jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liang Yan | Xu Jiang | Jian Ma | Yuhang Liu | Tian Bian | Qichao Wang | Asit P. Basu | Yu Rong | Tingyang Xu | Pengcheng Wu | Le Song | Imran Razzak | Junchi Yan | Zengfeng Huang | Yutong Xie
Source: Preprints.org (preprint, 2026-02-27)
Field: Materials Science — Materials Chemistry

A Comprehensive Survey of Multimodal LLMs for Scientific Discovery Recent advances in artificial intelligence (AI), especially large language models, have accelerated the integration of multimodal data in scientific research. Given that scientific fields involve diverse data types, ranging from text and images to complex biological sequences, graphs, and structures, multimodal large language models (MLLMs) have emerged as powerful tools to bridge these modalities, enabling more comprehensive data analysis and intelligent decision-making. This work, S3-Bench, provides a comprehensive overview of recent advances in MLLMs, focusing on their diverse applications across science. We systematically review the progress of MLLMs in key scientific domains, including drug discovery, molecular &amp;amp; protein design, materials science, and genomics. The work highlights model architectures, domain-specific adaptations, benchmark datasets, and promising future directions. More importantly, we benchmarked open-source MLLMs on a range of critical molecular and protein property prediction tasks. Our work aims to serve as a valuable resource for both researchers and practitioners interested in the rapidly evolving landscape of multimodal AI for science. Key (lock) ; Bridge (graph theory) ; Benchmark (surveying) ; Scientific literature ; Resource (disambiguation) ; Multimodality Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: Ethan Holbrook | Juan C. Verduzco | Alejandro Strachan
Source: ArXiv.org (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

Evaluating LLM-generated code for domain-specific languages: molecular dynamics with LAMMPS Large language models (LLMs) are changing the way researchers interact with code and data in scientific computing. While their ability to generate general-purpose code is well established, their effectiveness in producing scientifically valid code/input scripting for domain-specific languages (DSLs) remains largely unexplored. We propose an evaluation procedure that enables domain experts (who may not be experts in the DSL) to assess the validity of LLM-generated input files for LAMMPS, a widely used molecular dynamics (MD) code, and to use those assessments to evaluate the performance of state-of-the-art LLMs and identify common issues. Key to the evaluation procedure are a normalization step to generate canonical files and an extensible parser for syntax analysis. The following steps isolate common errors without incurring costly tests (in time and computational resources). Once a working input file is generated, LLMs can accelerate verification tests. Our findings highlight limitations of LLMs in generating scientific DSLs and a practical path forward for their integration into domain-specific computational ecosystems by domain experts. Scripting language ; Parsing ; Domain (mathematical analysis) ; Code (set theory) ; Key (lock) ; Syntax ; Normalization (sociology) ; Compiler Machine Learning in Materials Science ; Natural Language Processing Techniques ; Model-Driven Software Engineering Techniques
Authors: Arjun Sundaram | Mohit Chakraborty | Sai Mani Kumar Devathi | B. Pabitramohan Prusty | Rohit Batra
Source: ArXiv.org (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design The design of sustainable materials requires access to materials performance and sustainability data from literature corpus in an organized, structured and automated manner. Natural language processing approaches, particularly large language models (LLMs), have been explored for materials data extraction from the literature, yet often suffer from limited accuracy or narrow scope. In this work, an LLM-based pipeline is developed to accurately extract alloy-related information from both textual descriptions and tabular data across the literature on high-entropy (or multicomponent) alloys (HEA). Specifically two databases with 37,711 and 148,069 entries respectively are retrieved; one from the literature text, consisting of alloy composition, processing conditions, characterization methods, and reported properties, and other from the literature tables, consisting of property names, values, and units. The pipeline enhances materials-domain sensitivity through prompt engineering and retrieval-augmented generation and achieves F1-scores of 0.83 for textual extraction and 0.88 for tabular extraction, surpassing or matching existing approaches. Application of the pipeline to over 10,000 articles yields the largest publicly available multicomponent alloy database and reveals compositional and processing-property trends. The database is further employed for sustainability-aware materials selection in three application domains, i.e., lightweighting, soft magnetic, and corrosion-resistant, identifying multicomponent alloy candidates with more sustainable production while maintaining or exceeding benchmark performance. The pipeline developed can be easily generalized to other class of materials, and assist in development of comprehensive, accurate and usable databases for sustainable materials design. Pipeline (software) ; USable ; Data extraction ; Benchmark (surveying) ; Matching (statistics) ; Information extraction ; Pipeline transport ; Class (philosophy) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Material Selection and Properties
Authors: Aritra Roy | Enrico Grisan | John Buckeridge | Chiara Gattinoni
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

ComProScanner: a multi-agent based framework for composition-property structured data extraction from scientific literature ComProScanner is an end-to-end multi-agentic autonomous platform that extracts, validates and visualises machine-readable compositions, properties and synthesis data from journal articles via Text and Data Mining APIs for database creation. Information extraction ; Scientific literature ; Semi-structured data ; Data extraction ; Knowledge extraction ; Data collection Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Sebastian Pagel | Michael Jirásek | Leroy Cronin
Source: Communications Chemistry (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Verification and execution of the scientific literature via chemputation augmented by large language models Chemputation is the process of programming chemical robots to do experiments using a universal symbolic language, but literature can be error-prone and hard to read due to ambiguities. Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains, including natural language processing, robotic control, and more recently, chemistry. Despite significant advancements in standardizing synthetic chemistry data, automatic reproduction and verification of reported syntheses remains labor-intensive task. We introduce an LLM-based chemical research agent workflow for automatic verification of synthetic literature procedures. Our workflow can autonomously extract synthetic procedures and analytical data from extensive documents, translate these procedures into universal XDL code, simulate the execution in a hardware-specific setup, and ultimately execute the procedure on an XDL-controlled robotic system for synthetic chemistry to confirm the procedure works in the real world. This demonstrates the potential of LLM-based workflows for autonomous chemical synthesis with Chemputers. While recent LLM-based agents have demonstrated remarkable success in autonomous experiment planning, a robust workflow for the faithful digitization and verification of existing literature remains a challenge. Our approach bridges this gap by providing six realistic examples of syntheses directly executed from synthetic literature on two robotic platforms. Our workflow will significantly enhance automation in robotically driven synthetic chemistry research, streamline data extraction, improve the reproducibility, scalability, and safety of synthetic chemistry. Workflow ; Automation ; Synthetic data ; Process (computing) ; Digitization ; Automated reasoning Machine Learning in Materials Science ; Scientific Computing and Data Management ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Lianduan Zeng | Xiao Zhou | Xueru Zheng | Ning Gao | Lei Liu | Yunxuan Cao | Hongjian Chen | Zhongyang Wang | Tongxiang Fan
Source: ArXiv.org (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

The HTC-Claw: Automating Discovery through High-Throughput Computational Campaigns With the advancement of the Materials Genome Initiative, high-throughput computation has become central to accelerating materials discovery. However, conventional first-principles workflows are cumbersome and error-prone. Existing high-throughput tools, while efficient at batch job submission, lack intelligence: they cannot automatically plan tasks based on scientific objectives or dynamically adapt workflows according to intermediate results. To address these limitations, this paper proposes and implements HTC-Claw, an intelligent high-throughput computational platform built upon the OpenClaw framework. The key innovations of HTC-Claw are: 1) An agent-based framework for automatic decomposition of high-level research goals into parallelizable task sets; 2) A closed-loop execution engine that integrates real-time analysis and reporting; 3) Adaptive decision-making and workflow iteration capabilities based on intermediate results; and 4) A decoupled, modular architecture that separates the scheduling system from functional modules, enhancing extensibility and robustness. Case studies demonstrate that HTC-Claw enables an intelligent, end-to-end workflow from user intent to final reporting in materials exploration Workflow ; Modular design ; Workflow engine ; Workflow management system ; Task (project management) ; Extensibility ; Workflow technology ; Scheduling (production processes) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Yun-Wen Mao | Roman V. Krems
Source: ArXiv.org (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Bayesian Optimization in Chemical Compound Sub-Spaces using Low-Dimensional Molecular Descriptors Efficient optimization of molecules with targeted properties remains a significant challenge due to the vast size and discrete nature of chemical compound space. Conventional machine-learning-based optimization approaches typically require large datasets to construct accurate surrogate models, limiting their applicability in data-scarce settings. In this study, we present a Bayesian optimization (BO) framework that identifies optimal molecular structures with high precision using fewer than 2,000 training data points within a chemical subspace containing more than 133,000 molecules. The framework employs a low-dimensional and physics-informed molecular descriptor vector that facilitates data-efficient surrogate modelling and optimization. A key innovation of the proposed framework is a reliable inverse mapping scheme that translates optimized points in the descriptor space back into chemically valid molecular structures, thereby bridging continuous optimization and discrete molecular design. We demonstrate the effectiveness of our approach on the QM9 benchmark dataset, where the framework successfully identifies organic molecules with the target entropy and zero-point vibrational energy (ZPVE) values.For entropy optimization, our approach achieves a 100% success rate while requiring fewer than 1,000 molecular evaluations in more than 80% of test cases. For ZPVE, the success rate exceeds 80% for molecules containing more than two heavy atoms. These results highlight the critical role of low-dimensional, interpretable descriptors in enabling data-efficient optimization and robust inverse molecular design, and establish Bayesian optimization as a practical tool for molecular discovery in small-data regimes. Bayesian optimization ; Molecular descriptor ; Bayesian probability ; Chemical space ; Entropy (arrow of time) ; Subspace topology ; Benchmark (surveying) ; Inverse ; Optimization problem Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference
Authors: Songkai Liu | Yanqing Shen | Chi Zhang | Zhangli Hou | Xin Wang | Jianxi Luo | Zhinan Zhang
Source: Nature Communications (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

