jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gabriel Bianchin de Oliveira | Fahad Saeed
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

MolDeBERTa: Foundational Model for Physicochemical and Structural-Informed Molecular Representation Learning Foundational models that learn the "language" of molecules are essential for accelerating the material and drug discovery. These self-learning models can be trained on a large number of unlabelled molecules, enabling applications like property prediction, molecule design (de novo generation, optimization), and screening for specific functions. However, existing molecular language models are built upon first-generation transformer architectures and are pretrained using masked language modeling, a generic token-level objective that is agnostic to physicochemical and structural molecular properties. Here we introduce <i>MolDe-BERTa</i> , a structure-informed self-supervised molecular encoder that leverages a byte-level Byte-Pair Encoding (BPE) tokenization strategy. MolDeBERTa is pretrained on up to 123 million SMILES molecules from PubChem, representing one of the largest publicly available SMILES-based corpora. To achieve this, we introduce three novel pretraining objectives designed to inject strong inductive biases for molecular properties and structural similarity directly into the latent space, resulting in reduced gap between linguistic chemical representations and physical molecular properties. The model was then systematically investigated across three architectural scales, two dataset sizes, and five distinct pretraining objectives. MolDeBERTa when evaluated on 9 downstream MoleculeNet benchmarks outperformed existing masked language models, achieving up to a 16% reduction in regression error and improvements of up to 3.0 ROC-AUC points on classification benchmarks. MolDeBERTa advances unsupervised encoder-based foundational models at scale both for pretraining data and downstream evaluation, enabling data-efficient chemistry-informed representation learning. The source code is publicly available at https://github.com/pcdslab/MolDeBERTa , and Hugging Face at https://huggingface.co/collections/SaeedLab/moldeberta . All the pretraining datasets are available at https://huggingface.co/datasets/SaeedLab/MolDeBERTa . Encoder ; Encoding (memory) ; Representation (politics) ; Language model ; Inference ; Property (philosophy) ; Transformer ; Similarity (geometry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yiwen Gao | Ruochen Zhao | Yang Deng | Wenxuan Zhang
Source: ArXiv.org (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

DR-Arena: an Automated Evaluation Framework for Deep Research Agents As Large Language Models (LLMs) increasingly operate as Deep Research (DR) Agents capable of autonomous investigation and information synthesis, reliable evaluation of their task performance has become a critical bottleneck. Current benchmarks predominantly rely on static datasets, which suffer from several limitations: limited task generality, temporal misalignment, and data contamination. To address these, we introduce DR-Arena, a fully automated evaluation framework that pushes DR agents to their capability limits through dynamic investigation. DR-Arena constructs real-time Information Trees from fresh web trends to ensure the evaluation rubric is synchronized with the live world state, and employs an automated Examiner to generate structured tasks testing two orthogonal capabilities: Deep reasoning and Wide coverage. DR-Arena further adopts Adaptive Evolvement Loop, a state-machine controller that dynamically escalates task complexity based on real-time performance, demanding deeper deduction or wider aggregation until a decisive capability boundary emerges. Experiments with six advanced DR agents demonstrate that DR-Arena achieves a Spearman correlation of 0.94 with the LMSYS Search Arena leaderboard. This represents the state-of-the-art alignment with human preferences without any manual efforts, validating DR-Arena as a reliable alternative for costly human adjudication. Task (project management) ; Rubric ; Deep learning ; Controller (irrigation) ; Automation Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Topic Modeling
Authors: Martin Priessner | Richard J. Lewis | Magnus J. Johansson | Jonathan Goodman | Jon Paul Janet | Anna Tomberg
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Enhancing molecular structure elucidation with reasoning-capable LLMs We introduce a novel workflow that integrates reasoning-capable language models (LLMs) with chemical analysis tools to enhance molecular structure determination using NMR spectroscopy. Workflow ; Molecule ; Molecular model ; Protein structure ; Nuclear magnetic resonance spectroscopy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Jiahui Li | Liting Guo | Weiyi Cai | Juanjuan Mei | Jiayi Liu | Y Liu
Source: Drug Delivery (article, 2026-03-20)
Field: Materials Science — Biomaterials

Overcoming the blood‒brain barrier: nanomedicine strategies for targeted delivery and multimodal therapy in Alzheimer's disease Alzheimer's disease (AD) remains a significant therapeutic challenge, primarily because the formidable blood‒brain barrier (BBB), which drastically limits the brain bioavailability of most drugs. Nanoparticle-based drug delivery systems offer a promising strategy to overcome this central obstacle. This review systematically examines the design, mechanisms, and applications of nanomedicine in AD therapy. We analyze key strategies for enhancing BBB penetration through surface engineering and the utilization of various nanocarriers, including liposomes, exosomes, dendrimers, and carbon dots. Furthermore, we discuss how stimuli-responsive release mechanisms (e.g. responsive to pH, enzymes, reactive oxygen species, light, or ultrasound) enable targeted and precise drug delivery. A critical focus is placed on how these multifunctional nanoplatforms can address multiple AD pathogenic pathways simultaneously, such as amyloid-β and tau aggregation, cholinergic dysfunction, oxidative stress, neuroinflammation, and gut‒brain axis dysregulation. Although preclinical evidence is compelling, the clinical translation of these nanotherapies is hindered by challenges related to long-term biocompatibility, scalable manufacturing, patient heterogeneity, and regulatory frameworks. This review highlights the translational potential of nanomedicine in AD treatment while outlining the key hurdles that must be addressed for its successful implementation. Nanomedicine ; Drug delivery ; Disease ; Targeted drug delivery ; Drug ; Translational research ; Translational medicine ; Bioavailability Nanoparticle-Based Drug Delivery ; Barrier Structure and Function Studies ; Advanced Drug Delivery Systems
Authors: Julien Amblard | Niklas Groll | Matthew Tait | Mark Law | Gürkan Sin | Alessandra Russo
Source: ArXiv.org (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Failure Detection in Chemical Processes Using Symbolic Machine Learning: A Case Study on Ethylene Oxidation Over the past decade, Artificial Intelligence has significantly advanced, mostly driven by large-scale neural approaches. However, in the chemical process industry, where safety is critical, these methods are often unsuitable due to their brittleness, and lack of explainability and interpretability. Furthermore, open-source real-world datasets containing historical failures are scarce in this domain. In this paper, we investigate an approach for predicting failures in chemical processes using symbolic machine learning and conduct a feasibility study in the context of an ethylene oxidation process. Our method builds on a state-of-the-art symbolic machine learning system capable of learning predictive models in the form of probabilistic rules from context-dependent noisy examples. This system is a general-purpose symbolic learner, which makes our approach independent of any specific chemical process. To address the lack of real-world failure data, we conduct our feasibility study leveraging data generated from a chemical process simulator. Experimental results show that symbolic machine learning can outperform baseline methods such as random forest and multilayer perceptron, while preserving interpretability through the generation of compact, rule-based predictive models. Finally, we explain how such learned rule-based models could be integrated into agents to assist chemical plant operators in decision-making during potential failures. Interpretability ; Probabilistic logic ; Process (computing) ; Chemical process ; Context (archaeology) ; Artificial neural network ; Symbolic regression Machine Learning in Materials Science ; AI-based Problem Solving and Planning ; Fuzzy Logic and Control Systems
Authors: Runhua Gao | Hiroaki Tatsumi | Takanori Kobatake | Minoru Ueshima | Hiroshi Nishikawa
Source: Scientific Reports (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Study on pore features in sintered die-attach microstructures based on machine learning Low-temperature sintering of nano-sized metal particles is a promising approach for die-attach bonding, where the porous microstructure plays a critical role in determining bonding performance. However, the internal relationships among various microstructural features and their effectiveness in characterizing morphology remain unclear. In this study, a series of pore-related features were extracted from massive cross-sectional SEM images and analyzed using machine learning. Correlation analysis revealed mathematical relationships among features, and accordingly they can be categorized into two groups related to pore distribution and shape. Principal component analysis was employed to obtain representative but interpretable descriptors from the correlated features. Four machine learning models, including K-nearest neighbor, support vector machine, random forest, and artificial neural networks, were trained on the transformed dataset, achieving over 90% accuracy in classifying images captured from samples prepared under different conditions. In addition, a variational autoencoder-artificial neural network framework was constructed for comparison, demonstrating the effectiveness of the proposed physical feature extraction. Significance analysis based on the four models reveals the accuracy and pertinence of these descriptors in microstructure-property assessment. Artificial neural network ; Support vector machine ; Principal component analysis ; Microstructure ; Pattern recognition (psychology) ; Sintering ; Feature (linguistics) Machine Learning in Materials Science ; Injection Molding Process and Properties ; Advanced ceramic materials synthesis
Authors: Tatsuya Sagawa | Ryosuke Kojima
Source: ArXiv.org (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

How Well Do Large-Scale Chemical Language Models Transfer to Downstream Tasks? Chemical Language Models (CLMs) pre-trained on large scale molecular data are widely used for molecular property prediction. However, the common belief that increasing training resources such as model size, dataset size, and training compute improves both pretraining loss and downstream task performance has not been systematically validated in the chemical domain. In this work, we evaluate this assumption by pretraining CLMs while scaling training resources and measuring transfer performance across diverse molecular property prediction (MPP) tasks. We find that while pretraining loss consistently decreases with increased training resources, downstream task performance shows limited improvement. Moreover, alternative metrics based on the Hessian or loss landscape also fail to estimate downstream performance in CLMs. We further identify conditions under which downstream performance saturates or degrades despite continued improvements in pretraining metrics, and analyze the underlying task dependent failure modes through parameter space visualizations. These results expose a gap between pretraining based evaluation and downstream performance, and emphasize the need for model selection and evaluation strategies that explicitly account for downstream task characteristics. Downstream (manufacturing) ; Task (project management) ; Property (philosophy) ; Scaling ; Scale (ratio) ; Selection (genetic algorithm) ; Training (meteorology) ; Hessian matrix Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Atharva Sehgal | James Hou | Akanksha Sarkar | Ishaan Mantripragada | Swarat Chaudhuri | Jennifer J. Sun | Yisong Yue
Source: ArXiv.org (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Evaluating Agentic Optimization on Large Codebases Large language model (LLM) coding agents increasingly operate at the repository level, motivating benchmarks that evaluate their ability to optimize entire codebases under realistic constraints. Existing code benchmarks largely rely on synthetic tasks, binary correctness signals, or single-objective evaluation, limiting their ability to assess holistic optimization behavior. We introduce FormulaCode, a benchmark for evaluating agentic optimization on large, real-world codebases with fine-grained, multi-objective performance metrics. FormulaCode comprises 957 performance bottlenecks mined from scientific Python repositories on GitHub, each paired with expert-authored patches and, on average, 264.6 community-maintained performance workloads per task, enabling the holistic ability of LLM agents to optimize codebases under realistic correctness and performance constraints. Our evaluations reveal that repository-scale, multi-objective optimization remains a major challenge for frontier LLM agents. Project website at: https://formula-code.github.io Correctness ; Limiting ; Benchmark (surveying) ; Python (programming language) ; Source code ; Coding (social sciences) ; Code (set theory) Machine Learning in Materials Science ; Natural Language Processing Techniques ; Topic Modeling
Authors: Seong‐Hoon Jang | Di Zhang | Xue Jia | Hung Ba Tran | Linda Zhang | Ryuhei Sato | Yusuke Hashimoto | Toyoto Sato | K. Konno | Shin-ichi Orimo | Hao Li
Source: arXiv (Cornell University) (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Digital Hydrogen Platform (DigHyd): A Rigorously Curated Database for Hydrogen Storage Materials Empowered by AI-Assisted Literature Mining Solid-state hydrogen storage materials are promising candidates for safe and compact hydrogen storage; however, data-driven discovery in this field remains limited by the availability of large-scale, well-curated datasets. Here, we present the Digital Hydrogen Platform (DigHyd: www.dighyd.org), a rigorously curated database comprising $>4,000$ experimental literature sources and $>30,000$ data entries on hydrogen storage materials, constructed through AI-assisted literature mining combined with human-in-the-loop validation. In addition to gravimetric hydrogen storage density ($w$), DigHyd also covers thermodynamic parameters, specifically the enthalpy ($ΔH$) and entropy ($ΔS$) changes associated with hydrogenation reactions, primarily defined as $M + \frac{1}{2} {\rm H}_2 \rightleftarrows M{\rm H}$. These parameters were obtained by manually analyzing multi-temperature pressure-composition-temperature (PCT) data using van't Hoff analysis. By focusing on $ΔH$ and $ΔS$ rather than fixing equilibrium pressure at a single temperature, DigHyd enables flexible evaluation of equilibrium behavior under application-specific operating conditions. Statistical analyses reveal distinct distributions of thermodynamic parameters across material classes, together with broad compositional variability within representative hydride systems. Furthermore, both physically interpretable symbolic regression and black-box XGBoost models achieve comparable predictive performance for $w$ and equilibrium pressure at room temperature ($P_{\rm eq,RT}$), demonstrating internal consistency and learnable composition-property relationships within the curated dataset. Overall, DigHyd provides a rigorously curated thermodynamic dataset that serves as a reliable basis for data-driven analyses of hydrogen storage materials and supports systematic exploration of structure-property relationships. Hydrogen storage ; Consistency (knowledge bases) ; Hydrogen ; Enthalpy ; Field (mathematics) ; Gravimetric analysis Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion
Authors: Debjit Paul | Daniel Murphy | Milan Gritta | Ronald Cardenas | Victor Prokhorov | Lena Sophia Bolliger | Aysim Toker | Roy Miles | Andreea-Maria Oncescu | J. Sivakumar | Philipp Borchert | Ismail Elezi | Meiru Zhang | Ka Yiu Lee | Guchun Zhang | Jun Wang | Gerasimos Lampouras
Source: arXiv (Cornell University) (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

A Benchmark for Deep Information Synthesis Large language model (LLM)-based agents are increasingly used to solve complex tasks involving tool use, such as web browsing, code execution, and data analysis. However, current evaluation benchmarks do not adequately assess their ability to solve real-world tasks that require synthesizing information from multiple sources and inferring insights beyond simple fact retrieval. To address this, we introduce DEEPSYNTH, a novel benchmark designed to evaluate agents on realistic, time-consuming problems that combine information gathering, synthesis, and structured reasoning to produce insights. DEEPSYNTH contains 120 tasks collected across 7 domains and data sources covering 67 countries. DEEPSYNTH is constructed using a multi-stage data collection pipeline that requires annotators to collect official data sources, create hypotheses, perform manual analysis, and design tasks with verifiable answers. When evaluated on DEEPSYNTH, 11 state-of-the-art LLMs and deep research agents achieve a maximum F1 score of 8.97 and 17.5 on the LLM-judge metric, underscoring the difficulty of the benchmark. Our analysis reveals that current agents struggle with hallucinations and reasoning over large information spaces, highlighting DEEPSYNTH as a crucial benchmark for guiding future research. Benchmark (surveying) ; Pipeline (software) ; Code (set theory) ; Source code ; Simple (philosophy) ; Verifiable secret sharing Machine Learning in Materials Science ; Topic Modeling ; Artificial Intelligence in Healthcare and Education
Authors: Wei-Po Hsin | Ren-Hao Deng | Yao-Ting Hsieh | En-Ming Huang | Shih-Hao Hung
Source: ArXiv.org (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

EvolVE: Evolutionary Search for LLM-based Verilog Generation and Optimization Verilog's design cycle is inherently labor-intensive and necessitates extensive domain expertise. Although Large Language Models (LLMs) offer a promising pathway toward automation, their limited training data and intrinsic sequential reasoning fail to capture the strict formal logic and concurrency inherent in hardware systems. To overcome these barriers, we present EvolVE, the first framework to analyze multiple evolution strategies on chip design tasks, revealing that Monte Carlo Tree Search (MCTS) excels at maximizing functional correctness, while Idea-Guided Refinement (IGR) proves superior for optimization. We further leverage Structured Testbench Generation (STG) to accelerate the evolutionary process. To address the lack of complex optimization benchmarks, we introduce IC-RTL, targeting industry-scale problems derived from the National Integrated Circuit Contest. Evaluations establish EvolVE as the new state-of-the-art, achieving 98.1% on VerilogEval v2 and 92% on RTLLM v2. Furthermore, on the industry-scale IC-RTL suite, our framework surpasses reference implementations authored by contest participants, reducing the Power, Performance, Area (PPA) product by up to 66% in Huffman Coding and 17% in the geometric mean across all problems. The source code of the IC-RTL benchmark is available at https://github.com/weiber2002/ICRTL. Fuzz testing ; Huffman coding ; Leverage (statistics) ; Implementation ; Evolutionary algorithm ; Concurrency ; Benchmark (surveying) ; Coding (social sciences) Machine Learning in Materials Science ; Advanced Neural Network Applications ; Machine Learning and Data Classification
Authors: Tarjei Paule Hage | Markus J. Buehler
Source: ArXiv.org (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoning Can reinforcement learning with hard, verifiable rewards teach a compact language model to reason about physics, or does it primarily learn to pattern-match toward correct answers? We study this question by training a 1.5B-parameter reasoning model on beam statics, a classic engineering problem, using parameter-efficient RLVR with binary correctness rewards from symbolic solvers, without teacher-generated reasoning traces. The best BeamPERL checkpoint achieves a 66.7% improvement in Pass@1 over the base model. However, the learned competence is anisotropic: the model generalizes compositionally (more loads) but fails under topological shifts (moved supports) that require the same equilibrium equations. Intermediate checkpoints yield the strongest reasoning, while continued optimization degrades robustness while maintaining reward. These findings reveal a key limitation of outcome-level alignment: reinforcement learning with exact physics rewards induces procedural solution templates rather than internalization of governing equations. The precision of the reward signal - even when analytically exact - does not by itself guarantee transferable physical reasoning. Our results suggest that verifiable rewards may need to be paired with structured reasoning scaffolding to move beyond template matching toward robust scientific reasoning. Verifiable secret sharing ; Correctness ; Robustness (evolution) ; Pleading ; Reinforcement learning ; Competence (human resources) ; Reuse Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems