jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hänseroth, Jonas | Flötotto, Aaron | Qaisrani, Muhammad Nawaz | Dreßler, Christian
Source: ArXiv.org (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Fine-Tuning Unifies Foundational Machine-learned Interatomic Potential Architectures at ab initio Accuracy This work demonstrates that fine-tuning transforms foundational machine-learned interatomic potentials (MLIPs) to achieve consistent, near-ab initio accuracy across diverse architectures. Benchmarking five leading MLIP frameworks (MACE, GRACE, SevenNet, MatterSim, and ORB) across seven chemically diverse compounds reveals that fine-tuning universally enhances force predictions by factors of 5-15 and improves energy accuracy by 2-4 orders of magnitude. The investigated models span both equivariant and invariant, as well as conservative and non-conservative, architectures. While general-purpose foundation models are robust, they exhibit architecture-dependent deviations from ab initio reference data; fine-tuning eliminates these discrepancies, enabling quantitatively accurate predictions of atomistic and structural properties. Using datasets constructed from equidistantly sampled frames of short ab initio molecular dynamics trajectories, fine-tuning reduces force errors by an order of magnitude and harmonizes performance across all architectures. These findings establish fine-tuning as a universal route to achieving system-specific predictive accuracy while preserving the computational efficiency of MLIPs. To promote widespread adoption, we introduce the aMACEing Toolkit, which provides a unified and reproducible interface for fine-tuning workflows across multiple MLIP frameworks. Ab initio ; Ab initio quantum chemistry methods ; Work (physics) ; Interatomic potential ; Workflow ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Haojian Su | Shuai Lei | Yazhou Chen | Yanfei Lv | Quan Zhang
Source: ACS Applied Materials & Interfaces (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Human-Machine Collaborative Design of SnTe-Based Thermoelectric Materials via a Multiagent Framework Leveraging Large Language Models Herein, we present an innovative large language model (LLM)-driven multiagent collaborative framework for the human-machine collaborative design of SnTe-based thermoelectric materials. Composed of strategy planning and reasoning modules, this framework efficiently extracts implicit domain knowledge from extensive literature and integrates historical experimental data to deduce optimization strategies and compositional ratio ranges, representing a novel paradigm that transcends traditional empirical and computational approaches in material design. Under the guidance of LLM, experimental results validate its efficacy: the incorporation of Sb, Ge, and Cu elements not only optimizes carrier concentration but also induces multiscale defects, synergistically modulating electrical and thermal transport properties. Notably, the (Sn<sub>0.58</sub>Sb<sub>0.12</sub>Ge<sub>0.3</sub>Te)<sub>0.95</sub>(Cu<sub>2</sub>Te)<sub>0.05</sub> sample achieves a <i>zT</i> value of ∼1.2, marking a 40% increase compared to Sb-doped SnTe and a 267% rise relative to SnTe prepared by the same method, directly demonstrating that LLM guidance can significantly enhance material performance. This research not only showcases the potential of LLMs to revolutionize thermoelectric material development but also provides a valuable reference for high-performance material design in broader energy-related fields with implications for improving waste heat recovery and solid-state cooling technologies. Thermoelectric effect ; Domain (mathematical analysis) ; Thermoelectric materials ; Thermal ; Sample (material) ; Empirical research ; Multiscale modeling ; Value (mathematics) Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials
Authors: Tianren Zhang (4236601) | Dai-Bei Yang (10429702)
Source: Figshare (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

MultimodalMachine Learning with Large Language EmbeddingModel for Polymer Property Prediction Contemporary large language models (LLMs), such as GPT-4 and Llama, have harnessed extensive computational power and diverse text corpora to achieve remarkable proficiency in interpreting and generating domain-specific content, including materials science. To leverage the domain knowledge embedded within these models, we propose a simple yet effective multimodal architecture, PolyLLMem. By integrating text embeddings from Llama 3 with molecular structure embeddings from Uni-Mol, PolyLLMem enables the accurate prediction of polymer properties. Low-Rank Adaptation (LoRA) layers were integrated into our model during the property prediction stage to adapt the Llama 3 and Uni-Mol embeddings to our limited polymer data set, thereby enhancing their chemical relevance for polymer SMILES representation. Such a balanced fusion of fine-tuned textual and structural information enables PolyLLMem to robustly predict a variety of polymer properties despite the scarcity of training data. Its performance is comparable to, and in some cases exceeds, that of graph-based or transformer-based models that typically require pretraining on millions of polymer samples. These findings demonstrate that LLM, such as Llama, can effectively capture chemical information encoded in polymer SMILES (PSMILES), and underscore the efficacy of multimodal fusion of LLM embeddings and molecular structure embeddings in overcoming data scarcity and accelerating the discovery of advanced polymeric materials. Leverage (statistics) ; Embedding ; Domain adaptation ; Relevance (law) ; Property (philosophy) ; Variety (cybernetics) ; Scarcity Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Andres M. Bran | Tong Xie | Shai Pranesh | Jeffrey Meng | Xuan Thang Nguyen | Jeremy Goumaz | David Ming Segura | Ruizhi Xu | Dongzhan Zhou | Wenjie Zhang | Bram Hoex | Philippe Schwaller
Source: arXiv (Cornell University) (preprint, 2025-12-24)
Field: Materials Science — Materials Chemistry

MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models Large Language Models can develop reasoning capabilities through online fine-tuning with rule-based rewards. However, recent studies reveal a critical constraint: reinforcement learning succeeds only when the base model already assigns non-negligible probability to correct answers -- a property we term 'latent solvability'. This work investigates the emergence of chemical reasoning capabilities and what these prerequisites mean for chemistry. We identify two necessary conditions for RL-based chemical reasoning: 1) Symbolic competence, and 2) Latent chemical knowledge. We propose mid-stage scientific training (MiST): a set of mid-stage training techniques to satisfy these, including data-mixing with SMILES/CIF-aware pre-processing, continued pre-training on 2.9B tokens, and supervised fine-tuning on 1B tokens. These steps raise the latent-solvability score on 3B and 7B models by up to 1.8x, and enable RL to lift top-1 accuracy from 10.9 to 63.9% on organic reaction naming, and from 40.6 to 67.4% on inorganic material generation. Similar results are observed for other challenging chemical tasks, while producing interpretable reasoning traces. Our results define clear prerequisites for chemical reasoning training and highlight the broader role of mid-stage training in unlocking reasoning capabilities. Scientific reasoning ; Set (abstract data type) ; Property (philosophy) ; Lift (data mining) ; Model-based reasoning ; Training set ; Training (meteorology) ; Automated reasoning Machine Learning in Materials Science ; Topic Modeling ; Multimodal Machine Learning Applications
Authors: Jaehwan Choi (22380419) | Seongmin Kim (7533128) | Yousung Jung (1283016)
Source: Figshare (article, 2025-10-07)
Field: Materials Science — Materials Chemistry

Synthesis-AwareMaterials Redesign via Large LanguageModels We propose a novel framework that leverages large language models (LLMs) to transform synthetically infeasible inorganic crystal structures into synthetically feasible ones. Unlike previous studies on synthesis predictions, which focus primarily on estimating synthesizability, our method provides actionable solutions for redesigning unsynthesizable materials into synthesizable ones. By integrating an invertible structural representation and an iterative fine-tuning strategy, our framework not only predicts synthetic feasibility but also modifies unsynthesizable materials into viable candidates. As a result, we demonstrate that LLMs can effectively modify materials of various types, enhancing their synthesizability and increasing the likelihood of successful synthesis. As an indirect experimental validation, we demonstrate that 34 materials among the top 100 redesigned (but originally unsynthesizable) structures have indeed been experimentally reported in the literature. This approach addresses a critical gap between design and synthesis in materials science, and enables the discovery of experimentally realizable compounds by employing the “learn-and-regenerate” strategy in LLMs. Representation (politics) ; Focus (optics) ; Invertible matrix ; Language model ; Data modeling ; Synthetic data Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Catalysis and Oxidation Reactions
Authors: Aeyaz Ahmad Bhat | Noureddine Elboughdiri | Abhinav Kumar | Ankit D. Oza | Karim Kriaa | Chemseddine Maatki | Bilel Hadrich | Anjuman Ayub | Meraj Ahmad | Atif Khurshid Wani
Source: Applied Catalysis A General (article, 2025-11-25)
Field: Chemistry — Organic Chemistry

Sustainable catalytic strategies for carbon–carbon and carbon–heteroatom (C–S, C–N, C–O, C–Se) bond formation: Green pathways to advanced molecules Aldol reaction ; Organocatalysis ; Organic synthesis ; Nucleophile ; Enantioselective synthesis ; Catalysis ; Cornerstone Sulfur-Based Synthesis Techniques ; Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods
Authors: Ömer Özyıldırım
Source: ChemRxiv (preprint, 2025-03-06)
Field: Chemistry — Spectroscopy

A Novel Method to Simulate Molecular Collisions Although usually ignored for a more intuitive macroscopic understanding, molecular collisions are the foundation of almost every chemical event - from chemical reactions to transport phenomena. On the molecular level, a chemical reaction is an exchange (or sometimes knocking off) of atoms between molecules as a result of energetically significant collisions. The current simulation paradigm is based on simulating bulks of molecules, requiring high computational resources. In such simulations sometimes the molecules are not allowed to react, as in the case of classical MD (Molecular Dynamics), or require significant computational resources and time to conclude, as in the case of ab initio MD. Without any fine-tuning to allow a more outcome-oriented approach, the results of such simulations require even more computational resources and sometimes human effort to analyze for meaningful results. To answer these limitations, a novel methodology is developed based on a more fine-tuned approach for simulations. Its Python implementation based on current state-of-the-art libraries, and supporting applications based on globally accessible web technologies, are presented as a software framework named collider.py. It focuses on fine-tuning the collision parameters before running the simulation. Individual linear velocities, positions, and orientations of the molecules can be defined as well as the impact points on each molecule. The result of a proper collision usually involves re-arranging the atoms into new species, which reflects the output of such a chemical reaction. Our framework is based on Atomic Simulation Environment - a computational chemistry library for Python and consists of a web application to design the collisions in detail. Computer science ; Statistical physics ; Physics Mass Spectrometry Techniques and Applications ; Various Chemistry Research Topics ; Ion-surface interactions and analysis
Authors: Vatsalya Gupta (22297681) | Ramkrishna Sarkar (11557442)
Source: Figshare (article, 2025-09-23)
Field: Materials Science — Polymers and Plastics

Benzyl Thioether:A Dynamic Covalent Motif for CovalentAdaptable Networks Covalent adaptable networks (CANs) have emerged as a promising alternative to conventional thermoset polymers, offering reprocessability and recyclability through the incorporation of dynamic covalent linkages. However, dynamic linkages often compromise the thermal and chemical stability of the polymer, which is essential for many thermosets’ applications. Designing a chemistry that imparts both robustness and efficient dynamicity to the cross-linked polymers is a major challenge. To this end, this report introduces benzyl thioether as a robust and dynamic covalent motif for designing CANs. First, using the small molecular model studies, the dynamic exchange in the thioether was demonstrated and a linear polythioether bearing benzyl thioether was prepared. The CANs were prepared by solvent-free melt polymerization of trifunctional benzyl ether with a linear dithiol. Further on, a series of CANs was prepared, and the properties of these CANs, which include swelling, glass-transition temperature (<i>T</i><sub>g</sub>), and mechanical properties, were tailored by regulating the dithiol chain lengths. The CANs demonstrated excellent thermal and chemical stability owing to the presence of robust thioether linkages. The reprocessability and efficient relaxation of the imposed stress in the CANs at elevated temperature were made possible by the dynamic exchange capability of the benzyl thioether linkage. Impressively, the dynamic behaviors of the CANs were shown to be regulated by monomer chain lengths as well as the catalyst concentration. Finally, degradation of the CANs in the presence of excess thiol was demonstrated. To the best of our knowledge, this is the first study that introduces the benzyl thioether motif for designing the robust and dynamic transthioetherification-based CAN, allowing for the fine-tuning of material properties. This approach will pave the way for the design of robust and sustainable polymeric materials. Thioether ; Covalent bond ; Dynamic covalent chemistry ; Thermosetting polymer ; Polymerization ; Thermal stability ; Polymer ; Monomer Polymer composites and self-healing ; Hydrogels: synthesis, properties, applications ; Carbon dioxide utilization in catalysis
Authors: Kwon, Hyuna | Sadigh, Babak | Hamel, Sebastien | Lordi, Vincenzo | Klepeis, John | Zhou, Fei
Source: arXiv (Cornell University) (preprint, 2025-12-11)
Field: Materials Science — Materials Chemistry

A probabilistic foundation model for crystal structure denoising, phase classification, and order parameters Atomistic simulations generate large volumes of noisy structural data, but extracting phase labels, order parameters (OPs), and defect information in a way that is universal, robust, and interpretable remains challenging. Existing tools such as PTM and CNA are restricted to a small set of hand-crafted lattices (e.g.\ FCC/BCC/HCP), degrade under strong thermal disorder or defects, and produce hard, template-based labels without per-atom probability or confidence scores. Here we introduce a log-probability foundation model that unifies denoising, phase classification, and OP extraction within a single probabilistic framework. We reuse the MACE-MP foundation interatomic potential on crystal structures mapped to AFLOW prototypes, training it to predict per-atom, per-phase logits $l$ and to aggregate them into a global log-density $\log \hat{P}_θ(\boldsymbol{r})$ whose gradient defines a conservative score field. Denoising corresponds to gradient ascent on this learned log-density, phase labels follow from $\arg\max_c l_{ac}$, and the $l$ values act as continuous, defect-sensitive and interpretable OPs quantifying the Euclidean distance to ideal phases. We demonstrate universality across hundreds of prototypes, robustness under strong thermal and defect-induced disorder, and accurate treatment of complex systems such as ice polymorphs, ice--water interfaces, and shock-compressed Ti. Probabilistic logic ; Universality (dynamical systems) ; Robustness (evolution) ; Euclidean geometry ; Thermal ; Phase (matter) ; Probability distribution ; Set (abstract data type) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems
Authors: Д. В. Серебренников
Source: Доклады Российской академии наук Химия науки о материалах / Doklady Chemistry (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

CRYSTALLIZATION OF THE SILICOALUMOPHOSPHATE MOLECULAR SIEVE SAPO-5 FROM REACTION GELS WITH DIFFERENT SiO/AlO RATIOS AND ITS APPLICATION IN HYDROISOMERIZATION OF -HEXADECANE Microporous silicoaluminophosphate molecular sieves SAPO-5 are considered promising acidic catalysts for hydrocarbon conversion processes. However, their catalytic performance is hindered by diffusion limitations, which can be mitigated by reducing crystal size and fine-tuning the acidic properties. The effect of the initial SiO/AlO ratio in the synthesis gel on the structural and acidic features of SAPO-5 was investigated using XPS, XRD, SEM, N adsorption—desorption, NH-TPD, and IR spectroscopy. An increase in silicon content was found to decrease crystal size and enhance the external surface area. The concentration of Bronsted acid sites reaches a maximum, suggesting limited Si incorporation into the framework. In the hydroisomerization of -hexadecane, the highest catalytic activity and selectivity towards isoparaffins were observed for the SAPO-5 sample with the smallest crystals and the highest acidity. These findings demonstrate that the structural and acidic properties of SAPO-5 can be effectively controlled through adjustment of the synthesis gel composition. Molecular sieve ; Catalysis ; Crystallization ; Selectivity ; Crystal (programming language) ; Brønsted–Lowry acid–base theory ; Hydrocarbon Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis and Hydrodesulfurization Studies
Authors: Kwon, Hyuna | Sadigh, Babak | Hamel, Sebastien | Lordi, Vincenzo | Klepeis, John | Zhou, Fei
Source: ArXiv.org (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

A probabilistic foundation model for crystal structure denoising, phase classification, and order parameters Atomistic simulations generate large volumes of noisy structural data, but extracting phase labels, order parameters (OPs), and defect information in a way that is universal, robust, and interpretable remains challenging. Existing tools such as PTM and CNA are restricted to a small set of hand-crafted lattices (e.g.\ FCC/BCC/HCP), degrade under strong thermal disorder or defects, and produce hard, template-based labels without per-atom probability or confidence scores. Here we introduce a log-probability foundation model that unifies denoising, phase classification, and OP extraction within a single probabilistic framework. We reuse the MACE-MP foundation interatomic potential on crystal structures mapped to AFLOW prototypes, training it to predict per-atom, per-phase logits $l$ and to aggregate them into a global log-density $\log \hat{P}_θ(\boldsymbol{r})$ whose gradient defines a conservative score field. Denoising corresponds to gradient ascent on this learned log-density, phase labels follow from $\arg\max_c l_{ac}$, and the $l$ values act as continuous, defect-sensitive and interpretable OPs quantifying the Euclidean distance to ideal phases. We demonstrate universality across hundreds of prototypes, robustness under strong thermal and defect-induced disorder, and accurate treatment of complex systems such as ice polymorphs, ice--water interfaces, and shock-compressed Ti. Probabilistic logic ; Universality (dynamical systems) ; Robustness (evolution) ; Euclidean geometry ; Thermal ; Phase (matter) ; Probability distribution ; Set (abstract data type) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems
Authors: Shengdong Zhang | Ruijie Yu | Jidong Tian | Feng Zhu | Jiapeng Liu | Xiaokang Yang | Yaohui Jin | Yanyan Xu
Source: ArXiv.org (preprint, 2025-06-30)
Field: Materials Science — Materials Chemistry

ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data With the increasing interest in robotic synthesis in the context of organic chemistry, the automated extraction of chemical procedures from literature is critical. However, this task remains challenging due to the inherent ambiguity of chemical language and the high cost of human annotation required for developing reliable computer-aided extraction protocols. Here, we present ChemActor, a fully fine-tuned large language model (LLM), as a chemical executor to convert between unstructured experimental procedures and structured action sequences. We propose a sequential LLM-generated data framework to address the challenges of insufficient and low-quality annotated data. This framework integrates a data selection module that selects data based on distribution divergence, with a general-purpose LLM, to generate machine-executable actions from a single molecule input. Additionally, we introduce a novel multi-round LLMs circle review metric, which reflects the model's advanced understanding of chemical experimental procedures. Extensive experiments on reaction-to-description (R2D) and description-to-action (D2A) tasks demonstrate that ChemActor, augmented by LLM-generated data, achieves state-of-the-art performance, outperforming the baseline model by 10%. The code is available at: https://github.com/Zhanghahah/ChemActor. Context (archaeology) ; Task (project management) ; Annotation ; Code (set theory) ; Data extraction ; Executor ; Ambiguity ; Source code ; Action (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis