jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yiwen Gao | Ruochen Zhao | Yang Deng | Wenxuan Zhang
Source: arXiv (Cornell University) (preprint, 2026-01-15)
Field: Materials Science — Materials Chemistry

DR-Arena: an Automated Evaluation Framework for Deep Research Agents As Large Language Models (LLMs) increasingly operate as Deep Research (DR) Agents capable of autonomous investigation and information synthesis, reliable evaluation of their task performance has become a critical bottleneck. Current benchmarks predominantly rely on static datasets, which suffer from several limitations: limited task generality, temporal misalignment, and data contamination. To address these, we introduce DR-Arena, a fully automated evaluation framework that pushes DR agents to their capability limits through dynamic investigation. DR-Arena constructs real-time Information Trees from fresh web trends to ensure the evaluation rubric is synchronized with the live world state, and employs an automated Examiner to generate structured tasks testing two orthogonal capabilities: Deep reasoning and Wide coverage. DR-Arena further adopts Adaptive Evolvement Loop, a state-machine controller that dynamically escalates task complexity based on real-time performance, demanding deeper deduction or wider aggregation until a decisive capability boundary emerges. Experiments with six advanced DR agents demonstrate that DR-Arena achieves a Spearman correlation of 0.94 with the LMSYS Search Arena leaderboard. This represents the state-of-the-art alignment with human preferences without any manual efforts, validating DR-Arena as a reliable alternative for costly human adjudication. Task (project management) ; Rubric ; Deep learning ; Controller (irrigation) ; Automation Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Topic Modeling
Authors: Penglei Sun | Shuyan Chen | Xiang Liu | Longhan Zhang | Huajie You | Chang Yan | Yongqi Zhang | Chu, Xiaowen | Tong-Yi Zhang
Source: Open MIND (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

From Literature to Lab: Closed-Loop Advancement of Perovskite Solar Cells via Domain Knowledge Guided LLM Perovskite solar cells (PSCs) have been considered as a next-generation disruptive photovoltaic technology, yet their advancement is constrained by the complexity of perovskite recipe with high-dimensional material and process design space. Despite the impressive general reasoning of Large Language Models (LLMs), they struggle with two limitations for application in PSCs: an inability to align general semantics with the perovskite domain knowledge, and an inefficiency in navigating high-dimensional perovskite material and recipe design spaces. To address these limitations, we introduce a domain-knowledge-guided framework PVK-LLM, a specialized model to serve as an expert to bridge general semantics with perovskite domain knowledge. By integrating this domain knowledge into a hierarchical Bayesian Optimization workflow, our approach efficiently navigates the high-dimension design space on a solar cell simulator platform. The domain knowledge resolves cold-start problems while dynamically adapting to simulator feedback. Moreover, in an individual wet-lab experiment aimed at maximizing power conversion efficiency (PCE), our framework autonomously proposes a novel synergistic four-component recipe comprising specialized organic passivation recipe (3MTPAI, PDAI2, EDAI2, and PipDI) which has not been reported in existing literature. This AI-designed recipe effectively achieves a champion PCE value of over 26.0 %, approaching world records achieved through extensive expert trial-and-error. Our approach can effectively enable LLM comprehend the domain knowledge, which can efficiently navigate in a high-dimensional, capable to accelerate the advancement in real-world perovskite as well as other material science development. Recipe ; Semantics (computer science) ; Domain (mathematical analysis) ; Photovoltaic system ; Perovskite (structure) ; Domain knowledge ; Process (computing) ; Bridge (graph theory) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Model Reduction and Neural Networks
Authors: Penglei Sun | Shuyan Chen | Xiang Liu | Longhan Zhang | Huajie You | Chang Yan | Yongqi Zhang | Chu, Xiaowen | Tong-Yi Zhang
Source: arXiv (Cornell University) (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

From Literature to Lab: Closed-Loop Advancement of Perovskite Solar Cells via Domain Knowledge Guided LLM Perovskite solar cells (PSCs) have been considered as a next-generation disruptive photovoltaic technology, yet their advancement is constrained by the complexity of perovskite recipe with high-dimensional material and process design space. Despite the impressive general reasoning of Large Language Models (LLMs), they struggle with two limitations for application in PSCs: an inability to align general semantics with the perovskite domain knowledge, and an inefficiency in navigating high-dimensional perovskite material and recipe design spaces. To address these limitations, we introduce a domain-knowledge-guided framework PVK-LLM, a specialized model to serve as an expert to bridge general semantics with perovskite domain knowledge. By integrating this domain knowledge into a hierarchical Bayesian Optimization workflow, our approach efficiently navigates the high-dimension design space on a solar cell simulator platform. The domain knowledge resolves cold-start problems while dynamically adapting to simulator feedback. Moreover, in an individual wet-lab experiment aimed at maximizing power conversion efficiency (PCE), our framework autonomously proposes a novel synergistic four-component recipe comprising specialized organic passivation recipe (3MTPAI, PDAI2, EDAI2, and PipDI) which has not been reported in existing literature. This AI-designed recipe effectively achieves a champion PCE value of over 26.0 %, approaching world records achieved through extensive expert trial-and-error. Our approach can effectively enable LLM comprehend the domain knowledge, which can efficiently navigate in a high-dimensional, capable to accelerate the advancement in real-world perovskite as well as other material science development. Recipe ; Semantics (computer science) ; Domain (mathematical analysis) ; Photovoltaic system ; Perovskite (structure) ; Domain knowledge ; Process (computing) ; Bridge (graph theory) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Model Reduction and Neural Networks
Authors: Devi  Dutta Biswajeet | Sara Kadkhodaei
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Leveraging Large Language Models to Address Data Scarcity in Machine Learning for Graphene Synthesis Imputation (statistics) ; Feature engineering ; Missing data ; Support vector machine ; Normalization (sociology) ; Feature (linguistics) ; Feature vector ; Data modeling Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: B. Moses Abraham | Yury Gogotsi
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Machine Learning Toolkits and Frameworks for Materials Design ABSTRACT The rapid evolution of machine learning (ML) has advanced materials discovery, providing tools to explore, predict, and design materials with tailored properties. Here we present an overview of emerging ML tools for data‐driven materials innovation, including data curation, feature engineering, model development, interpretability, and inverse design. We highlight high‐throughput material databases in providing large‐scale, DFT‐computed datasets, and discuss the importance of descriptor libraries that encode compositional and structural information into machine‐readable inputs for model development. Advances in ML architectures, ranging from classical algorithms to graph neural networks, are discussed for their ability to capture complex structure–property relationships. Particular emphasis is given to inverse design frameworks using generative models and optimization strategies to enable property‐targeted materials generation. We further explore interpretability and uncertainty quantification techniques that are important for bridging ML predictions with experimental validation. Automation platforms are described as tools for closed‐loop, high‐throughput discovery pipelines. We outline grand challenges, including data sparsity, model generalizability, and experimental integration. Finally, we summarize future directions that include foundation models pre‐trained on broad, multimodal materials data; self‐supervised learning strategies to reduce dependence on labeled datasets; ML workflows that embed thermodynamic and symmetry constraints to enhance interpretability; and fully autonomous laboratories that couple ML guidance with robotic synthesis and real‐time feedback. This article is categorized under: Structure and Mechanism &gt; Computational Materials Science Data Science &gt; Artificial Intelligence/Machine Learning Interpretability ; Workflow ; Bridging (networking) ; Generative grammar ; Feature (linguistics) ; ENCODE ; Automation ; Graph Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Catalysis and Oxidation Reactions
Authors: Xin-De Wang | Zhi-Rui Chen | Wen-Kao Li | Peng-Jie Guo | Cheng Mu | Ze-Feng Gao | Zhong-Yi Lu
Source: Molecules (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Artificial Intelligence for Perovskite Additive Engineering: From Molecular Screening to Autonomous Discovery Additive engineering plays a crucial role in enhancing the performance of perovskite solar cells (PSCs), yet identifying suitable additives within the vast chemical space remains a significant challenge. This paper describes a paradigm shift in additive discovery from trial-and-error methods to AI-driven approaches. We first establish the physicochemical foundations of additive engineering and the descriptors commonly employed in machine learning algorithms. Next, we discuss intelligent process optimization, highlighting how active learning algorithms effectively tune complex precursor formulations with minimal experimental iterations. Additionally, we explore the role of AI in mechanism elucidation and the potential prospects of generative models in the field of additives. Finally, we emphasize the emerging trend of integrating large language models with autonomous laboratories for closed-loop autonomous discovery, offering a promising pathway to accelerate the commercialization of PSCs. Process (computing) ; Commercialization ; Generative grammar ; Chemical space ; Field (mathematics) ; Perovskite (structure) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Scientific Computing and Data Management
Authors: B. Y. Zhang | Thomas J. Summers | Logan J. Augustine | Michael G. Taylor | Andreas Geist | Rujun Li | Enrique R. Batista | Danny Perez | Ping Yang | Joshua Schrier
Source: Journal of the American Chemical Society (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Augmenting Large Language Models for Automated Discovery of F-Element Extractants Efficient separation of f-elements is a critical challenge for a wide range of emerging technologies. The chemical similarity among these elements makes the development of selective solvent extraction reagents both slow and difficult. Here, we present a quasi-autonomous AI-enabled workflow for the design and computational screening of selective extractant ligands. Molecular design is guided by <i>SAFE</i>-<i>MolGen</i>, a large language model-based agentic system that leverages curated extraction data to propose new ligands and preliminarily rank their performance using a supervised machine learning model trained on experimental data sets to consider the impact of realistic experimental conditions. Promising human-approved ligands are then passed to a second automated pipeline that constructs three-dimensional metal-ligand complexes and performs quantum mechanical free energy calculations to directly assess the metal selectivity. We demonstrate this approach for Am(III)/Eu(III) separations and report several newly designed ligands predicted to exhibit higher Am(III)/Eu(III) selectivity than the benchmark extractant CyMe<sub>4</sub>BTBP. This workflow accelerates computational exploration of the molecular space in this data-sparse field and provides a general strategy for the rapid generation and evaluation of novel lanthanide (Ln) and actinide (An) extractants. Workflow ; Pipeline (software) ; Benchmark (surveying) ; Field (mathematics) ; Range (aeronautics) ; Chemical space ; Rank (graph theory) ; Similarity (geometry) Machine Learning in Materials Science ; Extraction and Separation Processes ; Computational Drug Discovery Methods
Authors: Zhongcan Xiao | Leyi Zhang | Guannan Zhang | Xiaoping Wang
Source: ArXiv.org (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

NeuDiff Agent: A Governed AI Workflow for Single-Crystal Neutron Crystallography Large-scale facilities increasingly face analysis and reporting latency as the limiting step in scientific throughput, particularly for structurally and magnetically complex samples that require iterative reduction, integration, refinement, and validation. To improve time-to-result and analysis efficiency, NeuDiff Agent is introduced as a governed, tool-using AI workflow for TOPAZ at the Spallation Neutron Source that takes instrument data products through reduction, integration, refinement, and validation to a validated crystal structure and a publication-ready CIF. NeuDiff Agent executes this established pipeline under explicit governance by restricting actions to allowlisted tools, enforcing fail-closed verification gates at key workflow boundaries, and capturing complete provenance for inspection, auditing, and controlled replay. Performance is assessed using a fixed prompt protocol and repeated end-to-end runs with two large language model backends, with user and machine time partitioned and intervention burden and recovery behaviors quantified under gating. In a reference-case benchmark, NeuDiff Agent reduces wall time from 435 minutes (manual) to 86.5(4.7) to 94.4(3.5) minutes (4.6-5.0x faster) while producing a validated CIF with no checkCIF level A or B alerts. These results establish a practical route to deploy agentic AI in facility crystallography while preserving traceability and publication-facing validation requirements. Workflow ; Traceability ; Pipeline (software) ; Protocol (science) ; Consistency (knowledge bases) ; Key (lock) ; Encryption ; Limiting Machine Learning in Materials Science ; Nuclear Physics and Applications ; Enzyme Structure and Function
Authors: Zhongcan Xiao | Leyi Zhang | Guannan Zhang | Xiaoping Wang
Source: Open MIND (preprint, 2026-02-18)
Field: Materials Science — Materials Chemistry

NeuDiff Agent: A Governed AI Workflow for Single-Crystal Neutron Crystallography Large-scale facilities increasingly face analysis and reporting latency as the limiting step in scientific throughput, particularly for structurally and magnetically complex samples that require iterative reduction, integration, refinement, and validation. To improve time-to-result and analysis efficiency, NeuDiff Agent is introduced as a governed, tool-using AI workflow for TOPAZ at the Spallation Neutron Source that takes instrument data products through reduction, integration, refinement, and validation to a validated crystal structure and a publication-ready CIF. NeuDiff Agent executes this established pipeline under explicit governance by restricting actions to allowlisted tools, enforcing fail-closed verification gates at key workflow boundaries, and capturing complete provenance for inspection, auditing, and controlled replay. Performance is assessed using a fixed prompt protocol and repeated end-to-end runs with two large language model backends, with user and machine time partitioned and intervention burden and recovery behaviors quantified under gating. In a reference-case benchmark, NeuDiff Agent reduces wall time from 435 minutes (manual) to 86.5(4.7) to 94.4(3.5) minutes (4.6-5.0x faster) while producing a validated CIF with no checkCIF level A or B alerts. These results establish a practical route to deploy agentic AI in facility crystallography while preserving traceability and publication-facing validation requirements. Workflow ; Traceability ; Pipeline (software) ; Protocol (science) ; Consistency (knowledge bases) ; Key (lock) ; Encryption ; Limiting Machine Learning in Materials Science ; Nuclear Physics and Applications ; Enzyme Structure and Function
Authors: Cristian  Díaz Rodríguez | Paula Guzmán-Sastoque | Juan Esteban Rodríguez | Wilman Sanchez-Hernandez | Juan C. Cruz
Source: Journal of Nanotheranostics (article, 2026-02-06)
Field: Chemistry — Inorganic Chemistry

From Words to Frameworks: Transformer Models for Metal–Organic Framework Design in Nanotheranostics Metal–organic frameworks (MOFs) are among the most structurally diverse classes of crystalline nanomaterials, offering exceptional tunability, porosity, and chemical modularity. These characteristics have positioned MOFs as promising platforms for nanomedicine, bioimaging, and integrated nanotheranostic applications. However, the rational design of MOFs that satisfy stringent biomedical requirements, including high drug loading capacity, controlled and stimuli responsive release, selective targeting, physiological stability, biodegradability, and multimodal imaging capability, remains challenging due to the vast combinatorial design space and the complex interplay between physicochemical properties and biological responses. The objective of this review is to critically examine recent advances in artificial intelligence approaches based on Transformer architectures for the design and optimization of MOFs aimed at next-generation nanotheranostics. In contrast to prior reviews that broadly survey machine learning methods for MOF research, this article focuses specifically on Transformer-based models and their ability to capture long-range, hierarchical, and multiscale relationships governing MOF structure, chemistry, and functional behavior. We review state-of-the-art models, including MOFormer, MOFNet, MOFTransformer, and Uni MOF, and discuss graph-based and sequence-based representations used to encode MOF topology and composition. This review highlights how Transformer-based models enable predictive assessment of properties directly relevant to nanotheranostic performance, such as adsorption energetics, framework stability, diffusion pathways, pore accessibility, and surface functionality. By explicitly linking these predictive capabilities to drug delivery efficiency, imaging performance, targeted therapeutic action, and combined diagnostic and therapeutic applications, this work delineates the specific contribution of Transformer-based artificial intelligence to biomedical translation. Finally, we discuss emerging opportunities and remaining challenges, including generative Transformer models for inverse MOF design, self-supervised learning on hybrid experimental and computational datasets, and integration with autonomous synthesis and screening workflows. By defining the scope, novelty, and contribution of Transformer-based design strategies, this review provides a focused roadmap for accelerating the development of MOF-based platforms for next-generation nanotheranostics. ENCODE ; Transformer ; Rational design ; Synthetic biology ; Design space exploration ; Generative grammar Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis
Authors: Mingze Kong | Zikun Qu | Zhongquan Zhou | Pengyu Liang | Xiang Li | Zhiwei Shang | Zhi Hong | K. X. Huang | Zhiyong Wang | Zhongxiang Dai
Source: ArXiv.org (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Workflow-R1: Group Sub-sequence Policy Optimization for Multi-turn Workflow Construction The rapid evolution of agentic workflows has demonstrated strong performance of LLM-based agents in addressing complex reasoning tasks. However, existing workflow optimization methods typically formulate workflow synthesis as a static, one-shot code-centric generation problem. This paradigm imposes excessive constraints on the model's coding capabilities and restricts the flexibility required for dynamic problem-solving. In this paper, we present Workflow-R1, a framework that reformulates workflow construction as a multi-turn, natural language-based sequential decision-making process. To resolve the optimization granularity mismatch inherent in such multi-turn interactions, we introduce Group Sub-sequence Policy Optimization (GSsPO). While explicitly tailored to align with the interleaved Think-Action dynamics of agentic reasoning, GSsPO fundamentally functions as a structure-aware RL algorithm generalizable to a broad class of multi-turn agentic sequential decision-making tasks. By recalibrating the optimization unit to the composite sub-sequence, specifically the atomic Think-Action cycle, it aligns gradient updates with the semantic boundaries of these interactions, ensuring robust learning in complex multi-turn reasoning tasks. Through extensive experiments on multiple QA benchmarks, Workflow-R1 outperforms competitive baselines, validating GSsPO as a generalized solution for sequential reasoning and establishing Workflow-R1 as a promising new paradigm for automated workflow optimization. Workflow ; Granularity ; Flexibility (engineering) ; Class (philosophy) ; Coding (social sciences) ; Optimization problem ; Workflow technology Machine Learning in Materials Science ; Business Process Modeling and Analysis ; Scientific Computing and Data Management
Authors: Mingze Kong | Zikun Qu | Zhongquan Zhou | Pengyu Liang | Xiang Li | Zhiwei Shang | Zhi Hong | K. X. Huang | Zhiyong Wang | Zhongxiang Dai
Source: Open MIND (preprint, 2026-02-01)
Field: Materials Science — Materials Chemistry

Workflow-R1: Group Sub-sequence Policy Optimization for Multi-turn Workflow Construction The rapid evolution of agentic workflows has demonstrated strong performance of LLM-based agents in addressing complex reasoning tasks. However, existing workflow optimization methods typically formulate workflow synthesis as a static, one-shot code-centric generation problem. This paradigm imposes excessive constraints on the model's coding capabilities and restricts the flexibility required for dynamic problem-solving. In this paper, we present Workflow-R1, a framework that reformulates workflow construction as a multi-turn, natural language-based sequential decision-making process. To resolve the optimization granularity mismatch inherent in such multi-turn interactions, we introduce Group Sub-sequence Policy Optimization (GSsPO). While explicitly tailored to align with the interleaved Think-Action dynamics of agentic reasoning, GSsPO fundamentally functions as a structure-aware RL algorithm generalizable to a broad class of multi-turn agentic sequential decision-making tasks. By recalibrating the optimization unit to the composite sub-sequence, specifically the atomic Think-Action cycle, it aligns gradient updates with the semantic boundaries of these interactions, ensuring robust learning in complex multi-turn reasoning tasks. Through extensive experiments on multiple QA benchmarks, Workflow-R1 outperforms competitive baselines, validating GSsPO as a generalized solution for sequential reasoning and establishing Workflow-R1 as a promising new paradigm for automated workflow optimization. Workflow ; Granularity ; Flexibility (engineering) ; Class (philosophy) ; Coding (social sciences) ; Optimization problem ; Workflow technology Machine Learning in Materials Science ; Business Process Modeling and Analysis ; Scientific Computing and Data Management