jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elena Zamaraeva | Christopher M. Collins | George R. Darling | Matthew S. Dyer | Bei Peng | Rahul Savani | Dmytro Antypov | Vladimir V. Gusev | Judith Clymo | Paul G. Spirakis | Matthew J. Rosseinsky
Source: White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (preprint, 2025-06-04)
Field: Materials Science — Materials Chemistry

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Geometry optimization of atomic structures is a common and crucial task in computational chemistry and materials design. Following the learning to optimize paradigm, we propose a new multi-agent reinforcement learning method called Multi-Agent Crystal Structure optimization (MACS) to address periodic crystal structure optimization. MACS treats geometry optimization as a partially observable Markov game in which atoms are agents that adjust their positions to collectively discover a stable configuration. We train MACS across various compositions of reported crystalline materials to obtain a policy that successfully optimizes structures from the training compositions as well as structures of larger sizes and unseen compositions, confirming its excellent scalability and zero-shot transferability. We benchmark our approach against a broad range of state-of-the-art optimization methods and demonstrate that MACS optimizes periodic crystal structures significantly faster, with fewer energy calculations, and the lowest failure rate. Reinforcement learning ; Benchmark (surveying) ; Crystal structure ; Crystal structure prediction ; Energy minimization ; Scalability ; Task (project management) ; Crystal (programming language) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Topology Optimization in Engineering
Authors: José Ángel Villarreal-Hernández | María Lucila Morales-Rodríguez | Nelson Rangel-Valdez | Laura Cruz–Reyes | Claudia Gómez-Santillán
Source: International Journal of Combinatorial Optimization Problems and Informatics. (article, 2025-07-14)
Field: Chemistry — Spectroscopy

Multi-Agent Architecture for Parameter Configuration in Computational Chemistry Chemists often rely on computational prediction techniques to optimise laboratory experiments. However, selecting and configuring these techniques can be a daunting task for those without a background in computer science. A potential solution arises when chemists collaborate with computer scientists to define and implement the prediction task. Based on this collaboration, we propose a novel architecture that reflects key aspects of this interaction: the expertise of computer scientists, their commitment to addressing the prediction task, and the discussions that facilitate consensus. Our proposed architecture enables chemists to generate solution proposals across the various stages of the prediction process, leveraging consensus-based decisions made by intelligent agents and incorporating a lazy learning approach. Architecture ; Computer science ; Computational science ; Chemistry ; Art Analytical Chemistry and Chromatography
Authors: M. Tacke | M. Busch | Kian P. Abdolazizi | Jonas F. Eichinger | Kevin Linka | Christian J. Cyron | Roland C. Aydin
Source: ArXiv.org (preprint, 2025-12-01)
Field: Materials Science — Materials Chemistry

Automating modeling in mechanics: LLMs as designers of physics-constrained neural networks for constitutive modeling of materials Large language model (LLM)-based agentic frameworks increasingly adopt the paradigm of dynamically generating task-specific agents. We suggest that not only agents but also specialized software modules for scientific and engineering tasks can be generated on demand. We demonstrate this concept in the field of solid mechanics. There, so-called constitutive models are required to describe the relationship between mechanical stress and body deformation. Constitutive models are essential for both the scientific understanding and industrial application of materials. However, even recent data-driven methods of constitutive modeling, such as constitutive artificial neural networks (CANNs), still require substantial expert knowledge and human labor. We present a framework in which an LLM generates a CANN on demand, tailored to a given material class and dataset provided by the user. The framework covers LLM-based architecture selection, integration of physical constraints, and complete code generation. Evaluation on three benchmark problems demonstrates that LLM-generated CANNs achieve accuracy comparable to or greater than manually engineered counterparts, while also exhibiting reliable generalization to unseen loading scenarios and extrapolation to large deformations. These findings indicate that LLM-based generation of physics-constrained neural networks can substantially reduce the expertise required for constitutive modeling and represent a step toward practical end-to-end automation. Artificial neural network ; Generalization ; Field (mathematics) ; Class (philosophy) ; Benchmark (surveying) ; Constitutive equation ; Extrapolation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Elizabeth A. Moore
Source: Computer Science Bulletin (article, 2025-12-28)
Field: Materials Science — Materials Chemistry

