jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shukai Wu (22591781) | Yan Luo (255674) | Shuo Niu (15183131) | Wentao Lin (8795450) | Chao Fang (456315)
Source: Figshare (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

AI-InformedSolvation Engineering for ThermogalvanicElectrolytes with High Thermopower Thermogalvanic cells utilize redox couple ions to achieve continuous heat-to-electricity conversion, yet the development of high-performance electrolytes is limited by the complex relationship between solvation of redox couple ions and thermopower. We present a data-driven machine learning strategy to identify high-performance solvents for thermogalvanic electrolytes. Our approach parametrizes the Seebeck coefficient by leveraging the strong correlation between DFT-derived ion–solvent interaction features and limited experimental data. A hybrid ML framework combining high-throughput DFT and interval regression was developed to screen extensive solvent candidates with SHAP analysis identifying polarity-related features as dominant factors. Experimental validation enabled model augmentation via a binary feature filter that incorporates solvation-relevant molecular features beyond two-body interactions. The framework validated known effective solvents and identified new candidates, which were confirmed via solvation structure calculations and UV–vis spectroscopy. This solvation-driven ML approach accelerates electrolyte discovery while providing fundamental insights into design principles for large solvation entropy differences. Solvation ; Electrolyte ; Binary number ; Seebeck coefficient ; Ion ; Strong electrolyte ; Entropy (arrow of time) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Edoardo Monti | Peter Yatsyshin | Konstantinos Gkagkas | A. Duncan
Source: arXiv (Cornell University) (preprint, 2025-12-29)
Field: Materials Science — Materials Chemistry

Learning Density Functionals to Bridge Particle and Continuum Scales Predicting interfacial thermodynamics across molecular and continuum scales remains a central challenge in computational science. Classical density functional theory (cDFT) provides a first-principles route to connect microscopic interactions with macroscopic observables, but its predictive accuracy depends on approximate free-energy functionals that are difficult to generalize. Here we introduce a physics-informed learning framework that augments cDFT with neural corrections trained directly against molecular-dynamics data through adjoint optimization. Rather than replacing the theory with a black-box surrogate, we embed compact neural networks within the Helmholtz free-energy functional, learning local and nonlocal corrections that preserve thermodynamic consistency while capturing missing correlations. Applied to Lennard-Jones fluids, the resulting augmented excess free-energy functional quantitatively reproduces equilibrium density profiles, coexistence curves, and surface tensions across a broad temperature range, and accurately predicts contact angles and droplet shapes far beyond the training regime. This approach combines the interpretability of statistical mechanics with the adaptability of modern machine learning, establishing a general route to learned thermodynamic functionals that bridge molecular simulations and continuum-scale models. Interpretability ; Helmholtz free energy ; Consistency (knowledge bases) ; Statistical mechanics ; Density functional theory ; Artificial neural network ; Adaptability Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Phase Equilibria and Thermodynamics
Authors: Zhu, Tiannian | Fang, Zhong | Wu, Quansheng | Weng, Hongming
Source: arXiv (Cornell University) (preprint, 2025-10-30)
Field: Materials Science — Materials Chemistry

MaterialsGalaxy: A Platform Fusing Experimental and Theoretical Data in Condensed Matter Physics Modern materials science generates vast and diverse datasets from both experiments and computations, yet these multi-source, heterogeneous data often remain disconnected in isolated "silos". Here, we introduce MaterialsGalaxy, a comprehensive platform that deeply fuses experimental and theoretical data in condensed matter physics. Its core innovation is a structure similarity-driven data fusion mechanism that quantitatively links cross-modal records - spanning diffraction, crystal growth, computations, and literature - based on their underlying atomic structures. The platform integrates artificial intelligence (AI) tools, including large language models (LLMs) for knowledge extraction, generative models for crystal structure prediction, and machine learning property predictors, to enhance data interpretation and accelerate materials discovery. We demonstrate that MaterialsGalaxy effectively integrates these disparate data sources, uncovering hidden correlations and guiding the design of novel materials. By bridging the long-standing gap between experiment and theory, MaterialsGalaxy provides a new paradigm for data-driven materials research and accelerates the discovery of advanced materials. Bridging (networking) ; Generative grammar ; Core (optical fiber) ; Experimental data ; Sensor fusion ; Generative model ; Data modeling Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Inorganic Chemistry and Materials
Authors: Haris Hasić | Takashi Ishida
Source: Journal of Cheminformatics (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

The consolidation of open-source computer-assisted chemical synthesis data into a comprehensive database Over the past decade, computer-assisted chemical synthesis has resurfaced as a prominent research subject. Even though the idea of utilizing computers to assist chemical synthesis has existed for nearly as long as computers themselves, the inherent complexity repeatedly exceeded the available resources. However, recent machine learning approaches have exhibited the potential to break this tendency. The performance of such approaches is heavily dependent on data that suffers from limited quantity, quality, visibility, and accessibility, posing significant challenges to potential scientific breakthroughs. This research addresses these issues by consolidating all relevant open-source computer-assisted chemical synthesis data into a comprehensive database, providing a practical overview of the state of data in the process. The computer-assisted chemical synthesis or CaCS database is designed to be a central repository for storing and analyzing data, with the primary objective being easy integration and utilization within existing research projects. It provides the users with a programmatic interface to retrieve the data required for various tasks like predicting the outcomes of chemical synthesis and retrosynthetic analysis or retrosynthesis, estimating the synthesizability of chemical compounds, and planning and optimizing the chemical synthesis routes. The database archives the original data to ensure reusability and traceability in downstream tasks and stores the processed data in a more efficient manner. The advantages and disadvantages are highlighted through a realistic case study of how such a database would be utilized within a computer-assisted chemical synthesis research project today. The code and documentation relevant to the CaCS database are available on GitHub under the MIT license at https://github.com/neo-chem-synth-wave/ncsw-data .Scientific contribution: The primary scientific contribution of this research is the consolidation of all relevant open-source computer-assisted chemical synthesis data into a comprehensive database. The database archives the original data to ensure reusability and traceability in downstream tasks, efficiently stores the processed data, and provides the users with a programmatic interface to manage and query the stored data. Rather than improving the existing or introducing new data, such a database provides a systematic overview of the existing open data sources and an easily reproducible environment for transparent processing and benchmarking purposes. Documentation ; Chemical database ; USable ; Traceability ; Reusability ; Database application ; Data integration ; Scope (computer science) ; Interface (matter) ; Chemical company Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Process Optimization and Integration
Authors: Yichun Huang | Yizhen Huang
Source:  (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

An Active Learning Framework Based on a Heteroscedastic Graph Attention Network for Improving Molecular Solubility Prediction Accuracy and Sample Efficiency Accurate prediction of molecular properties is the key to chemical discovery, but traditional graph neural networks often ignore the inherent uncertainty differences between experimental data and molecular structure. To solve this problem, this paper proposes a hybrid framework that combines Heterogeneous Graph Attention Network (HeteroGAT) with hybrid active learning. By integrating randomness and cognitive uncertainty modeling, this method improves the prediction accuracy and confidence reliability at the same time. The hybrid sampling strategy combines uncertainty-driven exploration with sample diversity selection to achieve higher data utilization efficiency with a limited label budget. In the AqSolDB data set experiment, the proposed HeteroGAT hybrid model is significantly improved compared with the benchmark method: the mean square root error is reduced by 15%, and the confidence interval width is reduced by 12%. This method effectively enhances the accuracy of molecular property prediction and uncertainty correction ability and provides an efficient and reliable framework for chemical modeling and molecular screening tasks. Graph ; Active learning (machine learning) ; Sample (material) ; Heteroscedasticity ; Attention network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Yair Davidson | Aviad Philipp | Sabyasachi Chakraborty | Alex Bronstein | Renana Gershoni‐Poranne
Source: The Journal of Chemical Physics (article, 2025-04-08)
Field: Chemistry — Organic Chemistry

How local is “local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons We investigate the locality of magnetic response in polycyclic aromatic molecules using a novel deep-learning approach. Our method employs graph neural networks (GNNs) with a graph-of-rings representation to predict nucleus independent chemical shifts (NICS) in the space around the molecule. We train a series of models, each time reducing the size of the largest molecules used in training. The accuracy of prediction remains high (MAE < 0.5 ppm), even when training the model only on molecules with up to four rings, thus providing strong evidence for the locality of magnetic response. To overcome the known problem of generalization of GNNs, we implement a k-hop expansion strategy and succeed in achieving accurate predictions for molecules with up to 15 rings (almost 4 times the size of the largest training example). Our findings have implications for understanding the magnetic response in complex molecules and demonstrate a promising approach to overcoming GNN scalability limitations. Furthermore, the trained models enable rapid characterization, without the need for more expensive DFT calculations. Locality ; Chemical space ; Scalability ; Molecule ; Aromaticity ; Computer science ; Generalization ; Graph ; Representation (politics) ; Artificial intelligence ; Theoretical computer science ; Computational chemistry ; Machine learning ; Chemistry ; Mathematics ; Drug discovery ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Seungpyo Kang | Junho Song | Jinyoung Jeong | Taehyun Park | Joonghee Won | Jungim Han | Kyoungmin Min
Source: ACS Applied Materials & Interfaces (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

Evaluating Machine Learning Interatomic Potentials for Accurate and Scalable Modeling of Organometallic Precursors Accurate modeling of organometallic precursors is essential for developing atomic layer deposition (ALD) processes that are required for fabricating high-performance thin films. The melting point of these precursors is often challenging to measure experimentally due to its sensitivity to environmental conditions, making computational methods a practical alternative. While density functional theory (DFT) is reliable, its high computational cost limits large-scale applications. In contrast, machine learning interatomic potentials (MLIPs) offer an attractive alternative by providing both high accuracy and computational efficiency. In this study, we developed and evaluated MLIPs for modeling cyclopentadienyl-based organometallic precursors, comparing the performance of moment tensor potential (MTP), crystal Hamiltonian graph neural network (CHGNet), and scalable equivariance-enabled neural network (SevenNet). The impact of active learning and fine-tuning with high-energy (HE)-state configurations was further investigated to improve generalization. CHGNet and SevenNet, both based on message-passing neural networks (MPNN), outperformed the polynomial-based MTP. Notably, SevenNet demonstrates superior performance with a mean absolute error (MAE) of 0.001 eV/atom for energy, <i>R</i><sup>2</sup> of 1.000 for force, and a density MAE of 0.033 g/cm<sup>3</sup>, enabled by incorporation of rotational symmetries via E(3)-equivariant convolutions. Melting point calculations were performed using the solid-liquid phase coexistence method to assess MLIP applicability. SevenNet showed the closest agreement with the experimental values, with a deviation of 0.65-2.05% for CUFBAG, while deviations for other precursors ranged from 1.87% to 7.58%. This study provides a methodological framework for constructing accurate and scalable MLIPs, enabling efficient and reliable simulations of organometallic precursors in ALD applications. Artificial neural network ; Scalability ; Density functional theory ; Hamiltonian (control theory) ; Atomic layer deposition ; Tensor (intrinsic definition) ; Homogeneous space ; Interatomic potential Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Seungpyo Kang (14461252) | JunHo Song (19774908) | Jinyoung Jeong (2279911) | Taehyun Park (3632845) | Joonghee Won (6811979) | Jungim Han (5872757) | Kyoungmin Min (1473985)
Source: Figshare (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

EvaluatingMachineLearning Interatomic Potentialsfor Accurate and Scalable Modeling of Organometallic Precursors Accurate modeling of organometallic precursors is essential for developing atomic layer deposition (ALD) processes that are required for fabricating high-performance thin films. The melting point of these precursors is often challenging to measure experimentally due to its sensitivity to environmental conditions, making computational methods a practical alternative. While density functional theory (DFT) is reliable, its high computational cost limits large-scale applications. In contrast, machine learning interatomic potentials (MLIPs) offer an attractive alternative by providing both high accuracy and computational efficiency. In this study, we developed and evaluated MLIPs for modeling cyclopentadienyl-based organometallic precursors, comparing the performance of moment tensor potential (MTP), crystal Hamiltonian graph neural network (CHGNet), and scalable equivariance-enabled neural network (SevenNet). The impact of active learning and fine-tuning with high-energy (HE)-state configurations was further investigated to improve generalization. CHGNet and SevenNet, both based on message-passing neural networks (MPNN), outperformed the polynomial-based MTP. Notably, SevenNet demonstrates superior performance with a mean absolute error (MAE) of 0.001 eV/atom for energy, <i>R</i><sup>2</sup> of 1.000 for force, and a density MAE of 0.033 g/cm<sup>3</sup>, enabled by incorporation of rotational symmetries via E(3)-equivariant convolutions. Melting point calculations were performed using the solid–liquid phase coexistence method to assess MLIP applicability. SevenNet showed the closest agreement with the experimental values, with a deviation of 0.65–2.05% for CUFBAG, while deviations for other precursors ranged from 1.87% to 7.58%. This study provides a methodological framework for constructing accurate and scalable MLIPs, enabling efficient and reliable simulations of organometallic precursors in ALD applications. Artificial neural network ; Scalability ; Density functional theory ; Hamiltonian (control theory) ; Atomic layer deposition ; Interatomic potential ; Tensor (intrinsic definition) ; Absolute deviation Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Electronic and Structural Properties of Oxides
Authors: Kateřina Plachká (12230862) | Veronika Pilařová (12065749) | Tat′ána Gazárková (21312329) | Jean-Christophe Garrigues (19239259) | František Švec (8446605) | Lucie Nováková (1676893)
Source: Figshare (article, 2025-05-10)
Field: Chemistry — Spectroscopy

ArtificialNeural Networks: An Innovative ApproachUsed for Elucidation of Ionization Processes in Supercritical FluidChromatography-Mass Spectrometry Understanding and predicting mass spectrometry responses in supercritical fluid chromatography-mass spectrometry (SFC-MS) is critical for optimizing detection across diverse analytes and solvent compositions. We present a novel approach using artificial neural networks (ANN) to explore the complex relationships between molecular descriptors of analytes and MS responses in different makeup solvent compositions enabling SFC-MS coupling. 226 molecular descriptors were evaluated for compounds under standardized SFC conditions, with 24 makeup solvent compositions. These makeup solvents included pure alcohols and methanol with varying concentrations of volatile additives. Our results highlight distinct ionization processes for the two most commonly used soft ionization techniques: (i) electrospray ionization (ESI), primarily involving proton or cation transfer, and (ii) atmospheric pressure chemical ionization (APCI), associated with charged ion transfer. Principal component analysis of weights assigned to molecular descriptors reveals that, in positive detection mode, these descriptors effectively differentiate ionization efficiency between ESI and APCI. In contrast, this differentiation is less pronounced in negative mode, where the variance explained is more homogeneously distributed, with stronger discrimination observed when NH<sub>3</sub> is used as an additive to the organic modifier. These findings provide critical insights into the influence of molecular descriptors and solvent composition on ionization efficiency, serving as a foundation for future investigations into SFC-MS optimization. This proof-of-concept underscores the feasibility of using predictive models to advance understanding of ionization efficiency and offers a valuable framework for refining SFC-MS workflows in analytical chemistry. Ionization ; Mass spectrometry ; Atmospheric-pressure chemical ionization ; Supercritical fluid ; Electrospray ionization ; Desorption electrospray ionization ; Analyte ; Solvent ; Solvation Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: S Jayakumar | Jie Hou | Renzhi Cao | Dong Si
Source:  (article, 2025-05-05)
Field: Materials Science — Surfaces, Coatings and Films

Geometric Deep Learning for Multiple Conformation Generation from Electron Density Images In cryo-electron microscopy (cryo-EM) structural analysis, accurately predicting molecular conformations from image datasets is crucial. Despite advancements in deep learning, current solutions often produce suboptimal volume reconstructions that require additional processing for research use. To address this limitation, we introduce GraphConv, an innovative encoder model that embeds particle images into a latent space, replacing the conventional encoder used by CryoDRGN. Our approach employs a Graph Neural Network (GNN) architecture with multiple GraphConv and convolutional layers to capture richer information from particle images and accurately reconstruct 3D volumes. Rigorous testing on two authentic datasets and three simulated datasets demonstrates our model's efficacy, with improvements in reconstruction quality and resolution enhancements of up to 20 % compared to CryoDRGN. By leveraging the power of GNNs, our methodology significantly advances the fidelity and accuracy of cryo-EM volume reconstructions, contributing to the refinement of structural analysis techniques. Artificial intelligence ; Computer science ; Electron ; Computer vision ; Physics ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhe Su | Shuoran Song | Qi Wang | Jian-Hua Jiang
Source: arXiv (Cornell University) (preprint, 2025-12-21)
Field: Materials Science — Materials Chemistry

Topological surface phonons modulate thermal transport in semiconductor thin films While phonon topology in crystalline solids has been extensively studied, its influence on thermal transport-especially in nanostructures-remains elusive. Here, by combining first-principles-based machine learning potentials with the phonon Boltzmann transport equation and molecular dynamics simulations, we systematically investigate the role of topological surface phonons in the in-plane thermal transport of semiconductor thin films (Si, 4H -SiC, and c-BN). These topological surface phonons, originating from nontrivial acoustic phonon nodal lines, not only serve as key scattering channels for dominant acoustic phonons but also contribute substantially to the overall thermal conductivity. Remarkably, for these thin semiconductor films below 10 nm this contribution can be as large as over 30% of the in-plane thermal conductivity at 300 K, and the largest absolute contribution can reach 82 W/m-K, highlighting their significant role in nanoscale thermal transport in semiconductors. Furthermore, we demonstrate that both temperature and biaxial strain provide effective means to modulate this contribution. Our work establishes a direct link between topological surface phonons and nanoscale thermal transport, offering the first quantitative assessment of their role and paving the way for topology-enabled thermal management in semiconductors. Phonon ; Boltzmann equation ; Thermal conductivity ; Topology (electrical circuits) ; Scattering ; Semiconductor ; Nanoscopic scale Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques