jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stefan Schott | Holger Gohlke
Source: Journal of Chemical Information and Modeling (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Chat-Driven Computational (Bio)chemistry: Using LLM Agents to Accelerate Bio- and Chemoinformatics Large-language models (LLMs) have rapidly become essential in software engineering, evolving from simple code suggestion tools to autonomous agents that directly read, modify, compile, and test local code bases. Recent LLMs perform well in software engineering benchmarks, showing good performance on complex multifile projects, generating new options for improving and developing bio- and chemoinformatic tools. We showcase this capability with the AMBER molecular dynamics suite, where the setup program <i>LEaP</i> suffered an <i>O</i>(<i>N</i><sup>2</sup>) merge routine and a 32-bit integer overflow, limiting simulation systems to ∼6 million atoms. By using an LLM, we implemented an optimized unit merge algorithm and 64-bit indexing, cutting the parametrization time by more than 10-fold for mid-sized systems and allowing one to parametrize multimillion-molecule systems. This case illustrates how natural scientists can make use of LLM agents to modernize, optimize, and develop computational (bio)chemistry tools while also raising new challenges for software provenance and developer roles. Cheminformatics ; Merge (version control) ; Software ; Limiting ; Source code ; Simple (philosophy) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Hyo Jeong Kim | Seungyong Shin | Jihoon Kyhm | Soong Ju Oh | Ho Seong Jang
Source: Chemical Engineering Journal (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Primary RGB-emitting single core@quadruple-shell upconversion nanoparticle for full-color displays The realization of primary red, green, and blue (RGB) luminescence from a single upconversion nanoparticle (UCNP) enables multicolor fine-tuning of the upconversion luminescence through the combination of RGB emissions, ultimately making it suitable for color volumetric display applications. However, excitation-wavelength-orthogonal RGB-emitting UCNPs require complex nanostructures and the synthesis of such RGB-emitting single UCNPs remains a great challenge. Here, we report a core@quadruple-shell (C@4S) single UCNP that exhibits primary tricolor upconversion luminescence under near-infrared (NIR) excitations. We first synthesize NaYbF 4 :Ho which emits green and red light under low- and high-power 980 nm NIR light. Then the NaYbF 4 :Ho core is coated with a NaYF 4 shell followed by the successive growth of NaYF 4 :Yb,Tm, NaYF 4 :Nd,Yb, and NaYF 4 shells. The synthesized NaYbF 4 :Ho@NaYF 4 @NaYF 4 :Yb,Tm@NaYF 4 :Nd,Yb@NaYF 4 C@4S UCNPs emit blue light under 800 nm excitation. By adjusting the power and wavelength of NIR light, the C@4S UCNPs exhibit various emission colors, including blue, sky-blue, green, yellow-green, orange, red, white, and more. Finally, the transparent C@4S UCNP-polydimethylsiloxane composite is prepared and various color images are displayed by simply scanning NIR light within the composite, indicating the high potential of the C@4S UCNPs for full-color display applications. • Core@quadruple-shell (C@4S) upconversion nanoparticles (UCNPs) are facily synthesized. • The C@4S UCNPs show primary tricolor red/green/blue luminescence. • Full-color tuning is achieved from the C@4S UCNPs by adjusting the excitation condition. • Various color images are displayed in the C@4S UCNP-polydimethylsiloxane composite. Photon upconversion ; Luminescence ; Nanoparticle ; RGB color model ; Wavelength ; Blue light ; Primary color Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Perovskite Materials and Applications
Authors: Zhijiang Jin | Xinyu Yang | Hongyan Qu | Mingzhu Guo | Qian Gao | Xiaoli Sun
Source: Renewable Energy (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Manipulating Al-Ga-based alloys' hydrolysis performance for hydrogen supply by balancing grain growth and liquid metal embrittlement Hydrogen production ; Hydrogen ; Alloy ; Mold ; Hydrogen embrittlement ; Embrittlement ; Grain growth Hydrogen Storage and Materials ; Hydrogen embrittlement and corrosion behaviors in metals ; Hybrid Renewable Energy Systems
Authors: Jiahui Gao | Marc Besch | Ning Ding | Suhao He | Yuxin Zhao | Li Yixiao | Ye Shen
Source: SAE technical papers on CD-ROM/SAE technical paper series (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Experimental Evaluation of After-Treatment Solutions for Heavy-Duty Natural Gas Vehicles to Meet Future CNVII Regulations &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The heavy-duty truck market in China has seen a significant increase in the adoption of natural gas-powered engines over the past two years. Simultaneously, the anticipated release of the China VII emissions regulation proposal by the end of 2025 is expected to impose stricter emissions limits on all heavy-duty engines, including new particulate number (PN10) thresholds analogous to those in the Euro 7 regulation. While tailpipe oxides of nitrogen (NOx) and methane (CH&lt;sub&gt;4&lt;/sub&gt;) emissions from natural gas engines can be mitigated through tighter lambda control and adjustments to catalyst volume and precious metal (PGM) loading, addressing NOx and particulate number (PN) emissions necessitate more advanced after-treatment solutions. Although natural gas combustion is virtually soot-free, the entrainment of lubricating oil into the combustion chamber, especially during cold-start conditions, poses a challenge, leading to potential exceedance of the proposed future China VII limits. Additionally, PN emissions from natural gas vehicles are highly dependent on duty-cycles and the state of the actual engine, with applications involving frequent stop/go operation experiencing increased piston ring wear, and thus, higher oil consumption, and elevated PN emissions.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;This study aimed to evaluate the performance of different after-treatment solutions for natural gas engines in meeting future China VII emissions standards, with a particular focus on the efficacy of particle filters for controlling PN10 emissions. Three different after-treatment configurations, comprising close-coupled and underfloor three-way catalysts, as well as bare and coated filters, were tested on a 15L China VI commercial natural gas engine in a controlled laboratory environment. Emissions and PN10 data were collected over regulatory cold and hot World Harmonized Transient Cycle (WHTC) test cycles, and analyzed for light-off behavior, conversion efficiencies, system pressure drop, and filtration effectiveness for particles as small as 10nm. The relative advantages and challenges of each configuration are discussed. The results indicate that natural gas engines will likely require the integration of particle filter devices to comply with future China VII PN10 limits. The results also show that NOx compliance is challenging and fine-tuning of the lambda calibration is essential for CNVII.&lt;/div&gt;&lt;/div&gt; Natural gas ; Combustion ; Truck ; NOx ; Particulates ; Renewable natural gas ; Fuel oil ; Fugitive emissions ; Methane ; Volume (thermodynamics) Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Vehicle emissions and performance
Authors: Yuansheng Liu | Yuchen Pan | Na Liu | Zhihua Yan | Letian Ye | Edward A. Bayer | Houhui Song | Chenggang Xu
Source: Chemical Engineering Journal (article, 2026-01-08)
Field: Materials Science — Biomaterials

Design and assembly of extended artificial cellulosomes mediated by covalent self-assembly systems for enhanced cellulose degradation Cellulosome ; Clostridium thermocellum ; Cellulosic ethanol ; Cellulose ; Cellulase ; Bacterial cellulose Supramolecular Self-Assembly in Materials ; Catalysis for Biomass Conversion ; Biochemical and Structural Characterization
Authors: Maochen Sun | Youzhi Zhang | Gaofeng Meng
Source: ArXiv.org (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents Large language models are making autonomous drug discovery agents increasingly feasible, but reliable success in this setting is not determined by any single action or molecule. It is determined by whether the final returned set jointly satisfies protocol-level requirements such as set size, diversity, binding quality, and developability. This creates a fundamental control problem: the agent plans step by step, while task validity is decided at the level of the whole candidate set. Existing language-based drug discovery systems therefore tend to rely on long raw history and under-specified self-reflection, making failure localization imprecise and planner-facing agent states increasingly noisy. We present CACM (Constraint-Aware Corrective Memory), a language-based drug discovery framework built around precise set-level diagnosis and a concise memory write-back mechanism. CACM introduces protocol auditing and a grounded diagnostician, which jointly analyze multimodal evidence spanning task requirements, pocket context, and candidate-set evidence to localize protocol violations, generate actionable remediation hints, and bias the next action toward the most relevant correction. To keep planning context compact, CACM organizes memory into static, dynamic, and corrective channels and compresses them before write-back, thereby preserving persistent task information while exposing only the most decision-relevant failures. Our experimental results show that CACM improves the target-level success rate by 36.4% over the state-of-the-art baseline. The results show that reliable language-based drug discovery benefits not only from more powerful molecular tools, but also from more precise diagnosis and more economical agent states. Task (project management) ; Context (archaeology) ; Protocol (science) ; Set (abstract data type) ; Drug discovery ; Action (physics) ; Component (thermodynamics) Machine Learning in Materials Science ; AI-based Problem Solving and Planning ; Computational Drug Discovery Methods
Authors: Shreyas Vinaya Sathyanarayana | Shah Rahil Kirankumar | Sharanabasava D. Hiremath | Bharath Ramsundar
Source: ArXiv.org (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Protect$^*$: Steerable Retrosynthesis through Neuro-Symbolic State Encoding Large Language Models (LLMs) have shown remarkable potential in scientific domains like retrosynthesis; yet, they often lack the fine-grained control necessary to navigate complex problem spaces without error. A critical challenge is directing an LLM to avoid specific, chemically sensitive sites on a molecule - a task where unconstrained generation can lead to invalid or undesirable synthetic pathways. In this work, we introduce Protect$^*$, a neuro-symbolic framework that grounds the generative capabilities of Large Language Models (LLMs) in rigorous chemical logic. Our approach combines automated rule-based reasoning - using a comprehensive database of 55+ SMARTS patterns and 40+ characterized protecting groups - with the generative intuition of neural models. The system operates via a hybrid architecture: an ``automatic mode'' where symbolic logic deterministically identifies and guards reactive sites, and a ``human-in-the-loop mode'' that integrates expert strategic constraints. Through ``active state tracking,'' we inject hard symbolic constraints into the neural inference process via a dedicated protection state linked to canonical atom maps. We demonstrate this neuro-symbolic approach through case studies on complex natural products, including the discovery of a novel synthetic pathway for Erythromycin B, showing that grounding neural generation in symbolic logic enables reliable, expert-level autonomy. Generative grammar ; Natural language ; Inference ; Encoding (memory) ; State (computer science) ; Non-monotonic logic ; Process (computing) ; Intuition Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Shreyas Vinaya Sathyanarayana | Shah Rahil Kirankumar | Sharanabasava D. Hiremath | Bharath Ramsundar
Source: Open MIND (preprint, 2026-02-13)
Field: Materials Science — Materials Chemistry

Protect$^*$: Steerable Retrosynthesis through Neuro-Symbolic State Encoding Large Language Models (LLMs) have shown remarkable potential in scientific domains like retrosynthesis; yet, they often lack the fine-grained control necessary to navigate complex problem spaces without error. A critical challenge is directing an LLM to avoid specific, chemically sensitive sites on a molecule - a task where unconstrained generation can lead to invalid or undesirable synthetic pathways. In this work, we introduce Protect$^*$, a neuro-symbolic framework that grounds the generative capabilities of Large Language Models (LLMs) in rigorous chemical logic. Our approach combines automated rule-based reasoning - using a comprehensive database of 55+ SMARTS patterns and 40+ characterized protecting groups - with the generative intuition of neural models. The system operates via a hybrid architecture: an ``automatic mode'' where symbolic logic deterministically identifies and guards reactive sites, and a ``human-in-the-loop mode'' that integrates expert strategic constraints. Through ``active state tracking,'' we inject hard symbolic constraints into the neural inference process via a dedicated protection state linked to canonical atom maps. We demonstrate this neuro-symbolic approach through case studies on complex natural products, including the discovery of a novel synthetic pathway for Erythromycin B, showing that grounding neural generation in symbolic logic enables reliable, expert-level autonomy. Generative grammar ; Natural language ; Inference ; Encoding (memory) ; State (computer science) ; Non-monotonic logic ; Process (computing) ; Intuition Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Jielin Zha | Jiayue Liu | Xiaomei Lu | Xueli Hu | Zijing Wu | Min Zhou | Fengzhen Huang | Xuenong Ying | Jesse Zhu
Source: Journal of Alloys and Compounds (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Enhancement of energy storage performance in high-entropy BNT-based relaxor ferroelectric ceramics via tolerance factor tuning Energy storage ; Dielectric ; Ceramic ; Bismuth ; Ionic radius ; Ferroelectricity ; Relaxor ferroelectric ; Ferroelectric ceramics Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Multiferroics and related materials
Authors: Maochen Sun | Youzhi Zhang | Gaofeng Meng
Source: arXiv (Cornell University) (preprint, 2026-04-10)
Field: Materials Science — Materials Chemistry

Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents Large language models are making autonomous drug discovery agents increasingly feasible, but reliable success in this setting is not determined by any single action or molecule. It is determined by whether the final returned set jointly satisfies protocol-level requirements such as set size, diversity, binding quality, and developability. This creates a fundamental control problem: the agent plans step by step, while task validity is decided at the level of the whole candidate set. Existing language-based drug discovery systems therefore tend to rely on long raw history and under-specified self-reflection, making failure localization imprecise and planner-facing agent states increasingly noisy. We present CACM (Constraint-Aware Corrective Memory), a language-based drug discovery framework built around precise set-level diagnosis and a concise memory write-back mechanism. CACM introduces protocol auditing and a grounded diagnostician, which jointly analyze multimodal evidence spanning task requirements, pocket context, and candidate-set evidence to localize protocol violations, generate actionable remediation hints, and bias the next action toward the most relevant correction. To keep planning context compact, CACM organizes memory into static, dynamic, and corrective channels and compresses them before write-back, thereby preserving persistent task information while exposing only the most decision-relevant failures. Our experimental results show that CACM improves the target-level success rate by 36.4% over the state-of-the-art baseline. The results show that reliable language-based drug discovery benefits not only from more powerful molecular tools, but also from more precise diagnosis and more economical agent states. Task (project management) ; Context (archaeology) ; Protocol (science) ; Set (abstract data type) ; Drug discovery ; Action (physics) ; Component (thermodynamics) Machine Learning in Materials Science ; AI-based Problem Solving and Planning ; Computational Drug Discovery Methods
Authors: Bhupendra Adhikari | Selvam Raju | A. Locke | Raymond Femi Awoyemi | B. Donnadieu | Aruzhan Abdikaiym | Brad S. Pierce | David O. Wipf | Sean L. Stokes | Joseph P. Emerson
Source: Polyhedron (article, 2026-03-31)
Field: Chemistry — Organic Chemistry

Synthesis and characterization of unsymmetrical-CuNHC complexes; connecting the electronic influence of substituted pyridine rings to copper(II) This report describes the synthesis and characterization of a family of unsymmetrical copper(II) N-heterocyclic carbene (NHC) complexes. A series of tridentate NCN pincer ligands bearing unsymmetrical C-4 substitutions on one pyridine ring were prepared using a straightforward synthetic methodology. Substituents, including methoxy (OMe), methyl (Me), chloro (Cl), and trifluoromethyl (CF₃), were introduced to systematically evaluate their electronic effects on copper(II) coordination and reactivity. Ligand precursors and corresponding copper(II) complexes were characterized by multinuclear NMR spectroscopy ( 1 H, 13 C, 31 P, 19 F), electrospray ionization mass spectrometry (ESI-MS), X-ray crystallography, cyclic voltammetry, and UV–vis spectroscopy. Structural analysis indicates that variation of pyridyl substituents has minimal impact on the solid-state coordination environment. However, electrochemical and spectroscopic studies reveal measurable electronic effects in solution. Computational investigations show comparable Lewis acidity and related physical properties across the series, with largely invariant HOMOs but substituent-dependent variation in LUMO and LUMO + 1 energies. Notably, the CF₃-substituted complex exhibits distinct behavior, promoting solvent oxidation to generate copper(I) species and displaying promising Ullmann-type C N bond-forming reactivity. Overall, these results demonstrate that while carbene donation dominates reactivity, fine electronic tuning is achievable through controlled modification of the pyridyl donor. • Stepwise syntheses of unsymmetrical NHC pincer ligands reported providing a mechanism for fine tuning electronic effects in coordination complexes. • Pyridinyl donor effects impact electronics in solution, but largely do not impact the coordination structure. • Select C N coupling reactivity through CEL and Ullman reactions unaffected by electron donor and weak withdrawer substitution. • Deactivation of CF 3 substituted py in 5e leads to remarkably different physical and electronic properties. • Ground state electronic states in the series of unsymmetrical copper(II) NHC complexes unaffected by py donor properties, however excited state orbitals (LUMO and LUMO + 1) appear to be more influenced. Pincer movement ; Carbene ; Pyridine ; HOMO/LUMO ; Electronic effect ; Electrospray ionization ; Trifluoromethyl ; Coordination complex ; Ligand (biochemistry) ; Transition metal carbene complex N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Organometallic Complex Synthesis and Catalysis ; Organic Light-Emitting Diodes Research
Authors: Manish Kumar Parihar
Source: Open MIND (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