jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiaqing Xie | Weida Wang | Ben Gao | Zhuo Yang | Hai Wan | Shufei Zhang | Tianfan Fu | Yuqiang Li
Source: Journal of Chemical Information and Modeling (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

QCBench: Evaluating Large Language Models on Domain-Specific Quantitative Chemistry Quantitative chemistry is central to modern chemical research, yet the ability of large language models (LLMs) to perform its rigorous, step-by-step calculations remains underexplored. To fill this gap, we propose QCBench, a <b>Q</b>uantitative <b>C</b>hemistry oriented benchmark comprising 350 computational chemistry problems across 7 chemistry subfields, which contains analytical chemistry, bio-organic chemistry, general chemistry, inorganic chemistry, physical chemistry, polymer chemistry and quantum chemistry. To systematically evaluate the mathematical reasoning abilities of large language models (LLMs), they are categorized into three tiers: easy, medium, and difficult. Each problem, rooted in realistic chemical scenarios, is structured to prevent heuristic shortcuts and demand explicit numerical reasoning. QCBench enables fine-grained diagnosis of computational weaknesses, reveals model-specific limitations across difficulty levels, and lays the groundwork for future improvements such as domain-adaptive fine-tuning or multimodal integration. Evaluations on 24 LLMs demonstrate a consistent performance degradation with increasing task complexity, highlighting the current gap between language fluency and scientific computation accuracy. Code for QCBench is available at https://github.com/jiaqingxie/QCBench. Heuristic ; Benchmark (surveying) ; Computation ; Fluency ; Computational model ; Task (project management) ; Quantum chemistry ; Code (set theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Xinfang Jia | Yu Bao | Shuo Cao | Ye Su | Ping Qian
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Temperature Effects on Radiation Damage in Hcp-Zirconium: A Molecular Dynamics Study Using a Fine-Tuned Machine-Learned Potential Zirconium ; Molecular dynamics ; Radiation damage ; Dynamics (music) ; Radiation ; Materials science ; Chemical physics ; Nanotechnology ; Biological system ; Physics ; Chemistry ; Nuclear physics ; Computational chemistry ; Biology ; Metallurgy ; Acoustics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications
Authors: Adrian Mirza | Nawaf Alampara | Martiño Ríos-García | Mohamed Abdelalim | Jack Butler | Brian Connolly | Tunca Doğan | Marianna Nezhurina | Bünyamin Şen | Santosh Tirunagari | Mark Worrall | Adamo Young | Philippe Schwaller | Michael Pieler | Kevin Maik Jablonka
Source: ArXiv.org (preprint, 2025-05-18)
Field: Materials Science — Materials Chemistry

ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models Foundation models have shown remarkable success across scientific domains, yet their impact in chemistry remains limited due to the absence of diverse, large-scale, high-quality datasets that reflect the field's multifaceted nature. We present the ChemPile, an open dataset containing over 75 billion tokens of curated chemical data, specifically built for training and evaluating general-purpose models in the chemical sciences. The dataset mirrors the human learning journey through chemistry -- from educational foundations to specialized expertise -- spanning multiple modalities and content types including structured data in diverse chemical representations (SMILES, SELFIES, IUPAC names, InChI, molecular renderings), scientific and educational text, executable code, and chemical images. ChemPile integrates foundational knowledge (textbooks, lecture notes), specialized expertise (scientific articles and language-interfaced data), visual understanding (molecular structures, diagrams), and advanced reasoning (problem-solving traces and code) -- mirroring how human chemists develop expertise through diverse learning materials and experiences. Constructed through hundreds of hours of expert curation, the ChemPile captures both foundational concepts and domain-specific complexity. We provide standardized training, validation, and test splits, enabling robust benchmarking. ChemPile is openly released via HuggingFace with a consistent API, permissive license, and detailed documentation. We hope the ChemPile will serve as a catalyst for chemical AI, enabling the development of the next generation of chemical foundation models. Mirroring ; Foundation (evidence) ; Executable ; Scientific reasoning ; Concept learning ; Documentation Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Hyun‐June Jang | Rapti Ghosh | Wen Zhuang | Xiaoben Zhang | Yuqin Wang | Xiaoao Shi | Xingkang Huang | Hongting Pu | Byunghoon Ryu | Janan Hui | Mark C. Hersam | Junhong Chen
Source: ACS Applied Materials & Interfaces (article, 2025-02-11)
Field: Materials Science — Materials Chemistry

Fine Tuning of Electrical Characteristics of Inkjet Printed Graphene for Physical and Chemical Sensing This study highlights the versatile applications of inkjet-printed graphene/ethyl cellulose (EC) in various electronic devices. Key points include its p-type doping achieved through low-temperature annealing, its use as a temperature sensor at higher annealing temperatures, and its effectiveness in electrochemical sensing, such as phosphate detection. The precise tuning of the oxygen composition of EC in graphene via thermal annealing was crucial to these capabilities. Electrical characterization showed consistent p-type doping behavior in graphene/EC annealed at 200 °C across all inkjet-printed graphene field-effect transistors. Upon annealing at 400 °C, the conductive properties of graphene were used in a hand-held reader device, demonstrating reversible responses to temperature fluctuations from 20 to 115 °C with a linearity of 99.8%. Furthermore, integrating inkjet-printed graphene electrodes annealed at 600 °C into remote floating-gate field-effect transistors resulted in a notably low detection limit of 1 pg/mL for phosphate ions in water, maintaining a linear response from 1 pg/mL to 10 ng/mL with a linearity of 98.91%. These applications underscore the adaptability and precision of inkjet-printed graphene, solidifying its role in advancing electronic components across various technological fields. Graphene ; Materials science ; Annealing (glass) ; Nanotechnology ; Transistor ; Printed electronics ; Optoelectronics ; Electrode ; Field-effect transistor ; Electronics ; Doping ; Linearity ; Electrical conductor ; Flexible electronics ; Inkwell ; Composite material ; Electronic engineering ; Electrical engineering Graphene research and applications ; Supercapacitor Materials and Fabrication ; Electrochemical sensors and biosensors
Authors: Zhiyang Liu | Zhe‐Xiong Lian | Guanqun Zhu | W. Zhang | Qi Qi | Hong Yang
Source: Luminescence (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Fine‐Tuning Molecular Structure of Salicylaldehyde Schiff Base for Solid‐State Photochromic Mechanism Salicylaldehyde Schiff base moiety is the classic photochromic core. The study of the absorption and fluorescence color-changing mechanism of such molecules in the solid state remains a challenge. Herein, two similar organic small molecules were designed and synthesized by replacing the hydroxyl group in the salicylaldehyde Schiff base moiety with an ethoxy group. Their optical properties were compared to elucidate critical structure-photoresponse correlations. The hydroxyl-containing molecule can undergo the excited-state intramolecular proton transfer (ESIPT) process under continuous ultraviolet light stimulation in the solid state, transforming from enol forms to keto forms, causing bathochromic shifts in absorption and fluorescence spectra. It has obvious reversible photochromism and aggregation-induced emission (AIE) characteristics. Contrastingly, the ethoxy-substituted derivative shows no change in its absorption spectra and emits almost no fluorescence under the same conditions. The hydroxyl group plays a crucial role in the photochromic and solid-state fluorescence properties of this type of molecule. These findings offer useful guidance for designing stimuli-responsive salicylaldehyde Schiff base molecules. Photochromism ; Photochemistry ; Salicylaldehyde ; Moiety ; Schiff base ; Chemistry ; Bathochromic shift ; Intramolecular force ; Molecule ; Enol ; Alkoxy group ; Fluorescence ; Absorption spectroscopy ; Chromophore ; Absorption (acoustics) ; Polymer chemistry ; Materials science ; Organic chemistry ; Catalysis Photochromic and Fluorescence Chemistry ; Luminescence and Fluorescent Materials ; Porphyrin and Phthalocyanine Chemistry
Authors: Barbora Blahová Prudilová | Roman Gabriel | Michal Otyepka | Eva Otyepková
Source: Pharmaceuticals (article, 2025-09-29)
Field: Chemistry — Spectroscopy

Controlled Crystallization Enables Facile Fine-Tuning of Physical–Chemical Properties of Nicergoline Toward Easier Processability <b>Background/Objectives</b>: Crystallization is a key process in the manufacturing of active pharmaceutical ingredients (APIs), as it significantly affects the physical and chemical properties of the final product. Nicergoline, a clinically relevant ergot derivative, was chosen as a model compound to investigate how different crystallization strategies affect particle attributes. The objective of this study was to compare controlled and uncontrolled crystallization techniques and evaluate their impact on the physicochemical properties of nicergoline. <b>Methods</b>: Nicergoline was crystallized using controlled methods, including sonication-induced and seeding-induced crystallization, and uncontrolled methods, namely cubic and linear cooling, as well as acetone evaporation. The resulting powders were characterized by using a range of physicochemical techniques to assess particle morphology, size distribution, agglomeration behavior, and surface properties. <b>Results</b>: Uncontrolled crystallization methods produced particles prone to agglomeration, resulting in a broader particle size distribution ranging from 8 to 720 µm and heterogeneous surface characteristics. In contrast, controlled crystallization generated more uniform particles with reduced agglomeration and narrower particle size distributions. Among the evaluated methods, sonocrystallization provided the most effective control over particle size and morphology, demonstrated by a narrow size distribution ranging from 16 to 39 µm which correlated with improved flowability and surface energy. <b>Conclusions</b>: The study demonstrates that the choice of crystallization method significantly influences the structural and physicochemical properties of nicergoline. These findings highlight the importance of method selection for tailoring API properties to enhance downstream processing and product quality. Crystallization ; Economies of agglomeration ; Particle size ; Particle-size distribution ; Particle (ecology) ; Acetone Adsorption, diffusion, and thermodynamic properties of materials ; Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography
Authors: Li‐Ming Wu | Wenbing Huang | Ran Jiao | Jing Huang | Liwei Liu | Yipeng Zhou | Hao Sun | Yang Liu | Fuchun Sun | Yuxiang Ren | Ji-Rong Wen
Source: ArXiv.org (preprint, 2025-03-13)
Field: Materials Science — Materials Chemistry

Siamese Foundation Models for Crystal Structure Prediction Crystal Structure Prediction (CSP), which aims to generate stable crystal structures from compositions, represents a critical pathway for discovering novel materials. While structure prediction tasks in other domains, such as proteins, have seen remarkable progress, CSP remains a relatively underexplored area due to the more complex geometries inherent in crystal structures. In this paper, we propose Siamese foundation models specifically designed to address CSP. Our pretrain-finetune framework, named DAO, comprises two complementary foundation models: DAO-G for structure generation and DAO-P for energy prediction. Experiments on CSP benchmarks (MP-20 and MPTS-52) demonstrate that our DAO-G significantly surpasses state-of-the-art (SOTA) methods across all metrics. Extensive ablation studies further confirm that DAO-G excels in generating diverse polymorphic structures, and the dataset relaxation and energy guidance provided by DAO-P are essential for enhancing DAO-G's performance. When applied to three real-world superconductors ($\text{CsV}_3\text{Sb}_5$, $ \text{Zr}_{16}\text{Rh}_8\text{O}_4$ and $\text{Zr}_{16}\text{Pd}_8\text{O}_4$) that are known to be challenging to analyze, our foundation models achieve accurate critical temperature predictions and structure generations. For instance, on $\text{CsV}_3\text{Sb}_5$, DAO-G generates a structure close to the experimental one with an RMSE of 0.0085; DAO-P predicts the $T_c$ value with high accuracy (2.26 K vs. the ground-truth value of 2.30 K). In contrast, conventional DFT calculators like Quantum Espresso only successfully derive the structure of the first superconductor within an acceptable time, while the RMSE is nearly 8 times larger, and the computation speed is more than 1000 times slower. These compelling results collectively highlight the potential of our approach for advancing materials science research and development. Foundation (evidence) ; Crystal structure prediction ; Crystal structure ; Computation ; Energy (signal processing) ; Mean squared error Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Enzyme Structure and Function
Authors: Baobing Tang | Xueyue Yu | Guanghua Li | Xin Liu | Jiantang Li | Yunling Liu
Source: Chemical Science (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Ambidextrous fine-tuning of zeolite-like hydrogen-bonded organic frameworks (HOFs) <i>via</i> scalable green synthesis for efficient biogas upgrading Zeolite-like supramolecular assemblies (ZSAs) are a distinctive class of hydrogen-bonded organic frameworks (HOFs) that form zeolite-like structures using fundamental four-membered ring (4 MR) building units, known as metal-organic squares (MOSs). Herein, we developed a zeolite-like supramolecular assembly (ZSA) platform to precisely control hydrogen-bond directionality, coupled with confinement effects, ultimately synthesizing two isoreticular HOFs, ZSA-12 and ZSA-13, with distinct pore environments. Notably, owing to their microporous structures, ZSA-12 and ZSA-13 exhibited exceptional CO<sub>2</sub> adsorption capacity and ultra-high CO<sub>2</sub>/CH<sub>4</sub> separation selectivity. The IAST selectivity value of 94.8 sets a benchmark, surpassing that of all previously reported HOFs. Theoretical calculations and breakthrough experiments confirmed the outstanding separation capability of ZSA-12 and ZSA-13, both of which also demonstrated remarkable thermal and chemical stability. Furthermore, gram-scale synthesis of ZSA-12 and ZSA-13 was successfully achieved under mild conditions using pure water as the solvent-one of the few reported HOFs that can be synthesized in large quantities <i>via</i> a water-based process at an exceptionally low cost. Thus, ZSA-12 and ZSA-13 offer a new strategy for the synthesis of isoreticular HOFs and hold great promise for biogas purification and upgrading applications. Biogas ; Zeolite ; Scalability ; Business ; Materials science ; Chemical engineering ; Waste management ; Chemistry ; Computer science ; Organic chemistry ; Catalysis ; Engineering ; Database Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis
Authors: Qian Tan | Di Zhang | Ben Gao | Yuqiang Li | See PDF
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Developing ChemMLLM as a multimodal large language model for chemistry Modeling language ; Field (mathematics) ; Natural language ; Feature (linguistics) Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Computational Drug Discovery Methods
Authors: Hänseroth, Jonas | Flötotto, Aaron | Qaisrani, Muhammad Nawaz | Dreßler, Christian
Source: arXiv (Cornell University) (preprint, 2025-11-07)
Field: Materials Science — Materials Chemistry

Fine-Tuning Unifies Foundational Machine-learned Interatomic Potential Architectures at ab initio Accuracy This work demonstrates that fine-tuning transforms foundational machine-learned interatomic potentials (MLIPs) to achieve consistent, near-ab initio accuracy across diverse architectures. Benchmarking five leading MLIP frameworks (MACE, GRACE, SevenNet, MatterSim, and ORB) across seven chemically diverse compounds reveals that fine-tuning universally enhances force predictions by factors of 5-15 and improves energy accuracy by 2-4 orders of magnitude. The investigated models span both equivariant and invariant, as well as conservative and non-conservative, architectures. While general-purpose foundation models are robust, they exhibit architecture-dependent deviations from ab initio reference data; fine-tuning eliminates these discrepancies, enabling quantitatively accurate predictions of atomistic and structural properties. Using datasets constructed from equidistantly sampled frames of short ab initio molecular dynamics trajectories, fine-tuning reduces force errors by an order of magnitude and harmonizes performance across all architectures. These findings establish fine-tuning as a universal route to achieving system-specific predictive accuracy while preserving the computational efficiency of MLIPs. To promote widespread adoption, we introduce the aMACEing Toolkit, which provides a unified and reproducible interface for fine-tuning workflows across multiple MLIP frameworks. Ab initio ; Ab initio quantum chemistry methods ; Work (physics) ; Interatomic potential ; Workflow ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: John M. Bennett | Andrew Bortz | Zheyuan Wang | Muhammed Fastheem | Rudi Fasan
Source: Angewandte Chemie International Edition (article, 2025-08-04)
Field: Chemistry — Organic Chemistry

Chemoenzymatic Skeletal Editing of Natural Product Scaffolds via P450‐Controlled Site‐Selective Ring Expansion at Aliphatic C─H Sites Methods for introducing subtle modifications at the level of single atoms/bonds ("skeletal editing") are highly desirable in organic and medicinal chemistry, owing to their potential for fine-tuning the structure and biological activity of organic molecules. Here, we report a chemoenzymatic strategy for enabling the skeletal editing of organic frameworks via ring expansion at the level of one or more aliphatic (methylene) C─H sites, as achieved through the synergistic combination of P450-mediated site-selective oxidation with subsequent Baeyer-Villiger rearrangement or ketone homologation. Combining this approach with engineered P450 catalysts exhibiting divergent regioselectivity enabled the expeditious synthesis of a panel of ring-expanded analogs of various complex natural product substrates. Importantly, the skeletal modification was found to drastically altered the anticancer activity of some of these compounds. By the direct targeting of aliphatic C─H sites with tunable site-selectivity, this strategy provides a powerful tool to rapidly access skeletally edited derivatives of natural products and other bioactive molecules for applications in drug discovery and chemical biology. Natural product ; Regioselectivity ; Chemistry ; Ring (chemistry) ; Drug discovery ; Ketone ; Small molecule ; Combinatorial chemistry ; Catalysis ; Molecule ; Stereochemistry ; Chemical biology ; Organic chemistry ; Biochemistry Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions ; Synthetic Organic Chemistry Methods
Authors: Lili Deng | Qian Su | Zengxi Li | Weiguo Cheng
Source: Applied Materials Today (article, 2025-01-18)
Field: Chemistry — Inorganic Chemistry

