jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lion Frangoulis | Lorenzo A. Mariano. Vu Ha Anh Nguyen | Zahra Khatibi | Alessandro Lunghi
Source: arXiv (Cornell University) (preprint, 2026-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Maximizing the magnetic anisotropy of Dy complexes by fine tuning organic ligands: A systematic multireference high-throughput exploration of over 30k molecules The design of the coordination environment of magnetic ions is key to achieving properties such as large magnetic anisotropy and slow magnetic relaxation, but a systematic exploration of the relevant chemical space for these compounds is missing. Here, we automatically extract all entries of mononuclear Dy coordination complexes from crystallographic databases and use multireference ab initio methods to compute their magnetic anisotropy. In addition, we generate and simulate magnetic anisotropy for 25k new molecules with the general formula [Dy(H$_2$O)$_5$L$_2$]$^{n-}$ and pentagonal bipyramidal coordination geometry, a motif selected as very promising. While no molecule with record magnetic anisotropy is serendipitously identified in crystallography databases, molecules with crystal field splittings over 1600 cm$^{-1}$ are identified by systematically exploring new organic ligands. This corresponds to a ~100% increase of magnetic anisotropy over the reference compound, ~30% over any known pentagonal bipyramidal Dy complex, and approaching record values of pseudo bi-coordinated Dy ions. This study demonstrates that the fine-tuning of Dy's second coordination sphere by organic ligands design can significantly improve magnetic anisotropy and that automated computational screening is key to accelerating this chemically non-intuitive process. Magnetic anisotropy ; Anisotropy ; Molecule ; Pentagonal bipyramidal molecular geometry ; Ab initio ; Coordination sphere ; Ab initio quantum chemistry methods ; Ion Magnetism in coordination complexes ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced NMR Techniques and Applications
Authors: Idelfonso B. R. Nogueira | Carine M. Rebelloa | M. Enis Leblebici | Erick Giovani Sperandio Nascimento
Source: ArXiv.org (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

MultiPUFFIN: A Multimodal Domain-Constrained Foundation Model for Molecular Property Prediction of Small Molecules Predicting physicochemical properties across chemical space is vital for chemical engineering, drug discovery, and materials science. Current molecular foundation models lack thermodynamic consistency, while domain-informed approaches are limited to single properties and small datasets. We introduce MultiPUFFIN, a domain-constrained multimodal foundation model addressing both limitations simultaneously. MultiPUFFIN features: (i) an encoder fusing SMILES, graphs, and 3D geometries via gated cross-modal attention, alongside experimental condition and descriptor encoders; (ii) prediction heads embedding established correlations (e.g., Wagner, Andrade, van't Hoff, and Shomate equations) as inductive biases to ensure thermodynamic consistency; and (iii) a two-stage multi-task training strategy.Extending prior frameworks, MultiPUFFIN predicts nine thermophysical properties simultaneously. It is trained on a multi-source dataset of 37,968 unique molecules (40,904 rows). With roughly 35 million parameters, MultiPUFFIN achieves a mean $R^2 = 0.716$ on a challenging scaffold-split test set of 8,877 molecules. Compared to ChemBERTa-2 (pre-trained on 77 million molecules), MultiPUFFIN outperforms the fine-tuned baseline across all nine properties despite using 2000x fewer training molecules. Advantages are strikingly apparent for temperature-dependent properties, where ChemBERTa-2 lacks the architectural capacity to incorporate thermodynamic conditions.These results demonstrate that multimodal encoding and domain-informed biases substantially reduce data and compute requirements compared to brute-force pre-training. Furthermore, MultiPUFFIN handles missing modalities and recovers meaningful thermodynamic parameters without explicit supervision. Systematic ablation studies confirm the property-specific benefits of these domain-informed prediction heads. Chemical space ; Embedding ; Baseline (sea) ; Set (abstract data type) ; Property (philosophy) ; Foundation (evidence) ; Encoding (memory) ; Encoder ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Idelfonso B. R. Nogueira | Carine M. Rebelloa | M. Enis Leblebici | Erick Giovani Sperandio Nascimento
Source: arXiv (Cornell University) (preprint, 2026-03-01)
Field: Materials Science — Materials Chemistry

MultiPUFFIN: A Multimodal Domain-Constrained Foundation Model for Molecular Property Prediction of Small Molecules Predicting physicochemical properties across chemical space is vital for chemical engineering, drug discovery, and materials science. Current molecular foundation models lack thermodynamic consistency, while domain-informed approaches are limited to single properties and small datasets. We introduce MultiPUFFIN, a domain-constrained multimodal foundation model addressing both limitations simultaneously. MultiPUFFIN features: (i) an encoder fusing SMILES, graphs, and 3D geometries via gated cross-modal attention, alongside experimental condition and descriptor encoders; (ii) prediction heads embedding established correlations (e.g., Wagner, Andrade, van't Hoff, and Shomate equations) as inductive biases to ensure thermodynamic consistency; and (iii) a two-stage multi-task training strategy.Extending prior frameworks, MultiPUFFIN predicts nine thermophysical properties simultaneously. It is trained on a multi-source dataset of 37,968 unique molecules (40,904 rows). With roughly 35 million parameters, MultiPUFFIN achieves a mean $R^2 = 0.716$ on a challenging scaffold-split test set of 8,877 molecules. Compared to ChemBERTa-2 (pre-trained on 77 million molecules), MultiPUFFIN outperforms the fine-tuned baseline across all nine properties despite using 2000x fewer training molecules. Advantages are strikingly apparent for temperature-dependent properties, where ChemBERTa-2 lacks the architectural capacity to incorporate thermodynamic conditions.These results demonstrate that multimodal encoding and domain-informed biases substantially reduce data and compute requirements compared to brute-force pre-training. Furthermore, MultiPUFFIN handles missing modalities and recovers meaningful thermodynamic parameters without explicit supervision. Systematic ablation studies confirm the property-specific benefits of these domain-informed prediction heads. Chemical space ; Embedding ; Baseline (sea) ; Set (abstract data type) ; Property (philosophy) ; Foundation (evidence) ; Encoding (memory) ; Encoder ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Qingran Lin | Fengwei Yang | Chaolun Zhu
Source: arXiv (Cornell University) (preprint, 2026-03-18)
Field: Materials Science — Materials Chemistry

Harnessing the Power of Foundation Models for Accurate Material Classification Material classification has emerged as a critical task in computer vision and graphics, supporting the assignment of accurate material properties to a wide range of digital and real-world applications. While traditionally framed as an image classification task, this domain faces significant challenges due to the scarcity of annotated data, limiting the accuracy and generalizability of trained models. Recent advances in vision-language foundation models (VLMs) offer promising avenues to address these issues, yet existing solutions leveraging these models still exhibit unsatisfying results in material recognition tasks. In this work, we propose a novel framework that effectively harnesses foundation models to overcome data limitations and enhance classification accuracy. Our method integrates two key innovations: (a) a robust image generation and auto-labeling pipeline that creates a diverse and high-quality training dataset with material-centric images, and automatically assigns labels by fusing object semantics and material attributes in text prompts; (b) a prior incorporation strategy to distill information from VLMs, combined with a joint fine-tuning method that optimizes a pre-trained vision foundation model alongside VLM-derived priors, preserving broad generalizability while adapting to material-specific features.Extensive experiments demonstrate significant improvements on multiple datasets. We show that our synthetic dataset effectively captures the characteristics of real world materials, and the integration of priors from vision-language models significantly enhances the final performance. The source code and dataset will be released. Generalizability theory ; Key (lock) ; Foundation (evidence) ; Range (aeronautics) ; Pipeline (software) ; Semantics (computer science) ; Domain (mathematical analysis) ; Prior probability ; Object detection Machine Learning in Materials Science ; Advanced Neural Network Applications ; Multimodal Machine Learning Applications
Authors: Qingran Lin | Fengwei Yang | Chaolun Zhu
Source: ArXiv.org (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Harnessing the Power of Foundation Models for Accurate Material Classification Material classification has emerged as a critical task in computer vision and graphics, supporting the assignment of accurate material properties to a wide range of digital and real-world applications. While traditionally framed as an image classification task, this domain faces significant challenges due to the scarcity of annotated data, limiting the accuracy and generalizability of trained models. Recent advances in vision-language foundation models (VLMs) offer promising avenues to address these issues, yet existing solutions leveraging these models still exhibit unsatisfying results in material recognition tasks. In this work, we propose a novel framework that effectively harnesses foundation models to overcome data limitations and enhance classification accuracy. Our method integrates two key innovations: (a) a robust image generation and auto-labeling pipeline that creates a diverse and high-quality training dataset with material-centric images, and automatically assigns labels by fusing object semantics and material attributes in text prompts; (b) a prior incorporation strategy to distill information from VLMs, combined with a joint fine-tuning method that optimizes a pre-trained vision foundation model alongside VLM-derived priors, preserving broad generalizability while adapting to material-specific features.Extensive experiments demonstrate significant improvements on multiple datasets. We show that our synthetic dataset effectively captures the characteristics of real world materials, and the integration of priors from vision-language models significantly enhances the final performance. The source code and dataset will be released. Generalizability theory ; Key (lock) ; Foundation (evidence) ; Range (aeronautics) ; Pipeline (software) ; Semantics (computer science) ; Domain (mathematical analysis) ; Prior probability ; Object detection Machine Learning in Materials Science ; Advanced Neural Network Applications ; Multimodal Machine Learning Applications
Authors: Maksim Kuznetsov | Zulfat Miftahutdinov | Rim Shayakhmetov | Mikolaj Mizera | Roman Schutski | Bogdan Zagribelnyy | Ivan Ilin | Nikita Bondarev | Thomas MacDougall | Mathieu Reymond | Mihir Bafna | Kaeli Kaymak-Loveless | Eugene Babin | Maxim Malkov | Lechner, Mathias | Ramin Hasani | Alexander Amini | Vladimir Aladinskiy | Alex Aliper | Alex Zhavoronkov
Source: Open MIND (preprint, 2026-03-03)
Field: Materials Science — Materials Chemistry

MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks. Simply increasing model size or introducing reasoning tokens does not yield significant performance gains. To address this gap, we introduce the MMAI Gym for Science, a one-stop shop molecular data formats and modalities as well as task-specific reasoning, training, and benchmarking recipes designed to teach foundation models the 'language of molecules' in order to solve practical drug discovery problems. We use MMAI Gym to train an efficient Liquid Foundation Model (LFM) for these applications, demonstrating that smaller, purpose-trained foundation models can outperform substantially larger general-purpose or specialist models on molecular benchmarks. Across essential drug discovery tasks - including molecular optimization, ADMET property prediction, retrosynthesis, drug-target activity prediction, and functional group reasoning - the resulting model achieves near specialist-level performance and, in the majority of settings, surpasses larger models, while remaining more efficient and broadly applicable in the domain. Foundation (evidence) ; Drug discovery ; Benchmarking ; Modalities ; Training (meteorology) ; Discovery learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Aaditya Baranwal | Shruti Vyas
Source: arXiv (Cornell University) (preprint, 2026-02-03)
Field: Materials Science — Materials Chemistry

ChemPro: A Progressive Chemistry Benchmark for Large Language Models We introduce ChemPro, a progressive benchmark with 4100 natural language question-answer pairs in Chemistry, across 4 coherent sections of difficulty designed to assess the proficiency of Large Language Models (LLMs) in a broad spectrum of general chemistry topics. We include Multiple Choice Questions and Numerical Questions spread across fine-grained information recall, long-horizon reasoning, multi-concept questions, problem-solving with nuanced articulation, and straightforward questions in a balanced ratio, effectively covering Bio-Chemistry, Inorganic-Chemistry, Organic-Chemistry and Physical-Chemistry. ChemPro is carefully designed analogous to a student's academic evaluation for basic to high-school chemistry. A gradual increase in the question difficulty rigorously tests the ability of LLMs to progress from solving basic problems to solving more sophisticated challenges. We evaluate 45+7 state-of-the-art LLMs, spanning both open-source and proprietary variants, and our analysis reveals that while LLMs perform well on basic chemistry questions, their accuracy declines with different types and levels of complexity. These findings highlight the critical limitations of LLMs in general scientific reasoning and understanding and point towards understudied dimensions of difficulty, emphasizing the need for more robust methodologies to improve LLMs. Benchmark (surveying) ; Point (geometry) ; Broad spectrum ; Natural language ; Computational linguistics Machine Learning in Materials Science ; Text Readability and Simplification ; Topic Modeling
Authors: Tianyu Liu | Sihan Jiang | Fan Zhang | Kunyang Sun | Teresa Head-Gordon | Hongyu Zhao
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery Abstract Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generation, optimizing candidate prioritization, and enabling more scalable and cost-effective drug discovery pipelines. However there is currently a lack of objective assessments of LLM performance to ascertain their advantages and limitations over traditional drug discovery platforms. To tackle this emergent problem, we have developed DrugPlayGround, a framework to evaluate and benchmark LLM performance for generating meaningful text-based descriptions of physiochemical drug characteristics, drug synergism, drug-protein interactions, and the physiological response to perturbations introduced by drug molecules. Moreover, DrugPlayGround is designed to work with domain experts to provide detailed explanations for justifying the predictions of LLMs, thereby testing LLMs for chemical and biological reasoning capabilities to push their greater use at the frontier of drug discovery at all of its stages. Drug discovery ; Benchmarking ; Benchmark (surveying) ; Scalability ; Domain (mathematical analysis) ; Language model ; Drug Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Olga Bunkova | Lorenzo Di Fruscia | Sophia Rupprecht | Schweidtmann, Artur M. | Marcel J. T. Reinders | Jana M. Weber
Source: arXiv (Cornell University) (preprint, 2026-01-22)
Field: Materials Science — Materials Chemistry

Grounding Large Language Models in Reaction Knowledge Graphs for Synthesis Retrieval Large Language Models (LLMs) can aid synthesis planning in chemistry, but standard prompting methods often yield hallucinated or outdated suggestions. We study LLM interactions with a reaction knowledge graph by casting reaction path retrieval as a Text2Cypher (natural language to graph query) generation problem, and define single- and multi-step retrieval tasks. We compare zero-shot prompting to one-shot variants using static, random, and embedding-based exemplar selection, and assess a checklist-driven validator/corrector loop. To evaluate our framework, we consider query validity and retrieval accuracy. We find that one-shot prompting with aligned exemplars consistently performs best. Our checklist-style self-correction loop mainly improves executability in zero-shot settings and offers limited additional retrieval gains once a good exemplar is present. We provide a reproducible Text2Cypher evaluation setup to facilitate further work on KG-grounded LLMs for synthesis planning. Code is available at https://github.com/Intelligent-molecular-systems/KG-LLM-Synthesis-Retrieval. Knowledge graph ; Language model ; Graph ; Hallucinating ; Scheme (mathematics) ; Code (set theory) ; Path (computing) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Scientific Computing and Data Management
Authors: Joby Sebastian | Chalachew Mebrahtu | Feng Zeng | R. Palkovits
Source: JuSER Publikationsportal (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Strong Metal‐Support Interactions on TiO 2 ‐Supported Metal Catalysts for Fine‐Tuning Catalysis Catalysis ; Metal ; Transition metal ; Bimetallic strip ; Heterogeneous catalysis Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Catalysis and Oxidation Reactions
Authors: Can Polat | Erchin Serpedin | Mustafa Kurban | Hasan Kurban
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

SCALAR: Quantifying Structural Hallucination, Consistency, and Reasoning Gaps in Materials Foundation Models Large language models are increasingly applied to materials science reasoning, yet their behavior under physically structured distribution shifts remains poorly understood. We introduce SCALAR (Structural Consistency And Logic Across Regimes), a benchmark for evaluating geometric scale generalization and its connection to structural hallucination, consistency, and reasoning in materials foundation models. Given canonical crystal representations, models must reason about derived nanoparticle structures obtained through supercell expansion and geometric truncation across length scales spanning a few atoms to over 18,000 atoms, totaling $\approx$100,000 structures from DFT-validated unit cells. SCALAR defines three tasks. (i) CIF to property prediction. (ii) A Chain-of-Thought variant with explicit physics-grounded reasoning. (iii) Inverse retrieval identifying crystals from candidates given target properties. Outputs are evaluated via structured metrics capturing numeric error, hallucination, cross-prompt consistency, monotonic reasoning, output validity, and retrieval regret. Experiments across diverse foundation models reveal large, model-dependent shifts under explicit reasoning, often reducing hallucination and error, but frequently destabilizing consistency or validity. These results demonstrate that geometric scale generalization cannot be inferred from accuracy alone. Supplementary materials are available at https://github.com/KurbanIntelligenceLab/SCALAR. Generalization ; Consistency (knowledge bases) ; Scalar (mathematics) ; Property (philosophy) ; Foundation (evidence) ; Benchmark (surveying) ; Scale (ratio) ; Monotonic function Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topological and Geometric Data Analysis
Authors: Varvara Voinarovska | Roćıo Mercado | Mikhail Kabeshov | Samuel Genheden
Source: ChemRxiv (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Do humans and large language models agree on the quality of synthesis plans? Large language models (LLMs) have seen a widespread adoption in all spheres of science including chemistry and cheminformatics. Nevertheless, our knowledge of how they operate is limited, giving rise to exploration of their capabilities in different areas of science and different operation modes. Here, we investigated whether LLMs could mimic human experts on the challenging task of assessing retrosynthetic path feasibility in routes generated by a popular computer-aided synthesis planning tool (AiZynthFinder). We evaluated the agreement between LLMs and expert chemists on holistic evaluations of the proposed routes as well as the individual chemical reactions in them. We used four frontier LLMs (Claude Sonnet 4.5, GPT-4.1, GPT-o3, and Gemini 2.5 Pro) and employed 17 expert chemists to grade 50 retrosynthetic paths. We found out that when instructed with clear possible categories for the evaluation of a given reaction in a retrosynthetic tree, human experts tend to converge in their opinions. Another important finding was that Gemini 2.5 Pro performs remarkably well out of all the LLMs explored, GPT-o3 tends to be more pessimistic, and Claude and GPT-4.1 tend to be overly optimistic when compared to the majority vote of human experts. Retrosynthetic analysis ; Quality (philosophy) ; Task (project management) ; Expert system ; Frontier Machine Learning in Materials Science ; Language and cultural evolution ; Chemistry and Chemical Engineering