jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Baoming Shi | Majumdar, Apala | Lei Zhang
Source: Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural network-based tensor models for liquid crystals with molecular-level information The phenomenological Landau–de Gennes (LdG) model is a powerful continuum theory to describe macroscopic liquid crystal (LC) phases. However, it is invariably less accurate and less physically informed than molecular-level models. We propose a neural network-based tensor (NN-tensor) model for LCs, supervised by an underlying molecular model. Our NN-tensor model not only attains energy precision comparable to the molecular model, but it also accurately captures the isotropic-nematic phase transition, which the LdG model cannot achieve. By embedding the NN-tensor model within a second neural network, we can efficiently compute stable LC configurations in a domain-free and mesh-free manner. We validate this approach with multiple examples for nematic LCs, demonstrating its ability to find physically relevant nematic configurations in diverse scenarios. We further apply the NN-tensor model to the more complex smectic LC phase. Strikingly, the NN-tensor model can quantitatively predict the smectic layer thickness and capture intricate microstructures such as Omega and T-shaped grain boundaries—features that current conventional approaches fail to resolve. These results demonstrate that the NN-tensor framework is a unified, efficient, and physically faithful route for computing rich LC configurations across multiple phases. Liquid crystal ; Tensor (intrinsic definition) ; Embedding ; Artificial neural network ; Phenomenological model ; Phase (matter) ; Energy (signal processing) Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Material Dynamics and Properties
Authors: Haewon Kim | Taekgi Lee | Seongeun Hong | Kyeong-Ho Kim | Yongchul G. Chung
Source: The Journal of Chemical Physics (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Data-driven prediction of ionic conductivity in solid-state electrolytes with machine learning and large language models Solid-state electrolytes (SSEs) are attractive for next-generation lithium-ion batteries due to improved safety and stability, but their low room-temperature ionic conductivity hinders practical application. Experimental synthesis and testing of new SSEs remain time-consuming and resource-intensive. Machine learning offers an accelerated route for SSE discovery; however, composition-only models neglect structural factors important for ion transport, while graph neural networks are challenged by the scarcity of structure-labeled conductivity data and the prevalence of crystallographic disorder in crystal structures (CIFs). Here, we train two complementary predictors on the same room-temperature, structure-labeled dataset (n = 499). A gradient-boosted tree regressor model using stoichiometric descriptors alone achieves a test MAE of 1.108 in log(S/cm); adding geometric descriptors (combined MAE = 1.172) does not lower the test error but reveals complementary structural information through Shapley Additive exPlanations, which shows that stoichiometric descriptors, particularly the oxygen ratio, dominate feature importance (seven of the top ten features), with three geometric descriptors (density, Lmax, and Lmin) also contributing meaningfully. In parallel, we fine-tune large language models (LLMs) using compact text prompts derived from CIF metadata (formula with optional symmetry and disorder tags), avoiding direct use of raw atomic coordinates. Notably, while Mistral-7B achieves the lowest absolute error [MAE = 0.798 in log(S/cm)], Qwen3-8B demonstrates the best overall ranking performance (SRCC = 0.849) using formula and disorder information, eliminating the need for numerical feature extraction from CIF files. Together, these results show that global geometric descriptors improve tree-based predictions and enable interpretable structure-property analysis, while LLMs provide a competitive low-preprocessing alternative for rapid SSE screening. Feature (linguistics) ; Tree (set theory) ; Ranking (information retrieval) ; Artificial neural network ; Symmetry (geometry) ; Conductivity ; Test data ; Feature extraction ; Differentiable function Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Rubén Guerrero-Rivera | Norma A. García-Vidaña | Francisco J. Godínez-García | Zhipeng Wang | Morinobu Endo | Josué Ortiz-Medina
Source: AI Materials (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Spatial Prediction of Electronic Wavefunctions from Reciprocal Lattices: Visualization of Electronic Properties of 2D Materials Using Deep Convolutional Neural Networks The representation of electronic wavefunctions in real space grids, which are directly related to molecular orbitals and electronic densities either in molecular or crystalline systems, is a fundamental part of many studies at ab initio levels, since it contributes to the understanding of complex physical and chemical phenomena at the nanoscale. This work proposes the use of a deep convolutional neural network for the prediction of electronic wavefunctions at arbitrary positions along high-symmetry points within the reciprocal space (first Brillouin zone), which can be represented as isosurfaces in the real space. The proposed neural network algorithm is trained with data from density functional theory (DFT) calculations of monolayer 2D crystalline systems (i.e., pristine, B- and N-doped graphene, and MoS2) and was able to produce predictions of data for wavefunction representation on the real space, with accuracies in between 62% and 92%, from calculated determination coefficients. Moreover, the optimized method for generating spatial representations of electronic wavefunctions, based on Machine Learning, is at least 25× faster than the conventional DFT-based methodology, enabling an efficient way for a quick assessment of 2D material properties related to the spatial distribution of electronic wavefunctions in the real space, such as local charge density and molecular orbital visualization in crystalline systems, and including their dependence on the position within the reciprocal space. Wave function ; Reciprocal lattice ; Electronic structure ; Convolutional neural network ; Representation (politics) ; Brillouin zone ; Reciprocal ; Position (finance) ; Visualization Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: C. Eagan | M. Copus | E. Iacocca
Source: arXiv (Cornell University) (preprint, 2026-03-10)
Field: Materials Science — Materials Chemistry

Deep learning statistical defect models on magnetic material dynamic and static properties The modeling of realistic magnetic materials requires the inclusion of defects. Based on the pseudospectral Landau-Lifshitz description of magnetisation dynamics, we propose a statistical model that takes into account defects, specifically vacancies. This statistical model can be integrated with deep learning techniques that correlate defect thresholds with relevant physical observables. We develop a convolutional neural network and a physics-informed neural network combined with theory of functional connections to predict the dispersion relation given defect parameters and physical constraints. A two-branch convolutional neural network is developed to predict domain-wall widths depending on defects threshold, taking into account the spatial profile and domain-wall width separately to achieve a prediction. The proposed physics-informed approaches leverage deep-learning and achieve statistical predictions measured in physical units. This is a stepping stone towards the discovery of new materials and the determination of minimal defect thresholds required for desired dynamics, states, or topological textures. Leverage (statistics) ; Convolutional neural network ; Artificial neural network ; Statistical learning ; Deep learning ; Statistical model ; Relation (database) ; Multiscale modeling ; Magnetization Machine Learning in Materials Science ; Magnetic Properties and Applications ; Magnetic properties of thin films
Authors: Barack Ndenga | Maarouf Maman Moussa Maman
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-04-11)
Field: Materials Science — Materials Chemistry

Topological Adaptive Catalysts for CO₂ Activation: A Machine Learning and Energy Landscape Approach A new paradigm for CO₂ activation is introduced, where catalytic efficiency emerges from the topology of energy landscapes rather than static molecular structures. By combining density functional theory and machine learning, we identify adaptive catalytic systems that exploit low-energy reaction pathways. This trajectory-based framework opens new directions for catalyst design. Exploit ; Topology (electrical circuits) ; Energy (signal processing) ; Density functional theory ; Catalysis Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Christos Stavrogiannis | Filippos Sofos | Theodoros E. Karakasidis
Source: Archives of Computational Methods in Engineering (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

Machine Learning-Enhanced Molecular Dynamics: Current State, Challenges and Perspectives Atomic-scale simulations, such as molecular dynamics (MD), have undergone a rapid evolution during the past decades, leading to their broad adoption in various scientific and engineering fields, where they have unveiled hidden phenomena otherwise inaccessible through experiments. In recent years, the integration of machine learning (ML), either as a post-processing tool or embedded directly in a hybrid manner to the simulation workflow, has further enhanced the predictive power of MD by improving accuracy, reducing computational costs, and uncovering patterns in high-dimensional data. This review investigates the integration of MD with ML by identifying existing approaches for their combination within popular simulation platforms. It incorporates an analysis of research trends from 2020 to 2025 based on Scopus-indexed publications. By synthesizing recent developments and highlighting ongoing challenges, this review aims to guide future efforts in exploiting the full potential of MD/ML integration across scientific and engineering disciplines, such as computational chemistry and physics, biomolecular engineering, medicine, soft matter physics, materials science and nanotechnology. Computational model ; Current (fluid) ; Science and engineering ; Scientific discovery ; Power (physics) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Haewon Kim | Taekgi Lee | Seongeun Hong | Kyeong-Ho Kim | Yongchul G. Chung
Source: ArXiv.org (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Data-driven Prediction of Ionic Conductivity in Solid-State Electrolytes with Machine Learning and Large Language Models Solid-state electrolytes (SSEs) are attractive for next-generation lithium-ion batteries due to improved safety and stability but their low room-temperature ionic conductivity hinders practical application. Experimental synthesis and testing of new SSEs remain time-consuming and resource intensive. Machine learning (ML) offers an accelerated route for SSE discovery; however, composition-only models neglect structural factors important for ion transport while graph neural networks (GNNs) are challenged by the scarcity of structure-labeled conductivity data and the prevalence of crystallographic disorder in CIFs. Here, we train two complementary predictors on the same room-temperature, structure-labeled dataset (n = 499). A gradient-boosted tree regressor (GBR) combining stoichiometric and geometric descriptors achieves best performance (MAE = 0.543 in log(S cm-1)), and Shapley Additive exPlanations (SHAP) identifies probe-occupiable volume (POAV) and lattice parameters as key correlations for conductivity. In parallel, we fine-tune large language models (LLMs) using compact text prompts derived from CIF metadata (formula with optional symmetry and disorder tags), avoiding direct use of raw atomic coordinates. Notably, Llama-3.1-8B-Instruct achieves high accuracy (MAE = 0.657 in log(S cm-1)) using formula and symmetry information, eliminating the need for numerical feature extraction from CIF files. Together, these results show that global geometric descriptors improve tree-based predictions and enable interpretable structure-property analysis, while LLMs provide a competitive low-preprocessing alternative for rapid SSE screening. Artificial neural network ; Symmetry (geometry) ; Stability (learning theory) ; Feature (linguistics) ; Conductivity ; Lattice (music) ; Tree (set theory) ; Key (lock) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Nicholas Hadler | N. Ian Rinehart | Masha Elkin | Jeremy Nicolai | Golsa Gheibi | Jiaqing Chen | Matthew Avaylon | Ross Maciejewski | Gunther H. Weber | Michael W. Mahoney | Talita Perciano | John F. Hartwig
Source: ChemRxiv (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

A 3D, Structure-Based, Deep Learning Approach for Predicting the Regioselectivity of Transition-Metal Catalysis Rational development of transition-metal catalysts, even when guided by theory and mechanistic knowledge, involves significant trial and error. Although ML offers the potential to accelerate catalyst discovery and optimization, accurately modeling the complex structures of catalysts and the multistep mechanisms by which they react remains challenging with the limited sets of data available. Olefin hydroformylation is a quintessential example of this challenge: its catalytic cycle involves many, often reversible, steps, and decades of study have not yielded reliable structure-selectivity relationships. We report Libra-ML, a 3D structure-based deep learning approach for predicting experimental outcomes of transition-metal catalyzed reactions. To demonstrate the ability of Libra-ML to model the outcomes of complex catalytic reactions, we predicted the regioselectivity of hydroformylation with terminal olefins catalyzed by rhodium complexes. Comparisons to existing methods demonstrate the state-of-the-art performance of Libra-ML and illustrate the importance of capturing 3D structure to predict experimental outcomes with molecular catalysts. Regioselectivity ; Hydroformylation ; Olefin fiber ; Deep learning ; Catalysis ; Rhodium Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; CO2 Reduction Techniques and Catalysts
Authors: Rostislav Fedorov | Anastasiia Nihei | Ganna Gryn’ova
Source: Journal of Chemical Information and Modeling (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Multi-Solvent Graph Neural Network for Reduction Potential Prediction Across the Chemical Space Reduction potentials of redox-active molecules and materials are essential descriptors of their performance as catalysts, antioxidants, electrode materials, etc. For a given species, its practical applications often span a range of solvent environments, which profoundly impact its redox properties. In this work, we present a message passing graph neural network architecture with a Set Transformer readout trained on <i>ca</i>. 20,000 reduction potentials of chemically diverse closed- and open-shell organic redox-active molecules (the "ReSolved" data set), computed using a rigorously benchmarked density functional theory procedure. The predictor model affords high accuracy with mean absolute errors of <i>ca</i>. 0.2 eV and is uniquely able to generalize to previously unseen solvents. We couple this architecture with an evolutionary algorithm to inverse-design synthetically accessible candidate molecules with target reduction potentials for several battery-related practical applications. Chemical space ; Reduction (mathematics) ; Artificial neural network ; Graph ; Graph theory ; Set (abstract data type) ; Transformer ; Range (aeronautics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Graph Neural Networks
Authors: Yingjie Fan | Jun Hu | Jingsong Guan | Zhao Wang | Qiwen Guo | Dawei Zhang | Qingchun Yang
Source: Chemical Engineering Science (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

BiML-SyMO: Bridging multi-objective optimization of catalysts and chemical systems through a bilateral machine learning framework Robustness (evolution) ; Process optimization ; Process (computing) ; Hyperparameter ; Bridging (networking) ; Catalysis ; Markov decision process Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Ryuhei Sato | Peter I. C. Cooke | Maélie Caussé | Hung Ba Tran | Seong Hoon Jang | Di Zhang | Hao Li | Shin-ichi Orimo | Yasushi Shibuta | Chris J. Pickard
Source: ArXiv.org (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Competing Hydrogenation Pathways to Metastable CaH$_6$ Revealed by Machine-Learning-Potential Molecular Dynamics The synthesis of the high-$T_c$ superhydride CaH$_6$ has stimulated significant interest in understanding synthesis pathways for metastable hydrides. However, the microscopic mechanisms governing such hydrogenation reactions remain poorly understood. Here, we show that machine-learning potential molecular dynamics (MLP-MD) simulations can reproduce and distinguish competing reaction pathways leading to metastable and stable hydrides. By simulating hydrogenation reactions at CaH$_2$/H$_2$ and CaH$_4$/H$_2$ interfaces, we identify two distinct pathways that produce clathrate-type CaH$_6$ and A15-type CaH$_{5.75}$, respectively. CaH$_{5.75}$ lies on the convex hull but requires extensive Ca sublattice rearrangement and therefore forms only at elevated temperatures. In contrast, CaH$_6$ becomes kinetically accessible when CaH$_2$ is used as the precursor. The crystallographic compatibility between the Ca sublattice of CaH$_2$ and the bcc framework of CaH$_6$ enables a martensitic-like topotactic transformation that bypasses the reconstructive pathway leading to CaH$_{5.75}$. These results reveal how precursor structure and thermodynamic stability compete to determine superhydride formation pathways and demonstrate that machine-learning molecular dynamics can directly capture the kinetic selection of metastable phases in reactive materials systems. Metastability ; Molecular dynamics ; Kinetic energy ; Kinetics ; Chemical stability ; Reaction intermediate Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Hodaka Mori | Shunsuke Tonogai | Yu Miyazaki | Akihide Hayashi | Masayoshi Takayanagi
Source: The Journal of Physical Chemistry B (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Ready-to-Use Polymerization Simulations Combining Universal Machine Learning Interatomic Potential with Time-Dependent Bond Boosting for Polymer and Interface Design Although polymerization and curing reactions govern the performance of advanced materials, their simulation remains challenging owing to the need for accurate, transferable potentials and the rarity of chemical events. Conventional reactive force fields such as ReaxFF require system-specific parametrization, while universal machine learning interatomic potentials (uMLIPs) exhibit limited sampling efficiency. This paper introduces a novel simulation framework integrating a uMLIP with a time-dependent bond-boost scheme. The bias potential increases monotonically with time, and the use of a unified parameter set across reaction classes enables consistent acceleration without system-specific tuning. For radical polymerization of vinyl monomers, the proposed framework reproduces characteristic trends, such as linear molecular-weight growth with conversion, initiator-concentration scaling, and relative monomer reactivity trends. For step-growth polycondensation of nylon-6,6, it captures the characteristic sharp increase in molecular weight at high conversion rates, consistent with experimental behavior. Epoxy curing on a copper substrate reveals interfacial ring-opening and cross-linking events, consistent with spectroscopic evidence of Cu-O-C bond formation. Overall, coupling uMLIPs with a time-dependent bond boost enables practical and transferable simulations of polymerization and curing processes. The proposed framework reliably resolves mechanistic pathways and relative reactivity, offering molecular-level insights into polymer growth and interfacial adhesion. Polymerization ; Curing (chemistry) ; Polymer ; Monomer ; Epoxy ; ReaxFF ; Chemical bond ; Molecular dynamics Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Nuria H. Espejo | Pablo Llombart | Andrés González de Castilla | Jorge Ramírez Caro | Jorge R. Espinosa | Adiran Garaizar
Source: arXiv (Cornell University) (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

Thermodynamic Descriptors from Molecular Dynamics as Machine Learning Features for Extrapolable Property Prediction The limited extrapolative power of structure-based machine learning (ML) models is a critical bottleneck in chemical discovery, particularly for industrial R&amp;D, where navigating uncharted chemical space to find next-generation materials or drugs is paramount. These models, reliant on structural descriptors or graph neural networks (GNNs), often fail when predicting properties for molecules with novel chemotypes. Here, we introduce a physics-augmented ML framework that overcomes this limitation. Our approach replaces conventional structural inputs with thermodynamic properties such as cohesive energy, heat of vaporization, and density, derived directly from molecular dynamics (MD) simulations. While performing comparably to structure-based models on known organic compounds, our method uniquely maintains low error when extrapolating to dissimilar chemical spaces. Crucially, it accurately predicts boiling points for entire chemical classes absent from the training set, including inorganic compounds, salts, and molecules with elements such as Si, B, and Te. By learning from the intermolecular forces that govern phase transitions, our framework provides a more fundamental and generalizable strategy for molecular property prediction, enabling chemical exploration beyond established structural domains. Property (philosophy) ; Bottleneck ; Benchmark (surveying) ; Chemical space ; Molecular dynamics ; Encoding (memory) ; Molecular descriptor ; Key (lock) ; Feature (linguistics) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods