jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junyi An | Xinyu Lu | Yunfei Shi | Li‐Cheng Xu | Nannan Zhang | Chao Qu | Yuan Qi | Fenglei Cao
Source: arXiv (Cornell University) (preprint, 2026-03-23)
Field: Materials Science — Materials Chemistry

Suiren-1.0 Technical Report: A Family of Molecular Foundation Models We introduce Suiren-1.0, a family of molecular foundation models for the accurate modeling of diverse organic systems. Suiren-1.0 comprising three specialized variants (Suiren-Base, Suiren-Dimer, and Suiren-ConfAvg) is integrated within an algorithmic framework that bridges the gap between 3D conformational geometry and 2D statistical ensemble spaces. We first pre-train Suiren-Base (1.8B parameters) on a 70M-sample Density Functional Theory dataset using spatial self-supervision and SE(3)-equivariant architectures, achieving robust performance in quantum property prediction. Suiren-Dimer extends this capability through continued pre-training on 13.5M intermolecular interaction samples. To enable efficient downstream application, we propose Conformation Compression Distillation (CCD), a diffusion-based framework that distills complex 3D structural representations into 2D conformation-averaged representations. This yields the lightweight Suiren-ConfAvg, which generates high-fidelity representations from SMILES or molecular graphs. Our extensive evaluations demonstrate that Suiren-1.0 establishes state-of-the-art results across a range of tasks. All models and benchmarks are open-sourced. Foundation (evidence) ; Property (philosophy) ; Bridge (graph theory) ; Intermolecular force ; Range (aeronautics) ; Quantum ; Chain (unit) ; Statistical mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ting Liang | Ke Xu | Eric Lindgren | Zherui Chen | Rui Zhao | Jiahui Liu | Esmée Berger | Benrui Tang | Bohan Zhang | Yanzhou Wang | Keke Song | Penghua Ying | Nan Xu | Haikuan Dong | Shunda Chen | Paul Erhart | Zheyong Fan | Tapio Ala-Nissila | Jiayi Xu
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Dataset and models supporting "NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements" Description This repository contains the dataset, source code, demonstration files, and fine-tuning workflows for NEP89, a universal Neuroevolution Potential (NEP) foundation model covering 89 elements across the periodic table, as described in Liang et al., "NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements" [arXiv:2504.21286]. Included Files Dataset_for_NEP89.zip This archive contains the comprehensive dataset used to train the NEP89 model. It comprises carefully subsampled structures from diverse sources (e.g., OMAT24, MPtrj, SPICE, ANI-1xnr, UNEP-v1, solid-state electrolytes, water, protein, and CH systems), along with newly constructed CHONPS data. The dataset is organized into 11 distinct subsets, which can be used independently or combined into a unified dataset for machine-learned potential (MLP) training and development. GPUMD-5.0-NEP89-Demos.zip This archive provides the essential software environment and practical examples for the NEP89 model, including: Source code for NEP89 model deployment and performing Molecular Dynamics (MD) simulations. Out-of-the-box demos for rapid testing of the NEP89 model. Fine-tuning workflows for adapting the NEP89 model to specific chemical environments or high-accuracy requirements. Quick Links & Resources For the most up-to-date resources and detailed instructions, please refer to: NEP89 Model: The latest NEP89 potential files are available in the GPUMD GitHub Repository. Fine-tuning Tutorial: A step-by-step guide to fine-tuning NEP89 is available in the GPUMD-Tutorials. Software Requirement: For optimal performance and compatibility, especially during fine-tuning, we recommend using the latest version of GPUMD (version 5.0). Additional Information Further technical details and implementation notes are provided in the README files within each archive. For repository- and implementation-related inquiries, please contact: liangting.zj@gmail.com, phychensd@gmail.com, brucenju@gmail.com Workflow ; Neuroevolution ; Software ; Source code ; Code (set theory) ; Artificial neural network ; Open source Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Clara Chinchilla-Garzon | Eva Rivera-Chao | Miguel Ángel Gómez-Mendoza | Bruno J. C. Vieira | Neyvis Almora-Barrios | Pol Gimeno-Fonquernie | Jan Navrátil | Piotr Błoński | Víctor A De La Peña O'shea | João C. Waerenborgh | Carlos Martí-Gastaldo | Natalia M Padial
Source: ChemRxiv (article, 2026-03-16)
Field: Chemistry — Inorganic Chemistry

Cluster-Enabled Control of Single-Electron Photoredox Catalysis in Reticular Frameworks The development of sustainable catalytic strategies for the formation of carbon–carbon bonds is central to the future of synthetic chemistry. Photoredox catalysis has emerged as a powerful alternative to traditional redox chemistry, replacing harsh conditions and stoichiometric reagents with visible light and single-electron transfer (SET) processes. However, most current systems rely on homogeneous catalysts containing iridium or ruthenium complexes, which are expensive, scarce, and difficult to recover. The development of robust, recyclable, and earth-abundant platforms that can operate under photoredox conditions while maintaining high efficiency and selectivity remains a central goal in the field. This work presents a new strategy for achieving photoredox reactivity using heterometallic clusters embedded in reticular frameworks. By integrating titanium and iron into a redox-active metal–oxo cluster, we designed a pristine MOF capable of promoting light-driven single-electron transfer chemistry without the need for any additives, external photosensitizers, or noble metals. The ability to fine-tune the electronic properties of the cluster through metal selection opens the door to a new generation of porous, crystalline materials for photoredox transformations involving the formation of new C–X bonds. In the long term, such systems could support the development of flow-compatible, heterogeneous, and recyclable catalysts for pharmaceutical synthesis, fine chemicals, or sustainable feedstock conversion. Beyond catalysis, this cluster-based approach also provides a design principle for building functional materials that integrate light absorption, charge separation, and redox reactivity in a single solid-state platform. Photoredox catalysis ; Catalysis ; Ruthenium ; Reactivity (psychology) ; Redox ; Reagent ; Noble metal ; Iridium Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Metalloenzymes and iron-sulfur proteins
Authors: Vineeth Venugopal | Mahjoubi, Soroush | Elsa A. Olivetti
Source: arXiv (Cornell University) (preprint, 2026-03-02)
Field: Materials Science — Materials Chemistry

Probing Materials Knowledge in LLMs: From Latent Embeddings to Reliable Predictions Large language models are increasingly applied to materials science, yet fundamental questions remain about their reliability and knowledge encoding. Evaluating 25 LLMs across four materials science tasks -- over 200 base and fine-tuned configurations -- we find that output modality fundamentally determines model behavior. For symbolic tasks, fine-tuning converges to consistent, verifiable answers with reduced response entropy, while for numerical tasks, fine-tuning improves prediction accuracy but models remain inconsistent across repeated inference runs, limiting their reliability as quantitative predictors. For numerical regression, we find that better performance can be obtained by extracting embeddings directly from intermediate transformer layers than from model text output, revealing an ``LLM head bottleneck,'' though this effect is property- and dataset-dependent. Finally, we present a longitudinal study of GPT model performance in materials science, tracking four models over 18 months and observing 9--43\% performance variation that poses reproducibility challenges for scientific applications. Inference ; Reliability (semiconductor) ; Limiting ; Knowledge base ; Verifiable secret sharing ; Base (topology) ; Generality ; Uncertainty quantification Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Text Readability and Simplification
Authors: Lin Huang | Arthur Jiang | XiaoLi Liu | Zion Wang | Jason Ming Zhao | Chu Wang | Haocheng Lu | Chengxiang Huang | JiaJun Cheng | Yiyue Du | Jia Zhang
Source: Open MIND (preprint, 2026-02-13)
Field: Materials Science — Materials Chemistry

UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems All-atom molecular simulation serves as a quintessential ``computational microscope'' for understanding the machinery of life, yet it remains fundamentally limited by the trade-off between quantum-mechanical (QM) accuracy and biological scale. We present UBio-MolFM, a universal foundation model framework specifically engineered to bridge this gap. UBio-MolFM introduces three synergistic innovations: (1) UBio-Mol26, a large bio-specific dataset constructed via a multi-fidelity ``Two-Pronged Strategy'' that combines systematic bottom-up enumeration with top-down sampling of native protein environments (up to 1,200 atoms); (2) E2Former-V2, a linear-scaling equivariant transformer that integrates Equivariant Axis-Aligned Sparsification (EAAS) and Long-Short Range (LSR) modeling to capture non-local physics with up to ~4x higher inference throughput in our large-system benchmarks; and (3) a Three-Stage Curriculum Learning protocol that transitions from energy initialization to energy-force consistency, with force-focused supervision to mitigate energy offsets. Rigorous benchmarking across microscopic forces and macroscopic observables -- including liquid water structure, ionic solvation, and peptide folding -- demonstrates that UBio-MolFM achieves ab initio-level fidelity on large, out-of-distribution biomolecular systems (up to ~1,500 atoms) and realistic MD observables. By reconciling scalability with quantum precision, UBio-MolFM provides a robust, ready-to-use tool for the next generation of computational biology. Scalability ; Initialization ; Inference ; Granularity ; Benchmarking ; Observable ; Embedding ; Testbed Machine Learning in Materials Science ; Protein Structure and Dynamics ; Force Microscopy Techniques and Applications
Authors: Lin Huang | Arthur Jiang | XiaoLi Liu | Zion Wang | Jason Ming Zhao | Chu Wang | Haocheng Lu | Chengxiang Huang | JiaJun Cheng | Yiyue Du | Jia Zhang
Source: arXiv (Cornell University) (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems All-atom molecular simulation serves as a quintessential ``computational microscope'' for understanding the machinery of life, yet it remains fundamentally limited by the trade-off between quantum-mechanical (QM) accuracy and biological scale. We present UBio-MolFM, a universal foundation model framework specifically engineered to bridge this gap. UBio-MolFM introduces three synergistic innovations: (1) UBio-Mol26, a large bio-specific dataset constructed via a multi-fidelity ``Two-Pronged Strategy'' that combines systematic bottom-up enumeration with top-down sampling of native protein environments (up to 1,200 atoms); (2) E2Former-V2, a linear-scaling equivariant transformer that integrates Equivariant Axis-Aligned Sparsification (EAAS) and Long-Short Range (LSR) modeling to capture non-local physics with up to ~4x higher inference throughput in our large-system benchmarks; and (3) a Three-Stage Curriculum Learning protocol that transitions from energy initialization to energy-force consistency, with force-focused supervision to mitigate energy offsets. Rigorous benchmarking across microscopic forces and macroscopic observables -- including liquid water structure, ionic solvation, and peptide folding -- demonstrates that UBio-MolFM achieves ab initio-level fidelity on large, out-of-distribution biomolecular systems (up to ~1,500 atoms) and realistic MD observables. By reconciling scalability with quantum precision, UBio-MolFM provides a robust, ready-to-use tool for the next generation of computational biology. Scalability ; Initialization ; Inference ; Granularity ; Benchmarking ; Observable ; Embedding ; Testbed Machine Learning in Materials Science ; Protein Structure and Dynamics ; Force Microscopy Techniques and Applications
Authors: Vaibhav S. Marde | Ashutosh Kumar Mishra
Source: The Chemical Record (article, 2026-03-04)
Field: Chemistry — Organic Chemistry

Boranil: A Reflection on a Decade of Advancement in Design, Synthesis, Photophysics to Applications In the last decade, boranils have emerged as one of the mostcompetitive candidates among other boron-based dyes due to the ease of their synthesis and postfunctionalization. This review primarily focuses on rational molecular design concepts and postfunctionalization strategies, highlighting the structure-property relationships of boranils to fine-tune their photophysical properties, such as intramolecular charge transfer (ICT), aggregation-induced emission (AIE), circularly polarized luminescence (CPL), and solid-state emission. Overall, the past few decades have witnessed the gradual development of boranil from individual fluorescent molecules to functional building blocks, opening up a wide range of applications in material chemistry, biomedicine, and photocatalysis. Reflection (computer programming) ; Luminescence ; Charge (physics) ; Rational design ; Aggregation-induced emission ; Intramolecular force Organoboron and organosilicon chemistry ; Luminescence and Fluorescent Materials ; Boron Compounds in Chemistry
Authors: Jorge Vasconcelos
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-02-11)
Field: Chemistry — Organic Chemistry

Context-Independent Molecular Compatibility Without Operational Regimen Engineering: A Structural Admissibility Condition for Identity-Level Molecular Stability Description This prior-art / structural admissibility analysis establishes context-independent molecular compatibility as a necessary condition for molecular systems claiming stability, compatibility, or industrial deployability across standard operating environments. The document is strictly non-constructive and discloses no specific molecular structures, compounds, polymers, formulations, synthesis routes, stabilization strategies, or handling protocols. Instead, it formalizes invariant boundary conditions under which molecular behavior must remain stable across nominal operating envelopes without reliance on fine-tuned handling sequences, curated environmental control, co-agent stabilization, or procedural enforcement of compatibility. The condition constrains patentability at the level of enablement/sufficiency, inventive step, and industrial applicability and applies uniformly across small molecules, polymers, supramolecular systems, functionalized materials, reactive intermediates, and hybrid molecular assemblies. Compatibility (geochemistry) ; Enforcement ; Supramolecular chemistry ; Molecular recognition ; Extractive distillation ; Stability (learning theory) Supramolecular Chemistry and Complexes ; Advanced Polymer Synthesis and Characterization ; Supramolecular Self-Assembly in Materials
Authors: Jorge Vasconcelos
Source: Open MIND (preprint, 2026-02-11)
Field: Chemistry — Organic Chemistry

Context-Independent Molecular Compatibility Without Operational Regimen Engineering: A Structural Admissibility Condition for Identity-Level Molecular Stability Description This prior-art / structural admissibility analysis establishes context-independent molecular compatibility as a necessary condition for molecular systems claiming stability, compatibility, or industrial deployability across standard operating environments. The document is strictly non-constructive and discloses no specific molecular structures, compounds, polymers, formulations, synthesis routes, stabilization strategies, or handling protocols. Instead, it formalizes invariant boundary conditions under which molecular behavior must remain stable across nominal operating envelopes without reliance on fine-tuned handling sequences, curated environmental control, co-agent stabilization, or procedural enforcement of compatibility. The condition constrains patentability at the level of enablement/sufficiency, inventive step, and industrial applicability and applies uniformly across small molecules, polymers, supramolecular systems, functionalized materials, reactive intermediates, and hybrid molecular assemblies. Compatibility (geochemistry) ; Enforcement ; Supramolecular chemistry ; Molecular recognition ; Extractive distillation ; Stability (learning theory) Supramolecular Chemistry and Complexes ; Advanced Polymer Synthesis and Characterization ; Supramolecular Self-Assembly in Materials
Authors: Dingding Cao | MieowKee Chan | WanSieng Yeo | Alessandra Criscuoli | Said Bey | Alberto Figoli
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Intelligent Materials Modelling: Large Language Models Versus Partial Least Squares Regression for Predicting Polysulfone Membrane Mechanical Performance Partial least squares regression ; Ultimate tensile strength ; Polysulfone ; Mean squared error ; Regression ; Metric (unit) ; Modulus ; Estimator ; Linear regression Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Dingding Cao | Mieow Kee Chan | Wan Sieng Yeo | Said Bey | Alberto Figoli
Source: arXiv (Cornell University) (preprint, 2026-03-14)
Field: Materials Science — Materials Chemistry

Intelligent Materials Modelling: Large Language Models Versus Partial Least Squares Regression for Predicting Polysulfone Membrane Mechanical Performance Predicting the mechanical properties of polysulfone (PSF) membranes from structural descriptors remains challenging due to extreme data scarcity typical of experimental studies. To investigate this issue, this study benchmarked knowledge-driven inference using four large language models (LLMs) (DeepSeek-V3, DeepSeek-R1, ChatGPT-4o, and GPT-5) against partial least squares (PLS) regression for predicting Young's modulus (E), tensile strength (TS), and elongation at break (EL) based on pore diameter (PD), contact angle (CA), thickness (T), and porosity (P) measurements. These knowledge-driven approaches demonstrated property-specific advantages over the chemometric baseline. For EL, LLMs achieved statistically significant improvements, with DeepSeek-R1 and GPT-5 delivering 40.5% and 40.3% of Root Mean Square Error reductions, respectively, reducing mean absolute errors from $11.63\pm5.34$% to $5.18\pm0.17$%. Run-to-run variability was markedly compressed for LLMs ($\leq$3%) compared to PLS (up to 47%). E and TS predictions showed statistical parity between approaches ($q\geq0.05$), indicating sufficient performance of linear methods for properties with strong structure-property correlations. Error topology analysis revealed systematic regression-to-the-mean behavior dominated by data-regime effects rather than model-family limitations. These findings establish that LLMs excel for non-linear, constraint-sensitive properties under bootstrap instability, while PLS remains competitive for linear relationships requiring interpretable latent-variable decompositions. The demonstrated complementarity suggests hybrid architectures leveraging LLM-encoded knowledge within interpretable frameworks may optimise small-data materials discovery. Partial least squares regression ; Ultimate tensile strength ; Mean squared error ; Regression ; Estimator ; Polysulfone ; Metric (unit) ; Modulus ; Linear regression ; Ordinary least squares Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Nicolas Wong | Julia H. Yang
Source: arXiv (Cornell University) (preprint, 2026-03-10)
Field: Materials Science — Materials Chemistry

Bias in Universal Machine-Learned Interatomic Potentials and its Effects on Fine-Tuning Universal machine learned interatomic potentials (uMLIPs) embody a growing area of interest due to their transferability across the periodic table, displaying an error of about 0.6 kcal/mol against the Matbench Discovery test set. However, we show that achieving more accurate predictions on out-of-domain tasks requires fine-tuning. Additionally, we investigate the existence and influence of model biases in molecular dynamics (MD) by examining two approaches for data generation: from multiple MD trajectories in parallel, which we call naive fine-tuning, and from a single MD trajectory with fine-tuning after set intervals, which we call periodic fine-tuning. Our results find that naive fine-tuning generates constrained datasets that fail to represent MD simulations, and thus downstream fine-tuned models fail during extrapolation. In contrast, periodic fine-tuning yields models which are more generalizable and accurate, producing low-error dynamics. These findings indicate the role of uMLIP bias in fine-tuning, and highlights the need for multiple fine-tuning steps. Lastly, we relate unphysical behavior to principal component space, and quantify extrapolations through Q-residual analysis, which are useful as a proxy for epistemic uncertainty for larger simulations. Transferability ; Trajectory ; Set (abstract data type) ; Interatomic potential ; Data set ; Proxy (statistics) ; Component (thermodynamics) ; Dynamics (music) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Andrew Salij | R. Seaton Ullberg | Megan C. Davis | M. J. Cawkwell | Christopher J. Snyder | Cristina García–Cardona | Ivana Matanović | Wilton J. M. Kort-Kamp
Source: arXiv (Cornell University) (preprint, 2026-03-30)
Field: Materials Science — Materials Chemistry

Generative Chemical Language Models for Energetic Materials Discovery The discovery of new energetic materials remains a pressing challenge hindered by limited availability of high-quality data. To address this, we have developed generative molecular language models that have been pretrained on extensive chemical data and then fine-tuned with curated energetic materials datasets. This transfer-learning strategy extends the chemical language model capabilities beyond the pharmacological space in which they have been predominantly developed, offering a framework applicable to other data-spare discovery problems. Furthermore, we discuss the benefits of fragment-based molecular encodings for chemical language models, in particular in constructing synthetically accessible structures. Together, these advances provide a foundation for accelerating the design of next-generation energetic materials with demanding performance requirements. Generative grammar ; Chemical space ; Foundation (evidence) ; Generative model ; Constructed language ; Natural language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions