jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pelin Sezgin | Seda Keskın
Source: The Journal of Physical Chemistry C (article, 2025-07-04)
Field: Chemistry — Inorganic Chemistry

Integrating Molecular Simulations with Machine Learning to Discover Selective MOFs for CH<sub>4</sub>/H<sub>2</sub> Separation As the number of synthesized and hypothetical metal-organic frameworks (MOFs) continues to grow, identifying the most selective adsorbents for CH<sub>4</sub>/H<sub>2</sub> separation through experimental or computational methods has become increasingly complex. This study integrates molecular simulations with machine learning (ML) to evaluate the CH<sub>4</sub>/H<sub>2</sub> separation performance of 126605 distinct types of MOFs. Grand canonical Monte Carlo (GCMC) simulations were performed to produce CH<sub>4</sub> and H<sub>2</sub> adsorption data for synthesized MOFs at various pressures, which were then used to train ML models incorporating structural, chemical, and energetic features of the MOFs. These ML models were subsequently transferred to hypothetical MOFs, enabling the rapid and accurate screening of promising adsorbents for CH<sub>4</sub>/H<sub>2</sub> separation. The top-performing MOFs were identified based on their CH<sub>4</sub>/H<sub>2</sub> selectivities, and their key structural and chemical characteristics were analyzed. Synthesized (hypothetical) MOFs having narrow pores and pyridine-, histidine-, and imidazole-based (carboxylate-, benzoate-, and cubane-based) linkers demonstrated high selectivities up to 85 (115) at 1 bar and 298 K. Our findings highlight the potential of MOFs as superior alternatives to traditional adsorbent materials for CH<sub>4</sub>/H<sub>2</sub> separation. Metal-organic framework ; Adsorption ; Pyridine ; Quantum chemical ; Imidazole ; Materials science ; Molecule ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Chinedu E. Ekuma
Source: AIP Advances (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Computational toolkit for predicting thickness of 2D materials using machine learning and autogenerated dataset by large language model The thickness of 2D materials not only plays a crucial role in determining the performance of nanoelectronic and optoelectronic devices but also introduces complexities in predicting volume-dependent properties, such as energy storage capacity, due to the intrinsic vacuum within these materials. Although a plethora of experimental techniques, including but not limited to optical contrast, Raman spectroscopy, nonlinear optical spectroscopy, near-field optical imaging, and hyperspectral imaging, facilitate the measurement of 2D material thickness, comprehensive data for many materials remain elusive. Over the past decade, the exponential proliferation of 2D materials and their heterostructures has outstripped the capabilities of conventional experimental and computational approaches. In this evolving landscape, machine learning (ML) has emerged as an indispensable tool, offering a scalable approach to augment these traditional methodologies. Addressing the critical gap, we introduce THICK2D—Thickness Hierarchy Inference and Calculation Kit for 2D Materials. This Python-based computational framework harnesses an autogenerated thickness database, developed using large language models, and advanced ML algorithms to facilitate the rapid and scalable estimation of material thickness, relying solely on crystallographic data. To demonstrate the utility and robustness of THICK2D, we successfully used the toolkit to predict the thickness of more than 8000 2D-based materials, sourced from two extensive 2D materials databases. THICK2D is disseminated as an open-source utility, accessible on GitHub at https://github.com/gmp007/THICK2D, and archived on Zenodo at https://10.5281/zenodo.11216648. Computer science ; Artificial intelligence ; Machine learning ; Natural language processing 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Yuan Yin | Wenshuo Liang | Shuaiyi Shui | Wentao Zhou | Dezhong Wang
Source: The Journal of Physical Chemistry B (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Physicochemical Properties and Structure of FLiBeTh Salts: Insights from Machine Learning Accelerated Molecular Dynamics Simulations LiF-BeF<sub>2</sub>-ThF<sub>4</sub> (FLiBeTh) is a promising fuel salt for thorium-based molten salt reactors due to its excellent neutron economy and adjustable properties. However, experiments on such systems remain challenging due to high temperature, corrosiveness, and toxicity. To address these challenges, this study employs molecular dynamics simulations based on a machine learning potential. Using data sets from ab initio calculations and an iterative workflow, a highly accurate machine-learning model was developed, achieving energy and force prediction errors below 1 meV/atom and 50 meV/Å, respectively. This model accurately reproduces the AIMD-predicted radial distribution functions, coordination numbers, and angular distributions. Furthermore, MLMD simulations enabled the exploration of larger-scale or long-term structural characteristics, including coordination shell lifetime, ionic network formation, and physicochemical properties such as density, ionic diffusion, shear viscosity, and thermal conductivity. Results show that increasing ThF<sub>4</sub> concentration promotes the formation of networks composed of Be<sup>2+</sup>, Th<sup>4+</sup>, and F<sup>-</sup> ions, which significantly reduces ion mobility and changes the physicochemical properties of the molten salts. Molecular dynamics ; Ionic bonding ; Molten salt ; Thermal ; Work (physics) ; Artificial neural network ; Ab initio ; Interatomic potential ; Combining rules Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Chemical Physics Studies
Authors: Zhiyuan Chen | Daoyuan Yang | Xianghui Li | Jinfeng Li | Huiyu Yuan | Junyan Cui
Source: Materials (article, 2025-04-09)
Field: Materials Science — Ceramics and Composites

Machine-Learning-Assisted Multi-Element Optimization of Mechanical Properties in Spinel Refractory Materials Using machine learning models, this study innovatively introduces multi-element compositions to optimize the performance of spinel refractories. A total of 1120 spinel samples were fabricated at 1600 °C for 2 h, and an experimental database containing 112 data points was constructed. High-throughput performance predictions and experimental verifications were conducted, identifying the sample with the highest hardness, (Al<sub>2</sub>Fe<sub>0.25</sub>Zn<sub>0.25</sub>Mg<sub>0.25</sub>Mn<sub>0.25</sub>)O<sub>4</sub> (1770.6 ± 79.1 HV1, 3.35 times that of MgAl<sub>2</sub>O<sub>4</sub>), and the highest flexural strength, (Al<sub>2</sub>Cr<sub>0.5</sub>Zn<sub>0.1</sub>Mg<sub>0.2</sub>Mn<sub>0.2</sub>)O<sub>4</sub> (161.2 ± 9.7 MPa, 1.4 times that of MgAl<sub>2</sub>O<sub>4</sub>). Further analysis of phase composition and microstructure shows that the mechanism of hardness enhancement is mainly the solid solution strengthening of multi-element doping, the energy dissipation of the large-grain layered structure, and the reinforcement of the zigzag grain boundary. In addition to solid solution strengthening and a compact low-pore structure, the mechanism of improving bending strength also includes second-phase strengthening and phase concentration gradient distribution. This method provides a promising way to optimize the performance of refractory materials. Materials science ; Spinel ; Microstructure ; Flexural strength ; Bending ; Phase (matter) ; Grain boundary ; Strengthening mechanisms of materials ; Composite material ; Phase boundary ; Doping ; Zigzag ; Metallurgy ; Geometry Advanced ceramic materials synthesis ; Advanced materials and composites ; Aluminum Alloys Composites Properties
Authors: Min Cheng | Minggao Feng | Li Zhou | Shihui Wang | Zhiyuan Zhang | Chong Liu | Xu Ji
Source: Industrial & Engineering Chemistry Research (article, 2025-08-20)
Field: Chemistry — Inorganic Chemistry

A Machine Learning-Boosted High-Throughput Screening of Metal–Organic Frameworks for Ethane/Ethylene Separation: From Molecular Simulation to Process Modeling Ethane/ethylene (C2H6/C2H4) separation is crucial in the petrochemical industry. This study proposes a multiscale framework combining machine learning and molecular modeling to identify optimal C2H6-selective metal–organic frameworks (MOFs) for this separation. A database of 505,973 experimental/hypothetical MOFs underwent geometric/chemical prescreening (254,500 retained). Grand canonical Monte Carlo (GCMC) simulations on sampled MOFs trained a Random Forest model to predict adsorption behaviors, enabling rapid screening of the remaining candidates. This yielded 14,927 MOFs verified using GCMC, with 10 top performers selected. Process-level evaluation via vacuum pressure swing adsorption modeling confirmed their robustness under industrial conditions (varying temperatures, pressures, and gas compositions). Comparative analysis demonstrated superior performance over literature-reported MOFs, particularly under realistic operational parameters. The integrated approach establishes an efficient pathway for MOF discovery, bridging molecular properties with industrial process requirements while reducing reliance on energy-intensive distillation. Robustness (evolution) ; Bridging (networking) ; Process (computing) ; Monte Carlo method ; Petrochemical ; Process simulation ; Pressure swing adsorption ; Modeling and simulation Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Deep Choudhuri | Ranjana Chowdhury
Source: Computational Materials Science (article, 2025-10-04)
Field: Materials Science — Materials Chemistry

Untangling linker dynamics in a metal–organic framework using ab initio molecular dynamics and physics-informed machine learning Torsion (gastropod) ; Ab initio ; Anharmonicity ; Molecular dynamics ; Transverse plane ; Stiffness ; Artificial neural network ; Coupling (piping) Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Protein Structure and Dynamics
Authors: Wenjuan Li | Mingjie Wen | Jun Han | Zhixiang Zhang | Yingzhe Liu | Qingzhao Chu | Dongping Chen
Source: FirePhysChem (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

Thermal stability ranking of energetic crystals via a neural network potential-enabled MD simulation protocol • Developed an improved MD protocol for predicting thermal stability of EMs. • Particle structures reduce T d error by up to 400 K than periodic structures. • Lower heating rates (0.001 K/ps) reduce T d error to as low as 80 K. • Achieved strong correlation (R 2 = 0.969) between MD results and experiments. • Proposed a correction model to bridge MD-predicted and experimental T d values. Assessing the thermal stability of energetic materials (EMs) remains challenging due to the limitations of traditional experimental and computational methods. This study develops an optimized molecular dynamics (MD) protocol based on a neural network potential (NNP) to enable reliable quantitative prediction of EM thermal stability. Key improvements include the use of nanoparticle models and reduced heating rates.​ Systematic investigations on RDX show that nanoparticle structures mitigate decomposition temperature ( T d ) overestimation compared to periodic models, with surface effects dominating over particle size. Lower heating rates (e.g., 0.001 K/ps) further reduce deviation, bringing RDX T d within 80 K of experimental values (vs. >400 K in conventional simulations). Kissinger analysis of the heating rate- T d relationship supports the feasibility of optimizing heating rates to align with experimental T d .​ Applied to eight representative EMs, the optimized protocol yields thermal stability rankings in excellent agreement with experiments (R 2 =0.96), outperforming traditional periodic models (R 2 =0.85). This work establishes a robust computational framework for EM thermal stability evaluation, particularly valuable in data-limited scenarios. Ranking (information retrieval) ; Protocol (science) ; Stability (learning theory) ; Artificial neural network ; Thermal stability ; Computer science ; Biological system ; Environmental science ; Artificial intelligence ; Chemistry ; Machine learning ; Biology ; Medicine Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure ; Energetic Materials and Combustion
Authors: Tatsuya Yokoi | Susumu Fujii | Yu Ogura | Katsuyuki Matsunaga
Source: Physical review. B./Physical review. B (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Origin of reduced thermal conduction by native point defects in PbTe: Perturbed molecular dynamics with neural network potential Native point defects are fundamental building blocks of defect engineering in thermoelectric materials. However, it remains unclear which changes induced by point defects reduce lattice thermal conductivity 𝜅l at the atomic level. This study develops a neural-network potential (NNP) trained on density functional theory data for lead telluride (PbTe) and applies it to equilibrium and perturbed molecular dynamics (PMD) simulations. Isolated Pb and Te vacancies, antisites, interstitials and Schottky pairs are examined using sufficiently large supercells. The NNP is shown to reproduce harmonic and anharmonic vibrational properties and to accurately predict potential energy and atomic forces at finite temperatures. NNP-based PMD simulations reveal that local thermal transport near defects varies significantly depending on the defect type, exhibiting the lowest 𝜅l for interstitial Pb. Atom-resolved 𝜅l of point defects and their neighboring atoms correlate with the integrated absolute difference in vibrational density of states between those atoms and bulk atoms. This trend appears even within the harmonic approximation, which neglects anharmonic contributions. This suggests that the difference between the harmonic potential energies of pristine and defect-containing PbTe plays a central role in reducing 𝜅l , by modifying the force field around point defects through the second-order force constant. In contrast, atomic displacements around point defects show a weak correlation with atom-resolved 𝜅l and thus may not be a suitable metric for predicting 𝜅l of defect-containing PbTe, although relevant quantities are assumed to affect 𝜅l in conventional analytical models. The present NNP enables us to analyze physical quantities accessible via molecular simulations with high accuracy and to understand the underlying mechanisms of thermal transport reduction by point defects, without relying on analytical models. Thermal conduction ; Dynamics (music) ; Molecular dynamics ; Artificial neural network ; Thermal ; Point (geometry) ; Statistical physics ; Materials science ; Chemical physics ; Neuroscience ; Physics ; Computer science ; Biology ; Artificial intelligence ; Thermodynamics ; Mathematics ; Quantum mechanics ; Acoustics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Advanced Semiconductor Detectors and Materials
Authors: Leonard Galustian | Konstantin Mark | Johannes Karwounopoulos | Maximilian P.-P. Kovar | Esther Heid
Source: ChemRxiv (preprint, 2025-06-18)
Field: Materials Science — Materials Chemistry

GoFlow: Efficient Transition State Geometry Prediction with Flow Matching and E(3)-Equivariant Neural Networks Transition state (TS) geometries of chemical reactions are key to understanding reaction mechanisms and estimating kinetic properties. Inferring these directly from 2D reaction graphs offers chemists a powerful tool for rapid and accessible reaction analysis. Quantum chemical methods for computing TSs are computationally intensive and often infeasible for larger molecular systems. Recently, deep learning–based diffusion models have shown promise in generating TSs from 2D reaction graphs for single-step reactions. However, framing TS generation as a diffusion process, by design, requires a prohibitively large number of sampling steps during inference. Here we show that modeling TS generation as an optimal transport flow problem, solved via E(3)-equivariant flow matching with geometric tensor networks, achieves over a hundredfold speedup in inference while improving geometric accuracy compared to the state-of-the-art. This breakthrough increase in sampling efficiency and predictive accuracy enables the practical use of deep learning-based TS generators in high-throughput settings for larger and more complex chemical systems. Our method, GoFlow, thus represents a significant methodological advancement in machine learning-based TS generation, bringing it closer to widespread use in computational chemistry workflows. Computer science ; Speedup ; Deep learning ; Inference ; Workflow ; Artificial neural network ; Artificial intelligence ; Algorithm ; Theoretical computer science ; Machine learning ; Parallel computing Machine Learning in Materials Science ; Topic Modeling ; Scientific Computing and Data Management
Authors: Yunfei Deng | Yixu Lv | Xiaoyue Yang | Chunzhi Du | Xianglin Huang
Source: Thin-Walled Structures (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Determination of residual velocity model of finite thickness material based on Artificial Neural Network Artificial neural network ; Residual ; Finite element method ; Materials science ; Biological system ; Computer science ; Artificial intelligence ; Engineering ; Algorithm ; Structural engineering ; Biology High-Velocity Impact and Material Behavior ; Advanced machining processes and optimization ; Tunneling and Rock Mechanics
Authors: Sunjay Sethi | Arnab Dutta | Emil I. Jaffal | Nishant Yadav | Danila Shiryaev | Brian Hoang | Anirudh Machathi | Sangjoon Lee | Karabi Das | Partha P. Jana | Anton O. Oliynyk
Source: ChemRxiv (preprint, 2025-02-25)
Field: Materials Science — Materials Chemistry

Unsupervised Machine Learning Prediction of Novel 1:3 Intermetallic Phase with Synthesis of TbIr3 (PuNi3-type) as Experimental Validation Crystal structure classification of binary intermetallic structures with 1:3 stoichiometry was done with simple machine learning algorithms. The successful crystal structure segregation is attributed to the novel set of descriptors comprising both compositional and structural features. The dataset includes 97 features, and a total of 2366 reported compounds adopting six different structure types. The unsupervised learning method based on principal component analysis (PCA) followed by clustering using the K-means method was applied to cluster compounds belonging to different structure types. Using the recommendation engine, we predicted the expansion of the clusters and then identified cluster/structure-type overlap. PuNi3-type was among the clearly segregated structure types according to the unsupervised model, and a novel representative, TbIr3, was selected for experimental validation, adopting this structure. The final supervised machine learning predictions were done with PLS-DA, SVM, and XGBoost confidently predicting the novel TbIr3 to belong to the PuNi3-type with an accuracy of 96.6%, 99.8%, and 99.9% respectively. Analysis of the features reveals that the main contributing features to the AB3 crystal structure segregation are the average shortest distance count of A element, the total number of sites of B atom, and the total number of second shortest distance count of B atom. Given that the predicted PuNi3-type of the TbIr3 phase could be controversial due to the extensive study of the Tb–Ir phase diagram and the reports of the TbIr3 in two different structure types, we conducted two independent experimental structural validations to confirm the existence of the TbIr3 in PuNi3-type structure. Subsequent theoretical validation explains that Ir-Ir contacts are the primary stability factor of TbIr3 in PuNi3-type structure over other structure types. Intermetallic ; Phase (matter) ; Artificial intelligence ; Computer science ; Type (biology) ; Machine learning ; Unsupervised learning ; Materials science ; Chemistry ; Geology ; Metallurgy ; Paleontology Machine Learning in Materials Science ; Powder Metallurgy Techniques and Materials ; Nuclear Materials and Properties
Authors: Mamduh J. Aljaafreh | Sajjad Hussain Sumrra | Abrar U. Hassan | Sadaf Noreen
Source: Dyes and Pigments (article, 2025-07-27)
Field: Materials Science — Materials Chemistry

Simpler to better: A machine learning-multioutput Gaussian process analysis to design small molecule motors for visible range photo switching attributes Range (aeronautics) ; Gaussian process ; Process (computing) ; Molecule ; Molecular motor ; Computer science ; Motor learning ; Gaussian ; Artificial intelligence ; Biological system ; Machine learning ; Control engineering ; Chemistry ; Materials science ; Nanotechnology ; Engineering ; Psychology ; Computational chemistry ; Organic chemistry ; Neuroscience ; Biology Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Photochromic and Fluorescence Chemistry
Authors: Gavin Nop | Micah Mundy | Jonathan D. H. Smith | Durga Paudyal
Source: npj Computational Materials (article, 2025-07-26)
Field: Materials Science — Materials Chemistry

Faithful novel machine learning for predicting quantum properties Machine learning (ML) has accelerated the process of materials classification, particularly with crystal graph neural network (CGNN) architectures. However, advanced deep networks have hitherto proved challenging to build and train for quantum materials classification and property prediction. We show that faithful representations, which directly represent crystal structure and symmetry, both refine current ML and effectively implement advanced deep networks to accurately predict these materials and optimize their properties. Our new models reveal the previously hidden power of novel convolutional and pure attentional approaches to represent atomic connectivity and achieve strong performance in predicting topological properties, magnetic properties, and formation energies. With faithful representations, the state-of-the-art CGNN accurately predicts quantum chemistry materials and properties, accelerating the design and discovery and improving the implicit understanding of complex crystal structures and symmetries. On two separate benchmarks, our non-graphical neural networks achieve near parity with the CGNN architecture, making them viable alternatives. Computer science ; Quantum machine learning ; Artificial intelligence ; Quantum ; Machine learning ; Physics ; Quantum computer ; Quantum mechanics Machine Learning in Materials Science ; Topological Materials and Phenomena ; Electronic and Structural Properties of Oxides