iDesignGPT enhances conceptual design via large language model agentic workflows Conceptual engineering system design faces challenges from traditional methods and emerging AI tools to fully address its inherently complex, dynamic, and creativity-driven demands. iDesignGPT is a framework that integrates large language models with established design methodologies to enable dynamic multi-agent collaboration for problem refinement, information gathering, design space exploration, and evaluation. By incorporating design metrics such as coverage, diversity, and novelty, iDesignGPT provides quantitative insights for early-stage conceptual design. Performance evaluations across six public design challenges show that iDesignGPT achieves competitive novelty and consistently higher originality and modularity than GPT-4o zero-shot, GPT-4o chain-of-thought and Deepseek-r1, based on metrics and expert assessments. Two controlled user studies show positive reception across profiles and, for novice designers, lower mental demand than human-only design and clearer design flow with iDesignGPT. These results establish iDesignGPT as a practical framework for integrating language-model agents with established engineering design methods, enabling metrics-driven support for conceptual design by both expert and novice designers. Workflow ; Modularity (biology) ; Novelty ; Conceptual design ; Conceptual model ; Originality ; Conceptual framework ; Engineering design process Machine Learning in Materials Science ; Design Education and Practice ; Artificial Intelligence in Healthcare and Education
Authors: Aobo Zhang
Source: Research Square (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

A Chemically-Aware Validation Framework for Benchmarking Large Language Models in Materials Synthesis Planning Soundness ; Benchmarking ; Benchmark (surveying) ; Trustworthiness ; Reliability (semiconductor) ; Development (topology) ; Rationality ; Quality (philosophy) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Heeseung Lee | Hyuk Jun Yoo | Hye Su Jang | Byeongho Park | Yang Jeong Park | Sang Soo Han
Source: Materials Horizons (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Toward self-driving laboratory 2.0 for chemistry and materials discovery The convergence of laboratory automation, artificial intelligence (AI), and data-driven science has catalyzed the emergence of self-driving laboratories (SDLs), autonomous platforms capable of designing, executing, and analyzing experiments with minimal human input. While early SDLs (SDL 1.0) demonstrated the feasibility of closed-loop discovery, their impact has been constrained by limited scope, poor interoperability, and reliance on human-curated heuristics. This review outlines the vision of SDL 2.0: a new generation of flexible, scalable, and collaborative discovery engines for chemistry and materials science. We discuss recent advances in modular hardware design, AI-driven decision-making including Bayesian optimization, computer vision, and large language models, and orchestration software that integrate scheduling, data management, and safety protocols. Building on these foundations, we propose six defining characteristics for SDL 2.0: interoperable, collaborative, generalizable, orchestrated, safe, and creative. Together, these features establish SDLs as globally networked platforms, enabling reproducible experimentation, accelerated innovation, and democratized access to advanced research infrastructure. By embedding modularity, AI reasoning, and community-driven standards into their core, SDLs 2.0 promise to transform not only how experiments are conducted, but also who can participate in and benefit from the accelerating pace of scientific discovery. Convergence (economics) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Fatemeh Pourgholamali | Vajihe Nejadshafiee
Source: ChemRxiv (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

A Systematic Review of Prompt Engineering Paradigms in Organic Chemistry: Mining, Prediction, and Model Architectures Large language models (LLMs) have emerged as transformative tools in scientific research, offering a powerful alternative to traditional, resource-intensive machine learning methods. By leveraging the vast knowledge encoded during pre-training, prompt engineering—the systematic design and optimization of input instructions—enables researchers to guide LLMs toward accurate and domain-specific outputs without updating model parameters. This review presents the first systematic examination of prompt engineering techniques within organic chemistry, focusing on two critical application areas: text mining and predictive tasks. We analyze the core paradigms of prompt engineering, including prompt design, prompt learning, and prompt tuning, and clarify terminological inconsistencies in the literature. The discussion is contextualized within the three principal LLM architectures (encoder-only, decoder-only, and encoder-decoder), with an evaluation of their respective performances on chemistry-related tasks. Transformative learning ; Principal (computer security) ; Systematic review ; Core (optical fiber) ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xiangsen Chen | Ruilong Wu | Yanyan Lan | Ting Ma | Yang Liu
Source: ArXiv.org (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization Despite deep learning's success in chemistry, its impact is hindered by a lack of interpretability and an inability to resolve activity cliffs, where minor structural nuances trigger drastic property shifts. Current representation learning, bound by the similarity principle, often fails to capture these structural-activity discontinuities. To address this, we introduce MolEvolve, an evolutionary framework that reformulates molecular discovery as an autonomous, look-ahead planning problem. Unlike traditional methods that depend on human-engineered features or rigid prior knowledge, MolEvolve leverages a Large Language Model (LLM) to actively explore and evolve a library of executable chemical symbolic operations. By utilizing the LLM to cold start and an Monte Carlo Tree Search (MCTS) engine for test-time planning with external tools (e.g. RDKit), the system self-discovers optimal trajectories autonomously. This process evolves transparent reasoning chains that translate complex structural transformations into actionable, human-readable chemical insights. Experimental results demonstrate that MolEvolve's autonomous search not only evolves transparent, human-readable chemical insights, but also outperforms baselines in both property prediction and molecule optimization tasks. Interpretability ; Executable ; Property (philosophy) ; Representation (politics) ; Monte Carlo tree search ; Process (computing) ; Evolutionary algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yan Lin | Jonas A. Finkler | Tao Du | Jilin Hu | Morten M. Smedskjaer
Source: arXiv (Cornell University) (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

AMShortcut: An Inference- and Training-Efficient Inverse Design Model for Amorphous Materials Amorphous materials are solids that lack long-range atomic order but possess complex short- and medium-range order. Unlike crystalline materials that can be described by unit cells containing few up to hundreds of atoms, amorphous materials require larger simulation cells with at least hundreds or often thousands of atoms. Inverse design of amorphous materials with probabilistic generative models aims to generate the atomic positions and elements of amorphous materials given a set of desired properties. It has emerged as a promising approach for facilitating the application of amorphous materials in domains such as energy storage and thermal management. In this paper, we introduce AMShortcut, an inference- and training-efficient probabilistic generative model for amorphous materials. AMShortcut enables accurate inference of diverse short- and medium-range structures in amorphous materials with only a few sampling steps, mitigating the need for an excessive number of sampling steps that hinders inference efficiency. AMShortcut can be trained once with all relevant properties and perform inference conditioned on arbitrary combinations of desired properties, mitigating the need for training one model for each combination. Experiments on three amorphous materials datasets with diverse structures and properties demonstrate that AMShortcut achieves its design goals. Amorphous solid ; Inference ; Probabilistic logic ; Set (abstract data type) ; Inverse ; Inverse problem Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Metallic Glasses and Amorphous Alloys
Authors: Fernando Arrais Romero Dias Lima | Anas Abdelrehim | Ashutosh Bharambe | Marius Micluța-Câmpeanu | Dhairya Gandhi | Venkateshprasad Bhat | Anshul Singhvi | Morten Piibeleht | Argimiro R. Secchi | Mauricio Bezerra de Souza | M. Enis Leblebici | Christopher Rackauckas | Idelfonso B. R. Nogueira
Source: Research Square (preprint, 2026-01-13)
Field: Materials Science — Materials Chemistry

Assessment of the Dyad Agentic Modeling Language in Process Systems Engineering Dyad ; Context (archaeology) ; Computation ; Process (computing) ; Calibration ; Representation (politics) ; Nonlinear system ; Population Machine Learning in Materials Science ; Process Optimization and Integration ; Crystallization and Solubility Studies