jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sudheesh Kumar Ethirajan | Ambarish Kulkarni
Source: ChemRxiv (preprint, 2025-07-22)
Field: Chemistry — Electrochemistry

Modeling Diffusion in Metal-Organic Frameworks using On-the-fly Probability Enhanced Sampling-based Machine Learning Potentials Machine learning potentials (MLPs) can help bridge the length- and time-scale gaps required to study diverse physicochemical phenomena in nanoporous materials with ab initio accuracy. These MLPs are typically trained on quantum chemical data obtained from traditional molecular dynamics (MD) simulations that predominantly sample near-equilibrium configurations. This often limits the model’s ability to describe high-energy, off-equilibrium states required to study rare events. To address this capability gap, we introduce a novel active learning curriculum using the On-the-fly Probability Enhanced Sampling (OPES) method. Using imidazole diffusion in the SALEM-2 metal-organic framework (MOF) as a prototypical example, we apply time-dependent OPES biases with temperature- and distance-based collective variables to systematically sample the most relevant regions of the potential energy surface. This strategy enables nanosecond-scale MD simulations with near-DFT accuracy, capturing both the expected diffusion pathway across the 6-membered window and a previously unreported ring-opening mechanism through the more constrained 4-membered window. The latter process, which involves the transient dissociation of a Zn–N bond, cannot be captured using classical force fields and is prohibitively expensive with DFT. Thus, this study highlights how enhanced sampling methods can overcome data scarcity challenges associated with training MLPs for studying rare events in nanoporous materials. On the fly ; Sampling (signal processing) ; Diffusion ; Computer science ; Probability sampling ; Artificial intelligence ; Machine learning ; Biological system ; Physics ; Thermodynamics ; Computer vision Electrochemical Analysis and Applications
Authors: Zihan Liu | Xinyi Zhao | Yuai Duan | Yaping Li | Zhijia Wang | Zixuan Wang | Jiarong Zhang | Jing Yuan | Hua Geng | Tianyu Han
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

An intelligent tactile imaging-recognition sensor system enabled <i>via</i> a methoxynitrobenzene-salicylaldehyde fluorescent material Tactile sensors utilizing functional materials decode surface textures for object recognition. Herein, we engineer a donor-acceptor fluorescent material, MNIMP, that synergizes aggregation-induced emission (AIE) and twisted intramolecular charge transfer (TICT) mechanisms. Contact-induced nanoflake assembly on the MNIMP film triggers fluorescence amplification mediated by the combined AIE and TICT effects, through which the surface morphology of textured objects can be accurately visualized as fluorescent patterns. MNIMP maps micro-textures of materials such as rubber, fabrics, and elastic polymers under tactile pressure with kPa-level sensitivity, seamlessly integrating visual and tactile perceptions. These fluorescent signatures can be recognized using a deep-learning model with >98% accuracy. Hardware integration with the embedded algorithm model creates an intelligent tactile sensor system performing concurrent contact imaging, data analysis, and classification. This intelligent platform demonstrates micron-scale resolution and cost-effective manufacturability while maintaining high signal fidelity across diverse target objects. Fluorescence ; Tactile sensor ; Materials science ; Intramolecular force ; Salicylaldehyde ; Acceptor ; Aggregation-induced emission ; Nanotechnology ; Optoelectronics ; Computer science ; Artificial intelligence ; Chemistry ; Robot ; Optics ; Physics Luminescence and Fluorescent Materials ; Analytical Chemistry and Sensors ; Nanoplatforms for cancer theranostics
Authors: Brook Xhabrahimi (22320736) | Emil I. Jaffal (21148848) | Danila Shiryaev (21148854) | Nikhil K. Barua (22320739) | Madison Donohoe (22320742) | Natalia Pozdnyakova (22320745) | Mariam Ismail (11369334) | Balaranjan Selvaratnam (5584691) | Ehsan Niknam (20381690) | Holger Kleinke (1680766) | Anton O. Oliynyk (1936174)
Source: Figshare (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Explainable RecommendationEngines to Predict ComplexIntermetallics: Synthesis and Characterization of Gd<sub>10</sub>RuCd<sub>3</sub>, a Neutron Absorption Material The search for a neutron absorption material was aided by materials informatics tools that comprise the developed recommendation engines. A new member in the ternary RE<sub>10</sub>MCd<sub>3</sub> series, Gd<sub>10</sub>RuCd<sub>3</sub>, was predicted and synthesized as an experimental validation. The recommendation engine is based on partial least-squares-discriminant analysis (PLS-DA) crystallographic site processing to classify the site preference of elements in compounds crystallizing in the Y<sub>10</sub>RuCd<sub>3</sub>-type structure and upon projecting other elements onto Principal Component map, the titled compound emerged as the top candidate. The distinguishing feature of the developed recommendation engine is in the three explainable methods utilized in the predictive framework: unrestricted, conservative, and cluster methods. Predictions are visualized with convenient property projection tools. The prediction was validated by high-temperature synthesis. The structure was confirmed with single-crystal and powder X-ray diffraction. The cold-water-quenched samples quenched at 800 °C have a smaller unit cell volume than the samples quenched at 600 °C annealing temperature. Transport property measurements show an unusually low thermal conductivity (5–7 W·m<sup>–1</sup> K<sup>–1</sup>) and a trend change indicates a possible structure transition between 600 and 800 °C. The unusual low thermal conductivity was predicted with a machine-learning model that focuses on thermoelectric property prediction. DFT analysis reveals the presence of a 0D electride phenomenon. The neutron cross section and absorption based on the constituent elements put our material within the top 0.4% of neutron absorbers. The negative thermal expansion and high mass absorption suggest Gd<sub>10</sub>RuCd<sub>3</sub> as an attractive neutron absorption material. Characterization (materials science) ; Ternary operation ; Neutron diffraction ; Neutron ; Absorption (acoustics) ; Thermal conductivity ; Analytical Chemistry (journal) ; Neutron scattering Thermal Expansion and Ionic Conductivity ; Rare-earth and actinide compounds ; Machine Learning in Materials Science
Authors: Jyothish Joy (3630721) | Alex Kraus (17426306) | Spencer Ricks (21664599) | Daniel H. Ess (1323651)
Source: Figshare (article, 2025-07-05)
Field: Chemistry — Organic Chemistry

Pd–MethylBond EnergyProperty Correlations,Noncorrelations, Machine Learning Models, and Application to PolymerizationCatalysis Metal–carbon bonds are a key intermediate in a variety of homogeneous organometallic transformations and often determine the critical thermodynamics and kinetics of catalytic processes. Surprisingly, the influence of different ligands on metal–carbon bond strengths has been largely overlooked. Here we evaluated nearly 700 experimental Pd–methyl complexes by calculating their bond dissociation energies using density functional theory (DFT) and compared these bond strengths to several fundamental molecular properties, and this revealed several surprising correlations and noncorrelations. Most surprising was that several fundamental properties, such as the bond length, bond force constant, and bond electron density, have no correlation with bond strength, despite these correlations often holding for main-group compounds. We were indeed able to identify key ligand-dependent chemical features/descriptors that provided a highly accurate machine learning model and provided insight into the general factors that control the Pd–carbon bond strength, such as radical delocalization and nucleophilicity. Insights gained from the Pd–Me bond energy analysis were then applied to CO migratory insertion steps that are part of copolymerization reactions. Bond order ; Bond energy ; Chemical bond ; Bond ; Sextuple bond ; Catalysis ; Bond strength ; Bent bond ; Homogeneous Organometallic Complex Synthesis and Catalysis ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Bing Wang | Zenan Shi | Penghua Ying | Libo Li | Jin Zhang
Source: Physical review. B./Physical review. B (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Strain-induced conformation transition in two-dimensional covalent organic frameworks Two-dimensional (2D) covalent organic frameworks (COFs) are a promising class of materials exhibiting significant potential for diverse applications owing to their unique structural characteristics. In particular, some applications crucially rely on mechanoresponsive behaviors of 2D COFs, which thus require a comprehensive understanding. Herein, a machine-learning potential is developed for two representative monolayer 2D COFs (COF-1 and COF-5), which demonstrates both high computational efficiency and accuracy in molecular dynamics (MD) simulations of their mechanical behaviors. Our machine-learning MD simulations reveal distinct planar-to-nonplanar conformation transitions occurring in COF-1 and COF-5 at large tensile strain in the armchair direction. According to density functional theory calculations, the tension-induced conformation transitions in 2D COFs are driven by strong H-H steric repulsions between aryl rings within their deformed linker or knot components. The observed conformation transition is anticipated to provide a new strategy for modulating electronic properties, fracture behaviors, and gas adsorption characteristics of 2D COFs. Covalent bond ; Steric effects ; Density functional theory ; Monolayer ; Molecular dynamics ; Linker ; Molecular conformation Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Weiyi Xia | Maxim Moraru | Ying Wai Li | Cai‐Zhuang Wang
Source: ArXiv.org (preprint, 2025-10-01)
Field: Materials Science — Materials Chemistry

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials We present exa-AMD, an open-source, high-performance framework designed for accelerated materials discovery on modern supercomputers. exa-AMD overcomes key computational bottlenecks in large-scale structure prediction through task-based parallelization, adaptive load balancing, and optimized data management for CPU and GPU architectures. The framework automates the end-to-end workflow, from generating candidate structures to evaluating formation energies and updating phase diagrams. Its modular design allows users to easily replace or extend components with custom machine learning models, alternative initial structure templates, and future structure generators, enabling flexible integration with emerging AI approaches. We demonstrate strong scaling across high-performance computing platforms and highlight applications to Na-B-C, Ce-Co-B, and Fe-Co-Zr systems, establishing exa-AMD as a robust and exascale-ready tool for accelerating the discovery and design of functional materials. exa-AMD is publicly available on GitHub, with detailed documentation and reproducible test cases to support community engagement and collaborative research. Modular design ; Key (lock) ; Documentation ; Data structure ; Scaling ; Modularity (biology) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Materials Characterization Techniques
Authors: Matthew S. Johnson (1633756) | Hao-Wei Pang (8416053) | Anna C. Doner (6613109) | William H. Green (1318902) | Judit Zádor (1292451)
Source: Figshare (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

PySIDT: SubgraphIsomorphic Decision Trees for MolecularProperty Prediction Accurate molecular property prediction is important across all fields of chemistry. Deep neural networks (DNNs) have become increasingly popular due to their ability to train automatically, avoiding the incredibly tedious process of constructing and extending traditional property estimation schemes. However, DNNs require large amounts of training data, are challenging to interpret, require large amounts of memory to load even during inference, and have severe difficulties incorporating qualitative chemical knowledge, which are often desired for molecular property prediction tasks. Here we present PySIDT (https://github.com/zadorlab/PySIDT), a software for training and running inference on Subgraph Isomorphic Decision Trees (SIDTs). SIDTs are graph-based decision trees made of nodes associated with molecular substructures. Inference is done by descending target molecular structures down the decision tree to nodes with matching subgraph isomorphic substructures and making predictions based on the final (most specific) nodes matched. SIDTs scale down well to dataset sizes much smaller than is feasible for DNNs. As trees of molecular substructures, SIDTs are inherently readable and easy to visualize, making them easy to analyze. They are also straightforward to extend and retrain, facilitate uncertainty estimation, and enable easy integration of expert knowledge. We demonstrate the SIDT approach discussing its application to a diverse range of molecular prediction tasks: rate coefficient estimation, diffusion coefficient estimation, thermochemistry estimation, transition state bond stretch prediction, p<i>K</i><sub>a</sub> prediction, stability of molecular structures, stability of surface structures, and prediction of surface lateral interaction energetics. Additionally, we demonstrate the power of the SIDT algorithms in two direct learning curve vanilla comparisons with the popular DNN-based software Chemprop and the popular gradient boosted trees-based software XGBoost on enthalpy of formation and rate coefficient prediction tasks. In particular, in the enthalpy of formation case, vanilla PySIDT is able to outperform vanilla Chemprop and XGBoost across the full range of training/validation set sizes out to 11,560 data points. Property (philosophy) ; Inference ; Decision tree ; Range (aeronautics) ; Stability (learning theory) ; Matching (statistics) ; Artificial neural network ; Process (computing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Bikramaditya Mandal | Dmitri Babikov | P. C. Stancil | Robert C. Forrey | Roman V. Krems | N. Balakrishnan
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Neural network ensemble for computing cross sections of rotational transitions in H <sub>2</sub> O + H <sub>2</sub> O collisions Water (H<sub>2</sub>O) is one of the most abundant molecules in the universe and is found in a wide variety of astrophysical environments. Rotational transitions in H<sub>2</sub>O + H<sub>2</sub>O collisions are important for modeling environments rich in water molecules but they are computationally intractable using quantum mechanical methods. Here, we present a machine learning (ML) tool using an ensemble of neural networks (NNs) to predict cross sections to construct a database of rate coefficients for rotationally inelastic transitions in collisions of complex molecules such as water. The proposed methodology utilizes data computed with a mixed quantum-classical theory (MQCT). We illustrate that efficient ML models using NNs can be built to accurately interpolate in the space of 12 quantum numbers for rotational transitions in two asymmetric top molecules, spanning both initial and final states. We examine various architectures of data corresponding to each collision energy, symmetry of water molecules, and excitation/de-excitation rotational transitions, and optimize the training/validation data sets. Using only about 10% of the computed data for training, the NNs predict cross sections of state-to-state rotational transitions in H<sub>2</sub>O + H<sub>2</sub>O collisions with an average relative root mean squared error of 0.409. Thermally averaged cross sections, computed using the predicted state-to-state cross sections (∼90%) and the data used for training and validation (∼10%), were compared against those obtained entirely from MQCT calculations. The agreement is found to be excellent with an average percent deviation of about ∼13.5%. The methodology is robust, and thus applicable to other complex molecular systems. Artificial neural network ; Quantum ; Collision ; Rotational symmetry ; Molecule ; Inelastic collision ; Quantum number ; Space (punctuation) ; Mean squared error Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Advanced Chemical Physics Studies
Authors: Ka Hung Chan | Xinyue Huang | Nobumichi Tamura | Xian Chen
Source: ArXiv.org (preprint, 2025-06-12)
Field: Materials Science — Materials Chemistry

Physics-informed Machine Learning Analysis for Nanoscale Grain Mapping by Synchrotron Laue Microdiffraction Understanding the grain morphology, orientation distribution, and crystal structure of nanocrystals is essential for optimizing the mechanical and physical properties of functional materials. Synchrotron X-ray Laue microdiffraction is a powerful technique for characterizing crystal structures and orientation mapping using focused X-rays. However, when grain sizes are smaller than the beam size, mixed peaks in the Laue pattern from neighboring grains limit the resolution of grain morphology mapping. We propose a physics-informed machine learning (PIML) approach that combines a CNN feature extractor with a physics-informed filtering algorithm to overcome the spatial resolution limits of X-rays, achieving nanoscale resolution for grain mapping. Our PIML method successfully resolves the grain size, orientation distribution, and morphology of Au nanocrystals through synchrotron microdiffraction scans, showing good agreement with electron backscatter diffraction results. This PIML-assisted synchrotron microdiffraction analysis can be generalized to other diffraction-based probes, enabling the characterization of nanosized structures with micron-sized probes. Synchrotron ; Nanoscopic scale ; Electron backscatter diffraction ; Diffraction ; Orientation (vector space) ; Grain size ; Resolution (logic) ; Image resolution Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Microstructure and mechanical properties
Authors: Sakshi Agarwal | Abhishek K. Singh
Source: Advanced Theory and Simulations (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Recommendation System to Predict the d‐band Center of Core‐Shell Bimetallic Nanoparticles Catalysts Abstract Core‐shell nanoparticles are an important class of catalytic materials due to the presence of unsaturated bonds, phase‐separated metals, and synergistic effect of more than one elements. Exploring the electronic properties such as the ‐band center () and the related catalytic properties of these materials are quite challenging due to resource consuming experiments and computationally expensive modeling of the nanoparticles. Therefore, a density functional theory (DFT) coupled recommendation system based approach is developed to predict the of the core‐shell nanoparticles. Here, the recommendation system involves completion of a core‐shell interaction matrix, where each matrix element represents the of a unique core‐shell pair. Matrix completion by predicting the interaction matrix elements is carried out using neural network. The neural network generated a low dimensional representations for each core and shell metals, which are learned iteratively to predict corresponding of the pair. The learned representations brilliantly captures the similarities and dissimilarities between metals present in shell or core. A very low train/test RMSE of 0.009/0.009 is obtained with elemental features based model. The same model is further employed to predict the ‐band width (), suggesting its transferability. This approach recommends the optimum range of / along with the combination of core‐shell metals having most efficient adsorption of species for any reaction. The results presented in this study can help experimentally design the core‐shell nanoparticles having a desired catalytic activity. Bimetallic strip ; Nanoparticle ; Shell (structure) ; Catalysis ; Center (category theory) ; Core (optical fiber) ; Materials science ; Nanotechnology ; Chemistry ; Composite material ; Crystallography ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Reese E. Jones | Asghar Jadoon | Daniel Seidl | Jan N. Fuhg
Source: ArXiv.org (preprint, 2025-12-10)
Field: Materials Science — Materials Chemistry

A physics-augmented neural network framework for modeling and detecting thermo-visco-plastic behavior Although considerable attention has been devoted to the development of models for isothermal, rate-independent plasticity, many high-consequence performance assessments involve viscoplastic processes that generate substantial heat. In addition, materials may transit from a nearly isothermal, rate-independent regime to a viscous, temperature-dependent regime during these processes, which makes modeling more challenging. In this work, we develop a physics-augmented neural network (PANN) framework for modeling general temperature-dependent, rate-dependent inelastic processes firmly based on physical principles, including the second law of thermodynamics and coordinate equivariance. These embedded properties are enabled by a number of architectural innovations in the structure and training of an input convex and potential-based neural ordinary differential equation framework. The resulting neural network models are capable of representing a wide spectrum of rate- and temperature-dependence ranging from isothermal, rate-independent elastic-plastic phenomenology to rate-dependent fully viscous inelastic behavior, as we demonstrate. We also show that the framework is capable of modeling complex microstructural inelasticity and predicting the conversion of plastic work to heating when calibrated to stress-temperature observations. Artificial neural network ; Phenomenology (philosophy) ; Work (physics) ; Ordinary differential equation ; Representation (politics) ; Ranging ; Viscoplasticity Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Elasticity and Material Modeling