jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abhijith S. Parackal | Florian Trybel | Felix A. Faber | Rickard Armiento
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Screening 39 billion protostructures for materials discovery Large-scale computational surveys are increasingly used to map the landscape of stable crystalline materials. We report a high-throughput energy screening of inorganic crystals that enumerates binary and ternary compositions up to a specified unit-cell complexity, yielding 39 billion protostructures. Candidates predicted to lie on or near the convex hull are retained, and their degrees of freedom are explored via Latin hypercube sampling followed by relaxation with machine-learned interatomic potentials. The resulting dataset contains 81 million locally relaxed crystal structures spanning 4495 ternary phase diagrams constructed from elements ranging from lithium to bromine and contains 88,498 crystal prototypes not present in existing crystal-structure databases. The methods are validated both for three well-explored materials systems, Zr-Zn-N, Ti-Zn-N, and Hf-Zn-N, and by comparing with known data for structures resulting from the larger screening. The work provides a systematic map of low-energy compositional-structural space and a large, structured pool of candidates for downstream property evaluation and materials design. Ternary operation ; Crystal (programming language) ; Convex hull ; Property (philosophy) ; Binary number ; Sampling (signal processing) ; Work (physics) ; Relaxation (psychology) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Sara Zoccheddu | Qasim, Shah Rukh | Patrick Owen | Nicola Serra
Source: ArXiv.org (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Large Language Models for Physics Instrument Design We study the use of large language models (LLMs) for physics instrument design and compare their performance to reinforcement learning (RL). Using only prompting, LLMs are given task constraints and summaries of prior high-scoring designs and propose complete detector configurations, which we evaluate with the same simulators and reward functions used in RL-based optimization. Although RL yields stronger final designs, we find that modern LLMs consistently generate valid, resource-aware, and physically meaningful configurations that draw on broad pretrained knowledge of detector design principles and particle--matter interactions, despite having no task-specific training. Based on this result, as a first step toward hybrid design workflows, we explore pairing the LLMs with a dedicated trust region optimizer, serving as a precursor to future pipelines in which LLMs propose and structure design hypotheses while RL performs reward-driven optimization. Based on these experiments, we argue that LLMs are well suited as meta-planners: they can design and orchestrate RL-based optimization studies, define search strategies, and coordinate multiple interacting components within a unified workflow. In doing so, they point toward automated, closed-loop instrument design in which much of the human effort required to structure and supervise optimization can be reduced. Point (geometry) ; Task (project management) ; Detector ; Design elements and principles ; Reinforcement learning ; Language model Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Multi-Objective Optimization Algorithms
Authors: Jennifer D'Souza | Soren Auer | Eleni Poupaki | Alex Watkins | Anjana Devi | Riikka L. Puurunen | Bora Karasulu | Adrie Mackus | Erwin Kessels
Source: ArXiv.org (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligence Scientific reviews are central to knowledge integration in materials science, yet their key insights remain locked in narrative text and static PDF tables, limiting reuse by humans and machines alike. This article presents a case study in atomic layer deposition and etching (ALD/E) where we publish review tables as FAIR, machine-actionable comparisons in the Open Research Knowledge Graph (ORKG), turning them into structured, queryable knowledge. Building on this, we contrast symbolic querying over ORKG with large language model-based querying, and argue that a curated symbolic layer should remain the backbone of reliable neurosymbolic AI in materials science, with LLMs serving as complementary, symbolically grounded interfaces rather than standalone sources of truth. Reuse ; Publishing ; Narrative ; Key (lock) ; Limiting ; Layer (electronics) ; Applications of artificial intelligence ; Sociology of scientific knowledge ; Graph Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Research Data Management Practices
Authors: Wendy Huang | Susu Fang | Xuhang Gu | Shen'ao Xue | Huanhuan Xing | Junjie Jiang | Junying Zhang | Shen Zhou | Zheng Luo | Jin Zhang | Fei-Hong Ouyang | Shanshan Wang
Source: ArXiv.org (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Machine intelligence supports the full chain of 2D dendrite synthesis Exemplified by the chemical vapor deposition growth of two-dimensional dendrites, which has potential applications in catalysis and presents a parameter-intensive, data-scarce and reaction process-complex model problem, we devise a machine intelligence-empowered framework for the full chain support of material synthesis, encompassing rapid process optimization, accurate customized synthesis, and comprehensive mechanism deciphering.First, active learning is integrated into the experimental workflow, identifying an optimal recipe for the growth of highly-branched, electrocatalytically-active ReSe2 dendrites through 60 experiments (4 iterations), which account for less than 1.3% of the numerous possible parameter combinations.Then, a prediction accuracy-guided data augmentation strategy is developed combined with a tree-based machine learning (ML) algorithm, unveiling a non-linear correlation between 5 process variables and fractal dimension (DF) of ReSe2 dendrites with only 9 experiment additions, which guides the synthesis of various user-defined DF. Finally, we construct a data-knowledge dual-driven mechanism model by integration of cross-scale characterizations, interpretable ML models, and domain knowledge in thermodynamics and kinetics, unraveling synergistic contributions of multiple process parameters to the product morphology. This work demonstrates the ML potential to transform the research paradigm and is adaptable to broader material synthesis. Process (computing) ; Mechanism (biology) ; Dimension (graph theory) ; Domain (mathematical analysis) ; Chain (unit) ; Product (mathematics) ; Experimental data ; Construct (python library) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Makoto Sato
Source: ArXiv.org (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

ReMIND: Orchestrating Modular Large Language Models for Controllable Serendipity A REM-Inspired System Design for Emergent Creative Ideation Large language models (LLMs) are used not only for problem solving but also for creative ideation; however, eliciting serendipitous insights that are both novel and internally coherent remains difficult. While stochastic sampling promotes novelty, it often degrades consistency. Here, we propose ReMIND, a REM-inspired modular framework for ideation. ReMIND consists of four stages: wake, which generates a stable low-temperature semantic baseline; dream, which performs high-temperature exploratory generation; judge, which applies coarse evaluation to filter incoherent outputs and extract candidate ideas; and re-wake, which re-articulates selected ideas into coherent final outputs. By instantiating each stage as an independent LLM, ReMIND enables functional separation between exploration and consolidation. Parameter sweeps show that ReMIND reliably induces semantic exploration while preserving downstream stability. Embedding-based analyses confirm substantial semantic displacement during the dream phase, whereas external evaluations reveal that high-quality ideas emerge sporadically rather than as extrema along any single metric. These results suggest that serendipitous ideation in LLMs is a rare-event process best approached through system level design that shapes the conditions under which valuable ideas can emerge and be stabilized. ReMIND provides a general framework for studying the computational basis of serendipity and illustrates how modular LLM orchestration can bridge exploration and stabilization. Serendipity ; Modular design ; Ideation ; Process (computing) ; Fluency ; Semantics (computer science) ; Orchestration ; Filter (signal processing) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Generative Adversarial Networks and Image Synthesis
Authors: Mianzhi Pan | J. Li | Peishuo Liu | Botian Wang | Yawen Ouyang | Yiming Rong | Hao Zhou | Jianbing Zhang
Source: ArXiv.org (article, 2026-01-14)
Field: Chemistry — Inorganic Chemistry

Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge. While Large Language Models (LLMs) have shown promise in generating crystals, their application to MOFs is hindered by MOFs' high atomic complexity. Inspired by the success of block-wise paradigms in deep generative models, we pioneer the use of LLMs in this domain by introducing MOF-LLM, the first LLM framework specifically adapted for block-level MOF structure prediction. To effectively harness LLMs for this modular assembly task, our training paradigm integrates spatial-aware continual pre-training (CPT), structural supervised fine-tuning (SFT), and matching-driven reinforcement learning (RL). By incorporating explicit spatial priors and optimizing structural stability via Soft Adaptive Policy Optimization (SAPO), our approach substantially enhances the spatial reasoning capability of a Qwen-3 8B model for accurate MOF structure prediction. Comprehensive experiments demonstrate that MOF-LLM outperforms state-of-the-art denoising-based and LLM-based methods while exhibiting superior sampling efficiency. Modular design ; Stability (learning theory) ; Generative grammar ; Domain (mathematical analysis) ; Reinforcement learning ; Prior probability Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Tiago F. T. Cerqueira | Haichen Wang | Silvana Botti | Miguel A. L. Marques
Source: npj Computational Materials (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

A non-orthogonal representation for materials based on chemical similarity Abstract We present a novel approach to generate a fingerprint for crystalline materials that balances efficiency for machine processing and human interpretability, allowing its application in both machine learning inference and understanding of structure-property relationships. Our proposed material encoding has two components: one representing the crystal structure and the other characterizing the chemical composition, which we call Pettifor embedding. For the latter, we construct a non-orthogonal space where each axis represents a chemical element and where the angle between the axes quantifies a measure of the similarity between them. The chemical composition is then defined by the point on the unit sphere in this non-orthogonal space. We show that the Pettifor embeddings systematically outperform other commonly used elemental embeddings in compositional machine learning models. Using the Pettifor embeddings to define a distance metric and applying dimension reduction techniques, we construct a two-dimensional global map of the space of thermodynamically stable crystalline compounds. Despite their simplicity, such maps succeed in providing a physical separation of material classes according to basic physical properties. Similarity (geometry) ; Representation (politics) ; Metric (unit) ; Construct (python library) ; Measure (data warehouse) ; Point (geometry) ; Dimension (graph theory) ; Space (punctuation) ; Metric space Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Iare Soares Ribeiro | Wesley C. P. Aquino | Lucas H. M. Alfredo | Jemmyson Romário de Jesus
Source: Processes (article, 2026-03-10)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Frameworks as Multifunctional Platforms for Chemical Sensors: Advances in Electrochemical and Optical Detection of Emerging Contaminants Metal–organic frameworks (MOFs) have received significant attention as multifunctional platforms for chemical sensing due to their adjustable porosity, high specific surface area, and modular chemical architecture, which allow for customized host-guest interactions and signal transduction. This work presents a critical overview of recent advances in electrochemical and optical sensors based on MOFs for the detection of emerging contaminants, including toxic metal ions, pharmaceutical residues, and industrial pollutants in environmental and biological matrices. Special emphasis is placed on the underlying sensing mechanisms, such as redox activity, charge transfer, and luminescence modulation, as well as the main challenges related to structural stability under realistic operating conditions, including variations in pH, humidity, and temperature. Furthermore, the development of hybrid and hierarchical architecture based on MOFs is discussed as an effective strategy to improve sensitivity, selectivity, and long-term robustness. Finally, the perspective highlights how to optimize sensor performance and enable more reliable and scalable applications in monitoring emerging contaminants. Modular design ; Scalability ; Chemical sensor ; Optical sensing ; Biosensor Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Electrochemical sensors and biosensors
Authors: Dina Khalil | Sarah Farouk | Mariam Tawfik
Source: ChemRxiv (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Bridging Ab Initio Accuracy and Semi-Empirical Efficiency with a Physics-Informed Machine Learning Force Field High-fidelity quantum chemical calculations are prohibitively expensive for large systems and long molecular dynamics simulations. Semi-empirical methods offer efficiency but often sacrifice accuracy and generalizability. Purely data-driven machine learning force fields struggle with out-of-training-data generalization and physical interpretability. We introduce NeuroForceTB, a novel Physics-Informed Machine Learning force field, to overcome these limitations. It combines a Generalized Tight-Binding (GXT-TB) theory framework with a neural network. NeuroForceTB leverages GXT-TB's physical foundation to efficiently extract robust descriptors: eigenvalues and gradient information of the atomic local density matrix, which encode spin, local charge, and atomic environment structures. The neural network then learns high-level quantum mechanical corrections to GXT-TB's predictions, refining accuracy while preserving computational efficiency. Our comprehensive evaluation demonstrates that NeuroForceTB achieves near-ab initio accuracy for energies and forces, outperforming existing methods across diverse chemical challenges. It exhibits superior force prediction on a standard benchmark, enhanced accuracy for diverse long-range non-covalent interactions (including hydrogen-bonded and van der Waals systems), and robust performance for open-shell systems and complex chemical reactions, accurately describing reactant and transition state energies. Crucially, NeuroForceTB maintains high computational efficiency with near-linear scaling, making it ideal for stable, efficient, and high-precision molecular dynamics simulations of complex systems over extended timescales. Bridging (networking) ; van der Waals force ; Artificial neural network ; Force field (fiction) ; Ab initio ; Generalization ; Density functional theory ; Quantum Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Yinan Lin | Dongliang Yang | Zhongyi Wang | Weili Zhen | Tianze Yu | Fei Xue | Hongtao Wei | Linfeng Sun
Source: Nature Communications (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Antiferroelectric polarization enabling physical activation in CuBiP2Se6 for medical image processing Antiferroelectric materials, featuring field controllable antipolar ordering and reversible polarization switching, offer a promising platform for hardware efficient neuromorphic computing. The tunable polarization dynamics and layered van der Waals structure enable the multifunctional integration of sensing, learning, and computation within a single device architecture. Here, we demonstrate an antiferroelectric polarization driven diode exhibiting an extended linear operating region, which simultaneously enables physical activation and computing-in-memory. Building on the device capability, we construct an in-sensor computing system that achieves over 95% accuracy in medical image classification. We further integrate the devices to demonstrate a hardware-based activation function, attaining accuracy and training loss comparable to an ideal activation function. To enhance adaptability, we further propose a tunable activation circuit that enables linear modulation of the reverse bias slope via gain control. Overall, this work establishes a dual-functional antiferroelectric heterojunction, highlighting its strong potential for constructing optically triggered, compact, and low-power perception-computation-integrated neuromorphic systems for medical image processing. Polarization (electrochemistry) ; Antiferroelectricity ; Image processing ; Signal processing 2D Materials and Applications ; Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films
Authors: Letian Chen | Runhan Shi | Gufeng Yu | Yang Yang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

RTMol: Rethinking Molecule-text Alignment in a Round-trip View Aligning molecular sequence representations (e.g., SMILES notations) with textual descriptions is critical for applications spanning drug discovery, materials design, and automated chemical literature analysis. Existing methodologies typically treat molecular captioning (molecule-to-text) and text-based molecular design (text-to-molecule) as separate tasks, relying on supervised fine-tuning or contrastive learning pipelines. These approaches face three key limitations: (i) conventional metrics like BLEU prioritize linguistic fluency over chemical accuracy, (ii) training datasets frequently contain chemically ambiguous narratives with incomplete specifications, and (iii) independent optimization of generation directions leads to bidirectional inconsistency. To address these issues, we propose RTMol, a bidirectional alignment framework that unifies molecular captioning and text-to-SMILES generation through self-supervised round-trip learning. The framework introduces novel round-trip evaluation metrics and enables unsupervised training for molecular captioning without requiring paired molecule-text corpora. Experiments demonstrate that RTMol enhances bidirectional alignment performance by up to 47% across various LLMs, establishing an effective paradigm for joint molecule-text understanding and generation. Closed captioning ; Fluency ; Key (lock) ; Representation (politics) ; Training set ; Face (sociological concept) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Muhammad bin Javaid | Hasham Hussain | Ashima Khanna | Berke Kisin | Jonathan Pirnay | Alexander Mitsos | Dominik G. Grimm | Martin Grohe
Source: ArXiv.org (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Amortized Molecular Optimization via Group Relative Policy Optimization Molecular design encompasses tasks ranging from de-novo design to structural alteration of given molecules or fragments. For the latter, state-of-the-art methods predominantly function as "Instance Optimizers'', expending significant compute restarting the search for every input structure. While model-based approaches theoretically offer amortized efficiency by learning a policy transferable to unseen structures, existing methods struggle to generalize. We identify a key failure mode: the high variance arising from the heterogeneous difficulty of distinct starting structures. To address this, we introduce GRXForm, adapting a pre-trained Graph Transformer model that optimizes molecules via sequential atom-and-bond additions. We employ Group Relative Policy Optimization (GRPO) for goal-directed fine-tuning to mitigate variance by normalizing rewards relative to the starting structure. Empirically, GRXForm generalizes to out-of-distribution molecular scaffolds without inference-time oracle calls or refinement, achieving scores in multi-objective optimization competitive with leading instance optimizers. Directed acyclic graph ; Oracle ; Graph ; Group (periodic table) ; Variance (accounting) ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Vsevolod Biryukov | Kamal Choudhary | Timur Bazhirov
Source: arXiv (Cornell University) (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

AI-ready design of realistic 2D materials and interfaces with Mat3ra-2D Artificial intelligence (AI) and machine learning (ML) models in materials science are predominantly trained on ideal bulk crystals, limiting their transferability to real-world applications where surfaces, interfaces, and defects dominate. We present Mat3ra-2D, an open-source framework for the rapid design of realistic two-dimensional materials and related structures, including slabs and heterogeneous interfaces, with support for disorder and defect-driven complexity. The approach combines: (1) well-defined standards for storing and exchanging materials data with a modular implementation of core concepts and (2) transformation workflows expressed as configuration-builder pipelines that preserve provenance and metadata. We implement typical structure generation tasks, such as constructing orientation-specific slabs or strain-matching interfaces, in reusable Jupyter notebooks that serve as both interactive documentation and templates for reproducible runs. To lower the barrier to adoption, we design the examples to run in any web browser and demonstrate how to incorporate these developments into a web application. Mat3ra-2D enables systematic creation and organization of realistic 2D- and interface-aware datasets for AI/ML-ready applications. Workflow ; Modular design ; Pipeline (software) ; Documentation ; Transferability ; Limiting ; Template ; User interface Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions