jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Manish Kumar Parihar
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

REACTOR: Training a Transformer in O(N) Time With Zero Gradient Steps — The Manish Principle (48 Proven Laws of Linear Structure in Neural Networks) Description: This paper proves that backpropagation is mathematically unnecessary for transformer training. We introduce the Manish Principle: every operation that appears nonlinear in the wrong coordinate system becomes exactly linear in its correct natural space. Applied to GPT-Neo transformers, this means every weight matrix — Wq, Wk, Wv, Wo, W1, W2 — is a perfectly linear map at its activation boundary, recoverable in one closed-form least-squares solve with R² = 1.000000. We organize this finding into 48 proven laws covering all transformer operations: attention projections, FFN matrices, GeLU/SiLU/ReLU/Sigmoid/Tanh/Softmax activations, LayerNorm, and the full bilinear cross-token structure. 36 laws achieve machine-precision R² = 1.000000. Zero laws failed. From this principle we build three systems: Crystal Engine — the complete GPT-Neo transformer implemented in pure NumPy. Zero PyTorch, zero CUDA, zero autograd. 100% token match with PyTorch. 3.42× faster. REACTOR — transformer training via 48 closed-form lstsq solves. Zero gradient steps. O(N) time. 100% token match with the original trained model. ~6 seconds on a laptop GPU. REACTOR-SCRATCH — transformer trained from raw text with no teacher model and no gradients, achieving 33.54% test accuracy (16,854× over random baseline) in 26 seconds. We prove the 78/22 Decomposition Law: 78% of predictive structure pre-exists in raw token embeddings before any layer computation; the remaining 22% is the cross-token bilinear component, also pre-existing in the tensor algebra of the input embeddings. Transformer layers assemble structure — they do not create it. We prove the V16 Intelligence Transfer Law: two models can be combined geometrically in closed form using embedding space alignment, midpoint blending, and orthogonalization — zero fine-tuning, zero gradient steps, R² = 1.000000 throughout. All experiments on roneneldan/TinyStories-1M and TinyStories-8M (GPT-Neo architecture). Hardware: Intel i5 12th gen, 16 GB RAM, RTX 4050 Laptop GPU (6 GB VRAM).Important Note : - Reactor Used Tautology approch for traning, but laws are valid. Keywords: transformer, backpropagation-free training, natural space, least squares, analytical training, Crystal Engine, 78/22 law, intelligence transfer, linear algebra, GPT-Neo, language model, weight extraction, zero gradient Bilinear interpolation ; Security token ; Transformer ; Nonlinear system ; Embedding ; Control theory (sociology) ; Linear variable differential transformer ; Flyback transformer Machine Learning in Materials Science ; Big Data and Digital Economy ; Stochastic Gradient Optimization Techniques
Authors: Jihed Makni | K. Riahi | Mayssa Yengui | Wissem Cheikhrou-Koubaa
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Biomaterials

Molybdenum-doped La <sub>0.7</sub> Sr <sub>0.3</sub> MnO <sub>3</sub> nanoparticles: tuning magnetic and heating properties for magnetic hyperthermia Self-limited magnetic hyperthermia offers a promising approach for cancer therapy by exploiting materials whose Curie temperature (<i>T</i> <sub>C</sub>) intrinsically constrains overheating during treatment. In this work, ultrafine La<sub>0.7</sub>Sr<sub>0.3</sub>Mn<sub>1-<i>x</i></sub> Mo <sub><i>x</i></sub> O<sub>3</sub> (<i>x</i> = 0.10, 0.15, 0.20) nanoparticles were synthesized <i>via</i> the glycine-nitrate process. All samples crystallized in a rhombohedral (<i>R</i>3̄<i>c</i>) structure, with particle size decreasing from ∼230 nm to ∼105 nm as Mo content increased. Magnetic measurements revealed ferromagnetic-paramagnetic transitions with decreasing <i>T</i> <sub>C</sub>. The substitution of Mo<sup>6+</sup> for Mn<sup>3+</sup>/Mn<sup>4+</sup> disrupted double-exchange interactions and altered the Mn<sup>3+</sup>/Mn<sup>4+</sup> balance, leading to reduced magnetization and a progressive decrease in <i>T</i> <sub>C</sub> (from 350 to 290 K). Under an alternating magnetic field, the nanoparticles exhibited rapid initial heating followed by a plateau near <i>T</i> <sub>C</sub>, demonstrating self-limited heating behavior. Specific absorption rate values were moderate (∼14-16 W g<sup>-1</sup> for intermediate doping and ∼7 W g<sup>-1</sup> at the highest doping), consistent with magnetic dilution and surface spin effects. These results show that La<sub>0.7</sub>Sr<sub>0.3</sub>Mn<sub>1-<i>x</i></sub> Mo <sub><i>x</i></sub> O<sub>3</sub> nanoparticles possess intrinsically self-limited heating suggesting their promise for magnetic hyperthermia. Further fine-tuning Mo doping, particle size, and surface properties could more precisely adjust the Curie temperature and enhance heating efficiency, advancing these nanoparticles toward safe and effective hyperthermia applications. Curie temperature ; Nanoparticle ; Magnetic hyperthermia ; Overheating (electricity) ; Magnetic nanoparticles ; Magnetization ; Specific absorption rate ; Particle size Nanoparticle-Based Drug Delivery ; Magnetic Properties and Synthesis of Ferrites ; Luminescence Properties of Advanced Materials
Authors: Yan Yang | Shulong Wen | Jie Li | Yao Xiao | Zhe Su | Yu Xiong | Junhui Li | Jiayin Zhang | Xin Huang | Yang Miao | Huaiwu Zhang
Source: Journal of Alloys and Compounds (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Multi-ion substitution enhanced gyromagnetic and dielectric properties in garnet microwave ferrites for low-loss and miniaturized circulators Circulator ; Dielectric ; Ferrite (magnet) ; Dielectric loss ; Microstructure ; Microwave ; Miniaturization ; Curie temperature Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Magneto-Optical Properties and Applications
Authors: Zihao Jing | Qiuhao Zeng | Ruiyi Fang | Yan Yi Li | Yan Sun | Boyu Wang | Pingzhao Hu
Source: arXiv (Cornell University) (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

A Study of Adaptive Modeling Towards Robust Generalization Large language models (LLMs) increasingly support reasoning over biomolecular structures, but most existing approaches remain modality-specific and rely on either sequence-style encodings or fixed-length connector tokens for structural inputs. These designs can under-expose explicit geometric cues and impose rigid fusion bottlenecks, leading to over-compression and poor token allocation as structural complexity grows. We present a unified all-atom framework that grounds language reasoning in geometric information while adaptively scaling structural tokens. The method first constructs variable-size structural patches on molecular graphs using an instruction-conditioned gating policy, enabling complexity-aware allocation of query tokens. It then refines the resulting patch tokens via cross-attention with modality embeddings and injects geometry-informed tokens into the language model to improve structure grounding and reduce structural hallucinations. Across diverse all-atom benchmarks, the proposed approach yields consistent gains in heterogeneous structure-grounded reasoning. An anonymized implementation is provided in the supplementary material. Security token ; Language model ; Generalization ; Encoding (memory) ; Key (lock) ; Minification ; Scaling Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Biomedical Text Mining and Ontologies
Authors: Hyosoon Jang | Hyunjin Seo | Yunhui Jang | Seonghyun Park | Sungsoo Ahn
Source: Open MIND (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

Boltz is a Strong Baseline for Atom-level Representation Learning Foundation models in molecular learning have advanced along two parallel tracks: protein models, which typically utilize evolutionary information to learn amino acid-level representations for folding, and small-molecule models, which focus on learning atom-level representations for property prediction tasks such as ADMET. Notably, cutting-edge protein-centric models such as Boltz now operate at atom-level granularity for protein-ligand co-folding, yet their atom-level expressiveness for small-molecule tasks remains unexplored. A key open question is whether these protein co-folding models capture transferable chemical physics or rely on protein evolutionary signals, which would limit their utility for small-molecule tasks. In this work, we investigate the quality of Boltz atom-level representations across diverse small-molecule benchmarks. Our results show that Boltz is competitive with specialized baselines on ADMET property prediction tasks and effective for molecular generation and optimization. These findings suggest that the representational capacity of cutting-edge protein-centric models has been underexplored and position Boltz as a strong baseline for atom-level representation learning for small molecules. Baseline (sea) ; Property (philosophy) ; Representation (politics) ; Granularity ; Feature learning ; Key (lock) ; Focus (optics) ; Quality (philosophy) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Hyosoon Jang | Hyunjin Seo | Yunhui Jang | Seonghyun Park | Sungsoo Ahn
Source: arXiv (Cornell University) (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Boltz is a Strong Baseline for Atom-level Representation Learning Foundation models in molecular learning have advanced along two parallel tracks: protein models, which typically utilize evolutionary information to learn amino acid-level representations for folding, and small-molecule models, which focus on learning atom-level representations for property prediction tasks such as ADMET. Notably, cutting-edge protein-centric models such as Boltz now operate at atom-level granularity for protein-ligand co-folding, yet their atom-level expressiveness for small-molecule tasks remains unexplored. A key open question is whether these protein co-folding models capture transferable chemical physics or rely on protein evolutionary signals, which would limit their utility for small-molecule tasks. In this work, we investigate the quality of Boltz atom-level representations across diverse small-molecule benchmarks. Our results show that Boltz is competitive with specialized baselines on ADMET property prediction tasks and effective for molecular generation and optimization. These findings suggest that the representational capacity of cutting-edge protein-centric models has been underexplored and position Boltz as a strong baseline for atom-level representation learning for small molecules. Baseline (sea) ; Property (philosophy) ; Representation (politics) ; Granularity ; Feature learning ; Key (lock) ; Focus (optics) ; Quality (philosophy) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Dirk Peschka
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

mini-workshop: AI-assisted scientific computing Abstract: The goal of this mini-workshop is to introduce key principles, workflows, and broader considerations involved in using large language models as programming tools. These models offer significant potential for supporting research software development, yet their effective use requires both a shift in mindset and careful, critical oversight. The workshop will present practical examples and, equally importantly, provide space for discussion and exchange of experiences. It is aimed at scientists at all levels of programming experience, from beginners to advanced developers, who wish to strengthen or expand their coding skills with the support of large language models. Experienced AI practitioners are equally welcome to contribute their perspectives and insights to the discussion.Description: This repository contains the material presented in a two-hour interactive workshop, including extensive discussion with the audience, at Leibniz MMS Days 2026 of the Leibniz Research Network “Mathematical Modeling and Simulation” (MMS), hosted by the Leibniz Institute for High Performance Microelectronics Frankfurt (Oder) (IHP).The workshop was delivered in keynote format and included pre-recorded AI interactions. The repository provides a PDF containing the presentation content and a video file without audio that also documents the pre-recorded AI interactions. Presentation (obstetrics) ; Mindset ; Key (lock) ; Coding (social sciences) ; Software ; Flexibility (engineering) Machine Learning in Materials Science ; Space Technology and Applications ; Parallel Computing and Optimization Techniques
Authors: Sofia M. Vignolo | Daniela M. Roth | May A.A. Fraga | Lillian Wu | Juliette Cosgrove | Avathamsa Athirasala | Angela S.P. Lin | Robert E. Guldberg | Luiz E. Bertassoni
Source: Acta Biomaterialia (article, 2026-02-01)
Field: Materials Science — Biomaterials

Engineering bioinspired, high-density collagen microgels with tunable intrafibrillar mineralization for accelerated osteogenesis in vitro and bone regeneration in vivo Mechanosensitive channels ; RUNX2 ; In vivo ; Extracellular matrix ; Self-healing hydrogels ; Tissue engineering ; Mesenchymal stem cell ; In vitro Collagen: Extraction and Characterization ; Bone Tissue Engineering Materials ; Cellular Mechanics and Interactions
Authors: Phonphiphat Bamrung | Juthamas Tantala | Pornchai Rachtanapun | Chitsiri Rachtanapun | Prapasiri Pongprayoon
Source: Journal of Food Engineering (article, 2026-01-31)
Field: Chemistry — Organic Chemistry

Molecular mechanisms of micelle formation of coco-glucosides in an aqueous solution Micelle ; Octyl glucoside ; Pulmonary surfactant ; Aqueous solution ; Alkyl ; Yield (engineering) ; van der Waals force Surfactants and Colloidal Systems ; Pickering emulsions and particle stabilization ; Advancements in Transdermal Drug Delivery
Authors: Aymane El Haji | Mohamed Karouchi | Abdelkebir Ejjabli | Hamza Errahoui | Abdelmounaim Laassouli | Moussab Harb | Youssef Lachtioui | Omar Bajjou
Source: International Journal of Hydrogen Energy (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

First-principles investigation of structural, mechanical, electronic, optical, and thermodynamic properties of perovskite hydrides XRuH3 (X=Mg, Ca, Sr, Ba) for hydrogen storage Perovskite hydrides have recently gained attention as solid-state hydrogen storage materials owing to their high density and tunable thermodynamic stability. In this work, we investigate the structural, mechanical, electronic, optical, thermodynamic and hydrogen storage properties of alkaline earth metal-ruthenium perovskite hydrides XRuH 3 (X = Mg, Ca, Sr, Ba) using density functional theory (DFT) as implemented in WIEN2k. Formation enthalpies confirm the thermodynamic stability of all XRuH 3 hydrides. The optimized lattice parameters of MgRuH 3 , CaRuH 3 , SrRuH 3 and BaRuH 3 are 3.47 Å, 3.63 Å, 3.76 Å and 3.91 Å, respectively. Mechanical analysis shows compliance with the Born criteria, while Pugh's ratios above 2.3 classify all compounds as ductile. Phonon dispersion curves confirm dynamical stability for CaRuH 3 , SrRuH 3 , and BaRuH 3 , whereas MgRuH 3 exhibits imaginary modes. AIMD simulations at 300 K further support the finite-temperature stability of CaRuH 3 and SrRuH 3 , while MgRuH 3 exhibits large temperature fluctuations consistent with its soft phonons. Electronic structure calculations show metallic character dominated by Ru 4d states near the Fermi level. Hydrogen storage analysis indicates gravimetric capacities range from 2.36 wt% for MgRuH 3 to 1.25 wt% for BaRuH 3 , while volumetric capacities (42.0 to 59.8 g H 2 /L) are higher than the U.S Department of Energy target of 30 g H 2 /L. Thermodynamic analysis shows heat capacities approaching the Dulong-Petit limit at higher temperatures while BaRuH 3 has a very low thermal expansion. Among the studied compounds, CaRuH 3 and SrRuH 3 display excellent structural integrity and volumetric performance, suggesting their suitability as frameworks for further development and optimization. Strategies to tailor the desorption thermodynamics toward practical hydrogen storage applications like partial Ru-site substitution or nanostructuring may help fine tune desorption thermodynamics toward practical hydrogen storage applications. • Ru-based XRuH 3 hydrides emerge as dense solid-state hydrogen carriers. • CaRuH 3 and SrRuH 3 confirmed mechanically, dynamically, and thermally stable. • Delivers gravimetric H 2 capacity up to 2.36 wt%. • Volumetric H 2 density up to 59.8 g H 2 /L, exceeding the DOE target of 30 g H 2 /L. • Metallic Ru-4d states govern electronic structure, optics and thermodynamics. Hydrogen storage ; Perovskite (structure) ; Chemical stability ; Hydrogen ; Density functional theory ; Enthalpy ; Fermi level ; Thermal stability ; Gravimetric analysis ; Thermogravimetric analysis Hydrogen Storage and Materials ; Thermal Expansion and Ionic Conductivity ; Electrocatalysts for Energy Conversion
Authors: Vo Quang Huy Phan | Jo' Del Gobbo | Mukundam Vanga | Deepika V. Karade | Cristina Marzano | Valentina Gandin | Laura Rigon | Carlo Santini | Maura Pellei | H V Rasika Dias
Source: Chemistry - A European Journal (article, 2026-03-04)
Field: Chemistry — Organic Chemistry

Structural and Biological Insights Into Copper(I) Phosphane Complexes With Boron‐Phenylated Poly(3‐(CF <sub>3</sub> )Pyrazolyl)‐ and Poly(6‐(CF <sub>3</sub> )‐2‐pyridyl)‐Borates Six new copper(I) phosphane complexes supported by B-phenylated poly(3-(CF<sub>3</sub>)pyrazolyl)- and poly(6-(CF<sub>3</sub>)-2-pyridyl)borate ligands as [Ph<sub>3</sub>B(3-(CF<sub>3</sub>)Pz)]<sup>-</sup> (1), [Ph<sub>2</sub>B(3-(CF<sub>3</sub>)Pz)<sub>2</sub>]<sup>-</sup> (2), [PhB(3-(CF<sub>3</sub>)Pz)<sub>3</sub>]<sup>-</sup> (3), [Ph<sub>3</sub>B(6-(CF<sub>3</sub>)Py)]<sup>-</sup> (4), [Ph<sub>2</sub>B(6-(CF<sub>3</sub>)Py)<sub>2</sub>]<sup>-</sup> (5) and [PhB(6-(CF<sub>3</sub>)Py)<sub>3</sub>]<sup>-</sup> (6) have been synthesized to systematically modulate steric and electronic properties at copper(I), (Pz = pyrazolyl, Py = pyridyl). The complexes [Cu(PPh<sub>3</sub>){Ph<sub>3</sub>B(3-(CF<sub>3</sub>)Pz)}] (7), [Cu(PPh<sub>3</sub>){Ph<sub>2</sub>B(3-(CF<sub>3</sub>)Pz)<sub>2</sub>}] (8), [Cu(PPh<sub>3</sub>){PhB(3-(CF<sub>3</sub>)Pz)<sub>3</sub>}] (9), [Cu(PPh<sub>3</sub>){Ph<sub>3</sub>B(6-(CF<sub>3</sub>)Py)}] (10), [Cu(PPh<sub>3</sub>){Ph<sub>2</sub>B(6-(CF<sub>3</sub>)Py)<sub>2</sub>}] (11) and [Cu(PPh<sub>3</sub>){PhB(6-(CF<sub>3</sub>)Py)<sub>3</sub>}] (12) were fully characterized by multinuclear NMR spectroscopy, electrochemistry, and single-crystal X-ray diffraction. All compounds display trigonal-planar copper centers, with mono(pyrazolyl)- and mono(pyridyl)borate complexes 7 and 10 exhibiting an unusual pseudo-κ<sup>2</sup> coordination supported by Cu•••C(ipso) interactions. Cyclic voltammetry revealed complex multielectron oxidation behavior, with poly(pyridyl)borate systems undergoing Cu(I)→Cu(II) oxidation at relatively lower potentials than their pyrazolyl analogues, consistent with stronger pyridyl σ-donation. We also conducted efficacy tests on newly synthesized copper(I) complexes and related free ligands against a panel of human cancer cell lines from various solid tumors to explore relevant structure-activity relationships. Studies on molecular target interaction, cellular redox modulation, and cell-death pathways, have also been conducted to elucidate their mode(s) of action. Altogether, these results highlight how fluorinated scorpionate ligand design enable fine tuning of structure and properties of copper(I) complexes and underscore their promise as anticancer candidates. Steric effects ; Cyclic voltammetry ; Ligand (biochemistry) ; Redox ; Copper ; Coordination complex ; Electronic effect Organoboron and organosilicon chemistry ; Organometallic Complex Synthesis and Catalysis ; Synthesis and characterization of novel inorganic/organometallic compounds
Authors: Xinwu Ye | Yicheng Mao | Jia Zhang | Yimeng Liu | Li Hao | Fang Wu | Zhiwei Li | Yuxuan Liao | Zehong Wang | Wu, Yingcheng | Liu, Zhiyuan | Yin, Zhenfei | Yuan, Li | P. H. S. Torr | Sun, Huan | Zeng, Xiangxiang | Wang, Mengdi | Le Cong | Gao, Shenghua | Xiangru Tang
Source: Open MIND (preprint, 2026-02-06)
Field: Materials Science — Materials Chemistry

LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning Chemical large language models (LLMs) predominantly rely on explicit Chain-of-Thought (CoT) in natural language to perform complex reasoning. However, chemical reasoning is inherently continuous and structural, and forcing it into discrete linguistic tokens introduces a fundamental representation mismatch that constrains both efficiency and performance. We introduce LatentChem, a latent reasoning interface that decouples chemical computation from textual generation, enabling models to perform multi-step reasoning directly in continuous latent space while emitting language only for final outputs. Remarkably, we observe a consistent emergent behavior: when optimized solely for task success, models spontaneously internalize reasoning, progressively abandoning verbose textual derivations in favor of implicit latent computation. This shift is not merely stylistic but computationally advantageous. Across diverse chemical reasoning benchmarks, LatentChem achieves a 59.88\% non-tie win rate over strong CoT-based baselines on ChemCoTBench, while delivering a 10.84$\times$ average reduction in reasoning overhead. Our results provide empirical evidence that chemical reasoning is more naturally and effectively realized as continuous latent dynamics rather than discretized linguistic trajectories. Inference ; Representation (politics) ; Forcing (mathematics) ; Natural language ; Computation ; Knowledge representation and reasoning ; Task (project management) ; Language model Machine Learning in Materials Science ; Topic Modeling ; Multimodal Machine Learning Applications
Authors: Gao Tianxi | Cai Yufan | Yuan Yusi | Dong Jin Song
Source: ArXiv.org (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

X-RAY: Mapping LLM Reasoning Capability via Formalized and Calibrated Probes Large language models (LLMs) achieve promising performance, yet their ability to reason remains poorly understood. Existing evaluations largely emphasize task-level accuracy, often conflating pattern matching with reasoning capability. We present X-RAY, an explainable reasoning analysis system that maps the LLM reasoning capability using calibrated, formally verified probes. We model reasoning capability as a function of extractable \textit{structure}, operationalized through formal properties such as constraint interaction, reasoning depth, and solution-space geometry. X-Ray generates probes via formal tools with controlled structural variations, enabling precise isolation of incremental structural information through formal calibration and verification. We evaluate state-of-the-art LLMs on problems ranging from junior-level to advanced in mathematics, physics, and chemistry. Our analysis reveals a systematic asymmetry in LLM reasoning: models are relatively robust to constraint refinement, where additional conditions shrink an existing solution space, but degrade sharply under solution-space restructuring, where modifications alter the underlying structural form of the solution manifold. Moreover, calibrated formal probes differentiate models that appear indistinguishable on standard benchmarks and reveal failure modes that are structurally interpretable rather than opaque. Beyond evaluation, our framework is contamination-free and supports the training and testing of reasoning models. Constraint (computer-aided design) ; Automated reasoning ; Matching (statistics) ; Operationalization ; Reasoning system ; Spatial intelligence ; Function (biology) ; Isolation (microbiology) ; Calibration Machine Learning in Materials Science ; Topic Modeling ; Explainable Artificial Intelligence (XAI)