jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhe Chu | Ruotong Liao
Source: Preprints.org (preprint, 2025-10-28)
Field: Materials Science — Materials Chemistry

ChemST-LLM: A Multi-Modal Spatiotemporal Question-Answering System for Dynamic Defect-Performance Synergy in Catalysts The development of high-performance electrocatalysts for reactions such as the oxygen evolution reaction (OER) is limited by the complex, dynamic evolution of defects under operando conditions. Existing methods and large language models (LLMs) face challenges in integrating and interpreting the diverse spatiotemporal data generated by advanced characterization techniques. To overcome this, we propose ChemST-LLM, a multi-modal spatiotemporal question-answering framework designed to understand and explain the relationship between defect evolution and OER performance in high-entropy layered double hydroxides (LDHs). ChemST-LLM features specialized temporal and graph encoders, a gated cross-modal fusion module, and an instruction-tuned LLM core with retrieval-augmented generation. We construct ChemVac-STQA, a large-scale dataset of experimental trajectories and spatiotemporal QA pairs, to support training and evaluation. Experimental results show that ChemST-LLM outperforms strong baselines across spatiotemporal QA tasks, achieving higher accuracy and robustness, particularly in challenging out-of-distribution scenarios. This work demonstrates a new paradigm for data-driven understanding and accelerated discovery of advanced electrocatalysts. Construct (python library) ; Oxygen evolution ; Graph ; Core (optical fiber) ; Work (physics) ; Face (sociological concept) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Cyprien Bone | Matthew Walker | Kuangdai Leng | Luis M. Antunes | Ricardo Grau‐Crespo | Amil Aligayev | Jessica Dominguez | Keith T. Butler
Source: ArXiv.org (preprint, 2025-11-26)
Field: Materials Science — Materials Chemistry

Discovery and recovery of crystalline materials with property-conditioned transformers Generative models have recently shown great promise for accelerating the design and discovery of new functional materials. Conditional generation enhances this capacity by allowing inverse design, where specific desired properties can be requested during the generation process. However, conditioning of transformer-based approaches, in particular, is constrained by discrete tokenisation schemes and the risk of catastrophic forgetting during fine-tuning. This work introduces CrystaLLM-π (property injection), a conditional autoregressive framework that integrates continuous property representations directly into the transformer's attention mechanism. Two architectures, Property-Key-Value (PKV) Prefix attention and PKV Residual attention, are presented. These methods bypass inefficient sequence-level tokenisation and preserve foundational knowledge from unsupervised pre-training on Crystallographic Information Files (CIFs) as textual input. We establish the efficacy of these mechanisms through systematic robustness studies and evaluate the framework's versatility across two distinct tasks. First, for structure recovery, the model processes high-dimensional, heterogeneous X-ray diffraction patterns, achieving structural accuracy competitive with specialised models and demonstrating applications to experimental structure recovery and polymorph differentiation. Second, for materials discovery, the model is fine-tuned on a specialised photovoltaic dataset to generate novel, stable candidates validated by Density Functional Theory (DFT). It implicitly learns to target optimal band gap regions for high photovoltaic efficiency, demonstrating a capability to map complex structure-property relationships. CrystaLLM-π provides a unified, flexible, and computationally efficient framework for inverse materials design. Robustness (evolution) ; Boosting (machine learning) ; Inverse ; Transformer ; Forgetting ; Generative grammar ; Discriminative model ; Property (philosophy) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Block Copolymer Self-Assembly
Authors: Mingxu Zhang | Dazhong Shen | Qi Zhang | Ying Sun
Source: ArXiv.org (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

ChemATP: A Training-Free Chemical Reasoning Framework for Large Language Models Large Language Models (LLMs) exhibit strong general reasoning but struggle in molecular science due to the lack of explicit chemical priors in standard string representations. Current solutions face a fundamental dilemma. Training-based methods inject priors into parameters, but this static coupling hinders rapid knowledge updates and often compromises the model's general reasoning capabilities. Conversely, existing training-free methods avoid these issues but rely on surface-level prompting, failing to provide the fine-grained atom-level priors essential for precise chemical reasoning. To address this issue, we introduce ChemATP, a framework that decouples chemical knowledge from the reasoning engine. By constructing the first atom-level textual knowledge base, ChemATP enables frozen LLMs to explicitly retrieve and reason over this information dynamically. This architecture ensures interpretability and adaptability while preserving the LLM's intrinsic general intelligence. Experiments show that ChemATP significantly outperforms training-free baselines and rivals state-of-the-art training-based models, demonstrating that explicit prior injection is a competitive alternative to implicit parameter updates. Interpretability ; Prior probability ; Adaptability ; Face (sociological concept) ; String (physics) ; Language model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Jian Jiang | Daixin Li | Guilin Wang | Nicole Hayes | Ya-Zhou Shi | Huahai Qiu | Bengong Zhang | Tianshou Zhou | Guo‐Wei Wei
Source: Acta Pharmaceutica Sinica B (review, 2025-12-12)
Field: Materials Science — Materials Chemistry

A review of recent advances in generative artificial intelligence models for biomolecular sciences Generative artificial intelligence (AI) models, a class of AI techniques that learn data distributions to synthesize novel samples, have emerged as impactful tools across scientific disciplines. In recent years, these models have found extensive applications in fields such as natural language processing and biomedical sciences. Despite their growing influence, comprehensive reviews on the application of generative models in biomolecular sciences remain limited. In this review, we provide a systematic overview of recent advances in generative models applied to biomolecular sciences. We discuss several prominent generative architectures, including variational autoencoders, generative adversarial networks, and diffusion models, highlighting their applications in molecular design and bioinformatics. Additionally, we examine how these models contribute to critical challenges such as molecular property prediction and molecular generation. Finally, we discuss key challenges that remain in this field, including model interpretability, scalability, and the need for high-quality molecular datasets. We highlight emerging research directions that aim to overcome these limitations and propose strategies for improving the reliability and applicability of generative models in biomolecular problems. Through this review, our objective is to provide researchers with a comprehensive understanding of the current landscape of generative modeling in biomolecular sciences and to inspire further advancements in this interdisciplinary area. Generative grammar ; Generative model ; Key (lock) ; Class (philosophy) ; Generative Design Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Generative Adversarial Networks and Image Synthesis
Authors: Ashwin Srinivasan | Tirtharaj Dash | A. Baskar | Sanjay Kumar Dey | Mainak Banerjee
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-16)
Field: Chemistry — Spectroscopy

Identifying a logical specification and a program for an LLM-based generator of lead molecules Abstract Our interest is in the generation of “lead” molecules in early-stage drug design. Leads are small molecules (ligands) that can bind to a part of pre-specified target and also satisfy multiple physico-chemical constraints. We propose using techniques developed in Inductive Logic Programming (ILP) to identify a logical specification of feasible molecules; and then using this specification to construct a program that uses a large language model (LLM) to generate new molecules. We ensure the program constructed is correct, in the sense that every molecule generated by the program is feasible according the specification. Our focus is on contributing to on-going drug-discovery research on novel inhibitors for Dopamine β -hydroxylase (DBH), an enzyme that plays a pivotal role in several diseases related to the brain and the heart. We find molecules comparable in affinity to the latest generation drugs currently in clinical trials, and chemical assessment of synthesisablity of the molecules generated. For completeness, we also provide results obtained on the classic benchmark datasets used in recent work reported in [1]. Generator (circuit theory) ; Lead (geology) ; Computer science ; Programming language ; Physics ; Power (physics) ; Biology Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Anthony J. Hickey | C. C. Huang | Anil Misra | Vasanthakumar Balasubramanian | Brij M. Moudgil | Anthony D. Rosato | Luís Marcelo Tavares | Qiang Zhang | Wei‐Ning Wang
Source: KONA Powder and Particle Journal (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Perspectives: Past, Present, and Future Developments in Particle Science and Technology Members of the American Editorial Board of the KONA Powder and Particle Journal recently convened to consider current and future perspectives in the fields of particle science and technology. This brief overview presents their observations regarding current and emerging areas of interest in select domains of particle and powder technologies. The major areas of practical significance identified were particle characterization, milling, granular material handling, scale-up considerations, and particle design strategies for environmental and pharmaceutical applications. An attempt has been made to highlight the role of artificial intelligence (AI)/machine learning (ML) in generating robust modeling and simulations for designing particle and powder systems for future process and product innovations. Materials science ; Science, technology and society ; Engineering ethics ; Nanotechnology ; Engineering physics ; Engineering ; Sociology ; Social science Catalytic Processes in Materials Science
Authors: Liu, Gang | Alosious, Sobin | Mahajan, Subhamoy | Inae, Eric | Zhu, Yihan | Liu, Yuhan | Zhang, Renzheng | Xu, Jiaxin | Howard, Addison | Li, Ying | Luo, Tengfei | Jiang, Meng
Source: ArXiv.org (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Open Polymer Challenge: Post-Competition Report Machine learning (ML) offers a powerful path toward discovering sustainable polymer materials, but progress has been limited by the lack of large, high-quality, and openly accessible polymer datasets. The Open Polymer Challenge (OPC) addresses this gap by releasing the first community-developed benchmark for polymer informatics, featuring a dataset with 10K polymers and 5 properties: thermal conductivity, radius of gyration, density, fractional free volume, and glass transition temperature. The challenge centers on multi-task polymer property prediction, a core step in virtual screening pipelines for materials discovery. Participants developed models under realistic constraints that include small data, label imbalance, and heterogeneous simulation sources, using techniques such as feature-based augmentation, transfer learning, self-supervised pretraining, and targeted ensemble strategies. The competition also revealed important lessons about data preparation, distribution shifts, and cross-group simulation consistency, informing best practices for future large-scale polymer datasets. The resulting models, analysis, and released data create a new foundation for molecular AI in polymer science and are expected to accelerate the development of sustainable and energy-efficient materials. Along with the competition, we release the test dataset at https://www.kaggle.com/datasets/alexliu99/neurips-open-polymer-prediction-2025-test-data. We also release the data generation pipeline at https://github.com/sobinalosious/ADEPT, which simulates more than 25 properties, including thermal conductivity, radius of gyration, and density. Polymer ; Pipeline (software) ; Benchmark (surveying) ; Pipeline transport ; Scale (ratio) ; Core (optical fiber) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Suman Itani | Yibo Zhang | Ranjit Itani | Jiadong Zang
Source: ArXiv.org (preprint, 2025-12-11)
Field: Materials Science — Materials Chemistry

Large Language Models for Superconductor Discovery Large language models (LLMs) offer new opportunities for automated data extraction and property prediction across materials science, yet their use in superconductivity research remains limited. Here we construct a large experimental database of 78,203 records, covering 19,058 unique compositions, extracted from scientific literature using an LLM-driven workflow. Each entry includes chemical composition, critical temperature, measurement pressure, structural descriptors, and critical fields. We fine-tune several open-source LLMs for three tasks: (i) classifying superconductors vs. non-superconductors, (ii) predicting the superconducting transition temperature directly from composition or structure-informed inputs, and (iii) inverse design of candidate compositions conditioned on target Tc. The fine-tuned LLMs achieve performance comparable to traditional feature-based models and in some cases exceed them, while substantially outperforming their base versions and capturing meaningful chemical and structural trends. The inverse-design model generates chemically plausible compositions, including 28% novel candidates not seen in training. Finally, applying the trained predictors to the GNoME database identifies unreported materials with predicted Tc &gt; 10 K. Although unverified, these candidates illustrate how integrating an LLM-driven workflow can enable scalable hypothesis generation for superconductivity discovery. Workflow ; Superconductivity ; Scalability ; Property (philosophy) ; Construct (python library) ; Language model ; Experimental data ; Base (topology) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Inorganic Fluorides and Related Compounds
Authors: Ayesha Gull | Muhammad Usman Safder | Rania Elbadry | Zhang, Fan | Stoyanov, Veselin | Nakov, Preslav | Xie, Zhuohan
Source: arXiv (Cornell University) (preprint, 2025-11-03)
Field: Materials Science — Materials Chemistry

EngTrace: A Symbolic Benchmark for Verifiable Process Supervision of Engineering Reasoning Large Language Models (LLMs) are increasingly entering specialized, safety-critical engineering workflows governed by strict quantitative standards and immutable physical laws, making rigorous evaluation of their reasoning capabilities imperative. However, existing benchmarks such as MMLU, MATH, and HumanEval assess isolated cognitive skills, failing to capture the physically grounded reasoning central to engineering, where scientific principles, quantitative modeling, and practical constraints must converge. To enable verifiable process supervision in engineering, we introduce EngTrace, a symbolic benchmark comprising 90 templates across three major engineering branches, nine core domains and 20 distinct areas. Through domain-aware parameterization, we generate 1,350 unique, contamination-resistant test cases to stress-test generalization. Moving beyond outcome matching, we introduce a verifiable two-stage evaluation framework that uses a tiered protocol to validate intermediate reasoning traces alongside final answers through automated procedural checks and a heterogeneous AI Tribunal. Our evaluation of 24 leading LLMs reveals a distinct trade-off between numeric precision and trace fidelity, identifying a complexity cliff where abstract mathematical pre-training fails to translate into the integrative reasoning required for advanced engineering tasks. Verifiable secret sharing ; Workflow ; Process (computing) ; Benchmark (surveying) ; Qualitative reasoning ; Model-based reasoning ; TRACE (psycholinguistics) ; Automated reasoning Machine Learning in Materials Science ; Topic Modeling ; Explainable Artificial Intelligence (XAI)
Authors: Jan Vincent M. Madayag | Marcel Roy B. Domalanta | Reymark D. Maalihan | Eugene B. Caldona
Source: MRS Communications (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

Linking microscopy to predictive coating performance via machine learning Abstract Electron microscopy (EM) and machine learning are progressively utilized to quantify coating characteristics and performance by correlating micrographs and other features with models. Recent studies cover separate techniques, but a few integrate imaging, algorithms, and engineering targets by synthesis. Here, we examine how EM produces high-resolution images of morphology, interfaces, and defects, and how learning approaches convert these images into descriptors and models. We compare learning strategies, including unsupervised and classical image processing, deep learning with U Net for coating phase and pore segmentation, label-efficient EM methods, and tree-based models in predicting tribological response. This review also recommends using benchmark datasets with complete metadata, cross-lab validation, and hybrid training accompanied by expert evaluation. Looking forward, the goal is to guide researchers in developing predictive and prescriptive processes that accelerate coating formulation and processing. Graphical abstract Coating ; Benchmark (surveying) ; Microscopy ; Predictive modelling ; Phase (matter) ; Pattern recognition (psychology) ; Deep learning Corrosion Behavior and Inhibition ; Surface Modification and Superhydrophobicity ; Force Microscopy Techniques and Applications
Authors: W. Luo | Yi Yuan | Cheng‐Hui Li | Yue Zhao | Jing‐Lin Zuo
Source: ArXiv.org (preprint, 2025-12-03)
Field: Materials Science — Materials Chemistry

Generative Refinement:A New Paradigm for Determining Single Crystal Structures Directly from HKL Data Single-crystal X-ray diffraction (SC-XRD) is the gold standard technique to characterize crystal structures in solid state. Despite significant advances in automation for structure solution, the refinement stage still depends heavily on expert intervention and subjective judgment, limiting accessibility and scalability. Herein, we introduce RefrActor, an end-to-end deep learning framework that enables crystal structure determination directly from HKL data. By coupling a physics-informed reciprocal-space encoder (ReciEncoder) with a symmetry-aware diffusion-based generator (StruDiffuser), RefrActor produces fully refined atomic models without requiring initial structural guesses or manual input. Comprehensive evaluations on the GenRef-10k benchmark demonstrates that RefrActor achieves low R1-factors across diverse systems, including low-symmetry, light-atom, and heavy-atom crystals. Case studies further confirm that RefrActor can correctly resolve hydrogen positions, elemental assignments, and moderate disorder. This work establishes a new data-driven paradigm for autonomous crystallographic analysis, offering a foundation for fully automated, high-throughput crystal structure determination. Diffraction ; Single crystal ; Benchmark (surveying) ; Generator (circuit theory) ; Representation (politics) ; Crystal structure ; Automation ; Refracting telescope Machine Learning in Materials Science ; Enzyme Structure and Function ; X-ray Diffraction in Crystallography
Authors: Shuyuan Zhang | Alexei A. Lapkin
Source: Research Square (preprint, 2025-12-09)
Field: Materials Science — Materials Chemistry

Enhancing molecular property prediction via transformer with dual graph representation Interpretability ; Molecular graph ; Topology (electrical circuits) ; Graph ; Transformer ; Property (philosophy) ; Representation (politics) ; Benchmark (surveying) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Sanchit Kabra | Shobhnik Kriplani | Parshin Shojaee | Chandan K. Reddy
Source: arXiv (Cornell University) (preprint, 2025-11-13)
Field: Materials Science — Materials Chemistry

SURFACEBENCH: A Geometry-Aware Benchmark for Symbolic Surface Discovery Equation discovery from data is a central challenge in machine learning for science, which requires the recovery of concise symbolic expressions that govern complex physical and geometric phenomena. Recent large language model (LLM) approaches have shown promise in symbolic regression, yet existing benchmarks predominantly evaluate low-dimensional scalar functions and rely on string-level or regression-based metrics that fail to capture structural and geometric equivalence. We introduce SURFACEBENCH, the first geometry-aware benchmark for symbolic discovery of three-dimensional surfaces. Unlike scalar curve-fitting tasks, SURFACEBENCH targets surface-level reasoning, where multi-variable coupling, coordinate transformations, and geometric structure must be inferred directly from data. The benchmark comprises 183 analytically constructed, science-inspired surface equations across 15 categories and three representation paradigms: explicit, implicit, and parametric forms. Each task includes variable semantics and synthetically sampled 3D data, and is designed to stress symbolic composition, structural ambiguity, and representational non-uniqueness while mitigating memorization. To evaluate discovery quality, SURFACEBENCH incorporates symbolic equivalence checks with geometric metrics of the object-space (Chamfer and Hausdorff distances) and regression-based error measures, allowing evaluation of functional fidelity beyond algebraic syntax. Empirical evaluation across evolutionary, neural, and LLM-driven frameworks reveals that no current method achieves consistent performance across representation types, with LLM-based approaches exhibiting strong structural priors but limited robustness in parameter calibration and multi-equation reasoning.The code and data are available at this link: github.com/deep-symbolic-mathematics/surfacebench. Symbolic trajectory evaluation ; Robustness (evolution) ; Parametric statistics ; Benchmark (surveying) ; Representation (politics) ; Equivalence (formal languages) ; Data structure ; Geometric data analysis Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Graph Neural Networks
Authors: Hongteng Xu | Fanmeng Wang | Shan Mei | Wenxuan Guo | Hongshuai Wang | Qi Ou | Zhifeng Gao
Source: Research Square (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

Unifying Polymer Modeling and Design via a Conformation-Centric Generative Foundation Model Foundation (evidence) ; Generative grammar ; Construct (python library) ; Sequence (biology) ; Field (mathematics) ; Polymer ; Autoregressive model Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Generative Adversarial Networks and Image Synthesis