jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohan Teja Dronadula | N. R. Aluru
Source: ACS Nano (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Catalysis-Mediated, Ion-Size Modulation-Driven Separation of Transition Metal Complexes Critical minerals have become integral to modern society due to their indispensable applications in energy, defense, construction, electronics, medicine, and other sectors. However, their limited availability and susceptibility to supply chain vulnerabilities make the extraction and separation of these minerals an important area of research. Among these critical minerals are tantalum and niobium, transition metals with invaluable applications in aerospace, energy, construction, and medical imaging. These minerals have very similar physical and chemical properties and almost always occur together in nature as oxide ores, making their separation using conventional approaches challenging and resource-intensive. In this study, we employ density functional theory, ab initio molecular dynamics, and machine learning simulation methods to explore the use of catalytically active 2D membranes, called MXenes, in separating these minerals. Our approach utilizes catalytic reactions to separate the fluoro-complexes of tantalum and niobium (formed by dissolving the oxide ore in hydrofluoric acid), which have similar physical properties in solution. The catalytic process dissociates the ionic complexes to yield modified complexes with distinct physical properties. By leveraging these differences, we observed faster translocation rates for tantalum complexes through nanopores in the 2D membrane. Further analysis shows that these distinct physical properties result in different interactions between the nanopore wall and the dissociated ionic complexes, leading to varying translocation barriers, which explain the different flux rates. Overall, this work establishes a catalysis-driven route for separating mineral complexes with nearly identical properties, offering a fundamentally different approach to critical mineral separation. Niobium ; Catalysis ; Dissolution ; Oxide ; Tantalum ; Ionic bonding ; Materials science ; Nanotechnology ; Chemical physics ; Membrane ; Chemical engineering ; Inorganic chemistry ; Chemistry ; Ion ; Physical chemistry ; Organic chemistry ; Metallurgy MXene and MAX Phase Materials ; Graphene research and applications ; Nanopore and Nanochannel Transport Studies
Authors: Zheng-Xin Yang | Xin-Tian Xie | Zhen-Xiong Wang | Dong-Xiao Chen | Zi-Xing Guo | Jia-Jie Du | Qi-Ming Liang | Qian-Yu Liu | Cheng Shang | Zhi-Pan Liu
Source: Science China Chemistry (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

High-order pair-reduced neural network architecture for global potential energy surface exploration across the periodic table Benchmarking ; Artificial neural network ; Inference ; Table (database) ; Representation (politics) ; Energy (signal processing) ; Exploit ; Spherical harmonics ; Harmonics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Nadia Tahir | Tayyaba Najam | Muhammad Altaf Nazir | Ayesha Arif | Ayman Nafady | Manzar Sohail | Syed Shoaib Ahmad Shah
Source: Chinese Chemical Letters (article, 2025-12-01)
Field: Chemistry — Inorganic Chemistry

Metal-organic frameworks: Nanomachines for efficient water purification Merge (version control) ; Propulsion ; Water pollutants ; Portable water purification ; Pollutant ; Water treatment Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Nanopore and Nanochannel Transport Studies
Authors: Rebecca Brew | Ian A. Nelson | Meruyert Binayeva | Amlan Nayak | W. Kyle Simmons | Joseph J. Gair | Corin Wagen
Source: Journal of Chemical Theory and Computation (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Wiggle150: Benchmarking Density Functionals and Neural Network Potentials on Highly Strained Conformers Accurate benchmarks are key to assessing the accuracy and robustness of computational methods, yet most available benchmark sets focus on equilibrium geometries, limiting their utility for applications involving nonequilibrium structures such as ab initio molecular dynamics and automated reaction-path exploration. To address this gap, we introduce Wiggle150, a benchmark comprising 150 highly strained conformations of adenosine, benzylpenicillin, and efavirenz. These geometries─generated via metadynamics and scored using DLPNO-CCSD(T)/CBS reference energies─exhibit substantially larger deviations in bond lengths, angles, dihedrals, and relative energies than other conformer benchmarks. We evaluate a diverse array of computational methods, including density-functional theory, composite quantum chemical methods, semiempirical models, neural network potentials, and force fields, on predicting relative energies for this challenging benchmark set. The results highlight multiple methods along the speed-accuracy Pareto frontier and identify AIMNet2 as particularly robust among the NNPs surveyed. We anticipate that Wiggle150 will be used to validate computational protocols involving nonequilibrium systems and guide the development of new density functionals and neural network potentials. Benchmarking ; Conformational isomerism ; Computer science ; Artificial neural network ; Density functional theory ; Computational chemistry ; Statistical physics ; Chemical physics ; Artificial intelligence ; Chemistry ; Physics ; Molecule ; Business ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Zakariya El‐Machachi | Bowen Cheng | Volker L. Deringer
Source: Chemical Communications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Mechanical properties of graphene oxide from machine-learning-driven simulations Graphene oxide (GO) materials have complex chemical structures that are linked to their macroscopic properties. Here we show that first-principles simulations with a machine-learned interatomic potential can predict the mechanical properties of GO sheets in agreement with experiment and provide atomistic insights into the mechanisms of strain and fracture. Our work marks a step towards understanding and controlling mechanical properties of carbon-based materials with the help of atomistic machine learning. Graphene ; Oxide ; Materials science ; Nanotechnology ; Computer science ; Metallurgy Graphene research and applications ; Machine Learning in Materials Science ; Graphene and Nanomaterials Applications
Authors: Rebecca Brew | I. Nelson | Meruyert Binayeva | Amlan Nayak | W. Kyle Simmons | Joseph J. Gair | Corin Wagen
Source: ChemRxiv (preprint, 2025-01-24)
Field: Materials Science — Materials Chemistry

Wiggle150: Benchmarking Density Functionals And Neural Network Potentials On Highly Strained Conformers Accurate benchmarks are key to assessing the accuracy and robustness of computational methods, yet most available benchmark sets focus on equilibrium geometries, limiting their utility for applications involving non-equilibrium structures such as ab initio molecular dynamics and automated reaction-path exploration. To address this gap, we introduce Wiggle150, a benchmark comprising 150 highly strained conformations of adenosine, benzylpenicillin, and efavirenz. These geometries—generated via metadynamics and scored using DLPNO-CCSD(T)/CBS reference energies—exhibit substantially larger deviations in bond lengths, angles, dihedrals, and relative energies than other conformer benchmarks. We evaluate a diverse array of computational methods, including density-functional theory, composite quantum chemical methods, semiempirical models, neural network potentials, and force fields, on predicting relative energies for this challenging benchmark set. The results highlight multiple methods along the speed–accuracy Pareto frontier and identify AIMNet2 as particularly robust among the NNPs surveyed. We anticipate that Wiggle150 will be used to validate computational protocols involving non-equilibrium systems and guide the development of new density functionals and neural network potentials. Benchmarking ; Conformational isomerism ; Artificial neural network ; Density functional theory ; Materials science ; Statistical physics ; Computational chemistry ; Computer science ; Chemical physics ; Physics ; Artificial intelligence ; Chemistry ; Business ; Molecule ; Quantum mechanics Machine Learning in Materials Science
Authors: Sakib Matin | Alice Allen | Emily Shinkle | Aleksandra Pachalieva | Galen T. Craven | Benjamin Nebgen | Justin S. Smith | Richard A. Messerly | Ying Wai Li | Sergei Tretiak | Kipton Barros | Nicholas Lubbers
Source: ArXiv.org (preprint, 2025-02-07)
Field: Materials Science — Materials Chemistry

Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures trained on larger datasets. The resulting increase in computational and memory costs may prohibit the application of these MLIPs to perform large-scale MD simulations. Here, we present a teacher-student training framework in which the latent knowledge from the teacher (atomic energies) is used to augment the students' training. We show that the light-weight student MLIPs have faster MD speeds at a fraction of the memory footprint compared to the teacher models. Remarkably, the student models can even surpass the accuracy of the teachers, even though both are trained on the same quantum chemistry dataset. Our work highlights a practical method for MLIPs to reduce the resources required for large-scale MD simulations. Training (meteorology) ; Mathematics education ; Computer science ; Machine learning ; Training set ; Artificial intelligence ; Psychology ; Physics Machine Learning in Materials Science
Authors: Yong Pan | Tingting Liu | Chengyu Li | Siyu Shen | Xiaofeng Hou | Xuedong Wang
Source: Coordination Chemistry Reviews (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Synthetic scenarios, enzyme-mimetic activities, and colorimetric/fluorescent sensing applications based on carbon-based nanozymes for sensitively detecting environmental pollutants Limiting ; Environmental analysis ; Pollutant ; Nanomaterials ; Catalysis ; Environmental monitoring Advanced Nanomaterials in Catalysis ; Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications
Authors: Tian-Wei Hua | Gui‐Xiang Huang | Chen Qian | Hao Zeng | Jun Jiang | Han‐Qing Yu
Source: ChemRxiv (preprint, 2025-03-28)
Field: Materials Science — Materials Chemistry

Data-driven recursive kinetic modeling for chemical reactions Kinetic models are crucial for analyzing reaction mechanisms and optimizing conditions. However, traditional models suffer from limitations such as lack of accuracy, narrow applicability, and difficulty in handling complex reaction conditions. Here, we develop a data-driven recursive kinetic model capable of predicting kinetic profiles from initial reaction conditions. The model captures reaction kinetics by leveraging recursive relationships between reactant or product concentrations at different times, which is learned through a machine learning algorithm, rather than traditional concentration-time equations. Moreover, we integrate a multiple estimation strategy into the model for performance enhancement. This model demonstrates superior accuracy, broad application scope, robustness, and few-shot learning capability on a simulated dataset including 18 chemical reaction types. Furthermore, its applicability to real-world chemical reactions is confirmed on the datasets of four practical reactions with complex kinetics. In addition, its strong capacity for mechanism interpretation and condition optimization is showcased using the experimental dataset of Fenton reaction. This work provides inspiration for the development of chemical kinetic models, potential to accelerate chemical research via advanced kinetic analysis. Kinetic energy ; Chemical reaction ; Statistical physics ; Computer science ; Chemistry ; Physics ; Classical mechanics ; Organic chemistry Machine Learning in Materials Science ; Advanced Control Systems Optimization
Authors: Rajni Garg | Rishav Garg | Ansar Anjum | N. Eddy | Hamimullah Watandost
Source: Discover Food (article, 2025-11-14)
Field: Materials Science — Polymers and Plastics

Valorization of natural fiber composites for sustainable food packaging materials Traditional food packaging materials, such as plastics and synthetic polymers, negatively impact the environment. On the other hand, natural fibers (NFs) obtained from renewable resources can be used as alternative packaging materials to mitigate pollution and waste management issues. This review provides a critical analysis of natural fiber composites (NFCs) as a sustainable alternative, moving beyond a descriptive listing of studies to a synthesized evaluation. The review focuses distinctly on materials, performance enhancement techniques, analytical properties, and functional applications of NFCs. The study provides a comparative analysis of NFs, systematically categorizing surface modification techniques, and integrating performance data to draw meaningful conclusions. Key technical limitations, such as poor fiber-matrix compatibility and moisture sensitivity, have been discussed in the context of specific material systems and modification strategies. Furthermore, this review identifies and elaborates on specific future research directions, including the role of machine learning in material optimization, the challenges of regulatory compliance for food-contact materials, and the hurdles of industrial scalability. This work offers a structured, analytical overview intended to guide future research and development in green food packaging materials. Compatibility (geochemistry) ; Food packaging ; Sustainability ; Context (archaeology) ; Sustainable development ; Natural resource ; Renewable energy ; Renewable resource Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging ; Advanced Cellulose Research Studies
Authors: Changjie Ding | Xiaoyang Wang | Xiangyan Li | Wenjun Yang | Xiaolin Li | Yange Zhang | Yi-chun Xu | C.S. Liu | Xuebang Wu
Source: Journal of Material Science and Technology (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Machine learning-based interatomic potential for simulating irradiation damage mechanisms in ZrC Irradiation ; Interatomic potential ; Radiation damage ; Materials science ; Nuclear engineering ; Computer science ; Molecular dynamics ; Nuclear physics ; Chemistry ; Physics ; Engineering ; Computational chemistry Nuclear Materials and Properties ; Fusion materials and technologies ; Machine Learning in Materials Science
Authors: Rokas Elijošius | Fabian Zills | Ilyes Batatia | Sam Walton Norwood | Dávid Péter Kovács | Christian Holm | Gábor Cśanyi
Source: Nature Communications (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Zero shot molecular generation via similarity kernels Generative modelling aims to accelerate the discovery of novel chemicals by directly proposing structures with desirable properties. Recently, score-based, or diffusion, generative models have significantly outperformed previous approaches. Key to their success is the close relationship between the score and physical force, allowing the use of powerful equivariant neural networks. However, the behaviour of the learnt score is not yet well understood. Here, we analyse the score by training an energy-based diffusion model for molecular generation. We find that during the generation the score resembles a restorative potential initially and a quantum-mechanical force at the end, exhibiting special properties in between that enable the building of large molecules. Building upon these insights, we present Similarity-based Molecular Generation (SiMGen), a new zero-shot molecular generation method. SiMGen combines a time-dependent similarity kernel with local many-body descriptors to generate molecules without any further training. Our approach allows shape control via point cloud priors. Importantly, it can also act as guidance for existing trained models, enabling fragment-biased generation. We also release an interactive web tool, ZnDraw, for online SiMGen generation ( https://zndraw.icp.uni-stuttgart.de ). Computer science ; Generative grammar ; Similarity (geometry) ; Artificial intelligence ; Kernel (algebra) ; Generative model ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Min Liu | Shihori Koyama | Norihiro Oyama | Hideyuki Mizuno
Source: Journal of Applied Physics (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Quasi-localized vibrations arise from liquid-like structures in glasses: Insights from deep neural networks Glasses possess material properties that differ significantly from those of crystals. These properties are closely linked to quasi-localized vibrations (QLVs) present in glasses, thus understanding that QLVs are crucial for comprehending the material properties of glasses. However, the connection between QLVs and amorphous structures of glasses remains poorly understood. To address this issue, we employ a deep neural network approach to characterize the amorphous structures. By training the neural network using data on the structures from both liquid and solid (glass) samples, the network is able to identify the liquid-like and solid-like regions within a sample. An examination of the spatial correlations of the QLVs with these liquid-like and solid-like structural distributions reveals that the QLVs originate from the liquid-like regions. Our findings indicate that, in glasses, the QLVs emerging from the liquid-like structures contribute to glassy phenomena such as the dynamic heterogeneity of supercooled liquids and the non-affine elasticity of glasses. Vibration ; Artificial neural network ; Materials science ; Physics ; Computer science ; Acoustics ; Artificial intelligence Material Dynamics and Properties ; Glass properties and applications ; Metallic Glasses and Amorphous Alloys
Authors: Jesun Firoz | Franco Pellegrini | Mario Geiger | Darren J. Hsu | Jenna A. Bilbrey | Huey Wen Chou | Maximilian Stadler | Markus Höhnerbach | Tingyu Wang | Dejun Lin | Emine Küçükbenli | Henry W. Sprueill | Ilyes Batatia | Sotiris S. Xantheas | Mal‐Soon Lee | Christopher J. Mundy | Gábor Cśanyi | Justin S. Smith | P. Sadayappan | Sutanay Choudhury
Source:  (article, 2025-07-20)
Field: Materials Science — Materials Chemistry

Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE Chemistry Foundation Models (CFMs) that leverage Graph Neural Networks (GNNs) operating on 3D molecular graph structures are becoming indispensable tools for computational chemists and materials scientists. These models facilitate the understanding of matter and the discovery of new molecules and materials. In contrast to GNNs operating on large homogeneous graphs, GNNs used by CFMs process a large number of geometric graphs of varying sizes, requiring different optimization strategies than those developed for large homogeneous GNNs. This paper presents optimizations for two critical phases of CFM training: data distribution and model training, targeting MACE - a state-of-the-art CFM. We address the challenge of load balancing in data distribution by formulating it as a multi-objective bin packing problem. We propose an iterative algorithm that provides a highly effective, fast, and practical solution, ensuring efficient data distribution. For the training phase, we identify symmetric tensor contraction as the key computational kernel in MACE and optimize this kernel to improve the overall performance. Our combined approach of balanced data distribution and kernel optimization significantly enhances the training process of MACE. Experimental results demonstrate a substantial speedup, reducing per-epoch execution time for training from 12 to 2 minutes on 740 GPUs with a 2.6M sample dataset. Kernel (algebra) ; Leverage (statistics) ; Graph ; Training set ; Kernel method ; Artificial neural network ; Process (computing) ; Iterative and incremental development Machine Learning in Materials Science ; Machine Learning and Algorithms ; Reservoir Engineering and Simulation Methods
Authors: Zhixin Liang | Yunlong Wang | Chi Ding | Junjie Wang | Hui‐Tian Wang | Dingyu Xing | Jian Sun
Source: Chinese Physics Letters (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Hot-Ham: an Accurate and Efficient <i>E</i> (3)-Equivariant Machine-Learning Electronic Structures Calculation Framework Abstract The combinations of machine learning with ab initio methods have attracted much attention for their potential to resolve the accuracy-efficiency dilemma and facilitate calculations for large-scale systems. Recently, equivariant message passing neural networks (MPNNs) that explicitly incorporate symmetry constraints have demonstrated promise for interatomic potential and density functional theory (DFT) Hamiltonian predictions. However, the high-order tensors used to represent node and edge information are coupled through the Clebsch–Gordan tensor product, leading to steep increases in computational complexity and seriously hindering the performance of equivariant MPNNs. Here, we develop high-order tensor machine-learning Hamiltonian (Hot-Ham), an E (3) equivariant MPNN framework that combines two advanced technologies: local coordinate transformation and Gaunt tensor product to efficiently model DFT Hamiltonians. These two innovations significantly reduce the complexity of tensor products from O ( L 6 ) to O ( L 3 ) or O ( L 2 log 2 L ) for the max tensor order L , and enhance the performance of MPNNs. Benchmarks on several public datasets demonstrate its state-of-the-art accuracy with relatively few parameters, and applications to multilayer twisted moiré systems, heterostructures, and allotropes showcase its generalization ability and high efficiency. Our Hot-Ham method provides a new perspective for developing efficient equivariant neural networks and would be a promising approach for investigating the electronic properties of large-scale materials systems. Equivariant map ; Hamiltonian (control theory) ; Tensor (intrinsic definition) ; Tensor product ; Generalization ; Artificial neural network ; Topology (electrical circuits) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks