jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: David J. Sousa | Erving Ximendes | Luís D. Carlos | Carlos D. S. Brites
Source: Advanced Intelligent Discovery (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Machine‐Learning‐Assisted Onset‐Time Determination in Transient Luminescence Thermometry Transient heating curves obtained from luminescence thermometry encode valuable information about nanoscale heat transport, yet extracting the onset time reliably remains challenging due to experimental noise and the need for manual signal‐processing pipelines. Here, we introduce a machine‐learning‐assisted framework for automated analysis of transient heating curves measured using Ln 3+ ‐doped upconverting nanoparticles. Neural‐network models spanning several architectural families are trained using a balanced dataset combining 822 experimental transients with 822 physically motivated synthetic curves designed to extend the diversity of heating dynamics beyond experimentally accessible variability. We show that predictive robustness is governed primarily by the diversity and physical plausibility of the training data rather than by architectural complexity alone. Relative to a conventional discrete wavelet transform reference procedure, the best‐performing models achieve median onset‐time errors on the order of 2 s, and once trained, determine the onset time with sub‐second latency per transient curve. The resulting framework enables fully automated, operator‐independent analysis of transient luminescence signals and provides a scalable strategy for extracting thermal metrics from time‐resolved spectroscopic data. More broadly, this approach establishes a pathway toward data‐driven analysis of dynamic thermal processes in nanoscale systems and intelligent materials with integrated sensing capabilities. Luminescence ; Transient (computer programming) ; Robustness (evolution) ; Scalability ; Thermal ; Temperature measurement ; Nanoscopic scale ; Wavelet Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Machine Learning in Materials Science
Authors: Kevin Han | Haolin Cong | Bowen Deng | Amir Barati Farimani
Source: Open MIND (preprint, 2026-01-29)
Field: Materials Science — Materials Chemistry

Smooth Dynamic Cutoffs for Machine Learning Interatomic Potentials Machine learning interatomic potentials (MLIPs) have proven to be wildly useful for molecular dynamics simulations, powering countless drug and materials discovery applications. However, MLIPs face two primary bottlenecks preventing them from reaching realistic simulation scales: inference time and memory consumption. In this work, we address both issues by challenging the long-held belief that the cutoff radius for the MLIP must be held to a fixed, constant value. For the first time, we introduce a dynamic cutoff formulation that still leads to stable, long timescale molecular dynamics simulation. In introducing the dynamic cutoff, we are able to induce sparsity onto the underlying atom graph by targeting a specific number of neighbors per atom, significantly reducing both memory consumption and inference time. We show the effectiveness of a dynamic cutoff by implementing it onto 4 state of the art MLIPs: MACE, Nequip, Orbv3, and TensorNet, leading to 2.26x less memory consumption and 2.04x faster inference time, depending on the model and atomic system. We also perform an extensive error analysis and find that the dynamic cutoff models exhibit minimal accuracy dropoff compared to their fixed cutoff counterparts on both materials and molecular datasets. All model implementations and training code will be fully open sourced. Cutoff ; Inference ; Molecular dynamics ; Atom (system on chip) ; Graph ; Code (set theory) ; Cutoff frequency Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: S. Santhoshkumar | Yu-Chan Wu | Liu Bo-Yu | Wei-Lung Tseng
Source: Sensors and Actuators B Chemical (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Rapid identification of sulfur defect levels in MoS₂ and MoSe₂ nanomaterials and their reaction with thiol molecules through defect-induced dimerization of 5-thio-2-nitrobenzoic acid Sulfur ; Thiol ; Nanomaterials ; Absorbance ; Molybdenum disulfide ; Vacancy defect ; Molybdenum ; Catalysis 2D Materials and Applications ; Catalysis and Hydrodesulfurization Studies ; MXene and MAX Phase Materials
Authors: Satoshi Minamoto | Takuya Kadohira | Jun Fujima | Yasuhiro Fujiwara | Akihiro Endo | Chie Suematsu | Koyo Daimaru | Hitoshi Izuno | Junya Sakurai | Masahiko Demura
Source: Science and Technology of Advanced Materials Methods (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

<i>pinax</i> : a provenance management system for materials data science We developed pinax, a provenance management system that serves as an integrated platform for materials data science. The system addresses three key challenges of applying machine learning to materials research: (i) the complexity of analytical workflows, (ii) the requirement for reproducibility, and (iii) effective knowledge sharing. The system records each step, from raw data ingestion and preprocessing to model training, evaluation, and prediction comprehensively. By representing these analytical processes as ‘provenance information’ in a graph-based structure, pinax allows systematic accumulation and reuse of analytical knowledge, which facilitates more efficient data-centric materials research. Furthermore, pinax integrates with the key analytical functions and databases of the NIMS (National Institute for Materials Science) data integration and utilization platform, thereby contributing to the establishment of an integrated infrastructure for materials data science. This integration advances the digital transformation (DX) of materials science by enabling more effective and seamless utilization of data across research activities. The effectiveness of the system was demonstrated through case studies of machine learning applications in materials engineering. Reuse ; Key (lock) ; Data integration ; System integration ; Raw data ; Data management ; Preprocessor ; Management system Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Bokun Li | Yushan Yan | Yuqing Wang | Yixiang Shi
Source: Energy and AI (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

A machine learning based method for hydrogen oxidation mechanisms classification in solid oxide cells • A machine learning model was developed to identify the reaction mechanism. • Data were produced in the one-dimensional button cell model. • Electrochemical impedance spectroscopy serves as the reaction mechanism criterion. • The accuracy of identifying reaction mechanisms exceeds 80%. The investigation of the reaction mechanism in solid oxide cells can establish a theoretical basis for improving the electrode structure, optimizing operational conditions, and enhancing cell performance. The conventional approach to mechanism research involves generating the cell's performance curve through experiments or constructing a basic reaction model, which is intricate and susceptible to human error. In this study, we introduce a novel mechanism identification method based on machine learning. This model can extract kinetic details from electrochemical impedance spectra with high precision and categorize the four hydrogen oxidation mechanisms accurately. The outcomes demonstrate that utilizing advanced artificial intelligence tools can simplify complex traditional electrochemical analyses into automated black-box models, offering a promising solution to liberate researchers from expensive and laborious experiments. Mechanism (biology) ; Dielectric spectroscopy ; Identification (biology) ; Oxide ; Electrochemistry ; Electrode ; Reaction mechanism Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Qi Xin | Fangzhen Tian | Huanhuan Qiao | Guo Li | Yili Wang | Li Li | Lijie Zhang | Nan Song | Ke Chen | Yuxing Yan | Shasha Li | Huizhen Ma | Haile Liu | Hao Wang | Jiang Yang | Si Sun | Yonghui Li | Xiao-Dong Zhang
Source: Materials Today (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Machine learning assisted engineering of atomically precise cluster-derived artificial hydrolases for lipid metabolism diseases Lipid metabolism ; Lipase ; Lipid droplet ; Nonalcoholic fatty liver disease ; Enzyme ; Metabolism Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Enzyme Catalysis and Immobilization
Authors: Chi Chen | Tao Jiang | Xiaodong Wei | Yanming Wang
Source: arXiv (Cornell University) (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

PolyCrysDiff: Controllable Generation of Three-Dimensional Computable Polycrystalline Material Structures The three-dimensional (3D) microstructures of polycrystalline materials exert a critical influence on their mechanical and physical properties. Realistic, controllable construction of these microstructures is a key step toward elucidating structure-property relationships, yet remains a formidable challenge. Herein, we propose PolyCrysDiff, a framework based on conditional latent diffusion that enables the end-to-end generation of computable 3D polycrystalline microstructures. Comprehensive qualitative and quantitative evaluations demonstrate that PolyCrysDiff faithfully reproduces target grain morphologies, orientation distributions, and 3D spatial correlations, while achieving an $R^2$ over 0.972 on grain attributes (e.g., size and sphericity) control, thereby outperforming mainstream approaches such as Markov random field (MRF)- and convolutional neural network (CNN)-based methods. The computability and physical validity of the generated microstructures are verified through a series of crystal plasticity finite element method (CPFEM) simulations. Leveraging PolyCrysDiff's controllable generative capability, we systematically elucidate how grain-level microstructural characteristics affect the mechanical properties of polycrystalline materials. This development is expected to pave a key step toward accelerated, data-driven optimization and design of polycrystalline materials. Crystallite ; Microstructure ; Context (archaeology) ; Finite element method ; Artificial neural network ; Plasticity ; Orientation (vector space) ; Markov process Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Jakub Grudzien Kuba | Pieter Abbeel | Sergey Levine
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Cliqueformer: Model-Based Optimization with Structured Transformers Large neural networks excel at prediction tasks, but their application to design problems, such as protein engineering or materials discovery, requires solving offline model-based optimization (MBO) problems. While predictive models may not directly translate to effective design, recent MBO algorithms incorporate reinforcement learning and generative modeling approaches. Meanwhile, theoretical work suggests that exploiting the target function’s structure can enhance MBO performance. We present Cliqueformer, a transformer- based architecture that learns the black-box function’s structure through functional graphical models (FGM), addressing distribution shift without relying on explicit conservative approaches. Across various domains, including chemical and genetic design tasks, Cliqueformer demonstrates superior performance compared to existing methods. Artificial neural network ; Reinforcement learning ; Transformer ; Graphical model ; Generative Design ; Generative grammar ; Encoding (memory) Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Machine Learning and Data Classification
Authors: Tianrui Zeng | Hao Yang | Shimu Wang | Di Wu | E Tang | Yilin Yang | Shanzhe Zhang | Xiaorong Sun | Cuiling Liu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Classification of Fermentation Methods of Fermented Products Using Fluorescence Spectroscopy Combined with Deep Learning Fermentation ; Raw material ; Deep learning ; Fluorescence spectroscopy ; Yeast ; Fermentation in food processing ; Lactobacillus Spectroscopy and Chemometric Analyses ; Biopolymer Synthesis and Applications ; GABA and Rice Research
Authors: Chi Chen | Tao Jiang | Xiaodong Wei | Yanming Wang
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

PolyCrysDiff: Controllable Generation of Three-Dimensional Computable Polycrystalline Material Structures The three-dimensional (3D) microstructures of polycrystalline materials exert a critical influence on their mechanical and physical properties. Realistic, controllable construction of these microstructures is a key step toward elucidating structure-property relationships, yet remains a formidable challenge. Herein, we propose PolyCrysDiff, a framework based on conditional latent diffusion that enables the end-to-end generation of computable 3D polycrystalline microstructures. Comprehensive qualitative and quantitative evaluations demonstrate that PolyCrysDiff faithfully reproduces target grain morphologies, orientation distributions, and 3D spatial correlations, while achieving an $R^2$ over 0.972 on grain attributes (e.g., size and sphericity) control, thereby outperforming mainstream approaches such as Markov random field (MRF)- and convolutional neural network (CNN)-based methods. The computability and physical validity of the generated microstructures are verified through a series of crystal plasticity finite element method (CPFEM) simulations. Leveraging PolyCrysDiff's controllable generative capability, we systematically elucidate how grain-level microstructural characteristics affect the mechanical properties of polycrystalline materials. This development is expected to pave a key step toward accelerated, data-driven optimization and design of polycrystalline materials. Crystallite ; Microstructure ; Context (archaeology) ; Finite element method ; Artificial neural network ; Plasticity ; Orientation (vector space) ; Markov process Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Michael Kim | Wei Cai
Source: ChemRxiv (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Accelerated Langevin Dynamics Simulation via Neural Network-Driven Importance Sampling Atomistic simulations are powerful tools for understanding the dynamical processes that control material behavior at the molecular scale. Yet the study of many important processes is currently limited by the timescales achievable by direct simulation. This limitation often arises from potential energy landscapes featuring deep energy basins, corresponding to metastable states, in which the system remains trapped for extended periods of time. The long-term evolution of such systems is governed by the transitions between these metastable states, which are rare events that are very inefficient to capture by brute-force simulations. We present an importance sampling framework to accelerate the time scale of Langevin dynamics simulations. Using an importance function to bias the dynamics, this approach significantly enhances the efficiency of sampling the rare transitions, while preserving the relative probabilities between different transition paths. The importance function is parameterized using a neural network for scalability to high-dimensional systems. We also provide a rigorous mathematical formulation to compute the original transition rates between metastable states from the biased dynamics. To control the statistical variance of the estimated transition rates, we introduce a branching random walk (BRW) algorithm to manage the weights of the sampled transition paths. We validate the proposed importance sampling framework on a two-dimensional system and a fourteen-dimensional system to demonstrate its robustness and scalability. Rare events ; Langevin dynamics ; Sampling (signal processing) ; Metastability ; Importance sampling ; Robustness (evolution) ; Scalability ; Complex system ; Random walk Machine Learning in Materials Science ; Quantum many-body systems ; Protein Structure and Dynamics
Authors: Alexander Yu. Sokolov
Source: Open MIND (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

From Ground to Excited: Modern Strategies for Modeling Light–Matter Interactions Abstract: Understanding and predicting how molecules and materials interact with light is central to modern computational chemistry, underpinning research in photochemistry, photovoltaics, spectroscopy, and biology. Yet, accurately describing electronically excited states remains a formidable task due to the challenges of simulating electronic structure, nuclear dynamics, and relativistic effects using high-level theoretical methods. In this lecture, I will survey the conceptual and practical challenges in simulating electronically excited states and discuss how different theoretical frameworks ranging from single-reference excited-state approaches to multireference methods address these issues. I will then focus on recent methodological developments from our group that enable accurate yet efficient treatment of complex excited-state phenomena in molecules and materials. Examples will illustrate how these new tools can be used to predict excitation energies, analyze photochemical pathways, and interpret experimental spectra. Finally, I will outline current frontiers and open questions in excited-state theory, highlighting opportunities for innovation at the interface of electronic structure theory, nonadiabatic dynamics, machine learning, and high-performance computing. The goal of this talk is to provide both conceptual insight and a practical perspective for students and researchers interested in exploring the rapidly evolving field of excited-state simulations. Keywords: Electronic structure theory, excited states, multireference methods, nonadiabatic dynamics, spectroscopy Field (mathematics) ; Excited state ; Perspective (graphical) ; Underpinning ; Task (project management) ; Focus (optics) ; Computational model ; Common ground Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Andrea Giunto | Yuxing Fei | Pragnay Nevatia | Bernardus Rendy | Nathan J. Szymanski | Gerbrand Ceder
Source: Research Square (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

Accurate SEM‑EDS Quantification, Automation, and Machine Learning Enable High‑Throughput Compositional Characterization of Powders Characterization (materials science) ; Reliability (semiconductor) ; Limiting ; Identification (biology) ; Scalability ; Limit (mathematics) X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Alexander Yu. Sokolov
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

From Ground to Excited: Modern Strategies for Modeling Light–Matter Interactions Abstract: Understanding and predicting how molecules and materials interact with light is central to modern computational chemistry, underpinning research in photochemistry, photovoltaics, spectroscopy, and biology. Yet, accurately describing electronically excited states remains a formidable task due to the challenges of simulating electronic structure, nuclear dynamics, and relativistic effects using high-level theoretical methods. In this lecture, I will survey the conceptual and practical challenges in simulating electronically excited states and discuss how different theoretical frameworks ranging from single-reference excited-state approaches to multireference methods address these issues. I will then focus on recent methodological developments from our group that enable accurate yet efficient treatment of complex excited-state phenomena in molecules and materials. Examples will illustrate how these new tools can be used to predict excitation energies, analyze photochemical pathways, and interpret experimental spectra. Finally, I will outline current frontiers and open questions in excited-state theory, highlighting opportunities for innovation at the interface of electronic structure theory, nonadiabatic dynamics, machine learning, and high-performance computing. The goal of this talk is to provide both conceptual insight and a practical perspective for students and researchers interested in exploring the rapidly evolving field of excited-state simulations. Keywords: Electronic structure theory, excited states, multireference methods, nonadiabatic dynamics, spectroscopy Field (mathematics) ; Excited state ; Perspective (graphical) ; Underpinning ; Task (project management) ; Focus (optics) ; Computational model ; Common ground Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Jilong Wang (2887142) | Yaqi Fan | Wan Zheng | Xiangxiang Shen | Xinyue Zhao (1755088) | Ziwen Niu | Xintong Li (3110796) | Xianchen Gong | Jingang Jiang | Zhendong Wang (578111) | Yi Luo (143206) | Yejun Guan | Xian Wei (4326325) | Hao Xu (6530) | Yanhang Ma (1803754) | Xiao He (223527) | Weimin Yang (803653) | Peng Wu (210686)
Source: Figshare (article, 2026-03-04)
Field: Chemistry — Inorganic Chemistry

Artificial IntelligencePredicted OSDAs Enable DirectSynthesis of Interlayer-Expanded Zeolites Zeolite crystallization is a metastable process under harsh conditions with poorly understood mechanisms, making the directed synthesis of specific frameworks challenging. Organic structure-directing agents (OSDAs) are key to framework control, but their discovery remains dominated by trial-and-error screening. Here, we develop a domain knowledge-informed machine learning model to predict OSDAs, which enables the successful synthesis of three novel zeolites, namely, ECNU-30, ECNU-34, and ECNU-40 (named after East China Normal University), validating the efficacy of the model. Traditional descriptor-based machine learning models exhibit limited predictive performance in screening OSDAs for unknown zeolite frameworks. Combining an end-to-end architecture with active learning, the ECNU-Zeoformer effectively overcomes this limitation, enabling more accurate prediction of OSDA-zeolite binding energies for selecting suitable OSDAs and superior generalizability to different framework topologies. Generalizability theory ; Process (computing) ; Key (lock) ; Domain (mathematical analysis) ; Artificial neural network ; Applicability domain ; Zeolite Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced NMR Techniques and Applications