jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Juejing Liu | Haydn Anderson | Noah I. Waxman | Vsevolod Kovalev | Barbara Fisher | Elizabeth Li | Xiaofeng Guo
Source: ArXiv.org (preprint, 2025-07-09)
Field: Materials Science — Materials Chemistry

Thermodynamic Prediction Enabled by Automatic Dataset Building and Machine Learning New discoveries in chemistry and materials science, with increasingly expanding volume of requisite knowledge and experimental workload, provide unique opportunities for machine learning (ML) to take critical roles in accelerating research efficiency. Here, we demonstrate (1) the use of large language models (LLMs) for automated literature reviews, and (2) the training of an ML model to predict chemical knowledge (thermodynamic parameters). Our LLM-based literature review tool (LMExt) successfully extracted chemical information and beyond into a machine-readable structure, including stability constants for metal cation-ligand interactions, thermodynamic properties, and other broader data types (medical research papers, and financial reports), effectively overcoming the challenges inherent in each domain. Using the autonomous acquisition of thermodynamic data, an ML model was trained using the CatBoost algorithm for accurately predicting thermodynamic parameters (e.g., enthalpy of formation) of minerals. This work highlights the transformative potential of integrated ML approaches to reshape chemistry and materials science research. Transformative learning ; Stability (learning theory) ; Work (physics) ; Experimental data ; Enthalpy ; Chemical stability Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Shaolei Zhang | Ju Fan | Peili Fan | Guoliang Li | Xiaoyong Du
Source: ArXiv.org (preprint, 2025-10-19)
Field: Materials Science — Materials Chemistry

DeepAnalyze: Agentic Large Language Models for Autonomous Data Science Autonomous data science, from raw data sources to analyst-grade deep research reports, has been a long-standing challenge, and is now becoming feasible with the emergence of powerful large language models (LLMs). Recent workflow-based data agents have shown promising results on specific data tasks but remain fundamentally limited in achieving fully autonomous data science due to their reliance on predefined workflows. In this paper, we introduce DeepAnalyze-8B, the first agentic LLM designed for autonomous data science, capable of automatically completing the end-toend pipeline from data sources to analyst-grade deep research reports. To tackle high-complexity data science tasks, we propose a curriculum-based agentic training paradigm that emulates the learning trajectory of human data scientists, enabling LLMs to progressively acquire and integrate multiple capabilities in real-world environments. We also introduce a data-grounded trajectory synthesis framework that constructs high-quality training data. Through agentic training, DeepAnalyze learns to perform a broad spectrum of data tasks, ranging from data question answering and specialized analytical tasks to open-ended data research. Experiments demonstrate that, with only 8B parameters, DeepAnalyze outperforms previous workflow-based agents built on most advanced proprietary LLMs. The model, code, and training data of DeepAnalyze are open-sourced, paving the way toward autonomous data science. Pipeline (software) ; Raw data ; Trajectory ; Data modeling ; Deep learning ; Data-driven ; Big data ; Training set ; Language understanding Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling
Authors: Zhilong Song | Shuaihua Lu | Qionghua Zhou | Jinlan Wang
Source: Acta Physico-Chimica Sinica (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

T2MAT (text-to-material): A universal agent for generating material structures with goal properties from a single sentence Property (philosophy) ; Graph ; Sentence ; Transformer ; Artificial neural network ; Stability (learning theory) ; Reliability (semiconductor) Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Polyoxometalates: Synthesis and Applications
Authors: Shusuke Ito | Koki Muraoka | Akira Nakayama
Source: ChemRxiv (preprint, 2025-01-30)
Field: Materials Science — Materials Chemistry

Knowledge-informed molecular design for zeolite synthesis using ChatGPT towards human-machine collaboration The design of organic molecules lies at the heart of solving numerous chemistry-related challenges, necessitating effective collaboration between human intuition and computational power. This study demonstrates how general-purpose Large Language Models (LLMs) can facilitate the design of molecules, leveraging feedback from empirical knowledge through natural language. We used this approach to design organic structure-directing agents (OSDAs) that guide the crystallization of zeolites. In our computational workflow, the LLM proposes OSDA candidates that are evaluated by empirical knowledge and atomistic simulation. Feedback is then provided to the LLM in natural language to refine subsequent proposals, thus progressively enhancing the proposed OSDAs and promoting the exploration of chemical space. The predicted candidates encompassed experimentally validated OSDAs, structurally analogous ones, and novel ones with superior affinity scores, underscoring the robust capability of the LLM. The human-machine collaboration, utilizing natural language as the communication interface, holds potential for application in other molecular design tasks, including drug design. Workflow ; Intuition ; Computer science ; Chemical space ; Natural language ; Empirical research ; Human–computer interaction ; Knowledge management ; Artificial intelligence ; Software engineering ; Cognitive science ; Drug discovery ; Chemistry ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Mengsong Wu | Yafei Wang | Yidong Ming | Y. An | Yuwei Wan | Wenliang Chen | Binbin Lin | Yuqiang Li | Tong Xie | Dongzhan Zhou
Source: ArXiv.org (preprint, 2025-06-09)
Field: Materials Science — Materials Chemistry

CheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning Large language models (LLMs) have recently demonstrated promising capabilities in chemistry tasks while still facing challenges due to outdated pretraining knowledge and the difficulty of incorporating specialized chemical expertise. To address these issues, we propose an LLM-based agent that synergistically integrates 137 external chemical tools created ranging from basic information retrieval to complex reaction predictions, and a dataset curation pipeline to generate the dataset ChemToolBench that facilitates both effective tool selection and precise parameter filling during fine-tuning and evaluation. We introduce a Hierarchical Evolutionary Monte Carlo Tree Search (HE-MCTS) framework, enabling independent optimization of tool planning and execution. By leveraging self-generated data, our approach supports step-level fine-tuning (FT) of the policy model and training task-adaptive PRM and ORM that surpass GPT-4o. Experimental evaluations demonstrate that our approach significantly improves performance in Chemistry QA and discovery tasks, offering a robust solution to integrate specialized tools with LLMs for advanced chemical applications. All datasets and code are available at https://github.com/AI4Chem/ChemistryAgent . Pipeline (software) ; Tree (set theory) ; Code (set theory) ; Chemical space Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Li, Zongwei | Li, Zhonghang | Guo, Zirui | Ren, Xubin | Huang, Chao
Source: ArXiv.org (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

DeepCode: Open Agentic Coding Recent advances in large language models (LLMs) have given rise to powerful coding agents, making it possible for code assistants to evolve into code engineers. However, existing methods still face significant challenges in achieving high-fidelity document-to-codebase synthesis--such as scientific papers to code--primarily due to a fundamental conflict between information overload and the context bottlenecks of LLMs. In this work, we introduce DeepCode, a fully autonomous framework that fundamentally addresses this challenge through principled information-flow management. By treating repository synthesis as a channel optimization problem, DeepCode seamlessly orchestrates four information operations to maximize task-relevant signals under finite context budgets: source compression via blueprint distillation, structured indexing using stateful code memory, conditional knowledge injection via retrieval-augmented generation, and closed-loop error correction. Extensive evaluations on the PaperBench benchmark demonstrate that DeepCode achieves state-of-the-art performance, decisively outperforming leading commercial agents such as Cursor and Claude Code, and crucially, surpassing PhD-level human experts from top institutes on key reproduction metrics. By systematically transforming paper specifications into production-grade implementations comparable to human expert quality, this work establishes new foundations for autonomous scientific reproduction that can accelerate research evaluation and discovery. Search engine indexing ; Context (archaeology) ; Coding (social sciences) ; Source code ; Key (lock) ; Code (set theory) ; Implementation ; Debugging Machine Learning in Materials Science ; Topic Modeling ; Machine Learning and Algorithms
Authors: Li, Zongwei | Li, Zhonghang | Guo, Zirui | Ren, Xubin | Huang, Chao
Source: arXiv (Cornell University) (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

DeepCode: Open Agentic Coding Recent advances in large language models (LLMs) have given rise to powerful coding agents, making it possible for code assistants to evolve into code engineers. However, existing methods still face significant challenges in achieving high-fidelity document-to-codebase synthesis--such as scientific papers to code--primarily due to a fundamental conflict between information overload and the context bottlenecks of LLMs. In this work, we introduce DeepCode, a fully autonomous framework that fundamentally addresses this challenge through principled information-flow management. By treating repository synthesis as a channel optimization problem, DeepCode seamlessly orchestrates four information operations to maximize task-relevant signals under finite context budgets: source compression via blueprint distillation, structured indexing using stateful code memory, conditional knowledge injection via retrieval-augmented generation, and closed-loop error correction. Extensive evaluations on the PaperBench benchmark demonstrate that DeepCode achieves state-of-the-art performance, decisively outperforming leading commercial agents such as Cursor and Claude Code, and crucially, surpassing PhD-level human experts from top institutes on key reproduction metrics. By systematically transforming paper specifications into production-grade implementations comparable to human expert quality, this work establishes new foundations for autonomous scientific reproduction that can accelerate research evaluation and discovery. Search engine indexing ; Context (archaeology) ; Coding (social sciences) ; Source code ; Key (lock) ; Code (set theory) ; Implementation ; Debugging Machine Learning in Materials Science ; Topic Modeling ; Machine Learning and Algorithms
Authors: Mahesh. A. Bote | Naeem A. Shaikh | Shruti Pandit | A. C. Bhagali | Abdulrahim Mulla
Source: Journal of Mines Metals and Fuels (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

AI Driven Discovery and Design of Smart Materials Artificial Intelligence (AI) is revolutionising the discovery, characterisation, and application of smart materials. This review comprehensively explores AI’s role in smart materials research, focusing on Machine Learning (ML) for property prediction, generative models for inverse design, and autonomous research systems. AI advancements have accelerated the optimisation of materials like shape memory alloys, piezoelectric materials, and self-healing polymers, enhancing their functionality. However, challenges such as data quality, model interpretability, and scalability persist. This paper addresses these innovations, their limitations, and future directions for AI-driven materials science. Major Findings: AI-driven methods, including ML and generative models, significantly enhance smart material discovery by improving prediction accuracy and enabling inverse design. Autonomous systems streamline experimental processes, but data quality and model interpretability remain critical challenges. Emerging trends like foundation models and active learning promise further advancements. Interpretability ; Scalability ; Generative grammar ; Big data ; Smart material ; Property (philosophy) ; Quality (philosophy) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Labate, Anton Bulle | de Sousa, Valesca Moura | Fiorini, Sandro Rama | Azevedo, Leonardo Guerreiro | Thiago, Raphael Melo | da Silva, Viviane Torres
Source: arXiv (Cornell University) (preprint, 2025-11-27)
Field: Materials Science — Materials Chemistry

Solving Context Window Overflow in AI Agents Large Language Models (LLMs) have become increasingly capable of interacting with external tools, granting access to specialized knowledge beyond their training data - critical in dynamic, knowledge-intensive domains such as Chemistry and Materials Science. However, large tool outputs can overflow the LLMs' context window, preventing task completion. Existing solutions such as truncation or summarization fail to preserve complete outputs, making them unsuitable for workflows requiring the full data. This work introduces a method that enables LLMs to process and utilize tool responses of arbitrary length without loss of information. By shifting the model's interaction from raw data to memory pointers, the method preserves tool functionality, allows seamless integration into agentic workflows, and reduces token usage and execution time. The proposed method is validated on a real-world Materials Science application that cannot be executed with conventional workflows, and its effectiveness is demonstrated via a comparative analysis where both methods succeed. In this experiment, the proposed approach consumed approximately seven times fewer tokens than the traditional workflow. Workflow ; Automatic summarization ; Context (archaeology) ; Task (project management) ; Process (computing) ; Security token ; Window (computing) ; Abstraction Machine Learning in Materials Science ; Scientific Computing and Data Management ; Explainable Artificial Intelligence (XAI)
Authors: Labate, Anton Bulle | de Sousa, Valesca Moura | Fiorini, Sandro Rama | Azevedo, Leonardo Guerreiro | Thiago, Raphael Melo | da Silva, Viviane Torres
Source: ArXiv.org (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Solving Context Window Overflow in AI Agents Large Language Models (LLMs) have become increasingly capable of interacting with external tools, granting access to specialized knowledge beyond their training data - critical in dynamic, knowledge-intensive domains such as Chemistry and Materials Science. However, large tool outputs can overflow the LLMs' context window, preventing task completion. Existing solutions such as truncation or summarization fail to preserve complete outputs, making them unsuitable for workflows requiring the full data. This work introduces a method that enables LLMs to process and utilize tool responses of arbitrary length without loss of information. By shifting the model's interaction from raw data to memory pointers, the method preserves tool functionality, allows seamless integration into agentic workflows, and reduces token usage and execution time. The proposed method is validated on a real-world Materials Science application that cannot be executed with conventional workflows, and its effectiveness is demonstrated via a comparative analysis where both methods succeed. In this experiment, the proposed approach consumed approximately seven times fewer tokens than the traditional workflow. Workflow ; Automatic summarization ; Context (archaeology) ; Task (project management) ; Process (computing) ; Security token ; Window (computing) ; Abstraction Machine Learning in Materials Science ; Scientific Computing and Data Management ; Explainable Artificial Intelligence (XAI)
Authors: Straughair, Daniel
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-09)
Field: Materials Science — Materials Chemistry

Triadic Agent v7.0: A Universal Mechanistic Reasoning Engine Using NEG–NEU–POS Causal Decomposition Triadic Agent v7.0 is a domain-general mechanistic reasoning engine that decomposes any scientific process into three structured causal states: NEGATIVE (initial physical components), NEUTRAL (interaction mechanisms), and POSITIVE (realized physical conditions). This system enforces strict physical causality using a modular pipeline of large language model prompts and filtering steps. Triadic Agent converts narrative explanations into deterministic mechanistic structures by applying consistent constraints across physics, chemistry, biology, engineering, and astrophysics. The architecture uses one model to generate physically grounded explanations and another to extract components, mechanisms, and realized states with strict rule-based filtering. The result is a transparent, interpretable reasoning system that highlights causal gaps, inconsistencies, or missing mechanisms. This repository contains the full Python implementation of Triadic Agent v7.0, along with cross-domain examples demonstrating stable decomposition across diverse scientific phenomena such as Hawking radiation, stellar fusion, catalysis, neuron signaling, protein folding, transformers, rectifiers, and more. Triadic Agent aims to support scientific education, research transparency, mechanistic reasoning, and future AI systems designed for discovery, hypothesis generation, and structured scientific understanding. Causality (physics) ; Decomposition ; Process (computing) ; Modular design ; Non-monotonic logic ; Causal model ; Causal reasoning ; Narrative Machine Learning in Materials Science ; Scientific Computing and Data Management ; Philosophy and History of Science
Authors: Feifei Luo (300216) | Jinglang Zhang (20989405) | Qilong Wang (228726) | Chunpeng Yang (1504366)
Source: Figshare (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Leveraging PromptEngineering in Large Language Modelsfor Accelerating Chemical Research Artificial intelligence (AI) using large language models (LLMs) such as GPTs has revolutionized various fields. Recently, LLMs have also made inroads in chemical research even for users without expertise in coding. However, applying LLMs directly may lead to “hallucinations”, where the model generates unreliable or inaccurate information and is further exacerbated by limited data set and inherent complexity of chemical reports. To counteract this, researchers have suggested prompt engineering, which can convey human ideas formatively and unambiguously to LLMs and simultaneously improve LLMs’ reasoning capability. So far, prompt engineering remains underutilized in chemistry, with many chemists barely acquainted with its principle and techniques. In this Outlook, we delve into various prompt engineering techniques and illustrate relevant examples for extensive research from metal–organic frameworks and fast-charging batteries to autonomous experiments. We also elucidate the current limitations of prompt engineering with LLMs such as incomplete or biased outcomes and constraints imposed by closed-source limitations. Although LLM-assisted chemical research is still in its early stages, the application of prompt engineering will significantly enhance accuracy and reliability, thereby accelerating chemical research. Set (abstract data type) ; Human language ; Basic research ; Natural language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Shusuke Ito | Koki Muraoka | Akira Nakayama
Source: ChemRxiv (preprint, 2025-03-21)
Field: Materials Science — Materials Chemistry

Knowledge-Informed Molecular Design for Zeolite Synthesis Using General-Purpose Pretrained Large Language Models Toward Human-Machine Collaboration The design of organic molecules lies at the heart of solving numerous chemistry-related challenges, necessitating effective collaboration between human intuition and computational power. This study demonstrates how general-purpose Large Language Models (LLMs) can facilitate the design of molecules, leveraging feedback from empirical knowledge through natural language. We used this approach to design organic structure-directing agents (OSDAs) that guide the crystallization of zeolites. In our computational workflow, the LLM proposes OSDA candidates that are evaluated by empirical knowledge and atomistic simulation. Feedback is then provided to the LLM in natural language to refine subsequent proposals, thus progressively enhancing the proposed OSDAs and promoting the exploration of chemical space. The predicted candidates encompassed experimentally validated OSDAs, structurally analogous ones, and novel ones with superior affinity scores, underscoring the robust capability of the LLM. The human-machine collaboration, utilizing natural language as the communication interface, holds potential for application in other molecular design tasks. Computer science ; Zeolite ; Human–computer interaction ; Knowledge management ; Natural language processing ; Artificial intelligence ; Chemistry ; Biochemistry Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Haidi Wang | Yufan Yao | Hanwen Song | Xiaofeng Liu | Zhao Chen | Weiwei Chen | Weiduo Zhu | Zhongjun Li | Jinlong Yang
Source: ArXiv.org (preprint, 2025-06-08)
Field: Materials Science — Materials Chemistry

Accelerating Two-Dimensional Materials Research via a Universal Interatomic Potential and Large Language Model Agent Accurate interatomic potentials (IAPs) are essential for modeling the potential energy surfaces (PES) that govern atomic interactions in materials. However, most existing IAPs are developed for bulk materials and struggle to accurately and efficiently capture the diverse chemical environment of two-dimensional (2D) materials. This limitation poses a significant barrier to the large-scale design and simulation of emerging 2D systems. To address this challenge, we present a universal interatomic potential tailored for 2D materials. Our model is trained on a dataset comprising 327,062 structure-energy-force-stress mappings derived from 20,114 2D materials, spanning 89 chemical elements. The results show high predictive accuracy, with mean absolute errors of 6 meV/atom for energies, 80 meV/Åfor atomic forces, and 0.067 GPa for stress tensors. It demonstrates broad applicability across a range of atomistic tasks, including structural relaxation, lattice dynamics, molecular dynamics, material discovery, and so on. To further enhance usability and accessibility, we introduce an intelligent agent powered by a large language model (LLM), enabling natural language interaction for 2D materials property simulations. Our work provides not only a precise and universal IAP for 2D systems, but also an intelligent, user-friendly platform that enables high-throughput screening, property prediction, and theoretical exploration, thereby accelerating advances in 2D materials research. Interatomic potential ; Property (philosophy) ; Usability ; Range (aeronautics) ; Lattice (music) ; Work (physics) ; Molecular dynamics Machine Learning in Materials Science ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research