jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: P. A. Fuchs | Julija Zavadlav
Source: ArXiv.org (preprint, 2025-12-03)
Field: Materials Science — Materials Chemistry

Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions Foundational Machine Learning Potentials can resolve the accuracy and transferability limitations of classical force fields. They enable microscopic insights into material behavior through Molecular Dynamics simulations, which can crucially expedite material design and discovery. However, insufficiently broad and systematically biased reference data affect the predictive quality of the learned models. Often, these models exhibit significant deviations from experimentally observed phase transition temperatures, in the order of several hundred kelvins. Thus, fine-tuning is necessary to achieve adequate accuracy in many practical problems. This work proposes a fine-tuning strategy via top-down learning, directly correcting the wrongly predicted transition temperatures to match the experimental reference data. Our approach leverages the Differentiable Trajectory Reweighting algorithm to minimize the free energy differences between phases at the experimental target pressures and temperatures. We demonstrate that our approach can accurately correct the phase diagram of pure Titanium in a pressure range of up to 5 GPa, matching the experimental reference within tenths of kelvins and improving the liquid-state diffusion constant. Our approach is model-agnostic, applicable to multi-component systems with solid-solid and solid-liquid transitions, and compliant with top-down training on other experimental properties. Therefore, our approach can serve as an essential step towards highly accurate application-specific and foundational machine learning potentials. Experimental data ; Phase transition ; Matching (statistics) ; Trajectory ; Range (aeronautics) ; Phase diagram ; Work (physics) ; Stability (learning theory) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Xiang-Yang Liu (1408399) | Dongyi Xiao (22185291) | Wei-Hai Fang (1268982) | Ganglong Cui (1329708)
Source: Figshare (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

Excited-State WaveFunctions and Energies Predictedby Machine Learning Based on Graph Neural Networks Accurate and efficient simulation of photoinduced dynamics in materials remains a significant challenge due to the computational cost of excited-state electronic structure calculations and the necessity to account for excitonic effects. In this work, we present a machine learning (ML)-accelerated approach to nonadiabatic molecular dynamics simulations that incorporates excitonic effects by predicting excited-state wave functions via configuration interaction coefficients and excitation energies using a graph neural network (GNN) architecture. The GNN model leverages molecular orbital information from ground-state calculations to construct input graphs, enabling efficient and accurate prediction of relevant excited-state wave functions and energies required for ab initio-based fewest-switches surface hopping simulations. Benchmarking on a zinc phthalocyanine-fullerene (ZnPc-C<sub>6</sub><sub>0</sub>) donor–acceptor system reveals that these ML-predicted properties agree closely with those obtained from linear-response time-dependent density functional theory calculations while boosting the computational efficiency significantly. The ML-accelerated simulations reproduce excited-state dynamics with high fidelity, demonstrating the methodological capability to study complex photodynamical processes in large systems. This work provides a general and scalable framework for efficient excited-state dynamics simulations in materials where excitonic effects play a vital role. Wave function ; Scalability ; Artificial neural network ; Boosting (machine learning) ; Benchmarking ; Molecular dynamics ; Graph ; Curse of dimensionality Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Agus Arip Munawar | Hizir Hizir | Devianti Devianti
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Robust and Update NIRS Models for Predicting Quality Attributes on Intact Mango by means of optimized Machine Learning and Artificial Neural Networks Artificial neural network ; Partial least squares regression ; Support vector machine ; Random forest ; Regression ; Linear regression ; Regression analysis ; Extreme learning machine ; Boosting (machine learning) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management
Authors: Mohamed A. Fouad M. Gaber | Basem Zaghloul | Ahmad Baraka | Osama Abuzalat
Source: Journal of Physics Conference Series (article, 2025-08-01)
Field: Chemistry — Inorganic Chemistry

A Mini Review on Recent Advances in Luminescent Coordination Polymers as Sensors for Dichromate Detection Abstract The detection of hazardous pollutants, particularly chromium(VI) species such as dichromate (Cr 2 O 7 2– ), remains a significant environmental and public health challenge due to their toxicity and carcinogenic nature. Conventional analytical techniques, while highly accurate, suffer from drawbacks such as high cost, complexity, and the need for specialized infrastructure, making real-time and on-site monitoring difficult. Luminescent coordination polymers (LCPs) have emerged as promising alternatives for Cr(VI) detection, offering benefits like tunable luminescence, high discernment, and fast response. This review explores recent advances in LCP-based sensors, emphasizing design principles, synthesis strategies, and fluorescence quenching mechanisms, including electron transfer, energy transfer, and the inner filter effect. The role of metal centers and organic ligands in enhancing sensing performance is examined, alongside the impact of structural modifications, nanotechnology-driven innovations, and composite materials. Additionally, we discuss the integration of computational modeling and machine learning in optimizing sensor performance. Challenges related to stability, selectivity, and real-world applicability are addressed, along with potential strategies to improve sensor robustness under diverse conditions. Finally, we outline future directions, including hybrid materials, miniaturized platforms, and green chemistry approaches, underscoring the potential of LCP sensors for sustainable and efficient Cr(VI) detection. This review provides a comprehensive perspective on the progress, challenges, and future outlook for LCP-based sensors in environmental monitoring. Luminescence ; Nanotechnology ; Polymer ; Luminescent Measurements ; Materials science ; Combinatorial chemistry ; Chemistry ; Optoelectronics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Polyoxometalates: Synthesis and Applications
Authors: Hongcai Liu (11924795) | Shuai Guan (6521855) | Zhuofan Wang (22478883) | Zhongjun Zhou (53993) | Zexing Qu (1684801) | Wenjian Liu (577482)
Source: Figshare (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Machine Learned FockMatrix This work introduces a novel machine learning integral-based self-consistent field method, called <i>mi</i>SCF, designed for the efficient prediction of molecular electronic structures. By incorporating detailed information from the atomic information and geometric features to predict the molecular Fock matrix, this approach enhances data sharing among similar systems and facilitates effective data transfer within chemically analogous systems. This enables precise predictions of various electronic properties of the systems with only a small amount of training data, significantly reducing computational costs while maintaining high accuracy. Testing results on some representative small molecules, H<sub>2</sub> and H<sub>2</sub>O chain as well as ice structures, demonstrate that <i>mi</i>SCF shows good performance in the prediction of energies, wave function, and electron densities, showcasing high precision, efficiency, and good transferability. Thus, the <i>mi</i>SCF method provides an efficient tool for quantum chemical calculations, laying a solid foundation for further applications in potential energy surface construction, ab initio molecular dynamics, and chemical reaction simulations. Ab initio ; Field (mathematics) ; Electronic structure ; Fock space ; Fock matrix ; Work (physics) ; Experimental data ; Matrix (chemical analysis) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Sema Dogmaz | Levent Çavaş
Source: International Journal of Hydrogen Energy (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

From a marine waste to hydrogen gas: Ulva lactuca Ulva lactuca ; Environmental science ; Hydrogen ; Waste management ; Environmental chemistry ; Chemistry ; Algae ; Ecology ; Biology ; Engineering Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Chemical Synthesis and Characterization
Authors: Zhao Yang (358244) | Shuai Yang (645019) | Xueyi Wang (335442) | Jixiong Xiao (21447216) | Hang Wang (137566)
Source: Figshare (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Prediction of GlobalWarming Potential for Gases Basedon Group Contribution Method and Chemical Activity Descriptor To assess the environmental performance of SF<sub>6</sub> substitute gases, it is essential to develop a predictive model for the global warming potential. In this study, 165 molecules are first selected to construct machine learning models using group contribution method. The predictive performance of various models is analyzed, including Artificial Neural Network, Random Forest, Gradient Boosted Decision Trees, and Support Vector Machines. Then 58 chemical activity descriptors are calculated using the M06–2X method and def2-TZVP basis, and the key descriptors are identified through Pearson correlation coefficient. These descriptors are used to build several machine learning models. The performance of these models constructed by the two approaches is compared. The result indicates that the descriptor-based models outperform the group-based models, with the descriptor-based Random Forest model achieving the best performance. The <i>R</i><sup>2</sup> of test set reached 0.82, with an MSE of 0.015, an RMSE of 0.024, and an MAE of 0.09. Moreover, the descriptor-based model demonstrated higher stability and robustness across 1000 training iterations. Random forest ; Support vector machine ; Robustness (evolution) ; Stability (learning theory) ; Test set ; Correlation ; Training set ; Predictive modelling ; Mean squared error Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Hong Liu | Shuai Guan | Zhuofan Wang | Zhong‐Jun Zhou | Zexing Qu | Wenjian Liu
Source: JACS Au (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Machine Learned Fock Matrix This work introduces a novel machine learning integral-based self-consistent field method, called <i>mi</i>SCF, designed for the efficient prediction of molecular electronic structures. By incorporating detailed information from the atomic information and geometric features to predict the molecular Fock matrix, this approach enhances data sharing among similar systems and facilitates effective data transfer within chemically analogous systems. This enables precise predictions of various electronic properties of the systems with only a small amount of training data, significantly reducing computational costs while maintaining high accuracy. Testing results on some representative small molecules, H<sub>2</sub> and H<sub>2</sub>O chain as well as ice structures, demonstrate that <i>mi</i>SCF shows good performance in the prediction of energies, wave function, and electron densities, showcasing high precision, efficiency, and good transferability. Thus, the <i>mi</i>SCF method provides an efficient tool for quantum chemical calculations, laying a solid foundation for further applications in potential energy surface construction, ab initio molecular dynamics, and chemical reaction simulations. Ab initio ; Field (mathematics) ; Electronic structure ; Fock space ; Fock matrix ; Work (physics) ; Experimental data ; Matrix (chemical analysis) Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Qing Lou | Yu Lan | Guangsong Zheng | Run‐Wei Song | Jing-Nan Hao | Jialu Liu | Cheng‐Long Shen | Jinyang Zhu | Sheng Cao | Jialong Zhao | Chongxin Shan
Source: Research Square (preprint, 2025-02-19)
Field: Materials Science — Materials Chemistry

Low-temperature molten salt enabled synthesis of solid-state emissive carbon dots with high endurance and &amp;gt;99% quantum yields Quantum dot ; Molten salt ; Carbon quantum dots ; Carbon fibers ; Salt (chemistry) ; Materials science ; Solid-state ; Chemical engineering ; Nanotechnology ; Chemistry ; Metallurgy ; Organic chemistry ; Composite material ; Physical chemistry ; Engineering Carbon and Quantum Dots Applications ; Graphene research and applications ; Catalytic Processes in Materials Science
Authors: Ardiana Topi | Majlinda Sana | Marjeta Daja | Dritan Topi
Source: Asian Journal of Dairy and Food Research (article, 2025-11-05)
Field: Chemistry — Analytical Chemistry

Applications of Machine Learning Models in Near-infrared Spectroscopy for Small-grain Quality Control Background: Wheat quality and safety are crucial for global food security and regional agricultural economies. Conventional analytical methods for assessing grain composition and quality-while accurate-are often time-consuming, destructive and resource-intensive. In contrast, near-infrared spectroscopy (NIR) has emerged as a rapid, non-destructive alternative that provides detailed chemical and physical insights. Recent advancements in machine learning have enhanced NIR’s analytical capabilities by enabling the development of predictive models that accurately categorize, quantify and monitor grain quality parameters. These models-spanning from linear regression methods to intricate neural networks-enable researchers to discern significant patterns from spectral data, even amidst noise or overlapping signals. Methods: This study used NIR spectroscopy and machine learning models to evaluate wheat quality across multiple Albanian regions during the 2023 harvest. NIR spectra were preprocessed using standard normalization and smoothing techniques. Machine learning models-including PLSR, SVM and random forest-were trained to predict key quality traits such as protein content and moisture. Model performance was validated using cross-validation metrics (RMSE, R², accuracy), demonstrating the potential of NIR-ML integration for rapid, non-destructive grain assessment. Result: Protein content ranged from 9.6% to 15.0%, gluten from 19.4% to 37.5% and starch from 66.3% to 71.5%, indicating consistent nutritional profiles across samples. Sedimentation index values (21.1-57.8 cm³) and moisture levels (9.8-12.5%) confirmed compliance with EU food safety standards. The integration of NIR spectroscopy with machine learning enabled rapid, non-destructive prediction of wheat quality traits, supporting scalable decision-making in agri-food systems. Support vector machine ; Grain quality ; Random forest ; Quality (philosophy) ; Normalization (sociology) ; Artificial neural network ; Partial least squares regression ; Quality assurance Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Wheat and Barley Genetics and Pathology
Authors: Nicholas Jose | Alexei A. Lapkin
Source: Apollo (University of Cambridge) (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

A roadmap toward closed-loop autonomous experimentation for engineered nanomaterials Commercialization ; Automation ; Nanomaterials ; Key (lock) ; Production (economics) Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Nanowire Synthesis and Applications
Authors: Jiaxin Ju | Yizhen Zheng | Huan Yee Koh | Can Wang | Shirui Pan
Source: ArXiv.org (preprint, 2025-08-12)
Field: Materials Science — Materials Chemistry

$\text{M}^{2}$LLM: Multi-view Molecular Representation Learning with Large Language Models Accurate molecular property prediction is a critical challenge with wide-ranging applications in chemistry, materials science, and drug discovery. Molecular representation methods, including fingerprints and graph neural networks (GNNs), achieve state-of-the-art results by effectively deriving features from molecular structures. However, these methods often overlook decades of accumulated semantic and contextual knowledge. Recent advancements in large language models (LLMs) demonstrate remarkable reasoning abilities and prior knowledge across scientific domains, leading us to hypothesize that LLMs can generate rich molecular representations when guided to reason in multiple perspectives. To address these gaps, we propose $\text{M}^{2}$LLM, a multi-view framework that integrates three perspectives: the molecular structure view, the molecular task view, and the molecular rules view. These views are fused dynamically to adapt to task requirements, and experiments demonstrate that $\text{M}^{2}$LLM achieves state-of-the-art performance on multiple benchmarks across classification and regression tasks. Moreover, we demonstrate that representation derived from LLM achieves exceptional performance by leveraging two core functionalities: the generation of molecular embeddings through their encoding capabilities and the curation of molecular features through advanced reasoning processes. Representation (politics) ; Task (project management) ; Molecular graph ; Encoding (memory) ; Property (philosophy) ; Graph ; Knowledge representation and reasoning ; Feature learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Nicholas Miklaucic | Lai Wei | Rongzhi Dong | Nihang Fu | Sadman Sadeed Omee | Qingtian Li | Sourin Dey | Victor Fung | Jianjun Hu
Source: ArXiv.org (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

Facet: highly efficient E(3)-equivariant networks for interatomic potentials Computational materials discovery is limited by the high cost of first-principles calculations. Machine learning (ML) potentials that predict energies from crystal structures are promising, but existing methods face computational bottlenecks. Steerable graph neural networks (GNNs) encode geometry with spherical harmonics, respecting atomic symmetries -- permutation, rotation, and translation -- for physically realistic predictions. Yet maintaining equivariance is difficult: activation functions must be modified, and each layer must handle multiple data types for different harmonic orders. We present Facet, a GNN architecture for efficient ML potentials, developed through systematic analysis of steerable GNNs. Our innovations include replacing expensive multi-layer perceptrons (MLPs) for interatomic distances with splines, which match performance while cutting computational and memory demands. We also introduce a general-purpose equivariant layer that mixes node information via spherical grid projection followed by standard MLPs -- faster than tensor products and more expressive than linear or gate layers. On the MPTrj dataset, Facet matches leading models with far fewer parameters and under 10% of their training compute. On a crystal relaxation task, it runs twice as fast as MACE models. We further show SevenNet-0's parameters can be reduced by over 25% with no accuracy loss. These techniques enable more than 10x faster training of large-scale foundation models for ML potentials, potentially reshaping computational materials discovery. Perceptron ; Artificial neural network ; Tensor (intrinsic definition) ; Equivariant map ; Spherical harmonics ; Superposition principle ; Relaxation (psychology) ; Translation (biology) ; Grid Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Inorganic Chemistry and Materials
Authors: Huang Shilei | Zhang Chenchen | Jiao Ruiting | Song Likun
Source:  (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Coupled Study of Insulation Material Aging Mechanisms and Electric Field Distribution in UHV Converter Transformer Valve-Side Outlet Devices Based on Equivalent Modeling and Deep Learning During long-term operation, the valve-side oil–paper insulation of ultra-high-voltage (UHV) converter transformers is exposed to combined electrical, thermal, mechanical, and environmental stresses, leading to insulation aging, altered electric field distribution, and potential safety risks. To address this, this paper presents a method based on the Debye-XY model, finite element simulation, and a multi-constraint inversion algorithm, combined with deep learning to process frequency domain spectroscopy (FDS) data. Coupling a genetic algorithm with finite element simulation enables aging state inversion, while a CNN optimized with MPIA-KNN is used to classify 30 aging–moisture states with high accuracy. Experiments show the method achieves 98.5% classification accuracy, significantly improving insulation assessment and electric field prediction, and offering valuable support for UHV equipment operation and maintenance. Transformer ; Distribution (mathematics) ; Electric field ; Deep learning ; Finite element method High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; High-Voltage Power Transmission Systems
Authors: Jiawei Zhang | Yifei Zhang | Baozhao Yi | Yao Ren | Qi Jiao | Hanyu Bai | Weiran Jiang | Ziyou Song
Source: ArXiv.org (preprint, 2025-08-09)
Field: Chemistry — Physical and Theoretical Chemistry

Discovery Learning accelerates battery design evaluation Fast and reliable validation of novel designs in complex physical systems such as batteries is critical to accelerating technological innovation. However, battery research and development remain bottlenecked by the prohibitively high time and energy costs required to evaluate numerous new design candidates, particularly in battery prototyping and life testing. Despite recent progress in data-driven battery lifetime prediction, existing methods require labeled data of target designs to improve accuracy and cannot make reliable predictions until after prototyping, thus falling far short of the efficiency needed to enable rapid feedback for battery design. Here, we introduce Discovery Learning (DL), a scientific machine-learning paradigm that integrates active learning, physics-guided learning, and zero-shot learning into a human-like reasoning loop, drawing inspiration from learning theories in educational psychology. DL can learn from historical battery designs and actively reduce the need for prototyping, thus enabling rapid lifetime evaluation for unobserved material-design combinations without requiring additional data labeling. To test DL, we present 123 industrial-grade large-format lithium-ion pouch cells, spanning eight material-design combinations and diverse cycling protocols. Trained solely on public datasets of small-capacity cylindrical cells, DL achieves 7.2% test error in predicting the average cycle life under unknown device variability. This results in savings of 98% in time and 95% in energy compared to industrial practices. This work highlights the potential of uncovering insights from historical designs to inform and accelerate the development of next-generation battery technologies. DL represents a key advance toward efficient data-driven modeling and helps realize the promise of machine learning for accelerating scientific discovery and engineering innovation. Battery (electricity) ; Key (lock) ; Efficient energy use ; Energy (signal processing) ; Work (physics) ; Data-driven ; Deep learning Various Chemistry Research Topics