Generative Design of Polymer Formulations via Conditional Graph Diffusion with Feasibility Filters The inverse design of polymer formulations—identifying molecular structures and mixture ratios that satisfy specific property constraints—remains a grand challenge in materials informatics. Traditional combinatorial chemistry and high-throughput screening are often constrained by the vastness of the chemical space and the non-linear structure-property relationships inherent in macromolecular systems. In this work, we propose a novel generative framework based on Conditional Graph Diffusion with Feasibility Filters (CGD-FF). Unlike conventional variational autoencoders or generative adversarial networks, which often suffer from mode collapse or generate thermodynamically unstable structures, our approach models the generation process as a reverse diffusion process on molecular graphs. We introduce a conditional guidance mechanism that directs the denoising trajectory towards regions of the latent space associated with desired target properties, such as glass transition temperature and tensile strength. Furthermore, we integrate a physics-based feasibility filter that evaluates synthetic accessibility and thermodynamic miscibility in real-time during the generation phase. Our experimental results demonstrate that CGD-FF outperforms state-of-the-art baselines in generating valid, unique, and novel polymer structures while adhering to strict property targets. The integration of domain knowledge via feasibility filters significantly reduces the generation of chemically invalid candidates, bridging the gap between computational design and experimental realizability. Bridging (networking) ; Inverse ; Generative grammar ; Graph ; Process (computing) ; Filter (signal processing) ; Generative model ; Chemical space ; Process design Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Model Reduction and Neural Networks
Authors: Carl N. Edwards | Chi Han | Gyuhong Lee | Thao Nguyen | Sara Szymkuć | Chetan Kumar Prasad | Bowen Jin | Jiawei Han | Ying Diao | Ge Liu | Hao Peng | Bartosz A. Grzybowski | Martin D. Burke | Heng Ji
Source: arXiv (Cornell University) (preprint, 2025-05-18)
Field: Materials Science — Materials Chemistry

mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules Despite their ability to understand chemical knowledge, large language models (LLMs) remain limited in their capacity to propose novel molecules with desired functions (e.g., drug-like properties). In addition, the molecules that LLMs propose can often be challenging to make, and are almost never compatible with automated synthesis approaches. To better enable the discovery of functional small molecules, LLMs need to learn a new molecular language that is more effective in predicting properties and inherently synced with automated synthesis technology. Current molecule LLMs are limited by representing molecules based on atoms. In this paper, we argue that just like tokenizing texts into meaning-bearing (sub-)word tokens instead of characters, molecules should be tokenized at the level of functional building blocks, i.e., parts of molecules that bring unique functions and serve as effective building blocks for real-world automated laboratory synthesis. This motivates us to propose mCLM, a modular Chemical-Language Model that comprises a bilingual language model that understands both natural language descriptions of functions and molecular blocks. mCLM front-loads synthesizability considerations while improving the predicted functions of molecules in a principled manner. Experiments on FDA-approved drugs showed that mCLM is capable of significantly improving chemical functions. mCLM, with only 3B parameters, also achieves improvements in synthetic accessibility relative to 7 other leading generative AI methods including GPT-5. When tested on 122 out-of-distribution medicines using only building blocks/tokens that are compatible with automated modular synthesis, mCLM outperforms all baselines in property scores and synthetic accessibility. mCLM can also reason on multiple functions and iteratively self-improve to rescue drug candidates that failed late in clinical trials ("fallen angels"). Modular design ; Property (philosophy) ; Natural language ; Function (biology) ; Generative model ; Drug discovery ; Generative grammar Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Xin Zhang | Peiliang Zhang | Jingling Yuan | Lin Li
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Zero-Shot Learning for Materials Science Texts: Leveraging Duck Typing Principles Materials science text mining (MSTM), involving tasks like property extraction and synthesis action retrieval, is pivotal for advancing research by deriving critical insights from scientific literature. Descriptors, serving as essential task labels, often vary in meaning depending on researchers' usage purposes across different mining tasks. (e.g., 'Material' can refer to both synthesis components and participants in fuel cell experiment). This meaning difference makes it difficult for existing methods, fine-tuned to specific task, to handle the same descriptors in other tasks. To overcome above limitation, we propose MatDuck, a simple and effective approach for Zero-Shot MSTM by evoking material knowledge within Large Language Models (LLMs). Specifically, inspired by the Duck Typing principles in programming languages, we present a ClassDefinition-Style Descriptor generation method that evokes task-specific characteristics to address usage variation. Subsequently, we introduce code-style in-context learning for zero-shot tasks, reframing them into code to leverage LLMs' proficiency in code understanding. Extensive experiments on eight benchmark datasets demonstrate that MatDuck, as a plug-and-play approach, significantly improves the Zero-Shot MSTM performance of LLMs by an average of 11.3% across seven tasks. Zero (linguistics) ; Shot (pellet) ; One shot ; Typing ; Computer science ; Mathematics education ; Artificial intelligence ; Mathematics ; Engineering ; Materials science ; Linguistics ; Mechanical engineering ; Speech recognition ; Philosophy Nuclear Materials and Properties
Authors: Dong-Shan Jian | Xiang Li | Chenxu Yan | Hui-Wen Zheng | Ziyi Bian | Fang Ye | R. D. He | Jingtian Zhang | Chengnuo Meng | Meng, Ling-Shi | Baoping Gong | Sheng‐Qi Zhang | Yan-Qing Ma
Source: ArXiv.org (preprint, 2025-11-13)
Field: Materials Science — Materials Chemistry

Mastering Olympiad-Level Physics with Artificial Intelligence Olympiad-level physics problem-solving significantly challenges both humans and artificial intelligence (AI), as it requires integrating appropriate modeling, application of physical principles, and precise calculation within long reasoning processes. In this paper, we introduce LOCA (LOgical Chain Augmentation), an AI agent framework designed for complex physics reasoning. LOCA decomposes long reasoning into serialized atomic and verifiable steps, refining the solution through an augment-review loop. We evaluate LOCA on the 2025 Chinese Physics Olympiad (CPhO) theory examination, a rigorous testbed renowned for its depth and complexity. The framework achieves a near-perfect score of 313 out of 320 points, significantly surpassing the top human competitor and other baseline methods. Furthermore, LOCA attains a near-perfect score of 28.6 out of 30 on the IPhO 2025 examination, demonstrating its strong generalizability across different contexts. Our work points toward the development of trustworthy AI partners in both research and education. Testbed ; Generalizability theory ; Verifiable secret sharing ; Trustworthiness ; Artificial life ; On the fly Machine Learning in Materials Science ; Science Education and Pedagogy ; Computational Physics and Python Applications
Authors: Lorenzo Di Fruscia | Jana M. Weber
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Leveraging large language models for enzymatic reaction prediction and characterization We present a systematic study of multitask Large Language Models, fine-tuned to predict enzyme commission numbers and to perform forward and retrosynthesis of enzymatic reactions. Characterization (materials science) ; Language model ; Key (lock) ; Scale (ratio) ; Retrosynthetic analysis ; Representation (politics) Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Bioinformatics and Genomic Networks
Authors: Noah van der Vleuten | Amanda Nicole Flores | Saurabh Mathur | Maksim Rakitin | Tim Hopkins | Kevin G. Yager | Esther H. R. Tsai
Source: ArXiv.org (preprint, 2025-11-13)
Field: Materials Science — Materials Chemistry

EnvTrace: Simulation-Based Semantic Evaluation of LLM Code via Execution Trace Alignment -- Demonstrated at Synchrotron Beamlines Evaluating large language models (LLMs) for instrument control requires methods that go beyond standard, stateless algorithmic benchmarks, since the behavior of physical systems cannot be fully captured by unit tests alone. Here we introduce EnvTrace, a simulation-based method that evaluates execution traces to assess semantic code equivalence. EnvTrace is demonstrated with a beamline control-logic digital twin to facilitate the evaluation of instrument control code, with the digital twin itself also enabling the pre-execution validation of live experiments. Over 30 LLMs were evaluated using trace alignment to generate a multi-faceted score for functional correctness across key behavioral dimensions, showing that many top-tier models can approach human-level performance in rapid control-code generation. This is a first step toward a broader vision where LLMs and digital twins work symbiotically: LLMs providing intuitive control and agentic orchestration, and digital twins offering safe and high-fidelity environments, paving the way towards autonomous embodied AI. TRACE (psycholinguistics) ; Correctness ; Key (lock) ; Code (set theory) ; Executable ; Control (management) ; Embodied cognition ; Software Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Computational Physics and Python Applications
Authors: Michael Sun | Weize Yuan | Gang Liu | Wojciech Matusik | Jie Chen
Source: ArXiv.org (preprint, 2025-05-29)
Field: Materials Science — Materials Chemistry

Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages Recent data-efficient molecular generation approaches exploit graph grammars to introduce interpretability into the generative models. However, grammar learning therein relies on expert annotation or unreliable heuristics for algorithmic inference. We propose Foundation Molecular Grammar (FMG), which leverages multi-modal foundation models (MMFMs) to induce an interpretable molecular language. By exploiting the chemical knowledge of an MMFM, FMG renders molecules as images, describes them as text, and aligns information across modalities using prompt learning. FMG can be used as a drop-in replacement for the prior grammar learning approaches in molecular generation and property prediction. We show that FMG not only excels in synthesizability, diversity, and data efficiency but also offers built-in chemical interpretability for automated molecular discovery workflows. Code is available at https://github.com/shiningsunnyday/induction. Interpretability ; Generative grammar ; Heuristics ; Grammar ; Rule-based machine translation ; Graph ; Foundation (evidence) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Zehua Zhao | Zhixian Huang | Junren Li | Siyu Lin | Junting Zhou | Fengqi Cao | Kun Zou | Rui Ge | Tingting Long | Yuexiang Zhu | Luping Liu | Jie Zheng | Junnian Wei | Rong Zhu | Peng Zou | Wenyu Li | Zekai Cheng | Tian Ding | Yaxuan Wang | Yizhao Yan | Tong Wei | Hai Ming | Weimin Mao | Chen Sun | W. Liu | Zichen Wang | Zhang Zuo | Tong Yang | Hao Ma | Zhen Gao | Jian Pei
Source: ArXiv.org (preprint, 2025-12-01)
Field: Materials Science — Materials Chemistry

SUPERChem: A Multimodal Reasoning Benchmark in Chemistry Current benchmarks for evaluating the chemical reasoning capabilities of Large Language Models (LLMs) are limited by oversimplified tasks, lack of process-level evaluation, and misalignment with expert-level chemistry skills. To address these issues, we introduce SUPERChem, a benchmark of 500 expert-curated reasoning-intensive chemistry problems, covering diverse subfields and provided in both multimodal and text-only formats. Original content and an iterative curation pipeline eliminate flawed items and mitigate data contamination. Each problem is paired with an expert-authored solution path, enabling Reasoning Path Fidelity (RPF) scoring to evaluate reasoning quality beyond final-answer accuracy. Evaluations against a human baseline of 40.3% accuracy show that even the best-performing model, GPT-5 (High), reaches only 38.5%, followed closely by Gemini 2.5 Pro (37.9%) and DeepSeek-V3.1-Think (37.3%). SUPERChem elicits multi-step, multimodal reasoning, reveals model-dependent effects of visual information, and distinguishes high-fidelity reasoners from heuristic ones. By providing a challenging benchmark and a reliable evaluation framework, SUPERChem aims to facilitate the advancement of LLMs toward expert-level chemical intelligence. The dataset of the benchmark is available at https://huggingface.co/datasets/ZehuaZhao/SUPERChem. Benchmark (surveying) ; Heuristic ; Pipeline (software) ; Fidelity ; Baseline (sea) ; Path (computing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Nayantara Mudur | Hao Cui | Subhashini Venugopalan | Paul Raccuglia | Michael P. Brenner | Peter Norgaard
Source: ArXiv.org (preprint, 2025-04-08)
Field: Materials Science — Materials Chemistry

FEABench: Evaluating Language Models on Multiphysics Reasoning Ability Building precise simulations of the real world and invoking numerical solvers to answer quantitative problems is an essential requirement in engineering and science. We present FEABench, a benchmark to evaluate the ability of large language models (LLMs) and LLM agents to simulate and solve physics, mathematics and engineering problems using finite element analysis (FEA). We introduce a comprehensive evaluation scheme to investigate the ability of LLMs to solve these problems end-to-end by reasoning over natural language problem descriptions and operating COMSOL Multiphysics$^\circledR$, an FEA software, to compute the answers. We additionally design a language model agent equipped with the ability to interact with the software through its Application Programming Interface (API), examine its outputs and use tools to improve its solutions over multiple iterations. Our best performing strategy generates executable API calls 88% of the time. LLMs that can successfully interact with and operate FEA software to solve problems such as those in our benchmark would push the frontiers of automation in engineering. Acquiring this capability would augment LLMs' reasoning skills with the precision of numerical solvers and advance the development of autonomous systems that can tackle complex problems in the real world. The code is available at https://github.com/google/feabench Executable ; Benchmark (surveying) ; Automated reasoning ; Multiphysics ; Scheme (mathematics) ; Automation ; Software ; Interface (matter) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Topic Modeling
Authors: Shankar, Shashwat | Pandey, Subhranshu | Mochahari, Innocent Dengkhw | Mali, Bhabesh | Chowdhury, Animesh Basak | Bhattacharjee, Sukanta | Karfa, Chandan
Source: ArXiv.org (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

David vs. Goliath: Can Small Models Win Big with Agentic AI in Hardware Design? Large Language Model(LLM) inference demands massive compute and energy, making domain-specific tasks expensive and unsustainable. As foundation models keep scaling, we ask: Is bigger always better for hardware design? Our work tests this by evaluating Small Language Models coupled with a curated agentic AI framework on NVIDIA's Comprehensive Verilog Design Problems(CVDP) benchmark. Results show that agentic workflows: through task decomposition, iterative feedback, and correction - not only unlock near-LLM performance at a fraction of the cost but also create learning opportunities for agents, paving the way for efficient, adaptive solutions in complex design tasks. Task (project management) ; Verilog ; Inference ; Foundation (evidence) ; Language understanding ; Fraction (chemistry) Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Generative Adversarial Networks and Image Synthesis