Flexible chain tunning of metalloporphyrin ionic framework: Spatial microenvironment construction for enhancing urea alcoholysis Urea ; Ionic bonding ; Chemistry ; Chain (unit) ; Chemical engineering ; Organic chemistry ; Ion ; Engineering ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Guangli Xu | Hongda Chen | Xiaoming Wang
Source: Science China Chemistry (article, 2025-01-09)
Field: Chemistry — Organic Chemistry

Enantioselective β-alkenylation of α,β-unsaturated aldehydes via chiral biphosphine ligand modified dirhodium(II) catalysis Enantioselective synthesis ; Chemistry ; Carbene ; Conjugate ; Catalysis ; Ligand (biochemistry) ; Combinatorial chemistry ; Reactivity (psychology) ; Stereochemistry ; Organic chemistry ; Receptor Cyclopropane Reaction Mechanisms ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Kazu Okumura | Shinichiro Aikawa | Yuki Aoki | Anas Abdullahi | Mustapha Mohammed
Source: Innovation of Chemistry & Materials for Sustainability (article, 2025-06-05)
Field: Chemistry — Inorganic Chemistry

Rational Design of Pt Supported Catalysts for Hydrosilylation: Influence of Support and Calcination Temperature The hydrosilylation reaction plays a key role in organosilicon chemistry, enabling the formation of silicon-carbon bonds crucial for industrial and specialty chemical production. While homogeneous platinum catalysts are incredibly efficient, they come with drawbacks like limited recyclability and high Pt consumption, necessitating researchers to develop more durable, heterogeneous alternatives. By harnessing the synergies of support materials and thermal treatment, this study aims to develop highly active heterogeneous platinum-supported catalysts for the hydrosilylation reaction. We synthesized Pt-supported catalysts on nine different metal oxide supports using impregnation and heat treatment at 500 – 1000 oC. Among them, Pt/CeO2 900H, prepared by calcining Pt-supported CeO2 at 900 °C, followed by reducing it with hydrogen at 500 °C, emerged as the most effective catalyst for alkene hydrosilylation, achieving a turnover number (TON) of 6.9 × 106, comparable to the widely used Karstedt catalyst. For alkyne hydrosilylation, Pt/CeO2 600C and Pt/ZrO2 500C demonstrated outstanding performance. These catalysts, prepared by calcining Pt-impregnated CeO2 at 600 °C and ZrO2 at 500 °C, respectively, exhibited remarkable activity and stability, maintaining high performance over 10 reaction cycles. These findings highlight how the right combination of support material and thermal treatment can fine-tune Pt catalysts for more sustainable hydrosilylation processes. By providing a scalable and cost-effective alternative to traditional homogeneous catalysts, this work connects fundamental research with industrial applications, advancing greener and more efficient approaches to organosilicon synthesis. Hydrosilylation ; Calcination ; Catalysis ; Rational design ; Chemical engineering ; Materials science ; Chemistry ; Organic chemistry ; Nanotechnology ; Engineering Asymmetric Hydrogenation and Catalysis ; Nanomaterials for catalytic reactions ; Organoboron and organosilicon chemistry
Authors: Jifu Yang | Chunyu Ding | Mengyao He | Xinqing Wang | Junyu Chen | Dongming Qi | Yangyi Sun
Source: Journal of Colloid and Interface Science (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Charge-dominated phase separation synthesis method of Janus particles with well-defined separated lobes and patternable surface chemistries Janus particles ; Nanotechnology ; Polystyrene ; Janus ; Chemistry ; Polymerization ; Nucleation ; Pickering emulsion ; Fabrication ; Nanoparticle ; Chemical engineering ; Surface charge ; Materials science ; Polymer ; Organic chemistry ; Physical chemistry Pickering emulsions and particle stabilization ; Polymer Surface Interaction Studies ; Micro and Nano Robotics