
Source: Journal of Quantitative Spectroscopy and Radiative Transfer (article, 2026-03-18)
Field: Chemistry — Spectroscopy

An efficient and adaptable line-by-line gas absorption algorithm using a neural-network-based Voigt profile generator Voigt profile ; Radiative transfer ; Spectral line shape ; Absorption (acoustics) ; Convolution (computer science) ; Line (geometry) ; Infrared window ; Spectral line ; Sampling (signal processing) Spectroscopy and Laser Applications ; Atmospheric chemistry and aerosols ; Atmospheric aerosols and clouds
Authors: Alessandro Caruso | Jacopo Venturin | Lorenzo Giambagli | Edoardo Rolando | Zakariya El‐Machachi | Frank Noé | Cecilia Clementi
Source: Refubium (Universitätsbibliothek der Freien Universität Berlin) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Extending the range of graph neural networks with global encodings Graph Neural Networks (GNNs) are routinely used in molecular physics, social sciences, and economics to model many-body interactions in graph-like systems. However, GNNs are inherently local and can suffer from information flow bottlenecks. This is particularly problematic when modeling large molecular systems, where dispersion forces and local electric field variations drive collective structural changes. We introduce RANGE, a model-agnostic framework that employs an attention-based aggregation-broadcast mechanism that significantly reduces oversquashing effects, and achieves remarkable accuracy in capturing long-range interactions with linear scaling. Notably, RANGE integrates attention with positional encodings and regularization to dynamically expand virtual representations in virtual-node message-passing implementations. Across multiple state-of-the-art baselines, RANGE consistently restores long-range information, enabling the models to correctly predict electrostatic and dispersion-driven behavior even in out-of-distribution extrapolation tasks, where other unmodified baselines fail. Compared with other long-range paradigms, RANGE achieves the highest accuracy while requiring significantly less computational overhead, and it enables stable and scalable molecular dynamic simulations. RANGE offers accurate and efficient modeling of long-range interactions for simulating large molecular systems. Extrapolation ; Scalability ; Range (aeronautics) ; Graph ; Artificial neural network ; Regularization (linguistics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems