jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonathan D. Schultz | Kelsey A. Parker | Bashir Sbaiti | David N. Beratan
Source: ArXiv.org (preprint, 2025-03-19)
Field: Materials Science — Materials Chemistry

Using machine learning to map simulated noisy and laser-limited multidimensional spectra to molecular electronic couplings Two-dimensional electronic spectroscopy (2DES) has enabled significant discoveries in both biological and synthetic energy-transducing systems. Although deriving chemical information from 2DES is a complex task, machine learning (ML) offers exciting opportunities to translate complicated spectroscopic data into physical insight. Recent studies have found that neural networks (NNs) can map simulated multidimensional spectra to molecular-scale properties with high accuracy. However, simulations often do not capture experimental factors that influence real spectra, including noise and suboptimal pulse resonance conditions, bringing into question the experimental utility of NNs trained on simulated data. Here, we show how factors associated with experimental 2D spectral data influence the ability of NNs to map simulated 2DES spectra onto underlying intermolecular electronic couplings. By systematically introducing multisourced noise into a library of 356000 simulated 2D spectra, we show that noise does not hamper NN performance for spectra exceeding threshold signal-to-noise ratios (SNR) (&gt; 6.6 if background noise dominates vs. &gt; 2.5 for intensity-dependent noise). In stark contrast to human-based analyses of 2DES data, we find that the NN accuracy improves significantly (ca. 84% $\rightarrow$ 96%) when the data are constrained by the bandwidth and center frequency of the pump pulses. This result is consistent with the NN learning the optical trends described by Kasha's theory of molecular excitons. Our findings convey positive prospects for adapting simulation-trained NNs to extract molecular properties from inherently imperfect experimental 2DES data. More broadly, we propose that machine-learned perspectives of nonlinear spectroscopic data may produce unique and, perhaps, counterintuitive guidelines for experimental design. Counterintuitive ; Artificial neural network ; Experimental data ; Spectral line ; Noise (video) ; Imperfect ; Bandwidth (computing) ; Spectroscopy Machine Learning in Materials Science ; Various Chemistry Research Topics ; Electrochemical Analysis and Applications
Authors: Hasan Can Gülbalkan | Seda Keskın
Source: Chemical Engineering Journal Advances (article, 2025-12-02)
Field: Chemistry — Inorganic Chemistry

Leveraging molecular simulations and machine learning to assess CO2, O2, and N2 adsorption and separation performances of diverse MOF databases • Combined molecular simulations with ML to predict CO 2 , O 2 , and N 2 adsorption in MOFs. • Predicted gas uptakes of ∼130,000 MOFs in minutes using simple features. • Identified structural and chemical properties for high CO 2 /N 2 and O 2 /N 2 selectivity. We integrated molecular simulations and machine learning (ML) to comprehensively explore the gas adsorption and separation performances of both synthesized and hypothetical metal-organic frameworks (MOFs) available in five different MOF databases. Following the generation of CO 2 , O 2 , and N 2 adsorption data for synthesized MOFs at varying pressures through grand canonical Monte Carlo (GCMC) simulations, we developed ML models that can swiftly and accurately predict the gas adsorption properties of any MOF based on its structural, chemical, and energetic characteristics. These ML models were then transferred to four distinct hypothetical MOF databases consisting of nearly 130,000 structures to assess their CO 2 , O 2 , and N 2 adsorption properties in addition to CO 2 /N 2 and O 2 /N 2 separation performances as a very efficient alternative to computationally time and resource demanding molecular simulations. We identified the top-performing materials from each database to uncover their structural, chemical, and topological properties leading to high selectivities and concluded that synthesized MOFs with narrow pores, lanthanide metals, and linkers featuring oxalate, pyridine dicarboxylate, and fumarate offer the highest CO 2 /N 2 selectivities. Our work presents the most extensive dataset produced for CO 2 , O 2 , and N 2 gas adsorption in MOFs, composed of ∼3.9 million data points for materials’ structural, chemical, and energetic features, gas adsorption properties, and selectivities computed at different pressures to accelerate the materials design and discovery for CO 2 , O 2 , and N 2 adsorption and separation. Adsorption ; Work (physics) ; Pyridine ; Gas separation ; Monte Carlo method ; Separation (statistics) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Anuj Kumar Sirohi | Ajeet Kumar Yadav | Pradipta Bandyopadhyay
Source: ChemRxiv (preprint, 2025-06-16)
Field: Materials Science — Materials Chemistry

Hydration Free Energies for Small Molecules with Physics-based Descriptors: Graph Neural Network with Cross Attention Hydration free energy (HFE) is a fundamental thermodynamic property with broad relevance in both chemistry and biology, particularly in solvation processes. Traditional methods for computing HFE, such as molecular dynamics simulations, are often computationally intensive and require significant domain-specific calibration. Recent advances in machine learning (ML) have enabled more efficient HFE predictions, especially for small molecules. However, many existing ML models lack interpretability and often rely on large, opaque feature sets. In this study, a cross-attention-based graph neural network (GNN) is used for predicting the HFE of small molecules from the FreeSolv dataset. Our model integrates graph representations of solute and solvent molecules and captures their mutual interactions through a cross-attention mechanism during message passing. To enhance physical interpretability, we incorporate a compact set of six global molecular descriptors: approximate electrostatic energy computed via a closed-form Generalized Born (GB) model, polar surface area, logarithm of the octanol–water partition coefficient (log P), hydrogen bond donors, hydrogen bond acceptors, and the number of rotatable bonds. We benchmark our model against classical machine learning methods and recent GNN-based baselines. Our attention-based GNN not only improves prediction accuracy but also maintains transparency in feature importance. Our method outperforms existing baselines, achieving a mean absolute error (MAE) of 0.54 ± 0.04 kcal/mol and a root mean square error (RMSE) of 0.75 ± 0.03 kcal/mol, which is approximately 23% and 36% improvement as compared to the bestperforming baseline, respectively. The ablation study reveals that among the global descriptors used for the solute, electrostatic energy and polar surface area are the most critical in reducing prediction error, followed by features related to hydrogen bonding. This combination of high accuracy and strong interpretability makes our framework well-suited for large-scale, data-driven investigations of solvation free energies. Artificial neural network ; Graph ; Physics ; Molecule ; Statistical physics ; Computer science ; Artificial intelligence ; Theoretical computer science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Yue Zhu | Fengyu Gao | Yaxin Niu | Tingkai Xiong | Xinyu Xu | Honghong Yi | Shunzheng Zhao | Yuansong Zhou | Ya Wang | Xiaolong Tang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Based on machine learning prediction and experimental verification: the optimization of NH3-SCR performance on Ce/Co-modified MnNi catalysts NOx ; Flue gas ; Ternary operation ; Catalysis ; Adsorption ; Ternary numeral system ; Classifier (UML) ; Ionic bonding Catalytic Processes in Materials Science
Authors: Rushikesh Pawar (21626785) | Ashish Rout (21626788) | Satadeep Bhattacharjee (8180442) | Phani Motamarri (16407240)
Source: Figshare (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Robust Lightweight Graph Neural Network Frameworkfor Accelerating Crystal Structure Prediction This work presents a crystal structure prediction framework that employs a structural search using a derivative-free optimization method, with a supervised Graph Neural Network (GNN) model as the energy evaluator. We address the limitations of existing GNN-based crystal structure prediction (CSP) frameworks and propose methods for designing a robust and computationally efficient predictor. In particular, we first highlight the often-overlooked sensitivity of GNN models to weight initialization in crystal structure prediction, and to address this, we introduce a model selection framework that consistently identifies an appropriate GNN model for downstream crystal structure prediction tasks. Using this framework, we conduct a meaningful comparison of multiple GNN architectures for CSP involving a Bayesian optimization approach. Furthermore, we propose a data augmentation strategy that incorporates unrelaxed structures in the supervised training process, and additionally explore the impact of unsupervised GNN pretraining with and without augmentation on crystal structure prediction. Finally, we demonstrate that our proposed crystal structure prediction framework, in conjunction with the lightweight GNN architecture CGCNN, can achieve a level of performance comparable to that of more complex GNN architectures, which are typically computationally expensive to train and infer. The approaches introduced in this work are generic and can be extended to any GNN-based crystal structure prediction framework, paving the way for developing novel and high-throughput crystal structure predictors in the future. Initialization ; Crystal structure prediction ; Artificial neural network ; Bayesian optimization ; Graph ; Model selection ; Bayesian probability ; Bayesian network Machine Learning in Materials Science ; Enzyme Structure and Function ; Crystallography and molecular interactions
Authors: Lim Kyungjun | Freederia AI Researcher
Source: Figshare (article, 2025-08-20)
Field: Materials Science — Polymers and Plastics

Enhanced Polypropylene Degradation Prediction using Machine Learning and Data-Driven Material Fatigue Modeling **Abstract:** Traditional methods for predicting polypropylene (PP) degradation and lifespan rely on empirical testing and simplified models, often overlooking complex interplay of environmental factors and material microstructure. This paper introduces a novel framework, the Polypropylene Degradation Prediction Engine (PDPPE), leveraging machine learning (ML) and data-driven material fatigue modeling to significantly improve the accuracy of degradation prediction. PDPPE incorporates a multi-modal data ingestion layer, semantic decomposition, a layered evaluation pipeline incorporating advanced theorem proving and finite element simulation, and a meta-self-evaluation loop for continual learning. Results demonstrate a 30-45% improvement in lifespan prediction compared to conventional Arrhenius-based models, with immediate implications for polymer lifecycle management, waste reduction, and sustainable product design, particularly in applications such as automotive components and packaging. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.* Polypropylene ; Pipeline (software) ; Degradation (telecommunications) ; Product (mathematics) ; Finite element method ; Automotive industry ; Predictive modelling Polymer crystallization and properties ; Machine Learning in Materials Science ; Polymer Nanocomposites and Properties
Authors: Nikita Yu Nikitin | Andrey A. Stepashkin | Yulia Valerevna Odintsova | Victor V. Tcherdyntsev
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Estimation of the influence of technological parameters and mixture composition during mechanochemical synthesis of quasicrystals of Al-Cu-Fe and Al-Cu-Cr systems using convolutional neural network, support vector and extreme gradient boosting models Quasicrystal ; Convolutional neural network ; Monte Carlo method ; Artificial neural network ; Data set ; Convolution (computer science) ; Set (abstract data type) ; Composition (language) Quasicrystal Structures and Properties ; Machine Learning in Materials Science ; Microstructure and mechanical properties
Authors: Rajendra P. Pawar | Ashish Rout | Satadeep Bhattacharjee | Phani Motamarri
Source: Journal of Chemical Information and Modeling (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Robust Lightweight Graph Neural Network Framework for Accelerating Crystal Structure Prediction This work presents a crystal structure prediction framework that employs a structural search using a derivative-free optimization method, with a supervised Graph Neural Network (GNN) model as the energy evaluator. We address the limitations of existing GNN-based crystal structure prediction (CSP) frameworks and propose methods for designing a robust and computationally efficient predictor. In particular, we first highlight the often-overlooked sensitivity of GNN models to weight initialization in crystal structure prediction, and to address this, we introduce a model selection framework that consistently identifies an appropriate GNN model for downstream crystal structure prediction tasks. Using this framework, we conduct a meaningful comparison of multiple GNN architectures for CSP involving a Bayesian optimization approach. Furthermore, we propose a data augmentation strategy that incorporates unrelaxed structures in the supervised training process, and additionally explore the impact of unsupervised GNN pretraining with and without augmentation on crystal structure prediction. Finally, we demonstrate that our proposed crystal structure prediction framework, in conjunction with the lightweight GNN architecture CGCNN, can achieve a level of performance comparable to that of more complex GNN architectures, which are typically computationally expensive to train and infer. The approaches introduced in this work are generic and can be extended to any GNN-based crystal structure prediction framework, paving the way for developing novel and high-throughput crystal structure predictors in the future. Computer science ; Artificial neural network ; Graph ; Crystal structure prediction ; Artificial intelligence ; Machine learning ; Data mining ; Crystal structure ; Pattern recognition (psychology) ; Theoretical computer science ; Chemistry ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: А. F. Schmidt | А. А. Курохтина | Е. В. Ларина | Н. А. Лагода
Source: Kinetics and Catalysis (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Approaches to the Interpretation of Machine Learning Models Trained with Big Experimental Kinetic Data: An Example of the Suzuki–Miyaura Reaction This paper presents the results of the development and interpretation of machine learning models using big experimental datasets where different ways are implemented to take into account information on the kinetics of reaction using the Suzuki–Miyaura reaction as an example. An original method for interpreting the resulting models was proposed, which consists in the multistep ablation of predictors used in the models, allowing for the identification of the most important reaction parameters required for the successful operation of catalytic systems. The obtained sets of the parameters determining the success of the reaction (rate, selectivity, and yield of the target product; E-factor and other environmental metrics of the reaction, energy efficiency, cost, etc.) as the minimal sets of the predictors of ML models that describe satisfactorily the differential and/or integral quantitative reaction parameters allowed us for the first time to identify the patterns of the functioning mechanism of catalytic systems in the Suzuki–Miyaura reaction, consistent with big experimental kinetic data obtained in a wide range of reaction conditions (nature and amounts of substrates, reagents, catalyst precursors, bases, additives, and solvents; temperature, stirring speed, and type of an atmosphere in the reactor). Interpretation (philosophy) ; Yield (engineering) ; Range (aeronautics) ; Kinetic energy ; Reaction mechanism ; Catalysis ; Big data ; Identification (biology) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Cross-Coupling Reactions
Authors: Hongtao Xu | Zibo Wu | Mingzhen Li | Weile Jia
Source: ArXiv.org (preprint, 2025-06-30)
Field: Materials Science — Materials Chemistry

Large-scale Neural Network Quantum States for ab initio Quantum Chemistry Simulations on Fugaku Solving quantum many-body problems is one of the fundamental challenges in quantum chemistry. While neural network quantum states (NQS) have emerged as a promising computational tool, its training process incurs exponentially growing computational demands, becoming prohibitively expensive for large-scale molecular systems and creating fundamental scalability barriers for real-world applications. To address above challenges, we present \ours, a high-performance NQS training framework for \textit{ab initio} electronic structure calculations. First, we propose a scalable sampling parallelism strategy with multi-layers workload division and hybrid sampling scheme, which break the scalability barriers for large-scale NQS training. Then, we introduce multi-level parallelism local energy parallelism, enabling more efficient local energy computation. Last, we employ cache-centric optimization for transformer-based \textit{ansatz} and incorporate it with sampling parallelism strategy, which further speedup up the NQS training and achieve stable memory footprint at scale. Experiments demonstrate that \ours accelerate NQS training with up to 8.41x speedup and attains a parallel efficiency up to 95.8\% when scaling to 1,536 nodes. Speedup ; Scalability ; Quantum ; Quantum computer ; Ab initio ; Scaling ; Sampling (signal processing) ; Memory footprint ; Artificial neural network Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Hyeonbin Moon | Hanbin Cho | Wabi Demeke | Byungki Ryu | Seunghwa Ryu
Source: ArXiv.org (preprint, 2025-10-19)
Field: Materials Science — Materials Chemistry

Thermal Conductivity Estimation of Thermoelectric Materials with Uncertainty Quantification Using Bayesian Physics-Informed Neural Networks Characterizing the temperature-dependent thermal conductivity is challenging because the property varies strongly with temperature and reliable heat flow measurement, not just temperature sensing, is difficult under experimental conditions. Here, we present a physics informed deep learning framework that infers conductivity solely from sparse electric potential measurements. We first develop a deterministic physics-informed neural network (PINN) that embeds coupled thermoelectric transport equations as soft constraints, enabling simultaneous recovery of spatial temperature, voltage, and conductivity profiles without temperature data. The deterministic PINN achieves accurate inference under noise-free conditions, yet its predictions degrade when measurement noise is introduced. To address this, we extend the framework to a Bayesian PINN, which models network parameters probabilistically and employs Hamiltonian Monte Carlo (HMC) sampling for posterior inference. This extension produces robust thermal conductivity estimates and, importantly, provides credible intervals that quantify uncertainty from sparse and noisy data. Numerical experiments confirm that the Bayesian PINN not only preserves predictive accuracy under noise but also reveals inference bias and enables uncertainty aware interpretation of material properties. Together, the deterministic and Bayesian formulations establish a scalable and generalizable alternative to conventional methods for determining temperature-dependent properties, offering physics- consistent and risk-aware property inference for thermoelectric systems and other functional materials where direct temperature sensing is impractical Uncertainty quantification ; Thermal conductivity ; Bayesian inference ; Bayesian probability ; Inference ; Monte Carlo method ; Artificial neural network ; Noise (video) ; Scalability Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Zhen Jiang (846105) | Hua Tian (220297)
Source: Figshare (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

SFTGNN: An Efficient Spherical Fourier Transform-EnhancedGraph Neural Network for High-Accuracy Crystal Property Prediction Crystalline materials, characterized by periodic atomic arrangements, play a fundamental role in materials science due to the strong correlation between their structure and physical properties. While graph neural networks (GNNs) have shown promise in predicting crystal properties, many existing models either neglect crucial angular features such as bond angles or suffer from high computational costs, as seen in methods like ALIGNN with <i>O</i>(<i>k</i><sup>2</sup>) complexity for local angle calculations. In this work, we introduce SFTGNN, a novel architecture that integrates spherical Fourier transforms (SFT) with GNNs to efficiently incorporate three-dimensional geometric information into crystal property prediction. By projecting atomic local environments into the spherical harmonic domain, SFTGNN captures angular dependencies without the need to explicitly enumerate all bond angles, while reducing the computational complexity of bond angle encoding from <i>O</i>(<i>k</i><sup>2</sup>) to <i>O</i>(<i>k</i>) and obviating the requirement for additional graph construction. We have validated the effectiveness of this method and evaluated the predictive performance of the model on the Materials Project and Jarvis data sets. SFTGNN achieves state-of-the-art results in multiple crystal property prediction tasks, while training 5.3 times faster than ALIGNN. These results highlight the potential of combining spatial geometric representations with graph-based learning for efficient and accurate modeling of crystal structures. Property (philosophy) ; Fourier transform ; Graph ; Computational complexity theory ; Artificial neural network ; Crystal (programming language) ; Spherical harmonics ; Encoding (memory) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Inorganic Chemistry and Materials