jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anurag Acharya | Timothy Vega | Rizwan A. Ashraf | Anshu Sharma | Derek Parker | Robert Ralló
Source: ArXiv.org (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

A Cloud-based Multi-Agentic Workflow for Science As Large Language Models (LLMs) become ubiquitous across various scientific domains, their lack of ability to perform complex tasks like running simulations or to make complex decisions limits their utility. LLM-based agents bridge this gap due to their ability to call external resources and tools and thus are now rapidly gaining popularity. However, coming up with a workflow that can balance the models, cloud providers, and external resources is very challenging, making implementing an agentic system more of a hindrance than a help. In this work, we present a domain-agnostic, model-independent workflow for an agentic framework that can act as a scientific assistant while being run entirely on cloud. Built with a supervisor agent marshaling an array of agents with individual capabilities, our framework brings together straightforward tasks like literature review and data analysis with more complex ones like simulation runs. We describe the framework here in full, including a proof-of-concept system we built to accelerate the study of Catalysts, which is highly important in the field of Chemistry and Material Science. We report the cost to operate and use this framework, including the breakdown of the cost by services use. We also evaluate our system on a custom-curated synthetic benchmark and a popular Chemistry benchmark, and also perform expert validation of the system. The results show that our system is able to route the task to the correct agent 90% of the time and successfully complete the assigned task 97.5% of the time for the synthetic tasks and 91% of the time for real-world tasks, while still achieving better or comparable accuracy to most frontier models, showing that this is a viable framework for other scientific domains to replicate. Workflow ; Bridge (graph theory) ; Task (project management) ; Field (mathematics) ; Supervisor ; Cloud computing ; Marshalling Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Anurag Acharya | Timothy Vega | Rizwan A. Ashraf | Anshu Sharma | Derek Parker | Robert Ralló
Source: arXiv (Cornell University) (preprint, 2026-01-18)
Field: Materials Science — Materials Chemistry

A Cloud-based Multi-Agentic Workflow for Science As Large Language Models (LLMs) become ubiquitous across various scientific domains, their lack of ability to perform complex tasks like running simulations or to make complex decisions limits their utility. LLM-based agents bridge this gap due to their ability to call external resources and tools and thus are now rapidly gaining popularity. However, coming up with a workflow that can balance the models, cloud providers, and external resources is very challenging, making implementing an agentic system more of a hindrance than a help. In this work, we present a domain-agnostic, model-independent workflow for an agentic framework that can act as a scientific assistant while being run entirely on cloud. Built with a supervisor agent marshaling an array of agents with individual capabilities, our framework brings together straightforward tasks like literature review and data analysis with more complex ones like simulation runs. We describe the framework here in full, including a proof-of-concept system we built to accelerate the study of Catalysts, which is highly important in the field of Chemistry and Material Science. We report the cost to operate and use this framework, including the breakdown of the cost by services use. We also evaluate our system on a custom-curated synthetic benchmark and a popular Chemistry benchmark, and also perform expert validation of the system. The results show that our system is able to route the task to the correct agent 90% of the time and successfully complete the assigned task 97.5% of the time for the synthetic tasks and 91% of the time for real-world tasks, while still achieving better or comparable accuracy to most frontier models, showing that this is a viable framework for other scientific domains to replicate. Workflow ; Bridge (graph theory) ; Task (project management) ; Field (mathematics) ; Supervisor ; Cloud computing ; Marshalling Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Hao Li | He Cao | Shenyao Peng | Zijing Liu | Bin Feng | Yu Wang | Zhiyuan Yan | Yonghong Tian | Yu Li | Li Yuan
Source: Open MIND (preprint, 2026-01-25)
Field: Materials Science — Materials Chemistry

Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis Language models are revolutionizing the biochemistry domain, assisting scientists in drug design and chemical synthesis with high efficiency. Yet current approaches struggle between small language models prone to hallucination and limited knowledge retention, and large cloud-based language models plagued by privacy risks and high inference costs. To bridge this gap, we introduce ChemCRAFT, a novel framework leveraging agentic reinforcement learning to decouple chemical reasoning from knowledge storage. Instead of forcing the model to memorize vast chemical data, our approach empowers the language model to interact with a sandbox for precise information retrieval. This externalization of knowledge allows a locally deployable small model to achieve superior performance with minimal inference costs. To enable small language models for agent-calling ability, we build an agentic trajectory construction pipeline and a comprehensive chemical-agent sandbox. Based on sandbox interactions, we constructed ChemToolDataset, the first large-scale chemical tool trajectory dataset. Simultaneously, we propose SMILES-GRPO to build a dense chemical reward function, promoting the model's ability to call chemical agents. Evaluations across diverse aspects of drug design show that ChemCRAFT outperforms current cloud-based LLMs in molecular structure analysis, molecular optimization, and synthesis pathway prediction, demonstrating that scientific reasoning is not solely an emergent ability of model scale, but a learnable policy of tool orchestration. This work establishes a cost-effective and privacy-preserving paradigm for AI-aided chemistry, opening new avenues for accelerating molecular discovery with locally deployable agents. Code available at https://github.com/HowardLi1984/ChemCraft. Language model ; Reinforcement learning ; Abstraction ; Inference ; Pipeline (software) ; Encoding (memory) ; Tirapazamine ; Sandbox (software development) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Hao Li | He Cao | Shenyao Peng | Zijing Liu | Bin Feng | Yu Wang | Zhiyuan Yan | Yonghong Tian | Yu Li | Li Yuan
Source: ArXiv.org (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis Language models are revolutionizing the biochemistry domain, assisting scientists in drug design and chemical synthesis with high efficiency. Yet current approaches struggle between small language models prone to hallucination and limited knowledge retention, and large cloud-based language models plagued by privacy risks and high inference costs. To bridge this gap, we introduce ChemCRAFT, a novel framework leveraging agentic reinforcement learning to decouple chemical reasoning from knowledge storage. Instead of forcing the model to memorize vast chemical data, our approach empowers the language model to interact with a sandbox for precise information retrieval. This externalization of knowledge allows a locally deployable small model to achieve superior performance with minimal inference costs. To enable small language models for agent-calling ability, we build an agentic trajectory construction pipeline and a comprehensive chemical-agent sandbox. Based on sandbox interactions, we constructed ChemToolDataset, the first large-scale chemical tool trajectory dataset. Simultaneously, we propose SMILES-GRPO to build a dense chemical reward function, promoting the model's ability to call chemical agents. Evaluations across diverse aspects of drug design show that ChemCRAFT outperforms current cloud-based LLMs in molecular structure analysis, molecular optimization, and synthesis pathway prediction, demonstrating that scientific reasoning is not solely an emergent ability of model scale, but a learnable policy of tool orchestration. This work establishes a cost-effective and privacy-preserving paradigm for AI-aided chemistry, opening new avenues for accelerating molecular discovery with locally deployable agents. Code available at https://github.com/HowardLi1984/ChemCraft. Language model ; Reinforcement learning ; Abstraction ; Inference ; Pipeline (software) ; Encoding (memory) ; Tirapazamine ; Sandbox (software development) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Alexander Zhao | Achuth Chandrasekhar | Amir Barati Farimani
Source: arXiv (Cornell University) (preprint, 2026-04-02)
Field: Materials Science — Materials Chemistry

PolyJarvis: LLM Agent for Autonomous Polymer MD Simulations All-atom molecular dynamics (MD) simulations can predict polymer properties from molecular structure, yet their execution requires specialized expertise in force field selection, system construction, equilibration, and property extraction. We present PolyJarvis, an agent that couples a large language model (LLM) with the RadonPy simulation platform through Model Context Protocol (MCP) servers, enabling end-to-end polymer property prediction from natural language input. Given a polymer name or SMILES string, PolyJarvis autonomously executes monomer construction, charge assignment, polymerization, force field parameterization, GPU-accelerated equilibration, and property calculation. Validation is conducted on polyethylene (PE), atactic polystyrene (aPS), poly(methyl methacrylate) (PMMA), and poly(ethylene glycol) (PEG). Results show density predictions within 0.1--4.8% and bulk moduli within 17--24% of reference values for aPS and PMMA. PMMA glass transition temperature (Tg) (395~K) matches experiment within +10--18~K, while the remaining three polymers overestimate Tg by +38 to +47K (vs upper experimental bounds). Of the 8 property--polymer combinations with directly comparable experimental references, 5 meet strict acceptance criteria. For cases lacking suitable amorphous-phase experimental, agreement with prior MD literature is reported separately. The remaining Tg failures are attributable primarily to the intrinsic MD cooling-rate bias rather than agent error. This work demonstrates that LLM-driven agents can autonomously execute polymer MD workflows producing results consistent with expert-run simulations. Polystyrene ; Polymer ; Context (archaeology) ; Molecular dynamics ; Property (philosophy) ; Work (physics) ; Force field (fiction) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Alexander Zhao | Achuth Chandrasekhar | Amir Barati Farimani
Source: arXiv (Cornell University) (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

PolyJarvis: LLM Agent for Autonomous Polymer MD Simulations All-atom molecular dynamics (MD) simulations can predict polymer properties from molecular structure, yet their execution requires specialized expertise in force field selection, system construction, equilibration, and property extraction. We present PolyJarvis, an agent that couples a large language model (LLM) with the RadonPy simulation platform through Model Context Protocol (MCP) servers, enabling end-to-end polymer property prediction from natural language input. Given a polymer name or SMILES string, PolyJarvis autonomously executes monomer construction, charge assignment, polymerization, force field parameterization, GPU-accelerated equilibration, and property calculation. Validation is conducted on polyethylene (PE), atactic polystyrene (aPS), poly(methyl methacrylate) (PMMA), and poly(ethylene glycol) (PEG). Results show density predictions within 0.1--4.8% and bulk moduli within 17--24% of reference values for aPS and PMMA. PMMA glass transition temperature (Tg) (395~K) matches experiment within +10--18~K, while the remaining three polymers overestimate Tg by +38 to +47K (vs upper experimental bounds). Of the 8 property--polymer combinations with directly comparable experimental references, 5 meet strict acceptance criteria. For cases lacking suitable amorphous-phase experimental, agreement with prior MD literature is reported separately. The remaining Tg failures are attributable primarily to the intrinsic MD cooling-rate bias rather than agent error. This work demonstrates that LLM-driven agents can autonomously execute polymer MD workflows producing results consistent with expert-run simulations. Polystyrene ; Polymer ; Context (archaeology) ; Molecular dynamics ; Property (philosophy) ; Work (physics) ; Force field (fiction) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Mahule Roy | Adib Bazgir | Arthur da Silva Sousa Santos | Yuwen Zhang
Source: arXiv (Cornell University) (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

Autonomous Multi-Agent AI for High-Throughput Polymer Informatics: From Property Prediction to Generative Design Across Synthetic and Bio-Polymers We present an integrated multiagent AI ecosystem for polymer discovery that unifies high-throughput materials workflows, artificial intelligence, and computational modeling within a single Polymer Research Lifecycle (PRL) pipeline. The system orchestrates specialized agents powered by state-of-the-art large language models (DeepSeek-V2 and DeepSeek-Coder) to retrieve and reason over scientific resources, invoke external tools, execute domain-specific code, and perform metacognitive self-assessment for robust end-to-end task execution. We demonstrate three practical capabilities: a high-fidelity polymer property prediction and generative design pipeline, a fully automated multimodal workflow for biopolymer structure characterization, and a metacognitive agent framework that can monitor performance and improve execution strategies over time. On a held-out test set of 1,251 polymers, our PolyGNN agent achieves strong predictive accuracy, reaching R2 = 0.89 for glass-transition temperature (Tg ), R2 = 0.82 for tensile strength, R2 = 0.75 for elongation, and R2 = 0.91 for density. The framework also provides uncertainty estimates via multiagent consensus and scales with linear complexity to at least 10,000 polymers, enabling high-throughput screening at low computational cost. For a representative workload, the system completes inference in 16.3 s using about 2 GB of memory and 0.1 GPU hours, at an estimated cost of about $0.08. On a dedicated Tg benchmark, our approach attains R2 = 0.78, outperforming strong baselines including single-LLM prediction (R2 = 0.67), group-contribution methods (R2 = 0.71), and ChemCrow (R2 = 0.66). We further demonstrate metacognitive control in a polystyrene case study, where the system not only produces domain-level scientific outputs but continually monitors and optimizes its own behavior through tactical, strategic, and meta-strategic self-assessment. Task (project management) ; Inference ; Set (abstract data type) ; Workflow ; Property (philosophy) ; Data-driven ; Generative model ; Generative grammar Machine Learning in Materials Science ; Scientific Computing and Data Management ; Model Reduction and Neural Networks
Authors: Mahule Roy | Adib Bazgir | Arthur da Silva Sousa Santos | Yuwen Zhang
Source: Open MIND (preprint, 2026-01-25)
Field: Materials Science — Materials Chemistry

Autonomous Multi-Agent AI for High-Throughput Polymer Informatics: From Property Prediction to Generative Design Across Synthetic and Bio-Polymers We present an integrated multiagent AI ecosystem for polymer discovery that unifies high-throughput materials workflows, artificial intelligence, and computational modeling within a single Polymer Research Lifecycle (PRL) pipeline. The system orchestrates specialized agents powered by state-of-the-art large language models (DeepSeek-V2 and DeepSeek-Coder) to retrieve and reason over scientific resources, invoke external tools, execute domain-specific code, and perform metacognitive self-assessment for robust end-to-end task execution. We demonstrate three practical capabilities: a high-fidelity polymer property prediction and generative design pipeline, a fully automated multimodal workflow for biopolymer structure characterization, and a metacognitive agent framework that can monitor performance and improve execution strategies over time. On a held-out test set of 1,251 polymers, our PolyGNN agent achieves strong predictive accuracy, reaching R2 = 0.89 for glass-transition temperature (Tg ), R2 = 0.82 for tensile strength, R2 = 0.75 for elongation, and R2 = 0.91 for density. The framework also provides uncertainty estimates via multiagent consensus and scales with linear complexity to at least 10,000 polymers, enabling high-throughput screening at low computational cost. For a representative workload, the system completes inference in 16.3 s using about 2 GB of memory and 0.1 GPU hours, at an estimated cost of about $0.08. On a dedicated Tg benchmark, our approach attains R2 = 0.78, outperforming strong baselines including single-LLM prediction (R2 = 0.67), group-contribution methods (R2 = 0.71), and ChemCrow (R2 = 0.66). We further demonstrate metacognitive control in a polystyrene case study, where the system not only produces domain-level scientific outputs but continually monitors and optimizes its own behavior through tactical, strategic, and meta-strategic self-assessment. Task (project management) ; Inference ; Set (abstract data type) ; Workflow ; Property (philosophy) ; Data-driven ; Generative model ; Generative grammar Machine Learning in Materials Science ; Scientific Computing and Data Management ; Model Reduction and Neural Networks
Authors: Astha Yadav
Source: ChemistrySelect (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Accelerating Materials Discovery: Opportunities and Challenges ABSTRACT Artificial Intelligence integration in materials science has revolutionized discovery processes, accelerating timelines by 10–100× and identifying over 2.2 million stable inorganic materials through machine learning frameworks. This review examines current AI paradigms—foundation models, quantum machine learning, and autonomous laboratories—while addressing implementation challenges. Four transformative innovations are reshaping materials discovery: graph neural networks achieving &gt;90% accuracy in thermoelectric predictions, generative models enabling inverse design, natural language processing extracting protocols from 100,000+ papers, and autonomous laboratories with 71% synthesis validation success rates. These advances leverage large‐scale databases (Materials Project, NOMAD, OQMD), computational infrastructure, and algorithms spanning supervised learning, reinforcement learning, and deep generative models. Critical case studies include DeepMind's GNoME discovering 380,000 stable materials and Microsoft's MatterGen platform for crystal structure generation. However, persistent challenges remain: data scarcity affects 60% of materials classes, algorithmic bias exists in training datasets, and black‐box limitations restrict scientific insight. Emerging trends—physics‐informed machine learning, multimodal AI, federated learning, and human‐AI collaboration—promise further acceleration. This review provides state‐of‐the‐art assessment and actionable insights for navigating AI‐driven materials discovery transformation, emphasizing the paradigm shift from traditional trial‐and‐error approaches to predictive, autonomous research methodologies that fundamentally reshape scientific investigation and innovation processes. Transformative learning ; Generative grammar ; Leverage (statistics) ; Applications of artificial intelligence ; Big data ; Artificial neural network ; Scientific discovery ; Robotics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Peter V. Coveney | Roger R. Highfield
Source: Frontiers in Physics (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

AI needs physics more than physics needs AI Artificial intelligence (AI) is commonly depicted as transformative. Yet, after more than a decade of hype, its measurable impact remains modest outside a few high-profile scientific and commercial successes. The 2024 Nobel Prizes in Chemistry and Physics recognized AI’s potential, but broader assessments indicate the impact to date is often more promotional than technical. We argue that while current AI may influence physics, physics has significantly more to offer this generation of AI. Current architectures—large language models, reasoning models, and agentic AI–can depend on trillions of meaningless parameters, suffer from distributional bias, lack uncertainty quantification, provide no mechanistic insights, and fail to capture even elementary scientific laws. We review critiques of these limits, highlight opportunities in quantum AI and analogue computing, and lay down a roadmap for the adoption of ‘Big AI’: a synthesis of theory-based rigour with the flexibility of machine learning. Rigour ; Flexibility (engineering) ; Applications of artificial intelligence ; Thought experiment Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Explainable Artificial Intelligence (XAI)
Authors: Aivett Bilbao
Source: International Journal of Mass Spectrometry (article, 2026-03-09)
Field: Chemistry — Spectroscopy

From raw signals to AI and omics-agnostic algorithms for multidimensional mass spectrometry Mass spectrometry ; Chemometrics ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Multidimensional systems Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications