jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wang Xu (1653565) | Yuhuang Huang (22589832) | Zhuohan Lin (14094300) | Qiaohui Ruan (19949579) | PeiQing Yuan (22797385) | Yan Li (23143)
Source: Figshare (article, 2025-12-06)
Field: Chemistry — Inorganic Chemistry

Bayesian Optimizationof Hierarchical ZSM‑5for High-Efficiency Polyolefin Waste Recycling to Light Olefins Efficient zeolitic recycling of polyolefin waste into C<sub>2</sub>–C<sub>4</sub> light olefins offers an attractive route toward resource recovery; however, conventional zeolite catalysts suffer from severe mass transport limitations that lead to undesired secondary reactions and coke formation. Here, we report a “structure modulation and intelligent optimization” strategy to develop hierarchical ZSM-5 (H-ZSM-5), integrating tailored mesoporosity and crystal size control via Bayesian optimization (BO). Catalytic performance was evaluated in a two-stage system combining thermal pyrolysis and subsequent catalytic cracking. BO efficiently navigates this multivariate design space, identifying an optimal architecture (∼100 nm crystals, 4.0 nm mesopores) that achieved 92.6 wt % total gas yield and 85.1 wt % light olefin selectivity at 500 °C with a low catalyst-to-feed ratio (0.2). The nanoscale H-ZSM-5 also exhibited durability over 40 consecutive cycles and broad compatibility with postconsumer plastic mixtures. Mechanistic studies revealed that the synergy between nanoscale morphology and hierarchical porosity enhances mass transport and acid site accessibility, demonstrating that complex catalyst architectures can be precisely optimized via artificial intelligence strategies, such as BO. Polyolefin ; Olefin fiber ; Catalysis ; Coke ; Porosity ; Bayesian optimization ; Zeolite ; Polysulfone Zeolite Catalysis and Synthesis ; Thermochemical Biomass Conversion Processes ; Catalysis for Biomass Conversion
Authors: João Marcos T. Palheta | Octavio Rodrigues Filho | Mohammad Soleymanibrojeni | Alexandre C. Dias | Diego Guedes‐Sobrinho | Wolfgang Wenzel | Roland C. Aydin | Celso R. C. Rêgo | Maurício J. Piotrowski
Source: ArXiv.org (preprint, 2025-12-04)
Field: Materials Science — Materials Chemistry

Teaching a Transformer to Think Like a Chemist: Predicting Nanocluster Stability Atomically precise metal nanoclusters bridge the molecular and bulk regimes, but designing bimetallic motifs with targeted stability and reactivity remains challenging. Here we combine density functional theory (DFT) and physics-grounded predictive artificial intelligence to map the configurational landscape of 13-atom icosahedral nanoclusters X$_{12}$TM, with hosts X = (Ti, Zr, Hf), and Fe and a single transition--metal dopant spanning the 3$d$-5$d$ series. Spin-polarized DFT calculations on 240 bimetallic clusters reveal systematic trends in binding and formation energies, distortion penalties, effective coordination number, d-band centre, and HOMO-LUMO gap that govern the competition between core-shell (in) and surface-segregated (out) arrangements. We then pretrain a transformer architecture on a curated set of 2968 unary clusters from the Quantum Cluster Database and fine-tune it on bimetallic data to predict formation energies and in/out preference, achieving mean absolute errors of about $0.6-0.7$eV and calibrated uncertainty intervals. The resulting model rapidly adapts to an unseen Fe-host domain with only a handful of labelled examples. At the same time, attention patterns and Shapley attributions highlight size mismatch, $d$-electron count, and coordination environment as key descriptors. All data, code, and workflows follow FAIR/TRUE principles, enabling reproducible, interpretable screening of unexplored nanocluster chemistries for catalysis and energy conversion. Nanoclusters ; Bimetallic strip ; Icosahedral symmetry ; Density functional theory ; Cluster analysis ; Bridging (networking) ; Cluster (spacecraft) Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Electrocatalysts for Energy Conversion
Authors: Bowen Zhou | Ning Ding | Lei Bai | Hao Zhou
