jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andreas E. Robertson | Samuel B. Inman | Ashley Lenau | Ricardo A. Lebensohn | Dongil Shin | Brad Boyce | Remi Dingreville
Source: arXiv (Cornell University) (preprint, 2025-12-19)
Field: Materials Science — Materials Chemistry

Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins Aleatoric uncertainties - irremovable variability in microstructure morphology, constituent behavior, and processing conditions - pose a major challenge to developing uncertainty-robust digital twins. We introduce the Variational Deep Material Network (VDMN), a physics-informed surrogate model that enables efficient and probabilistic forward and inverse predictions of material behavior. The VDMN captures microstructure-induced variability by embedding variational distributions within its hierarchical, mechanistic architecture. Using an analytic propagation scheme based on Taylor-series expansion and automatic differentiation, the VDMN efficiently propagates uncertainty through the network during training and prediction. We demonstrate its capabilities in two digital-twin-driven applications: (1) as an uncertainty-aware materials digital twin, it predicts and experimentally validates the nonlinear mechanical variability in additively manufactured polymer composites; and (2) as an inverse calibration engine, it disentangles and quantitatively identifies overlapping sources of uncertainty in constituent properties. Together, these results establish the VDMN as a foundation for uncertainty-robust materials digital twins. Uncertainty quantification ; Nonlinear system ; Probabilistic logic ; Calibration ; Embedding ; Inverse problem ; Artificial neural network ; Inverse ; Propagation of uncertainty Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Multi-Objective Optimization Algorithms
Authors: Lin Wang (11986) | Tanjin He (2891264) | Bin Ouyang (452434)
Source: Figshare (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Impactof Domain Knowledge on the Property Predictionof Specialized Machine Learning Models Developing transferable machine learning models is trending in data-driven materials research. However, how to apply such models to a specific research domain remains unclear. In this work, we choose high-entropy materials as a platform with a specialized data set containing 145,323 DFT-relaxed materials. This data set is used to explore the role of domain-specific knowledge in training effective models. Our tests with three representative graph neural network architectures indicate the model complexity has much smaller influence on performance than the data itself. Specifically, the consideration of low-energy atomic ordering, structures with diverse elemental coverage, and high-order interactions significantly influences the model performance. We also find that domain knowledge-driven sampling can greatly enhance unsupervised learning techniques. This research highlights that developing specialized data sets is more beneficial than further complicating deep learning architectures. Additionally, physics-inspired sampling algorithms are crucially needed for better machine learning models for a specific materials research domain. Artificial neural network ; Domain (mathematical analysis) ; Property (philosophy) ; Domain knowledge ; Set (abstract data type) ; Training set ; Data modeling ; Graph ; Deep learning Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Quantum many-body systems
Authors: Ruiyu Wang | Vanessa Meraz | Pratyush Tiwary
Source: ChemRxiv (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Machine learning driven advances in molecular dynamics of bulk and interfacial aqueous systems Molecular dynamics ; Aqueous solution ; Graph ; Sampling (signal processing) ; Coupling (piping) ; Key (lock) ; Experimental data ; Scale (ratio) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Flóra Boróka Németh | Andrea Hamza | Beatrix Tugyi | Maya El-Ali | Luca Szegletes | Ádám Madarász | Imre Pápai
Source: ChemRxiv (preprint, 2025-06-20)
Field: Chemistry — Electrochemistry

PredPotS – A Web Tool for Predicting One-Electron Standard Reduction Potentials for Organic Molecules in Aqueous Phase An interactive web tool, PredPotS, has been developed for predicting one-electron standard reduction potentials of organic molecules in aqueous solutions. The predictions are generated using deep learning models trained and validated on a chemically diverse database comprising reduction potentials of approximately 8000 organic compounds. The reduction potentials of this database were computed using a composite computational protocol that combines the semiempirical quantum chemical method GFN2-xTB) and a well-established DFT approach (M06-2X functional along with the SMD solvent model). While this computational approach is cost-effective, it is subject to certain limitations, which are nonetheless duly accounted for in the development of the database. The applied graph-based deep learning methods perform remarkably well in terms of the standard performance metrics. By entering or uploading the SMILES codes of the molecules, PredPotS provides fast and sensible predictions for one-electron standard reduction potentials for a diverse set of organic molecules also in the range compatible with the electrochemical stability of aqueous electrolytes. The PredPotS web tool is particularly well-suited for screening redox-active candidates for aqueous organic redox flow batteries, but it may also prove useful in a variety of other electrochemical applications. Aqueous solution ; Computer science ; Redox ; Electrochemistry ; Organic molecules ; Reduction (mathematics) ; Molecule ; Chemistry ; Computational chemistry ; Mathematics ; Physical chemistry ; Inorganic chemistry ; Organic chemistry ; Electrode Electrochemical Analysis and Applications ; Advanced battery technologies research ; Electrocatalysts for Energy Conversion
Authors: Sander Vandenhaute
Source: Ghent University Academic Bibliography (Ghent University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerating molecular simulation using machine learning : from wave functions to thermodynamics Feature (linguistics) ; Work (physics) ; Stability (learning theory) ; Process (computing) ; Function (biology) Machine Learning in Materials Science ; Quantum many-body systems ; Spectroscopy and Quantum Chemical Studies
Authors: Harrison Walker | Eric R. Hoglund | De‐Liang Bao | Thomas W. Pfeifer | Patrick E. Hopkins | Jordan A. Hachtel | Sokrates T. Pantelides
Source: Microscopy and Microanalysis (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Machine-Learning Molecular Dynamics for Insights into Vibrational Electron Energy Loss Spectroscopy Electron energy loss spectroscopy ; Spectroscopy ; Chemical physics ; Molecular dynamics ; Materials science ; Vibrational energy ; Energy (signal processing) ; Dynamics (music) ; Electron ; Molecular physics ; Atomic physics ; Physics ; Chemistry ; Computational chemistry ; Nuclear physics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Luuk H.
E. Kempen (22406963) | Marius Juul Nielsen (22406966) | Mie Andersen (1527007)
Source: Figshare (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Breaking ScalingRelations with Inverse Catalysts:A Machine Learning Exploration of Trends in CO<sub>2</sub>‑to-FormateEnergy Barriers The conversion of CO<sub>2</sub> into useful products such as methanol is a key strategy for abating climate change and our dependence on fossil fuels. Developing improved catalysts for this process is costly and time-consuming and can thus benefit from computational exploration of possible active sites. However, this is complicated by the complexity of the materials and reaction networks. Here, we present a workflow for exploring transition states of elementary reaction steps at inverse catalysts, which is based on the training of a neural network-based machine learning interatomic potential. We focus on the crucial formate intermediate and its formation over nanoclusters of indium oxide supported on Cu(111). The speedup compared to an approach purely based on density functional theory allows us to probe a wide variety of active sites found at nanoclusters of different sizes and stoichiometries. Analysis of the obtained set of transition state geometries reveals different structure–activity trends at the edge or interior of the nanoclusters. Furthermore, the identified geometries allow for the breaking of linear scaling relations, which could be a key underlying reason for the catalytic performance of inverse catalysts observed in experiments. Nanoclusters ; Inverse ; Workflow ; Scaling ; Density functional theory ; Speedup ; Inverse problem Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Isaac Wilfried Sanou | Julien Baderot | Vincent Barra | Ali Hallal | Léo Mazauric | J. Foucher
Source: Journal of Micro/Nanopatterning Materials and Metrology (article, 2025-10-09)
Field: Materials Science — Surfaces, Coatings and Films

Deep-learning-driven automation of inner and outer object segmentation in SEM/TEM imaging for semiconductor metrology International audience Metrology ; Automation ; Object (grammar) ; Segmentation ; Image segmentation Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advanced Electron Microscopy Techniques and Applications
Authors: Tiantian Gao | Yufeng Wu
Source:  (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

A Self-Supervised Graph Attention Network for Accelerating the Discovery of High-Conductivity Solid-State Electrolytes Solid-state electrolytes (SSEs) are a critical component for the development of next-generation all-solid-state batteries, offering enhanced safety and energy density. However, the discovery of novel SSEs with high ionic conductivity is a timeconsuming and resource-intensive process, often relying on trial-and-error experimentation or computationally expensive firstprinciples calculations. To address this challenge, we propose a two-stage machine learning framework that combines selfsupervised pre-training with supervised fine-tuning to efficiently and accurately predict the ionic conductivity of crystalline materials. Our approach first leverages a vast repository of unlabeled crystal structures to pre-train a Graph Attention Network (GAT) encoder via a contrastive learning objective. This allows the model to learn universal, chemically-aware representations of atomic environments without requiring labeled data. Subsequently, the pre-trained encoder is fine-tuned on a smaller, curated dataset of materials with known ionic conductivities using a multi-layer perceptron (MLP) regression head. Our results demonstrate that this pre-training strategy significantly enhances prediction accuracy compared to a model trained from scratch, achieving a state-of-the-art performance in conductivity prediction. The proposed framework provides a powerful, data-driven paradigm for accelerating the rational design and discovery of novel solid-state electrolytes. Graph ; Encoder ; Component (thermodynamics) ; Multilayer perceptron ; Supervised learning ; Perceptron ; Artificial neural network ; Training set Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; CO2 Reduction Techniques and Catalysts
Authors: Юрій Нікітін
Source: Věda a perspektivy (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

ARTIFICIAL INTELLIGENCE FOR MATERIALS SCIENCE: CURRENT TRENDS Modern trends in the application of artificial intelligence to materials science have been identified, which use the growing availability of computer processing power and large databases of materials to discover new nextgeneration materials while reducing the time and cost of their development.Machine learning and deep learning artificial neural networks have been found to be the most popular approaches for extracting knowledge from large databases of materials and are considered as innovative means for successfully designing innovative new generation materials that address modern socio-economic and environmental challenges of society. Current (fluid) ; Computer science ; Data science ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Kunwoo Kim | Jejoong Yoo
Source: Biophysical Journal (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

BPS2025 - Deep learning-based force field for coarse-grained molecular dynamics Force field (fiction) ; Field (mathematics) ; Molecular dynamics ; Dynamics (music) ; Computer science ; Physics ; Artificial intelligence ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Hydrocarbon exploration and reservoir analysis
Authors: S. Kevin Zhou | Wanchen Zhao | Zhenglin Wan | Haoke Qiu | Xianbo Huang | Zhao‐Yan Sun
Source: Macromolecular Rapid Communications (article, 2025-09-27)
Field: Materials Science — Materials Chemistry

Multimodal Machine Learning with 3D‐Weighted‐Matrix Encoding for High‐Throughput Design of High‐Performance Polyurethanes Polyurethanes (PUs) are ubiquitous in our daily life, while facing fundamental challenges in designing materials with targeted mechanical properties due to their inherent structural complexity. To address this, we developed an extensible high-throughput screening framework that combines machine learning, multimodal feature engineering, and feature fusion strategy to enable the mechanical property prediction of PU materials. Specifically, an effective 3D-Weighted-Matrix encoding method was proposed to represent polyurethane monomers, indicating better performance than conventional molecular descriptors (23% improvement in feature discriminability). Synthesis process parameters were also digitized through logic-based encoding and fused with structural features (including chemical structure representations via 3D-Weighted-Matrix and molecular descriptors as well as synthesis process information) via an early fusion architecture, yielding a multimodal deep learning model capable of concurrent prediction of Young's modulus, tensile strength, and elongation at break with mean coefficient of determination ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msup><mml:mi>R</mml:mi> <mml:mn>2</mml:mn></mml:msup> <mml:annotation>${\rm R}^{2}$</mml:annotation></mml:semantics> </mml:math> ) values exceeding 0.86. With this model, we then performed combinatorial screening of more than 150 million molecular and process combinations, identifying optimal candidates that promote various mechanical performance metrics. This work enhances our comprehension of the intrinsic structure - property correlations in PU and introduces a powerful computational framework for the accelerated development of high - performance polyurethane materials. Feature (linguistics) ; Encoding (memory) ; Process (computing) ; Property (philosophy) ; Fusion ; Benchmark (surveying) ; Feature vector ; Pattern recognition (psychology) Machine Learning in Materials Science ; Polymer composites and self-healing ; Computational Drug Discovery Methods
Authors: Zisheng Zhang | X. Zhou
Source: MRS Communications (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Non-equilibrium restructurings in catalysis: A chemical space odyssey Restructuring ; Metastability ; Set (abstract data type) ; Chemical space ; Categorization ; Feature (linguistics) ; Space (punctuation) ; Perspective (graphical) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Ammonia Synthesis and Nitrogen Reduction
Authors: Seong-Min Kim | Joshua Schrier | Yousung Jung
Source: ChemRxiv (preprint, 2025-01-26)
Field: Materials Science — Materials Chemistry

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs using Large Language Models We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine-tuned large language models (LLMs) trained on a human-readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive-unlabeled learning model on a text-embedding representation of the structure. An LLM-based workflow can then be used to generate human-readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non-synthesizable hypothetical structures to make them more feasible for materials design. Crystal (programming language) ; Crystal structure prediction ; Computer science ; Crystallography ; Materials science ; Crystal structure ; Chemistry ; Programming language Machine Learning in Materials Science
Authors: Vijith Parambil | H. C. Goyal | U.K. Tripathi | Rohit Batra
Source: Materials Today Communications (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

TransChem: A hybrid transformer and cheminformatics based framework for enhanced polymer informatics Materials science ; Cheminformatics ; Informatics ; Polymer ; Transformer ; Nanotechnology ; Electrical engineering ; Composite material ; Engineering ; Bioinformatics ; Biology Machine Learning in Materials Science ; Semantic Web and Ontologies ; Business Process Modeling and Analysis
Authors: Jiayin Shi (22559427) | Xiangmin Feng (20255652) | Zhenming Xu (489339) | Xiaogang Zhang (1282050)
Source: Figshare (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Molecular DynamicsSimulation and Artificial Intelligence-DrivenDevelopment of New Lithium Electrolytes with High Ionic Conductivityand Understanding Ion Transport High ionic conductivity electrolytes are vital for ensuring robust lithium-ion battery performance, especially in low-temperature environments. In this study, we systematically investigated a novel chemical space comprising 2604 electrolyte formulations using high-throughput molecular dynamics (MD) simulations, integrating the OPLS-AA force field with the RESP2 charge model. This methodology accurately replicated Li<sup>+</sup> solvation shell structures and identified numerous innovative electrolytes exhibiting high room-temperature ionic conductivities more than 10 mS/cm, and many of which were experimentally validated for the first time. By leveraging MD data sets and the machine learning method, the composition-property relationships governing Li<sup>+</sup> solvation shell structure and ion transport in electrolytes were elucidated. Li<sup>+</sup> solvation shell structures are primarily influenced by solvent concentration, molecular topology, and surface charge distribution, with the higher solvent concentrations enhancing Li<sup>+</sup>-molecule coordination numbers. Ionic conductivity of electrolyte is predominantly determined by viscosity, and the low-viscosity components such as PF<sub>6</sub><sup>–</sup>, DOL, DME, EA, and DMC boost ionic conductivity, while TFSI<sup>–</sup>, DEC, and EMC tend to reduce it. Additionally, the high coordination numbers with weakly coordinating solvents leading to the larger localized Li<sup>+</sup> interactions further enhance ion transport in electrolyte. Molecular descriptors, including HallKierAlpha and MaxPartialCharge, exhibit strong correlations with ionic conductivity, serving as the effective metrics for the large-scale screening tasks. Consequently, the optimal high-conductivity electrolytes should incorporate low-viscosity solvents with high coordination numbers, strong Li<sup>+</sup> binding energies, elevated HallKierAlpha values, and reduced MaxPartialCharges. This synergistic integration of high-throughput simulations and machine learning offers a powerful approach for the discovery of advanced electrolytes. Electrolyte ; Solvation ; Ionic conductivity ; Solvation shell ; Molecular dynamics ; Ionic bonding ; Ion ; Conductivity ; Solvent Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: V. Cornette | Fernando S. Seijas | Rodrigo Delgado Mons | Raúl H. López
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of the Regularization Parameter for Pore Size Distribution Determination in Nanoporous Materials Regularization (linguistics) ; Nanoporous ; Distribution (mathematics) ; Pattern recognition (psychology) ; Extrapolation ; Support vector machine Machine Learning in Materials Science ; Nanoporous metals and alloys
Authors: Rauno Reitalu | Tatsiana Jarg | Riina Aav | Mario Öeren
Source: ChemRxiv (preprint, 2025-05-15)
Field: Chemistry — Inorganic Chemistry

Predicting the Host-guest Binding Gibbs Free Energy for Anion Guests Host-guest chemistry is an emerging approach in the design of functional molecular systems, with growing potential in areas such as targeted drug delivery, sensing, and adaptive materials. However, the development of new hosts and finding new guests for existing hosts remains experimentally challenging due to complex synthesis and the need for selective binding. To address this, we present a computational model that predicts Gibbs free energies of binding using a two-component machine learning model. Built on a carefully curated dataset of 247 host-guest systems from the literature, the model combines the electrostatic component of the complexes with three-dimensional molecular fingerprints and achieves an R² of 0.77 and MAE of 5.3 kJ per mol on an external test set. We demonstrate its applicability using newly aquiered experimental data for a mixHC[8] macrocycle, showing that the approach can distinguish between strong and weak binders; thus, enable efficient high-throughput screening for host-guest discovery. Gibbs free energy ; Host (biology) ; Ion ; Chemistry ; Computer science ; Biology ; Thermodynamics ; Physics ; Ecology ; Organic chemistry Inorganic Chemistry and Materials ; Crystallography and molecular interactions ; Machine Learning in Materials Science
Authors: Loukas Manitsas | George S. Fanourgakis
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Physically Motivated Machine Learning Model for Accurate Gas Adsorption Predictions in Nanoporous Materials Nanoporous ; Adsorption ; Computer science ; Materials science ; Chemistry ; Nanotechnology ; Physical chemistry Catalytic Processes in Materials Science