jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Javier Emilio Alfonso Ramos | Carlo Adamo | Éric Brémond | Thijs Stuyver
Source: Journal of Chemical Theory and Computation (article, 2025-02-02)
Field: Materials Science — Materials Chemistry

Improving the Reliability of, and Confidence in, DFT Functional Benchmarking through Active Learning Validating the performance of exchange-correlation functionals is vital to ensure the reliability of density functional theory (DFT) calculations. Typically, these validations involve benchmarking data sets. Currently, such data sets are usually assembled in an unprincipled manner, suffering from uncontrolled chemical bias, and limiting the transferability of benchmarking results to a broader chemical space. In this work, a data-efficient solution based on active learning is explored to address this issue. Focusing─as a proof of principle─on pericyclic reactions, we start from the BH9 benchmarking data set and design a chemical reaction space around this initial data set by combinatorially combining reaction templates and substituents. Next, a surrogate model is trained to predict the standard deviation of the activation energies computed across a selection of 20 distinct DFT functionals. With this model, the designed chemical reaction space is explored, enabling the identification of challenging regions, <i>i.e.</i>, regions with large DFT functional divergence, for which representative reactions are subsequently acquired as additional training points. Remarkably, it turns out that the function mapping the molecular structure to functional divergence is readily learnable; convergence is reached upon the acquisition of fewer than 100 reactions. With our final updated model, a more challenging─and arguably more representative─pericyclic benchmarking data set is curated, and we demonstrate that the functional performance has changed significantly compared to the original BH9 subset. Benchmarking ; Chemical space ; Pericyclic reaction ; Computer science ; Divergence (linguistics) ; Benchmark (surveying) ; Set (abstract data type) ; Density functional theory ; Reliability (semiconductor) ; Function (biology) ; Space (punctuation) ; Machine learning ; Data mining ; Artificial intelligence ; Theoretical computer science ; Computational chemistry ; Chemistry ; Physics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Nikita Rybin | Ivan S. Novikov | Alexander V. Shapeev
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Accelerating structure prediction of molecular crystals using actively trained moment tensor potential Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of inorganic crystals, we present a methodology that exploits moment tensor potentials (MTP) and active learning (based on maxvol algorithm) to accelerate structure prediction of molecular crystals. Benzene and glycine are used as test systems. The obtained potentials are able to rank different benzene and glycine polymorphs in good agreement with density-functional theory. Hence, we argue that MTP can be used to accelerate the computationally guided polymorph search. Moment (physics) ; Moment tensor ; Tensor (intrinsic definition) ; Statistical physics ; Computational chemistry ; Materials science ; Physics ; Computer science ; Mathematics ; Chemistry ; Classical mechanics ; Pure mathematics ; Magnitude (astronomy) Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; High-pressure geophysics and materials
Authors: Valdas Vitartas | Hanwen Zhang | Veronika Jurásková | Tristan Johnston-Wood | Fernanda Duarte
Source: Digital Discovery (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentials Atomistic simulations driven by machine-learned interatomic potentials (MLIPs) are a cost-effective alternative to <i>ab initio</i> molecular dynamics (AIMD). Yet, their broad applicability in reaction modelling remains hindered, in part, by the need for large training datasets that adequately sample the relevant potential energy surface, including high-energy transition state (TS) regions. To optimise dataset generation and extend the use of MLIPs for reaction modelling, we present a data-efficient and fully automated workflow for MLIP training that requires only a small number (typically five to ten) of initial configurations and no prior knowledge of the TS. The approach combines automated active learning with well-tempered metadynamics to iteratively and selectively explore chemically relevant regions of configuration space. Using data-efficient architectures, such as the linear Atomic Cluster Expansion, we illustrate the performance of this strategy in various organic reactions where the environment is described at different levels, including the S<sub>N</sub>2 reaction between fluoride and chloromethane in implicit water, the methyl shift of 2,2-dimethylisoindene in the gas phase, and a glycosylation reaction in explicit dichloromethane solution, where competitive pathways exist. The proposed training strategy yields accurate and stable MLIPs for all three cases, highlighting its versatility for modelling reactive processes. Metadynamics ; Workflow ; Active learning (machine learning) ; Molecular dynamics ; Cheminformatics ; QM/MM ; Interatomic potential ; Potential energy Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Hong-Wei Yang | Zongyi Deng | Minxian Shi | Zhixiong Huang
Source: Polymers (review, 2025-08-28)
Field: Materials Science — Materials Chemistry

High-Thermal-Conductivity Graphene/Epoxy Resin Composites: A Review of Reinforcement Mechanisms, Structural Regulation and Application Challenges As electronic devices advance toward higher power density, heat dissipation has emerged as a critical bottleneck limiting their reliability. Graphene oxide (GO)/epoxy resin (EP) composites, combining high-thermal-conductivity potential with polymer-matrix advantages, have become a key focus for overcoming the limitations of traditional metal heat-dissipation materials. This review systematically examines these composites, analyzing their thermal conductivity enhancement mechanisms, structural regulation strategies, and application challenges. We first elaborate on how GO's intrinsic properties influence its enhancement capability, then explore the roles of physical dispersion strategies and interfacial modification techniques in optimizing filler dispersion and reducing interfacial thermal resistance, revealing the effects of preparation processes on thermal conduction network construction. Their remarkable potential is demonstrated in applications such as electronic packaging and electromagnetic shielding. However, challenges including cross-scale structural design and multi-physics collaborative regulation remain. This review aims to provide theoretical foundations and technical guidance for transitioning these composites from lab research to industrial application and advancing thermal management in high-performance electronics. Epoxy ; Composite material ; Materials science ; Graphene ; Reinforcement ; Thermal conductivity ; Composite epoxy material ; Nanotechnology Graphene research and applications ; Thermal properties of materials ; Carbon Nanotubes in Composites
Authors: Fan Mo | Qixing Zhou | Pengfei Wang | Weitao Liu | Chuan Yin | Zelin Hou | Wendan Xue | Qi Wang | Jianling Wang | Tong Zheng | Zongxin Tao | Xiang Li
Source: Chemical Science (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Hydrogen/solvent-free plastic valorization <i>via</i> loosely coordinated ruthenium sites steering selective aromatization The development of hydrogen- and solvent-free catalytic systems for plastic valorization requires precise control over metal speciation to enhance both catalytic efficiency and selectivity. Ru atomic clusters (ACs) on HZSM-5 zeolite show a turnover frequency of 52.71 mg<sub>LDPE</sub> mg<sub>Ru</sub> <sup>-1</sup> h<sup>-1</sup> in low-density polyethylene (LDPE) conversion, a fivefold increase compared to traditional nanoparticles (NPs) at 10.63 mg<sub>LDPE</sub> mg<sub>Ru</sub> <sup>-1</sup> h<sup>-1</sup>. This improved catalytic performance is further evident in cyclohexane dehydro-aromatization, where atomic-scale Ru sites exhibit a 17.8-fold activity increase (544.34 <i>vs.</i> 30.48 μM mg<sub>Ru</sub> <sup>-1</sup> h<sup>-1</sup>), linking subnanometric structures to reaction pathway selectivity. Mechanistic analysis highlights two key effects: (1) optimized interactions between isolated Ru sites and intermediates lower dehydrogenation barriers, and (2) reduced cyclization energy barriers compared to NPs. Process optimization leads to 87% LDPE conversion with 69% aromatic selectivity, maintaining 70% efficiency across various polyolefins (including high-density PE and ultra-high-molecular-weight PE) and polystyrene. A techno-economic analysis shows the potential of AC catalysts, predicting a net present value of 1.78 million over five years, compared to an annual loss of 8.17 million for NPs. This study demonstrates the critical role of metal dispersion and acid-metal balance, forming the basis for a sustainable plastic-to-aromatics process with strong economic viability. Aromatization ; Catalysis ; Ruthenium ; Zeolite ; Selectivity ; Solvent ; Hydrogen ; Chemistry ; Combinatorial chemistry ; Organic chemistry Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Momang Tian | Ke‐Juan Meng | Kunyu Xiong | Iftikhar Hussain | Xinwen Ma | Kaili Zhang
Source: Nature Communications (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

MXene Templated assembly of hierarchical Al/CuO/V2C nanothermite with excellent energy release efficiency and highly tunable performance Nanothermite has gained significant attention for its exceptional energy release rate and reactivity, making it a promising material for energetic applications. However, nanoparticle agglomeration significantly hinders its energy release efficiency. To overcome this challenge, we developed an innovative bottom-up strategy and a straightforward preparation method to construct a hierarchical Al/CuO/V<sub>2</sub>C nanocomposite. This is achieved through the ordered self-assembly of Al and CuO nanoparticles in an ethanol suspension, using V<sub>2</sub>C MXene as a template. Our findings show that the Al/CuO/V<sub>2</sub>C nanocomposite exhibits distinct morphologies based on V<sub>2</sub>C concentration: nanosheets at lower concentrations and microspheres at higher concentrations. CuO and Al nanoparticles form the first and second layers outside the V<sub>2</sub>C, respectively, due to the interplay between electrostatic forces and covalent Cu-O-V bonds. This unique structure, along with high reactivity of V<sub>2</sub>C, enables complete Al oxidation, achieving a heat release of 3156.2 J g<sup>-1</sup> with a 10 wt% V<sub>2</sub>C addition-seven times greater than the Al/CuO control group. The concentration-dependent structure allows for tunable energetic performance, from a rapid 3-ms deflagration to a prolonged 16-ms combustion. Reduced gas release enhances safety, paving the way for applications in microinitiators and advancing nanothermite technology. Materials science ; Energy (signal processing) ; Nanotechnology ; Optoelectronics ; Physics MXene and MAX Phase Materials ; Aluminum Alloys Composites Properties ; Advanced Memory and Neural Computing
Authors: Vanshika Jain | Pramod P. Pillai
Source: Chemical Science (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

A path to perpetual chemical synthesis <i>via</i> photocatalytic cofactor regeneration Harnessing the power of the Sun for perpetual chemical synthesis is one of the most sustainable ways to reduce the carbon footprint in the chemical industry. In this regard, the natural photosynthetic machinery offers key insights into the sustainable production of chemical entities in a ceaseless manner. The natural process of photosynthesis couples light harvesting to produce cofactor molecules, which then participate in enzyme-driven dark cycles for continuous biocatalytic transformations. At the core of photosynthetic machinery is the constant regeneration and consumption of cofactors, which sustain the metabolic cycles continuously. Consequently, coupling the unique powers of photocatalysis and biocatalysis through cofactor shuttling emerges as an excellent opportunity for the ceaseless production of fine chemicals. The present Perspective highlights the design principles for integrating photocatalytically regenerated cofactors with natural enzymatic cycles for various chemical transformations. Further, we examine the existing limitations of the integrated system and highlight the efforts to alleviate them. Finally, we highlight the possibilities of incorporating ideas from different research fields, from material science to synthetic biology to organometallic chemistry, to develop robust cofactor-dependent photobiocatalytic systems for the perpetual synthesis of chemicals. Cofactor ; Biochemical engineering ; Artificial photosynthesis ; Synthetic biology ; Photosynthesis ; Chemistry ; Nanotechnology ; Biology ; Photocatalysis ; Materials science ; Organic chemistry ; Enzyme ; Engineering ; Computational biology ; Catalysis ; Biochemistry Porphyrin and Phthalocyanine Chemistry ; Enzyme Catalysis and Immobilization ; Advanced Photocatalysis Techniques
Authors: V. Pezzotta | Stefano Facchini | Cristiano Longarini | G. Lodato | P. Martire
Source: Astronomy and Astrophysics (article, 2025-01-18)
Field: Chemistry — Spectroscopy

The two-dimensional pressure structure of the HD 163296 protoplanetary disk as probed by multi-molecule kinematics Gas kinematics is a new and unique way to study planet-forming environments by an accurate characterization of disk velocity fields. High angular resolution ALMA observations allow deep kinematical analysis of disks, by observing molecular line emission at high spectral resolution. In particular, rotation curves are key tools for studying the disk pressure structure and efficiently estimating fundamental disk parameters, such as mass and radius. In this work we explore the potential of a multi-molecule approach to gas kinematics to provide a 2D characterization of the HD 163296 disk. From the high quality data of the MAPS Large Program we extracted the rotation curves of rotational lines from seven distinct molecular species ( 12 CO, 13 CO, C 18 O, HCN, H 2 CO, HCO + , C 2 H), spanning a wide range in the disk radial and vertical extents. To obtain reliable rotation curves for the HCN and C 2 H hyperfine lines, we extended standard methodologies to fit multi-component line profiles. We then sampled the likelihood of a thermally stratified model that reproduces all the rotation curves simultaneously, taking into account the molecular emitting layers ɀ( R ) and disk thermal structure T ( R , ɀ). From this exploration, we obtained dynamical estimates of three fundamental parameters: the stellar mass M ⋆ = 1.89 M ⊙ , the disk mass M d = 0.12 M ⊙ , and the scale radius R c = 143 au. We also explore how rotation curves, and consequently the parameter estimates, depend on the adopted emitting layers: the disk mass proves to be the most affected by these systematics, yet the main trends we find do not depend on the adopted parameterization. Finally, we investigated the impact of thermal structure on gas kinematics, and show that the thermal stratification can efficiently explain the measured rotation velocity discrepancies between tracers at different heights. Our results show that such a multi-molecule approach, tracing a large range of emission layers, can provide unique constraints on the ( R , ɀ) pressure structure of protoplanetary disks. Physics ; Astrophysics ; Galaxy rotation curve ; Protoplanetary disk ; RADIUS ; Rotation (mathematics) ; Planet ; Line (geometry) ; Geometry ; Galaxy ; Galaxy formation and evolution Molecular Spectroscopy and Structure ; Astrophysics and Star Formation Studies ; Thermodynamic properties of mixtures
Authors: Benjamin Walls | Suljo Linic
Source: ACS Catalysis (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

Use of Large Language Models for Extracting and Analyzing Data from Heterogeneous Catalysis Literature Extracting experimentally measured heterogeneous catalysis data from the text of research articles into structured databases would facilitate the rapid screening of catalysts with target properties and the development of models capable of directly predicting experimental outcomes. This text mining task has been transformed by the release of large language models (LLMs) capable of following general natural language instructions, which have made it possible to mine text without the need to train task-specific models or define comprehensive expression-matching rules. Here, we develop and share a text mining tool called CatMiner that extracts arbitrary user-specified structure–environment–property data using LLMs. It is agnostic to LLM choice, with both OpenAI GPT models and open-source Llama and DeepSeek models supported without modification. We benchmark the ability of CatMiner to rapidly extract useful data from abundant published literature by focusing on a case study of the oxidative coupling of methane. We explore how model choice and prompting strategies affect extraction quality. Key capabilities, including the use of domain knowledge, iterative prompting, and document-wide context handling are shown to be critical for effective performance. We identify situations where CatMiner struggles and suggest reporting standards for the community to make catalysis data easier to extract going forward. CatMiner enables the creation of machine-readable catalysis datasets, streamlining access to experimental insights buried in the literature. Catalysis ; Computer science ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling ; Data Quality and Management
Authors: Indhumathi Kamaraj | Santhosh Kamaraj | Ganesan Shanmugam
Source: Medicine in Novel Technology and Devices (article, 2025-09-09)
Field: Materials Science — Biomaterials

Emerging strategies and multifunctional applications of nanomaterials in modern nanomedicine The growing complexity of diseases, alongside the limitations of conventional therapies and the rise of multidrug resistance, underscores the pressing need for innovative treatment paradigms. Herein, we highlight the transformative potential of nanomaterials in modern nanomedicine, focusing on their ability to enable precise, targeted, and multifunctional therapeutic interventions. However, despite their promise, clinical translation remains constrained by several challenges, including immune clearance, systemic toxicity, scalability and a lack of long-term safety data. This review systematically presents emerging strategies that are redefining nanomaterial applications in medicine. These include surface functionalization strategies to enhance targeting specificity, hybrid nanomaterial systems for combined therapeutic and diagnostic (theranostic) functions, and stimuli-responsive strategies for controlled, site-specific drug release. We further examine biomimetic strategies that enable immune evasion by mimicking natural cellular membranes, and scaffold-based approaches that support tissue engineering and regenerative medicine. In the context of oncology, we explore strategies to overcome multidrug resistance through the co-delivery of chemotherapeutics and gene modulators. Finally, we emphasize the role of artificial intelligence (AI)-driven strategies in optimizing nanomaterial design, facilitating high-throughput screening, and predicting biological interactions. Collectively, these advancements offer a robust framework for developing next-generation nanotherapeutics that align with the goals of personalized medicine by improving precision, safety, and clinical efficacy. • Nanomaterials enable precise, targeted, and controlled drug delivery in nanomedicine. • Surface-functionalized nanomaterials improve targeting and minimize side effects. • Hybrid nanomaterials offer multifunctional theragnostic capabilities. • Stimuli-responsive nanomaterials provide smart drug release under physiological triggers. • AI accelerates nanomaterial design and enhances biological performance. Context (archaeology) ; Nanomedicine ; Drug delivery ; Nanomaterials ; Personalized medicine ; Targeted drug delivery ; Biopharmaceutical Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Sourav Ghosh | Amrita Chatterjee | Nilanjan Dey | Sunidhi Mishra | Shakshi Bhardwaj | Shiva Singh | Ujjal Tewary | Satyam S. Sahay | Madhuchhanda Maiti | Pradip K. Maji
Source: Nanoscale Advances (review, 2025-01-01)
Field: Chemistry — Organic Chemistry

Impact of nanofillers on vitrimerization and recycling strategies: a review The growing complexity of the waste from polymer nanocomposite materials has brought attention to the pressing need for creative recycling techniques. Vitrimerization, based on dynamic covalent bond exchange mechanisms, offers a transformative method to improve the recyclability, reprocessability, and long-term durability of polymer-based nanocomposites. This paper conveys a complete overview of vitrimer chemistry, vitrimer processing techniques, and their integration with diverse nanofillers. The significance of nanofiller addition in modifying vitrimer network dynamics, mechanical performance, thermal stability, and self-healing capacities is critically investigated. Case studies and performance tests illustrate the major advantages of vitrimerized nanocomposites, including better mechanical characteristics, energy-efficient recyclability, and prolonged service life. Furthermore, the study investigates the industrial significance of materials based on vitrimers in fields including biomedical engineering and the aerospace and automotive sectors. The potential of vitrimerization to achieve sustainable, circular material lifecycles is highlighted by a comparison with conventional recycling techniques. Finally, future research topics and obstacles linked to large-scale deployment are discussed. This review seeks to serve as a foundational reference for researchers exploring vitrimer-based recycling technologies for high-performance as well as ecologically responsible polymer nanocomposites. Nanocomposite ; Covalent bond ; Pressing ; Nanotechnology ; Materials science ; Composite material ; Chemistry ; Organic chemistry Nanomaterials for catalytic reactions ; Microplastics and Plastic Pollution ; biodegradable polymer synthesis and properties