Source: AI Open (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Advancing AI for science: From the revolution of tools to the tools for revolution Scientific research is not a linear pipeline but a dynamic system built upon the ever-shifting interactions among three elements — research objects, tools, and researchers . And sustained progress depends on how quickly insights circulate within this network, not on optimizing a single node in isolation. With the impending arrival of more general artificial intelligence, we stand at a critical point in how AI might change scientific research in a systemic manner. Recent “AI for Science” achievements – from protein-structure prediction to accelerated climate simulations – have proven the value of task-level AI-driven solutions. Yet, potential still remains unrealized when these advances are siloed in disciplinary “archipelagos”. This paper argues that the real prize is systemic: AI that simultaneously expands the research objects’ data landscape (AI for Data), rewires computational research tools (AI for Computation), and co-creates hypotheses with researchers (AI for Innovation). When these three pushes converge, AI stops being merely a revolution of tools but becomes the tool of revolution — a catalyst that raises the frequency, breadth, and depth of discovery across disciplines. By enhancing the full research triad rather than isolated nodes, AI can raise the overall tempo and scope of discovery in a measured, discipline-agnostic way. Pipeline (software) ; Scope (computer science) ; Scientific discovery ; Big data ; Technological revolution ; Scientific revolution ; Value (mathematics) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Genetics, Bioinformatics, and Biomedical Research
Authors: Thomas Knisley
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Materials Innovation for Thermal Interfaces: Unlocking AI Compute Efficiency The semiconductor industry is entering the Artificial Intelligence era, where compute density and energy efficiency are defining the pace of innovation. As chipmakers push toward advanced architectures—gate-all-around transistors, 3D-stacked logic and memory, and chiplet-based integration—thermal management at the chip level has emerged as a critical bottleneck. These architectures concentrate power in increasingly confined volumes, leading to localized hotspots that can exceed material and reliability thresholds. Traditional thermal solutions are no longer sufficient to manage the steep thermal gradients and interfacial resistances introduced by vertical integration and high-density interconnects. Without effective heat extraction at the die-to-die and die-to-package interfaces, performance throttling, electromigration, and premature failure become limiting factors. Thermal interface materials (TIMs) are now a key factor in improving AI compute efficiency. To meet the demands of next-generation packaging and 3D integration, TIMs must deliver ultra-low thermal resistance, mechanical compliance, and long-term stability—all while integrating seamlessly into high-volume manufacturing flows. Materials innovation is focused on tailoring interfacial chemistry, optimizing thermal flow, and deploying scalable deposition techniques such as atomic layer deposition and solution-based processing. This presentation will highlight how materials innovation in TIMs can unlock new levels of AI compute efficiency. We will discuss recent advances and directions in chip-level thermal solutions and show how aligning TIM development with device roadmap inflections is essential to enabling the next generation of high-performance systems. Thermal ; Scalability ; Thermal grease ; Reliability (semiconductor) ; Limiting ; Efficient energy use ; Vertical integration ; Thermal management of electronic devices and systems ; Chip Thermal properties of materials ; Transition Metal Oxide Nanomaterials ; Electronic Packaging and Soldering Technologies
Authors: Nunzio Cennamo | Stefano Toldo
Source:  (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Abstracts of the 5th International Electronic Conference on Applied Sciences The 5th International Electronic Conference on Applied Sciences (ASEC 2024) was held online from 4 to 6 December 2024. It represents an opportunity to gather an interdisciplinary community to discuss applied sciences. More specifically, after the success of the first four editions, this year’s edition focuses on eight thematic areas of applied sciences: Applied Biosciences and Bioengineering; Nanosciences, Chemistry and Materials Science; Computing and Artificial Intelligence; Electrical, Electronics and Communications Engineering; Mechanical and Aerospace Engineering; Energy, Environmental and Earth Science; Food Science and Technology; and Applied Physical Science. The participants of this conference exchanged novel ideas by presenting short oral presentations on research and development discoveries. Computer science ; Engineering ethics ; Engineering physics ; Library science ; Engineering Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Academic Publishing and Open Access
Authors: Fuyu Han | Yupeng Wu | Linchuan Cong | Fangbing Liu | X.Z. Li | Ciduo Wang | Kewei Ma | Xiang‐Gao Meng | Nan Lin | Haibo Lin
Source: Applied Catalysis B: Environmental (article, 2025-12-02)
Field: Chemistry — Organic Chemistry

Unlocking aqueous electrosynthesis of λ5-iodane: DFT-guided catalyst screening meets multivariate optimization Electrosynthesis ; Catalysis ; Aqueous solution ; Yield (engineering) ; Electrochemistry ; Response surface methodology ; Electrolyte Oxidative Organic Chemistry Reactions ; Electrocatalysts for Energy Conversion ; Advanced oxidation water treatment
Authors: Carlos Eduardo Riveros Berger
Source: Preprints.org (preprint, 2025-08-19)
Field: Chemistry — Physical and Theoretical Chemistry

Quaternionic–Fractional Kinetic Operator: An Entropy-Based Framework for Competitive and Uncertain Chemical Reactions This work proposes a quaternionic–fractional kinetic operator to generalize classical chemical kinetics by embedding entropy-driven corrections. In this framework, reaction pathways are represented as quaternionic weights, where the scalar component encodes global reactivity and the vector component introduces anisotropic interactions. An angular error operator is incorporated as an entropy-like metric of uncertainty, quantifying deviations between theoretical and experimental orientations. Additionally, fractional chemical potentials are introduced to account for memory effects, extending kinetics beyond the Markovian regime. Synthetic simulations demonstrate that the operator recovers classical laws as limiting cases, while predicting non-exponential and anisotropic trends when uncertainty, interference, and entropic memory dominate. Figures 1–12 illustrate the roles of quaternionic corrections, angular misalignment, and fractional persistence in shaping the kinetic response. The approach provides a unified interpretation of uncertainty, anisotropy, and memory within chemical kinetics, offering potential applications in heterogeneous catalysis, biochemical networks, and energy materials where classical exponential kinetics fail. While this study is based on numerical modeling, the results lay the groundwork for future experimental validation and integration with data-driven approaches. Kinetic energy ; Operator (biology) ; Entropy (arrow of time) ; Mathematics ; Chemistry ; Physics ; Thermodynamics ; Classical mechanics Various Chemistry Research Topics ; Thermal and Kinetic Analysis ; thermodynamics and calorimetric analyses
Authors: Yiming Shen (6941957) | Jiyizhe Zhang (22308827) | Xiao Liang (185891) | Kai Wang (21246)
Source: Figshare (article, 2025-09-25)
Field: Chemistry — Organic Chemistry

From Optimization to Mechanism: Bayesian Optimization-GuidedExploration of p‑Xylene Electro-oxidation Electrochemical synthesis, with its mild conditions and high atom economy, offers a sustainable alternative for creating high-value products. However, for electrochemical reactions, commonly involving a larger parameter space than traditional chemical reactions, it is challenging to efficiently identify optimal reaction conditions or investigate the underlying principles. In this article, a two-step framework of AI-guided exploration is proposed for investigating the Co(II)-mediated electro-oxidation of p-xylene. A multiobjective diversity-driven Bayesian optimization approach is applied for the first step to search the space of operation, identifying Pareto-optimal solutions that balance maximizing reaction yield and minimizing Faradaic contribution. These solutions are then analyzed by employing SHAP (SHapley Additive Explanations) and additional experiments in the second step to investigate the reaction mechanisms. The optimized conditions achieved higher yields and lower energy consumption compared to reported studies in the literature while avoiding the use of toxic solvents. The framework demonstrates the advantage of AI-guided optimization to accelerate the research of electrochemical reactions by significantly reducing the cost of experiments and reducing reagent consumption as well as waste generation, presenting a new path for exploring sustainable chemical processes. Bayesian optimization ; Multi-objective optimization ; Bayesian probability ; Path (computing) ; Yield (engineering) ; Energy consumption ; Consumption (sociology) Radical Photochemical Reactions ; Electrocatalysts for Energy Conversion ; Oxidative Organic Chemistry Reactions
Authors: Yuyang Chen (4483531) | Danhao Wang (5942921) | Dalei Jiang (11853407) | Zetian Mi (1371909)
Source: Figshare (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Semi-SupervisedVision Transformer Framework for AI-BasedRHEED Image Classification of Ferroelectric Nitride MBE Growth We report a semi-supervised Vision Transformer (ViT) framework for automated reflection high energy electron diffraction (RHEED) image classification of ferroelectric nitride (ScAlN) materials grown by molecular beam epitaxy (MBE). By incorporating pseudo-labeling, the new framework reduces the need for extensive manual annotations while maintaining robust performance across multiple substrate angles. The effects of three key hyperparameters: labeled-data proportion, the number of Transformer heads, and model depth on classification outcomes are explored. Our findings show that, although parameter tuning can yield incremental accuracy gains, simpler configurations (e.g., two heads and two layers) provide an optimal balance between accuracy and computational overhead. Adjusting embedding dimensions further refines the model without incurring excessive computational costs. Compared with fully supervised approaches, the proposed framework delivers equal or better accuracy using far fewer labeled samples and effortlessly adapts to diverse RHEED angles. These findings underscore the potential of semi-supervised ViT-based solutions to facilitate AI-driven standardization and optimization in semiconductor manufacturing. Molecular beam epitaxy ; Ferroelectricity ; Embedding ; Reflection high-energy electron diffraction ; Transformer ; Gallium nitride ; Niobium nitride ; Cathode ray Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Electron Microscopy Techniques and Applications
Authors: Jiacheng Gao
Source: Applied and Computational Engineering (article, 2025-06-27)
Field: Materials Science — Materials Chemistry

Study on Preparation Technology of Transition Metal Sulfide Nanomaterials Transition metal sulfide nanomaterials (nano TMS), with their unique size, surface and quantum effects, have been playing an increasingly important role in industries, such as semiconductors. However, the properties of nano TMS are still under investigation. In order to promote their widespread application, it has been a critical issue for material preparation technology to develop a proper processing method. This article summarizes common methods for preparing nano-TMS, including top-down preparation methods using mechanical or liquid ultrasonic exfoliation, bottom-up preparation methods involving hydrothermal and solvothermal methods, chemical vapor deposition, and electrochemical deposition (collectively known as bottom-up preparation methods), as well as biomineralization production based on sulfate-reducing bacteria metabolism. It also provides examples and corresponding selection criteria. Additionally, this article analyzes the existing shortcomings in nano-TMS preparation from four aspects: production stability, detection ways, cost control, and environmental damages, and identifies potential development directions such as high-throughput microreactor technology, more research on biomineralization mechanisms, and AI-assisted reaction conditions control. It offers valuable references for large-scale industrial production and application of nano TMS. Nanomaterials ; Sulfide ; Nanotechnology ; Transition metal ; Materials science ; Chemistry ; Metallurgy ; Organic chemistry ; Catalysis Quantum Dots Synthesis And Properties
Authors: Jack Yang | Ziqi Yin | Ao Lei | Sean Li
Source: ArXiv.org (preprint, 2025-10-21)
Field: Materials Science — Materials Chemistry

MACE Foundation Models for Lattice Dynamics: A Benchmark Study on Double Halide Perovskites Recent developments in materials informatics and artificial intelligence has led to the emergence of foundational energy models for material chemistry, as represented by the suite of MACE-based foundation models, bringing a significant breakthrough in universal potentials for inorganic solids. As to all method developments in computational materials science, performance benchmarking against existing high-level data with focusing on specific applications, is critically needed to understand the limitations in the models, thus facilitating the ongoing improvements in the model development process, and occasionally, leading to significant conceptual leaps in materials theory. Here, using our own published DFT (Density Functional Theory) database of room-temperature dynamic stability and vibrational anharmonicity for $\sim2000$ cubic halide double perovskites, we benchmarked the performances of four different variants of the MACE foundation models for screening the dynamic stabilities of inorganic solids. Our analysis shows that, as anticipated, the model accuracy improves with more training data. The dynamic stabilities of weakly anharmonic materials (as predicted by DFT) are more accurately reproduced by the foundation model, than those highly anharmonic and dynamically unstable ones. The predominant source of error in predicting the dynamic stability arises predominantly from the amplification of errors in atomic forces when predicting the harmonic phonon properties through the computation of the Hessian matrix, less so is the contribution from possible differences in the range of the configurational spaces that are sampled by DFT and the foundation model in molecular dynamics. We hope that our present findings will stimulate future works towards more physics-inspired approaches in assessing the accuracy of foundation models for atomistic modelling. Anharmonicity ; Foundation (evidence) ; LEAPS ; Phonon ; Lattice (music) ; Benchmarking ; Stability (learning theory) ; Computation Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Pin-Jing Su | Debby Li | Wenyu Deng | Liang-Yih Hung | Andrew H. Kang | Yu-Po Wang
Source:  (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Evaluation of low thermal resistance film type TIM for FCBGA package In the booming era of Artificial Intelligence (AI), the high-performance computing (HPC) products focus on the pursuit of processing large amounts of data and fast calculations for efficient training and inference of AI models. Furthermore, the product will be required to enhance the heat dissipation ability to maintain computing efficiency, due to the high-density transistor in chips and the evolution of high-power input design. For the package application, flip chip ball grid array (FCBGA) is the well-known solution that has high IO and fine line width and line space design features. Therefore, the thermal interface material (TIM) selection plays an important role in the performance of high-end products. There is a new film type material that is composed of liquid metal and carbon fiber. This composite film type material has the features to combine the workability of film type material and the thermal resistance reduction characteristics of metal type material. The melting point of the liquid metal alloy was measured to be approximately 31°C by DSC. Thus, it is a solid phase at room temperature that can keep the process method of film type.The present article focuses on the discussion of material thermal properties and process workability. First, the thermal properties of this novel film material were measured such as thermal conductivity and thermal resistance. All thermal properties are superior to those of conventional Graphite TIM. Secondly, the solutions of high-risk processes were evaluated. These processes included TIM attachment, add the heating stage under the substrate and temperature setting greater than 31°C that can prevent TIM shift risk in process because of the film material is non-polymer-based and non-adhesive. For HS process, when the TIM size is larger than die size, the liquid metal bleeding risk can be reduced. This is attributable to the extra TIMs around, which serve to enhance the resistance of liquid metal and thereby prevent bleeding out. To conduct a TIM coverage inspection by SAT, it is necessary to utilize a low-frequency probe that can enhance the penetration of ultrasound to get the readable TIM coverage image. In the meantime, the TIM material change of appearance was collected following the reliability conditions. The volumetric expansion of liquid metal alloy was found after uHAST condition, which directly influenced the TIM coverage. Therefore, the liquid metal alloy requires adjustment and enhancement in the subsequent stage. In conclusion, although this study did not achieve the expected results, it has provided new inspiration for research in related fields. Ball grid array ; Thermal grease ; Thermal conductivity ; Thermal resistance ; Thermal ; Material selection ; Graphite ; Material properties ; Thermal conduction ; Phase-change material Thermal properties of materials ; Electronic Packaging and Soldering Technologies ; 3D IC and TSV technologies