jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Soheila Molaei | Kam C. Poon | Chang Gao | Katharina H. S. Eisenhardt | Matilde Concilio | Gregory S. Sulley | David Kohan Marzagão | Georgina L. Gregory | David A. Clifton | Clive R. Siviour | Charlotte K. Williams
Source: Journal of the American Chemical Society (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics Oxygenated block polymers, comprising esters and carbonates, are priority materials to replace petrochemical polymers in a circular plastics economy. These materials should repopulate the thermomechanical property space mapped by current plastics and elastomers. Here, a novel machine learning approach, PolyReco, predicts structures of oxygenated block polymers meeting the mechanical performance thresholds for widely used and hard-to-replace petroleum derived hydrocarbon polymers. Triblock oxygenated polymers are represented as graphs, and a link prediction algorithm enables feature extraction to identify new block polymer combinations, and associated degrees of polymerization, to meet the target properties. PolyReco is paired with a visualization tool for further material down selection based on user requirements. Three case studies highlight and experimentally validate its predictive power for identifying high-performance oxygenated block polymers, with new block polymers prepared and tested. These new block polymers exhibit tensile mechanical properties in the range of high-impact polystyrene, poly(dimethylsiloxane), and styrenic elastomers; the experimental results indicate that PolyReco may help support the identification of sustainable materials that could reduce dependence on fossil-based polymer incumbents. Block (permutation group theory) ; Polymer ; Copolymer Machine Learning in Materials Science ; Polymer crystallization and properties ; Advanced Polymer Synthesis and Characterization
Authors: Kadri Muuga | Lisanne Knijff | Chao Zhang
Source: arXiv (Cornell University) (preprint, 2026-01-15)
Field: Materials Science — Materials Chemistry

Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions The molecular electrostatic potential (MEP) is a key quantity for describing and predicting intermolecular and ion-molecule interactions. Here, we assess the ability of machine-learning (ML) models to infer the MEP, based on the equivariant graph-convolutional neural network architecture PiNet2 and trained on dipole and quadrupole moments. For the established QM9 dataset, we find that including the quadrupole contribution in the ML models substantially improves their ability to recover the MEP compared to dipole-only models. This trend is confirmed on the SPICE dataset, which spans a much broader region of organic chemical space. Together, this study underscores the central role of the quadrupole moment as a fitting target for ML models aiming at rapid access to the MEP. Quadrupole ; Dipole ; Artificial neural network ; Moment (physics) ; Intermolecular force ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Haodong Feng | Yu Zhang | Peiyan Hu | Ruiqi Feng | Yue Wang | Tailin Wu | Dixia Fan
Source: ACS Earth and Space Chemistry (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Guaranteed Mass-Conserving Deep Learning Model for Stiff Kinetics in Atmospheric Chemistry Atmospheric chemistry modeling faces significant computational challenges due to stiff coupled ordinary differential equation (ODE) systems with reaction rates spanning multiple orders of magnitude. Traditional numerical solvers have been computational bottlenecks in chemical transport models (CTMs), while existing deep learning approaches suffer from mass conservation violations and catastrophic error accumulation. To overcome the above issues, we propose mass-conserving flow matching (MC-Flow), a theoretically guaranteed mass-conserving deep learning model for stiff atmospheric chemistry ODEs. MC-Flow employs generative modeling that directly predicts final states from initial conditions, bypassing intermediate transitions to eliminate error accumulation. Moreover, we theoretically derive a guaranteed mass conservation constraint and modify the training and inference procedures within the flow matching framework according to the theorem. We validate the MC-Flow on H2O2/OH/HO2 system, the Verwer system, and the tropospheric CH4–CO–NOx–HOx chemical system. Results demonstrate significant improvement in accuracy, both for single-step and long-term predictions with 11-fold computational speedup over numerical solvers. MC-Flow maintains guaranteed mass conservation, while comparison methods show conservation violations. The study offers a physically consistent and computationally efficient alternative to conventional numerical methods, enabling potentially an AI-driven module for atmospheric chemistry modeling with applications in air quality and climate modeling. Kinetics ; Deep learning ; Atmospheric chemistry ; Atmospheric pressure ; Atmospheric model ; Mathematical model Machine Learning in Materials Science ; Atmospheric chemistry and aerosols ; Advanced Physical and Chemical Molecular Interactions
Authors: Xiang Yin | Jun Ma | Shu Zhang | Guanglei Cui
Source: Computational Materials Today (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Machine learning drives a new paradigm in inorganic solid-state electrolytes research The development of next-generation solid-state batteries (SSBs) relies heavily on the discovery of high-performance inorganic solid-state electrolytes (SSEs). However, conventional experimental and computational methods are often time-consuming and inefficient in exploring the vast compositional and structural space. Machine learning (ML) has emerged as a powerful and transformative tool, driving a paradigm shift in SSE research. This review systematically summarizes recent advances in ML applications for inorganic SSEs, focusing on its role in predicting and optimizing key properties such as thermodynamic stability, electronic transport, electrochemical stability windows, ionic conductivity, migration barriers, phonon dynamics, mechanical strength, and interfacial stability. By enabling high-throughput screening, establishing structure-property relationships, and guiding inverse design, ML significantly accelerates the discovery and rational design of promising SSE candidates. Perspectives on the challenges such as data quality, model interpretability, and multi-property trade-offs are supplied. Finally, we recommend that future efforts prioritize the development of standardized databases, interpretable and physics-informed ML models, and closed-loop frameworks integrating computation, synthesis, and validation. This review will guide future directions to advance SSE technologies toward practical application in high-energy, safe solid-state batteries. Transformative learning ; Key (lock) ; Stability (learning theory) ; Electronics ; Development (topology) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Lucas B. T. de Kam | Jiaxin Zhu | Ankit Mathanker | Katharina Doblhoff-Dier | Nitish Govindarajan
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces Atomistic simulations of electrochemical interfaces remain challenging due to the long time scales required to adequately sample the structure of the electric double layer. The emergence of efficient, short-range machine learning interatomic potentials (MLIPs) offers a promising alternative to computationally expensive density functional theory-based molecular dynamics (DFT-MD) simulations in this regard. However, in standard periodic DFT calculations of metal surfaces, the surface charge is implicitly set by the number of counterions in the simulation cell, making it a global property that is difficult to represent with strictly local MLIPs. Here, we benchmark common MLIP architectures (DP, ACE, MACE) for charged Au/water interfaces containing solvated sodium ions. We find that MLIPs trained on datasets spanning multiple surface charge states yield inconsistent predictions of interfacial water orientation and ion distributions, although message-passing models with a larger receptive field exhibit greater robustness to training on mixed-charge datasets. In contrast, models trained on a single charge state produce consistent equilibrium interfacial properties. Finally, we assess the performance of the eSEN model trained on the recently released Open Catalyst 2025 dataset, which includes solid/liquid interfaces that span a wide range of surface charge densities. Overall, our results characterize the limitations of short-range MLIPs for simulations of electrochemical interfaces and provide practical guidance for constructing training datasets for simulations of charged metal/electrolyte interfaces. Molecular dynamics ; Robustness (evolution) ; Surface charge ; Charge density ; Charge (physics) ; Electrostatics ; Benchmark (surveying) ; Benchmarking ; Scaling ; Range (aeronautics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Mohd Ahsan | Chinmai Pindi | Souvik Sinha | Amun C. Patel | Giulia Palermo
Source: Current Opinion in Structural Biology (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Graph neural networks for molecular dynamics simulations ENCODE ; Complement (music) ; Graph ; Artificial neural network ; Molecular dynamics ; Key (lock) ; Deep neural networks Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Neural Networks and Applications
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 (Cornell University) (preprint, 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: Chenyu Wu | Chao Yang | Zhening Fang | Wenbo Xie | Daiqian Xie | P. Hu
Source: Journal of Chemical Theory and Computation (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Hitchhikers Guide To Training More General Machine Learning Potentials in Heterogeneous Catalysis Machine learning potentials (MLPs) have emerged as powerful simulation tools for heterogeneous catalysis. While current MD-active-learning workflows excel at fitting a specific system/reaction pathway through iterative structural sampling, the transition toward more general and transferable MLPs, designed to handle diverse structures and reactive events within a defined chemical space, presents fundamentally new challenges. Such generality often requires highly diverse, nonequilibrium training data, for which standard practices may confront challenges regarding training discipline and evaluation logic. Here, using our recently developed REICO method as an example to generate such data sets, we systematically investigate the distinct pathologies that arise when training on such diverse data, revealing critical deviations from standard system-specific MLP training. We further provide detailed recommendations on data cleaning, model selection, and error metrics for both numerical performance and physical validation, offering practical guidance for training MLPs with diverse and hybrid data sets. Generality ; Workflow ; Training set ; Experimental data ; Training (meteorology) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Chao Chen | Yiding Ma | Xiaokai He | Yu Li | Yingzhe Liu
Source: ChemRxiv (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Multi-Body Approximation Featurization to Broaden Machine Learning Application Range for Crystal Density Modelling Machine Learning (ML) is pivotal in forecasting the physicochemical properties of high-energy-density materials (HEDMs). However, the limited out-of-sample predictive capacity and interpretability of conventional models have constrained advancements beyond the performance boundaries defined by the training datasets. This research introduces a novel molecular descriptor, Multi-body Approximation (MBA), which enhances the prediction of crystal density (ρcryst)-a critical parameter for detonation performance-by incorporating molecular spatial information into atomic groups. Analytical functions for MBA volume terms were developed through Density Functional Theory simulations and theoretical derivation, substantially reducing the data requirements for constructing accurate ML models. The resulting general regression model achieved exceptional accuracy, with a mean absolute error (MAE) of 0.026 g/cm³. Furthermore, the MBA volume descriptor demonstrated excellent predictive capability for more complex co-crystal structures. Leveraging the SHAP explainer, we derived three interpretable design criteria for high-ρcryst materials and subsequently identified eight high-ρcryst candidates. These results demonstrate that the MBA volume descriptor effectively integrates chemical and physical principles into a mathematical framework, achieving multiple advanced functionalities: compatibility with small datasets, out-of-sample extrapolative capability, and intuitive model interpretability. We anticipate that extending MBA volume descriptors to other HEDM metrics, coupled with probabilistic structure generation models, holds significant potential to revolutionize HEDM design. Interpretability ; Probabilistic logic ; Range (aeronautics) ; Crystal structure prediction ; Volume (thermodynamics) ; Uncertainty quantification ; Big data ; Density estimation Machine Learning in Materials Science ; Energetic Materials and Combustion ; High-pressure geophysics and materials
Authors: Samak Boonpan | Weerachai Sarakorn | Krailikhit Latpala | Pornpimon Boriwan | Pornjuk Srepusharawoot
Source: Scientific Reports (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

BEmXRD-Nets framework for novel machine learning models to predict crystal energy with diversity structures This work proposes the BEmXRD-Nets framework, a novel machine learning framework that integrates fundamental atomic properties with learned embeddings from experimental X-ray diffraction (XRD) patterns to accurately predict the crystal energy in diverse and complex material structures. This framework is implemented to predict both formation and total energy for structures ranging from binary ([Formula: see text]) to complex quinary ([Formula: see text]) compositions. The elemental and structural features are extracted to create tailored representations for each crystal type, which are then utilized to train several machine learning models and combined using an optimized-weight stacking ensemble (STE) technique. The numerical results indicate that integrating elemental properties with XRD embeddings significantly enhances prediction accuracy and stability across all configurations. Our framework provides a robust and generalizable method for estimating crystal energies in complex structures, thereby advancing the application of machine learning in the discovery and design of novel materials. Quinary ; Crystal structure prediction ; Stability (learning theory) ; Stacking ; Binary number ; Energy (signal processing) ; Crystal (programming language) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: G. E. Ogwu | B. O. Adewuyi | H. K. Talabi | N. F. Efozia
Source: Advanced Journal of Science Technology and Engineering (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Prospective Revisions on the Use of Machine Learning in the Aluminum Alloy Development: A Review on Techniques and Future Applications Alloy design is the major driving force of next-generation materials technology. Traditionally alloy design has relied on empirical rules and iterative trial-and-error experimentation, with the process of identifying novel compositions being time-consuming, costly, and inefficient. The landscape has recently been revolutionized by advances in machine learning (ML) that enable data-driven methods to improve the efficiency of sophisticated alloy design, selection, and property prediction. ML algorithms can learn effectively the relationships between composition, processing, structure, and properties from existing data, and thus guide the discovery of novel alloys with target properties. In this review, a survey of ML approaches employed in alloy design is provided, including supervised and unsupervised learning, feature engineering, and combination with physical modeling frameworks, such as CALPHAD. Process (computing) ; Alloy ; Feature (linguistics) ; Property (philosophy) ; Iterative and incremental development ; Stability (learning theory) Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Aluminum Alloy Microstructure Properties
Authors: Badrinathan Sridharan | Maaz Salman | Yeong Seo Park | Eun Ju Lee | Soonhyuk Tak | Jegyeong Ryu | Junghwan Oh | Deukhee Lee | Hae Gyun Lim
Source: Microchimica Acta (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Decoding chemical composition of urinary crystals from ultrasonic echo signals via deep learning Ultrasonic sensor ; Echo (communications protocol) ; Deep learning ; Decoding methods ; Chemical composition ; Ultrasonic testing Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Ultrasonics and Acoustic Wave Propagation
Authors: Stiv Llenga | Alessandro Calzolari | Ganna Gryn'ova
Source: ChemRxiv (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Quantum-Inspired Machine Learning Representation for Periodic Materials Performance of the machine learning models for materials discovery critically depends on how atomic structures are represented. While many materials descriptors encode geometric and compositional information, representations that directly incorporate the features of the electronic structure remain comparatively scarce. In this work, quantum-inspired representations for materials derived from their oneelectron integrals, i.e., kinetic energy, nuclear attraction, and overlap matrix, are developed and tested in predicting the band gaps of metal-organic frameworks. Through benchmarking against other common materials representations, kinetic energy matrix is shown to consistently afford the lowest prediction errors across all training set sizes. This performance is attributed to the physical information encoded by the Laplacian operator, which captures the wavefunction curvature and orbital delocalisation-key factors governing electronic band gaps. A Leave-One-Element-Out analysis reveals that this new representation, termed TM, captures transferable trends across different metal centres. These results demonstrate that physics-based descriptors afford accurate and efficient machine learning on periodic materials and offer a promising direction for integrating quantum-mechanical information into data-driven materials discovery. Representation (politics) ; Curvature ; Benchmarking ; Set (abstract data type) ; Energy (signal processing) ; Matrix representation ; Feature learning ; ENCODE ; Artificial neural network Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Kangying Li | Yongfa Zheng | Jesse Zhu | Yuanxing Huang | Changsheng Qin | Tonghan Yang
Source: Journal of Materials Engineering and Performance (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Investigating the High-Temperature Oxidation Behavior of Ti-5Sn-xEr and Ti-6.5Ni-xEr Alloys Using Multi-scale Convolutional Neural Networks Convolutional neural network ; Kinetics ; Artificial neural network ; Diffusion ; Oxide ; Oxygen Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Intermetallics and Advanced Alloy Properties