jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wubshet Getachew Mengesha
Source: Discover Nano (review, 2025-12-01)
Field: Materials Science — Materials Chemistry

AI-driven design of multifunctional nanomaterials in revolutionizing high-temperature, high-power solutions for space technology: potentials, challenges and perspectives The extreme conditions of space-including intense thermal cycling, radiation, and micrometeoroid impacts-demand advanced materials that surpass the capabilities of conventional alloys and composites. This paper highlights the transformative potential of integrating artificial intelligence (AI) with multifunctional nanomaterials to overcome these challenges and revolutionize space technology. While nanomaterials like carbon nanotubes (CNTs), graphene, and boron nitride nanotubes (BNNTs) offer exceptional thermal, mechanical, optical and radiation-shielding properties, their development has been hindered by vast design spaces, synthesis complexities, and a lack of data for extreme environments. This paper addresses these gaps by hypothetically investigating how AI-driven methodologies for property prediction and multi-objective optimization can accelerate the discovery and optimization of next-generation nanomaterials. Key findings demonstrate that AI-guided design enables the creation of materials with tailored functionalities, including thermal interface materials with conductivities exceeding 200 W/m K, radiation-tolerant magnetic alloys with 50% less demagnetization, and self-cooling optical coatings maintaining high reflectivity after long thermal cycles. These advancements facilitate significant system-level improvements, such as mass reduction in shielding and thruster-specific impulse increase. The paper concludes that a synergistic AI-nanomaterial approach is essential to meet the escalating demands of future space exploration, though challenges in data scarcity, high- temperature modeling, and scalable manufacturing remain. Prioritizing hybrid AI-physics models and international collaboration for standardized testing is recommended to fully realize this potential. Nanomaterials ; Boron nitride ; Space (punctuation) ; Radiation shielding ; Nanomanufacturing ; Complementarity (molecular biology) ; Scalability Thermal properties of materials ; Thermal Expansion and Ionic Conductivity ; Boron and Carbon Nanomaterials Research
Authors: Jinming Hu | Hassan Nawaz | Hou, Yi-Fan | Yuting Rui | Lijie Chi | Chen, Yuxinxin | Arif Ullah | Pavlo O. Dral
Source: arXiv (Cornell University) (preprint, 2025-05-13)
Field: Materials Science — Materials Chemistry

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations We have developed Aitomia - a platform powered by AI to assist in performing AI-driven atomistic and quantum chemical (QC) simulations. This evolving intelligent assistant platform is equipped with chatbots and AI agents to help experts and guide non-experts in setting up and running atomistic simulations, analyzing simulation results, and summarizing them for the user in both textual and graphical forms. Aitomia combines LLM-based agents with the MLatom platform to support AI-driven atomistic simulations as well as conventional quantum-chemical calculations, including DFT, semiempirical methods such as GFN2-xTB, and selected high-level wavefunction-based methods, through interfaces to widely used programs such as Gaussian, ORCA, PySCF, and xtb, covering tasks from ground-state and excited-state calculations to geometry optimization, thermochemistry, and spectra simulations. The multi-agent implementation enables autonomous execution of complex computational workflows, such as reaction enthalpy calculations. Aitomia was the first intelligent assistant publicly launched on cloud computing platforms for broad-scope atomistic simulations (Aitomistic Lab@XMU at https://atom.xmu.edu.cn and Aitomistic Hub at https://aitomistic.xyz). Aitomia lowers the barrier to performing atomistic simulations, thereby democratizing simulations and accelerating research and development in relevant fields. Leverage (statistics) ; Scope (computer science) ; Cloud computing ; Computation ; Quantum computer ; Quantum chemical Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Uddalak Das
Source: Preprints.org (preprint, 2025-04-07)
Field: Materials Science — Materials Chemistry

Generative AI for Drug Discovery and Protein Design: The Next Frontier in AI-Driven Molecular Science Generative artificial intelligence (AI) has emerged as a disruptive paradigm in molecular science, enabling algorithmic navigation and construction of chemical and proteomic spaces through data-driven modeling. This review systematically delineates the theoretical underpinnings, algorithmic architectures, and translational applications of deep generative models&amp;mdash;including variational autoencoders (VAEs), generative adversarial networks (GANs), autoregressive transformers, and score-based denoising diffusion probabilistic models (DDPMs)&amp;mdash;in the rational design of bioactive small molecules and functional proteins. We examine the role of latent space learning, probabilistic manifold exploration, and reinforcement learning in inverse molecular design, focusing on optimization of pharmacologically relevant objectives such as ADMET profiles, synthetic accessibility, and target affinity. Furthermore, we survey advancements in graph-based molecular generative frameworks, LLM-guided protein sequence modeling, and diffusion-based structural prediction pipelines (e.g., RFdiffusion, FrameDiff), which have demonstrated state-of-the-art performance in de novo protein engineering and conformational sampling. Generative AI is also catalyzing a paradigm shift in structure-based drug discovery via AI-augmented molecular docking (e.g., DiffDock), end-to-end binding affinity prediction, and quantum chemistry-informed neural potentials. We explore the convergence of generative models with Bayesian retrosynthesis planners, self-supervised pretraining on ultra-large chemical corpora, and multimodal integration of omics-derived features for precision therapeutics. Finally, we discuss translational milestones wherein AI-designed ligands and proteins have progressed to preclinical and clinical validation, and speculate on the synthesis of generative AI, closed-loop automation, and quantum computing in future autonomous molecular design ecosystems. Frontier ; Generative grammar ; Drug discovery ; Computer science ; Data science ; Artificial intelligence ; Computational biology ; Bioinformatics ; Political science ; Biology Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research
Authors: Chao Peng | Bing Wang | Lie Wu | Haoqu Jin | Yutang Li | Wenxia Gao | Jie Zhou | Guolai Jiang | Chen Wang | Jiahong Wang | Xingchen He | Denis Kramer | Paul K. Chu | Xue‐Feng Yu
Source: Angewandte Chemie International Edition (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

AI‐Driven Discovery and Molecular Engineering Design for Enhancing Interface Stability of Black Phosphorus Molecular engineering offers significant potential for developing advanced interfacial materials, yet the complexity of organic molecules poses challenges in discovering optimal structures. This study leveraged large language model (LLM) and machine learning (ML) to accelerate molecular discovery and guide molecular engineering for enhancing the stability of black phosphorus (BP), a promising 2D semiconductor but rapidly degrades when exposed to oxygen and moisture. By utilizing GPT-4o, molecular groups such as ─SiR<sub>3</sub>, ─PR<sub>2</sub>, ─SH, and ═NH that interact effectively with BP were identified and a high-throughput workflow employing graph neural networks (GNNs) models was developed to successfully predict and screen 662 promising candidates from over 117 million molecules. These candidates were validated by density functional theory (DFT) simulations and experiments, with synthesis protocols guided by GPT-4o, achieving great interfacial stabilization of BP for up to 24 days under ambient conditions. Furthermore, a new synergistic molecular engineering strategy was proposed by incorporating functional head, linker, and tail groups of molecules to even enable the use of hydrophilic molecules to stabilize BP surface, overcoming traditional design limitations. This work highlights the AI technologies not only in optimizing BP interfacial stability but also in broader aspects of molecular engineering for various materials. Black phosphorus ; Workflow ; Computer science ; Molecular engineering ; Molecular machine ; Nanotechnology ; Interface (matter) ; Linker ; Materials science ; Molecule ; Stability (learning theory) ; Biochemical engineering ; Combinatorial chemistry ; Chemistry ; Machine learning ; Database ; Engineering ; Organic chemistry 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques
Authors: Aleksandar Kondinski | Nadiia I. Gumerova | Annette Rompel | Paolo Falcaro | Tobias Schreck
Source: Chemistry - A European Journal (article, 2025-09-07)
Field: Materials Science — Materials Chemistry

Data‐Driven Polyoxometalate Chemistry Polyoxometalates (POMs) are nanoscale, structurally versatile metal-oxo clusters with emerging applications in sustainability, energy, nanoelectronics, and life science technologies. Owing to their structural complexity, some all-inorganic POMs are often perceived as serendipitous outcomes from self-assembly processes, which poses challenges for scalable rational design. From this perspective, we therefore examine how the development of POM informatics and, more generally, data-driven POM exploration can pave the way for the molecular engineering of new POM-based materials targeting customized applications. In the process, we highlight recent successes in the digitalization of POM chemistry and outline which advanced technologies are necessary for progress in this promising area of research. Polyoxometalate ; Informatics ; Scalability ; Emerging technologies Polyoxometalates: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications
Authors: Sai Teja Potu | Rachana Niranjan Murthy | Akhil Thomas | Lokesh Mishra | Natalie Prange | Ali Riza Durmaz
Source: Scientific Reports (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Ontology-conformal recognition of materials entities using language models Extracting structured and semantically annotated materials information from unstructured scientific literature is a crucial step toward constructing machine-interpretable knowledge graphs and accelerating data-driven materials research. This is especially important in materials science, which is adversely affected by data scarcity. Data scarcity further motivates employing solutions such as foundation language models for extracting information which can in principle address several subtasks of the information extraction problem in a range of domains without the need of generating costly large-scale annotated datasets for each downstream task. However, foundation language models struggle with tasks like Named Entity Recognition (NER) due to domain-specific terminologies, fine-grained entities, and semantic ambiguity. The issue is even more pronounced when entities must map directly to pre-existing domain ontologies. This work aims to assess whether foundation large language models (LLMs) can successfully perform ontology-conformal NER in the materials mechanics and fatigue domain. Specifically, we present a comparative evaluation of in-context learning (ICL) with foundation models such as GPT-4 against fine-tuned task-specific language models, including MatSciBERT and DeBERTa. The study is performed on two materials fatigue datasets, which contain annotations at a comparatively fine-grained level adhering to the class definitions of a formal ontology to ensure semantic alignment and cross-dataset interoperability. Both datasets cover adjacent domains to assess how well both NER methodologies generalize when presented with typical domain shifts. Task-specific models are shown to significantly outperform general foundation models on an ontology-constrained NER. Our findings reveal a strong dependence on the quality of few-shot demonstrations in ICL to handle domain-shift. The study also highlights the significance of domain-specific pre-training by comparing task-specific models that differ primarily in their pre-training corpus. Computer science ; Ontology ; Domain (mathematical analysis) ; Natural language processing ; Named-entity recognition ; Information extraction ; Context (archaeology) ; Upper ontology ; Information retrieval ; Artificial intelligence ; Task (project management) ; Ontology language ; Biomedical text mining ; Data science ; Semantic Web ; Text mining Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Parisa Ghasemi | Mohsen Moghaddam
Source: Journal of Mechanical Design (article, 2025-01-10)
Field: Materials Science — Materials Chemistry

Vision-Language Models for Design Concept Generation: An Actor–Critic Framework Abstract We introduce a novel actor-critic framework that utilizes vision-language models (VLMs) and large language models (LLMs) for design concept generation, particularly for producing a diverse array of innovative solutions to a given design problem. By leveraging the extensive data repositories and pattern recognition capabilities of these models, our framework achieves this goal through enabling iterative interactions between two VLM agents: an actor (i.e., concept generator) and a critic. The actor, a custom VLM (e.g., GPT-4) created using few-shot learning and fine-tuning techniques, generates initial design concepts that are improved iteratively based on guided feedback from the critic—a prompt-engineered LLM or a set of design-specific quantitative metrics. This process aims to optimize the generated concepts with respect to four metrics: novelty, feasibility, problem–solution relevancy, and variety. The framework incorporates both long-term and short-term memory models to examine how incorporating the history of interactions impacts decision-making and concept generation outcomes. We explored the efficacy of incorporating images alongside text in conveying design ideas within our actor–critic framework by experimenting with two mediums for the agents: vision language and language only. We extensively evaluated the framework through a case study using the AskNature dataset, comparing its performance against benchmarks such as GPT-4 and real-world biomimetic designs across various industrial examples. Our findings underscore the framework’s capability to iteratively refine and enhance the initial design concepts, achieving significant improvements across all metrics. We conclude by discussing the implications of the proposed framework for various design domains, along with its limitations and several directions for future research in this domain. Computer science ; Variety (cybernetics) ; Novelty ; Process (computing) ; Set (abstract data type) ; Artificial intelligence ; Generator (circuit theory) ; Human–computer interaction ; Engineering design process ; Machine learning ; Programming language ; Engineering Machine Learning in Materials Science
Authors: Yuxinxin Chen | Pavlo O. Dral
Source: ChemRxiv (preprint, 2025-08-28)
Field: Materials Science — Materials Chemistry

One To Rule Them All: A Universal Interatomic Potential Learning Across Quantum Chemical Levels With the development of universal machine learning interatomic potentials, a rapidly growing number of chemical space datasets appear. One of the biggest challenges is that these datasets are mostly generated at different quantum chemical (QC) levels. However, a general framework scalable to learning across both chemical space and quantum chemical levels remains in need. In this work, we proposed the all-in-one approach that enables simultaneous learning on an arbitrary number of QC levels from various datasets, presenting a more general and easier-to-use alternative to transfer learning. We showcase the superiority of our all-in-one strategy by creating OMNI-P1 – the first-ever universal interatomic potential simultaneously learning and making predictions at different QC levels. The generalization capability of the universal model OMNI-P1 for organic molecules is comparable to semi-empirical GFN2-xTB and common density functional theory (DFT) methods with a double-ζ basis set, while the speed is orders of magnitude faster. Due to its unique ability to make predictions at different levels, a single model trained with our approach provides a straightforward way to also generate the corrections. This can be used in the Δ-learning models without the need to train a dedicated correcting model. We utilize this capability of OMNI-P1 to correct the DFT ωB97X-D4 level to obtain the Ω-ωB97X-D4 method with superior accuracy. Computer science ; Quantum chemical ; Generalization ; Scalability ; Chemical space ; Set (abstract data type) ; Space (punctuation) ; Density functional theory ; Quantum ; Machine learning ; Theoretical computer science ; Artificial intelligence ; Statistical physics ; Quantum mechanics ; Mathematics ; Physics ; Molecule ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Bing Zeng | Shuo Wu | Shufang Yao
Source: Polymers (review, 2025-11-11)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Paradigm for Polymer Aging Lifetime Prediction: Integrating Multi-Mechanism Coupling and Cross-Scale Modeling This review systematically examined the transformative role of machine learning in predicting polymer aging lifetime, addressing critical limitations of conventional methods such as the Arrhenius model, time-temperature superposition principle, and numerical fitting approaches. The primary objective was to establish a comprehensive framework that integrates multi-mechanism coupling with dynamic data-driven modeling to enhance prediction accuracy across complex aging scenarios. Four key machine learning categories demonstrate distinct advantages: support vector machines effectively capture nonlinear interactions in multi-stress environments; neural networks enable cross-scale modeling from molecular dynamics to macroscopic failure; decision tree models provide interpretable feature importance quantification; and hybrid approaches synergistically combine complementary strengths. These methodologies have shown significant success in critical industrial applications, including building trades, photovoltaic systems, and aerospace composites, creating an integrated predictive system that bridges molecular-level dynamics with service-life performance. By transforming life prediction from empirical extrapolation to mechanism-based simulation, this machine-learning-driven paradigm offers robust methodological support for engineering safety design in diverse polymer applications through its capacity to model complex environmental interactions, adapt to real-time monitoring data, and elucidate underlying degradation mechanisms. Interpretability ; Extrapolation ; Coupling (piping) ; Support vector machine ; Superposition principle ; Artificial neural network ; Nonlinear system ; Complex system ; Decision tree Machine Learning in Materials Science ; Polymer crystallization and properties ; Polymer composites and self-healing
Authors: Arash E. Zaghi
Source:  (preprint, 2025-04-07)
Field: Chemistry — Physical and Theoretical Chemistry

Relational Quantum Dynamics as a Topological and Categorical Graph Structure We develop a rigorous categorical and topological framework for Relational Quantum Dynamics (RQD), formalizing a dynamic network of observers as a relational graph enriched with quantum-theoretic structure. In RQD, quantum systems and observers have no intrinsic properties, only relationships: we prove via category theory (Yoneda’s lemma) that each object (observer/system) is uniquely determined (up to isomorphism) by its web of morphisms to all others, with no additional absolute identity. This network of interactions is formalized as a structured graph of observers – mathematically, a category (and higher-dimensional generalizations) enriched with weights capturing interaction strength. We show how quantum correlations and information flow can label the graph’s edges, yielding a rich combinatorial structure. Crucially, quantum contextuality and entanglement impose topological constraints on the observer network. Using presheaf topos theory and sheaf cohomology, we prove that the absence of a global assignment of outcomes (no single “God’s-eye” view) corresponds to a nontrivial cohomology class on the relational graph. In particular, in contextual scenarios the first Čech cohomology H1 of the presheaf of local measurement outcomes is nonzero, providing a rigorous obstruction to global consistency. These results endow the relational graph with a “twisted” topology reflecting quantum connectivity constraints. We present theorem-proof developments of these ideas and discuss implications: how RQD’s relational ontology naturally explains Bell inequality violations without spooky action, how multi-observer paradoxes (Wigner’s friend) are resolved by higher-category structures, and potential links to models of consciousness via integrated information. Categorical variable ; Quantum ; Computer science ; Topology (electrical circuits) ; Theoretical computer science ; Mathematics ; Physics ; Combinatorics ; Quantum mechanics ; Machine learning History and advancements in chemistry ; Topological and Geometric Data Analysis ; Complex Network Analysis Techniques
Authors: Daniel Widdowson | Vitaliy Kurlin
Source: Scientific Reports (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Geographic-style maps with a local novelty distance help navigate in the materials space With the advent of self-driving labs promising to synthesize large numbers of new materials, new automated tools are required for checking potential duplicates in existing structural databases before a material can be claimed as novel. To avoid duplication, we rigorously define the novelty metric of any periodic material as the smallest distance to its nearest neighbor among already known materials. Using ultra-fast structural invariants, all such nearest neighbors can be found within seconds on a typical computer even if a given crystal is disguised by changing a unit cell, perturbing atoms, or replacing chemical elements. This real-time novelty check is demonstrated by finding near-duplicates of the 43 materials produced by Berkeley's A-lab in the world's largest collections of inorganic structures, the Inorganic Crystal Structure Database and the Materials Project. To help future self-driving labs successfully identify novel materials, we propose navigation maps of the materials space where any new structure can be quickly located by its invariant descriptors similar to a geographic location on Earth. Novelty ; Style (visual arts) ; Space (punctuation) ; Computer science ; World Wide Web ; Data science ; Information retrieval ; Geography ; Psychology ; Archaeology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides