jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Akshay Subramanian | Elton Pan | Juno Nam | Maurice Weiler | Shuhui Qu | Cheol Woo Park | Tommi Jaakkola | Elsa A. Olivetti | Rafael Gomez-Bombarelli
Source: arXiv (Cornell University) (preprint, 2026-02-23)
Field: Materials Science — Materials Chemistry

PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment Organic molecular crystals underpin technologies ranging from pharmaceuticals to organic electronics, yet predicting solid-state packing of molecules remains challenging because candidate generation is combinatorial and stability is only resolved after costly energy evaluations. Here we introduce PackFlow, a flow matching framework for molecular crystal structure prediction (CSP) that generates heavy-atom crystal proposals by jointly sampling Cartesian coordinates and unit-cell lattice parameters given a molecular graph. This lattice-aware generation interfaces directly with downstream relaxation and lattice-energy ranking, positioning PackFlow as a scalable proposal engine within standard CSP pipelines. To explicitly steer generation toward physically favourable regions, we propose physics alignment, a reinforcement learning post-training stage that uses machine-learned interatomic potential energies and forces as stability proxies. Physics alignment improves physical validity without altering inference-time sampling. We validate PackFlow's performance against heuristic baselines through two distinct evaluations. First, on a broad unseen set of molecular systems, we demonstrate superior candidate generation capability, with proposals exhibiting greater structural similarity to experimental polymorphs. Second, we assess the full end-to-end workflow on two unseen CSP blind-test case studies, including relaxation and lattice-energy analysis. In both settings, PackFlow outperforms heuristics-based methods by concentrating probability mass in low-energy basins, yielding candidates that relax into lower-energy minima and offering a practical route to amortize the relax-and-rank bottleneck. Scalability ; Maxima and minima ; Reinforcement learning ; Stability (learning theory) ; Crystal structure prediction ; Ranging ; Relaxation (psychology) ; Heuristic ; Workflow Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Jingyi Cui | Jacob K. Christopher | Ankita Biswas | Prasanna V. Balachandran | Ferdinando Fioretto
Source: arXiv (Cornell University) (preprint, 2026-02-22)
Field: Materials Science — Materials Chemistry

Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects Point defects affect material properties by altering electronic states and modifying local bonding environments. However, high-throughput first-principles simulations of point defects are costly due to large simulation cells and complex energy landscapes. To this end, we propose a generative framework for simulating point defects, overcoming the limits of costly first-principles simulators. By leveraging a primal-dual algorithm, we introduce a constraint-aware diffusion model which outperforms existing constrained diffusion approaches in this domain. Across six defect configuration settings for Bi2Te3, the proposed approach provides state-of-the-art performance generating physically grounded structures. Crystallographic defect ; Point (geometry) ; Diffusion ; Relaxation (psychology) ; Energy (signal processing) ; Point process Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: Jingyi Cui | Jacob K. Christopher | Ankita Biswas | Prasanna V. Balachandran | Ferdinando Fioretto
Source: ArXiv.org (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects Point defects affect material properties by altering electronic states and modifying local bonding environments. However, high-throughput first-principles simulations of point defects are costly due to large simulation cells and complex energy landscapes. To this end, we propose a generative framework for simulating point defects, overcoming the limits of costly first-principles simulators. By leveraging a primal-dual algorithm, we introduce a constraint-aware diffusion model which outperforms existing constrained diffusion approaches in this domain. Across six defect configuration settings for Bi2Te3, the proposed approach provides state-of-the-art performance generating physically grounded structures. Crystallographic defect ; Point (geometry) ; Diffusion ; Relaxation (psychology) ; Energy (signal processing) ; Point process Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: Chenguang Wang | Zihan Zhou | Lei Bai | Tianshu Yu
Source: ArXiv.org (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching Template-free retrosynthesis methods treat the task as black-box sequence generation, limiting learning efficiency, while semi-template approaches rely on rigid reaction libraries that constrain generalization. We address this gap with a key insight: atom ordering in neural representations matters. Building on this insight, we propose a structure-aware template-free framework that encodes the two-stage nature of chemical reactions as a positional inductive bias. By placing reaction center atoms at the sequence head, our method transforms implicit chemical knowledge into explicit positional patterns that the model can readily capture. The proposed RetroDiT backbone, a graph transformer with rotary position embeddings, exploits this ordering to prioritize chemically critical regions. Combined with discrete flow matching, our approach decouples training from sampling and enables generation in 20--50 steps versus 500 for prior diffusion methods. Our method achieves state-of-the-art performance on both USPTO-50k (61.2% top-1) and the large-scale USPTO-Full (51.3% top-1) with predicted reaction centers. With oracle centers, performance reaches 71.1% and 63.4% respectively, surpassing foundation models trained on 10 billion reactions while using orders of magnitude less data. Ablation studies further reveal that structural priors outperform brute-force scaling: a 280K-parameter model with proper ordering matches a 65M-parameter model without it. Prior probability ; Oracle ; Sequence (biology) ; Transformer ; Limiting ; Bridging (networking) ; Exploit ; Position (finance) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Chenguang Wang | Zihan Zhou | Lei Bai | Tianshu Yu
Source: Open MIND (preprint, 2026-02-13)
Field: Materials Science — Materials Chemistry

Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching Template-free retrosynthesis methods treat the task as black-box sequence generation, limiting learning efficiency, while semi-template approaches rely on rigid reaction libraries that constrain generalization. We address this gap with a key insight: atom ordering in neural representations matters. Building on this insight, we propose a structure-aware template-free framework that encodes the two-stage nature of chemical reactions as a positional inductive bias. By placing reaction center atoms at the sequence head, our method transforms implicit chemical knowledge into explicit positional patterns that the model can readily capture. The proposed RetroDiT backbone, a graph transformer with rotary position embeddings, exploits this ordering to prioritize chemically critical regions. Combined with discrete flow matching, our approach decouples training from sampling and enables generation in 20--50 steps versus 500 for prior diffusion methods. Our method achieves state-of-the-art performance on both USPTO-50k (61.2% top-1) and the large-scale USPTO-Full (51.3% top-1) with predicted reaction centers. With oracle centers, performance reaches 71.1% and 63.4% respectively, surpassing foundation models trained on 10 billion reactions while using orders of magnitude less data. Ablation studies further reveal that structural priors outperform brute-force scaling: a 280K-parameter model with proper ordering matches a 65M-parameter model without it. Prior probability ; Oracle ; Sequence (biology) ; Transformer ; Limiting ; Bridging (networking) ; Exploit ; Position (finance) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Carlos Cardona-Giraldo | C. Fanelli | James Giroux | Cole G. Granger | Benjamin Nachman | Gerald Sabin
Source: arXiv (Cornell University) (preprint, 2026-03-27)
Field: Materials Science — Materials Chemistry

Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuning Modern particle physics experiments face an increasing demand for high-fidelity detector simulation as luminosities rise and computational requirements approach the limits of available resources. Deep generative models have emerged as promising surrogates for traditional Monte Carlo simulation, with recent advances drawing inspiration from large language models (LLM) and next-token prediction paradigms. In this work, we introduce a generalizable foundation model for calorimetry built on next-token transformer backbones, designed to support modular adaptation across materials, particle species, and detector configurations. Our approach combines Mixture-of-Experts pre-training with parameter-efficient fine-tuning strategies to enable controlled, additive model expansion without catastrophic forgetting. A pre-trained backbone is trained to generate electromagnetic showers across multiple absorber materials, while new materials are incorporated through the addition and tuning of lightweight expert modules. Extensions to new particle types are achieved via parameter-efficient fine-tuning and modular vocabularies, preserving the integrity of the base model. This design enables efficient, incremental knowledge integration as new simulation datasets become available, a critical requirement in realistic detector-development workflows. In addition, we demonstrate that next-token calorimeter models are computationally competitive with standard generative approaches under established LLM optimization procedures. These results establish next-token architectures as a viable path toward extensible, physics-aware foundation models for calorimetry and future high-energy physics experiments. Modular design ; Monte Carlo method ; Detector ; Transformer ; Particle filter ; Knowledge base Machine Learning in Materials Science ; Particle physics theoretical and experimental studies ; Nuclear physics research studies
Authors: Norhan Badawy | Rehab Mahmoud | Amna A. Kotp | Ahmed A. Farghali | Amal Zaher
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Mg–Fe layered double hydroxides as multifunctional materials for manganese removal and methanol electrooxidation This study evaluated how well Mg-Fe layered double hydroxides (LDH) removed manganese (Mn<sup>2+</sup>) from aqueous media and electrocatalytic methanol oxidation. The structural and physicochemical properties of the materials both prior to and after the adsorption process were characterized by X-ray diffraction, infrared analysis, particle size analysis, and field emission scanning electron microscopy. Adsorption was evaluated at different pH levels and adsorbent dosages, revealing that surface hydroxyl groups and interlayer anions are crucial for manganese uptake. The equilibrium data were well fitted by the Langmuir-Freundlich isotherm model, whereas the kinetic results followed those of the mixed 1,2-order and intraparticle diffusion models, indicating that monolayer adsorption was dominated by chemisorption mechanisms. Thermodynamic parameters confirmed that the adsorption process is spontaneous and exothermic, suggesting strong interactions between Mn<sup>2+</sup> ions and the LDH surface through surface complexation and chemical bonding. The Mg-Fe LDH achieved a manganese removal efficiency exceeding 70%, and <i>q</i> <sub>max</sub> was 198.99 mg g<sup>-1</sup>. Furthermore, electrochemical studies demonstrated that both pristine LDH and Mn-loaded LDH (LDH/Mn) exhibit enhanced electrocatalytic activity toward methanol oxidation. The improved LDH/Mn performance was attributed to a larger electroactive surface area and improved charge-transfer kinetics. These results highlight the dual functionality of Mg-Fe LDH for environmental remediation and electrochemical energy conversion applications. Manganese ; Layered double hydroxides ; Methanol ; Aqueous solution ; Aqueous medium Layered Double Hydroxides Synthesis and Applications ; Advanced oxidation water treatment ; Catalysis for Biomass Conversion
Authors: Natanel Nissan | Sherman W. Marcus | Dan Raviv | Giryes, Raja | Ariel Epstein
Source: Open MIND (preprint, 2026-03-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Harnessing Selective State Space Models to Enhance Semianalytical Design of Fabrication-Ready Multilayered Huygens' Metasurfaces: Part II - Generative Inverse Design (MetaMamba) We present a generative framework for inverse design of five-layer transmissive Huygens' metasurfaces (HMSs), addressing a longstanding challenge in achieving full-phase, high-efficiency unit cell designs with minimal full-wave simulations. The key to achieving this is our reliance on the field-based semianalytical (SA) scheme developed in Part I of this paper, which allows rapid and highly effective synthesis of such multilayer composites, however with limited accuracy. To overcome the prohibitive data demands of traditional pipelines, we employ Mamba, a selective state space model well suited for long-range sequence modeling as the backbone of our learning framework. A bidirectional Mamba (Bi-Mamba) forward surrogate is first trained on SA-generated data and subsequently fine-tuned with full-wave CST samples. An ablation over a 1080-sample CST pool shows that as few as 270 full-wave calibration samples suffice to reach near-CST-level agreement at a fraction of the simulation cost. An autoregressive Mamba inverse generator is subsequently trained on surrogate-augmented data, treating unit-cell synthesis as a sequential generation task. The resulting one-to-many generative model produces diverse unit cell geometries conditioned on target scattering responses. It achieves CST-validated designs with field transmission magnitude 0.9 across the full 0-$2π$ phase range at 20 GHz. Moreover, a CST-calibrated surrogate trained to accurately predict frequency responses (18-22 GHz) enables functional post-selection of inverse generated designs. Together, the hybrid SA-generative methodology in this two-part compilation establishes a scalable and data-efficient solution for multilayer HMS synthesis, with natural extensions toward broadband, oblique-incidence, and higher-dimensional electromagnetic inverse-design problems. Inverse ; Scalability ; Range (aeronautics) ; Generator (circuit theory) ; Sequence (biology) ; Field (mathematics) ; Transmission (telecommunications) ; Inverse problem ; Surrogate model ; Generative Design Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Wireless Communication Technologies
Authors: Mairi McCauley | Joel Martis | Ondrej L. Krivanek | B Plotkin-Swing | Andreas Mittelberger | Tolga Wagner | Hüseyin Çelik | Grigory Kornilov | Meng Zhao | Matthias Meffert | Luca Giuseppe Piazza | Tracy C. Lovejoy | Guillaume Radtke | Christoph Koch | Benedikt Haas
Source: ArXiv.org (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Platform and Framework for Time-Resolved Nanoscale Thermal Transport Measurements in STEM Understanding heat transport at the nanometer scale is critical for semiconductor devices, quantum materials, and thermal management of nanostructures, yet direct local measurements of thermal conductivity and heat capacity remain scarce. We developed a laser-excitation system integrated into a scanning transmission electron microscope (STEM) for nanoscale thermal transport measurements using ultra-high-resolution electron energy-loss spectroscopy (EELS). A fiber-coupled laser is introduced via a modified aperture mechanism, enabling flexible holder geometries and large tilt angles without optical elements in the polepiece gap. Synchronization of pulsed laser excitation with an externally gated direct electron detector provides temporal resolution about 50 ns at <10 meV energy resolution. Local temperatures are determined via the principle of detailed balance, and thermal transport parameters are extracted by fitting a forward-time central-space heat diffusion model including radiative losses. For amorphous carbon films, we obtain a thermal conductivity of 1.24 $\frac{W}{m\cdot K}$ and a heat capacity of 821 $\frac{J}{kg\cdot K}$, consistent with literature. This framework enables time-resolved nanoscale measurements of thermal transport in materials and devices. Thermal conductivity ; Laser ; Scanning thermal microscopy ; Thermal conduction ; Thermal ; Nanoscopic scale ; Thermal diffusivity ; Heat transfer ; Nanomanufacturing ; Bolometer Thermal properties of materials ; Advanced Electron Microscopy Techniques and Applications ; Thermal Radiation and Cooling Technologies
Authors: Sumangla Kadian
Source: Journal of Research in Chemistry (article, 2026-03-01)
Field: Materials Science — Biomaterials

Formulation, characterization, and enhanced antibacterial activity of Ag₂O-loaded levofloxacin nanoparticles (Ag₂O-LFNs) against multidrug-resistant Salmonella typhi The study was designed to develop and evaluate silver oxide-loaded levofloxacin nanoparticles (Ag₂O‑LFNs) as a nanotechnology-based strategy to improve the treatment of Salmonella typhi infections. Ag₂O nanoparticles were synthesized by controlled alkaline precipitation, loaded with levofloxacin via adsorption, and optimized for particle size, polydispersity, zeta potential, encapsulation efficiency, and drug loading. The optimized formulation exhibited nanoscale size (~140 nm), low PDI, a moderately negative zeta potential, and high encapsulation efficiency (78-93%) with drug loading in the range of 10-15%, indicating efficient association of levofloxacin with the Ag₂O matrix. FTIR analysis confirmed the coexistence of characteristic levofloxacin and Ag-O bands with minor shifts, suggesting noncovalent interactions without structural degradation of the drug. In vitro release studies demonstrated a biphasic, sustained-release profile for Ag₂O‑LFNs, with only ~42.6% drug released at 4 h and ~78.4% at 24 h compared with >90% release from free levofloxacin within 4 h, and the release data best fitted the Higuchi diffusion model (R² ≈ 0.99).​ Antibacterial assays against S. typhi showed larger inhibition zones for Ag₂O‑LFNs (27.4 ± 1.2 mm) than for free levofloxacin (20.1 ± 0.9 mm) or Ag₂O alone (14.7 ± 0.8 mm), and a four-fold reduction in MIC (1.0 vs. 4.0 µg/mL), indicating a pronounced synergistic effect between Ag₂O and levofloxacin. Overall, the results demonstrate that Ag₂O‑LFNs combine favorable physicochemical properties, sustained drug release, and enhanced antibacterial activity, supporting their potential as a promising nanoplatform for targeted therapy against typhoid fever. Levofloxacin ; Salmonella typhi ; Zeta potential ; Antibacterial activity ; Typhoid fever ; Nanoparticle ; Ofloxacin Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Advanced Drug Delivery Systems
Authors: Marhaina Ismail | Mohamad Azmi Bustam | Yin Fong Yeong | Norwahyu Jusoh
Source: Key engineering materials (article, 2026-03-04)
Field: Chemistry — Inorganic Chemistry

Mechanism of Carbon Dioxide Adsorption on Gallate-Based Metal-Organic Frameworks Carbon dioxide (CO 2 ) capture is a significant chemical process that has attracted considerable interest in both academic and industrial sectors. It is essential for mitigating climate change and its related impacts on the environment and human health. Various technologies are implemented for CO 2 capture, with physical adsorption using porous material standing out as one of the most widely employed methods. Gallate-based metal-organic frameworks (MOFs) are reported to offer remarkable CO 2 adsorption capacity values, with Mg-gallate exhibiting the highest capacity, followed by Co-gallate and Ni-gallate. The mechanism of CO 2 adsorption on gallate-based MOFs, however, lacks extensive discussion. A thorough understanding of the adsorption mechanism helps in designing and synthesizing MOFs with enhanced CO 2 capture performance. Therefore, this work aims to discuss the mechanism of CO 2 adsorption on gallate-based MOFs based on the experimental pure isotherms. The experimental isotherms exhibited S-shaped curves that are related to the occurrence of gate-opening effect. These S-shaped isotherms corresponded to multistep adsorption, classifying gallate-based MOFs as flexible MOFs. The flexibility of these frameworks can be controlled by the pressure and temperature, which is important for designing specific gas storage and separation systems. In addition, the intra-particle diffusion model supported that the CO 2 adsorption occurred at the surface and mesopore of gallate-based MOFs. Given these characteristics, gallate-based MOFs can be considered as the promising physisorbent for CO 2 capture. Adsorption ; Carbon dioxide ; Flexibility (engineering) ; Mechanism (biology) ; Porosity ; Carbon fibers ; Process (computing) ; Mesoporous material Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Isak Silander | Johan Zakrisson | Ove Axner | Martin Zelán
Source: Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena (article, 2026-03-30)
Field: Chemistry — Spectroscopy

Diffusion-isolated pressure equilibration for multigas refractometry Fabry–Pérot refractometry is a leading technique for high-accuracy pressure realization; however, multigas operation is typically constrained by gas-handling complexity and the risk of cross-contamination. In this work, we investigate whether the pronounced difference in time scales between rapid pressure equilibration and the much slower molecular diffusion in long, narrow connections can be exploited to enable two connected Fabry–Pérot cavity-based refractometers to operate with different gases at the same pressure. Diffusion modeling based on Fick’s second law, together with experiments using two refractometers, shows that gas mixing can remain below 1 ppm for a variety of measurement times and pressures. These results demonstrate a simple and robust approach to simultaneous multigas refractometry at a common pressure without the need for physical separation hardware. Refractometry ; Refractometer ; Mixing (physics) ; Diffusion ; Refractive index ; Pressure gradient ; Pressure measurement ; Radiation pressure Spectroscopy and Laser Applications ; Scientific Measurement and Uncertainty Evaluation ; Gas Dynamics and Kinetic Theory