jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Youmei Li | Kewei Zhang | Jintian Wu | Zixuan Fang | Ziqiang Xu | Mengqiang Wu
Source: Journal of Advanced Ceramics (article, 2025-04-15)
Field: Materials Science — Materials Chemistry

Advancing NASICON-type materials through high-entropy strategy: Synthesis and applications High-entropy materials (HEMs) have emerged as a promising frontier in electrochemical energy storage systems due to their unique compositional versatility and tunable physicochemical properties. By incorporating multiple principal elements with distinct chemical functionalities, HEMs exhibit tailored electronic/ionic configurations, enabling unprecedented structural adaptability and application potential. This review systematically analyzes the fundamental principles underpinning the entropy-driven optimization of electrochemical performance in battery materials, with a focus on the interplay between compositional disorder and functional enhancements. For the first time, we comprehensively review recent advances in NASICON-type HEMs spanning cathodes, solid-state electrolytes, and anodes. Through investigations, the profound impact of high-entropy strategies on critical material parameters were elucidate, including lattice strain modulation, interfacial stability reinforcement, charge-transfer kinetics optimization, and ion transport pathway regulation. Furthermore, we evaluate the current challenges in high-entropy NASICON-type battery design and propose actionable strategies for advancing next-generation high-entropy battery systems, emphasizing rational compositional screening, entropy-stabilized interface design, and machine learning-assisted property prediction. Fast ion conductor ; Structural material ; Materials science ; Nanotechnology ; Process engineering ; Chemical engineering ; Metallurgy ; Chemistry ; Engineering ; Electrolyte ; Physical chemistry Thermal properties of materials ; High-Temperature Coating Behaviors ; Transition Metal Oxide Nanomaterials
Authors: Zhiyang Chen | Huaxin Liu | Yinhua Ma | Fangjian Shang | Meiheng Lv | Danna Song | Shuhui Yin | Jianyong Liu
Source: Chemical Physics Letters (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

An ab-initio deep neural network potential to study the effect of density on the thermal decomposition mechanism of RDX Ab initio ; Mechanism (biology) ; Thermal decomposition ; Decomposition ; Artificial neural network ; Computational chemistry ; Chemistry ; Chemical physics ; Computer science ; Artificial intelligence ; Physics ; Organic chemistry ; Quantum mechanics Thermal and Kinetic Analysis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jingkuan Lyu | Jing Qian | Zhucheng Yang | Jianping Xie
Source: Nanoscale Horizons (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Synthesis planning for atomically precise metal nanoclusters The rational design and synthesis of materials with tailored properties remains a long-standing goal in advanced materials science. Metal nanoclusters (MNCs), distinguished by their atomic precision and molecule-like properties-including discrete energy levels, strong photoluminescence, and high property tunability-represent promising platforms for applications spanning catalysis to biomedicine. This perspective presents a comprehensive synthesis planning framework comprising three critical stages, <i>i.e.</i>, target design, route development, and condition optimization, systematically addressing MNC rational design and synthesis with special emphasis on thiolate-protected gold nanoclusters as exemplary systems. We first discuss design considerations for core and ligand shell engineering based on their profound influence on overall material properties. Subsequently, we examine methods and synthetic mechanisms for atomic-level tailoring of core and ligand shells to achieve target MNC synthesis. We then elucidate condition parameter tuning considerations based on their deterministic roles in reaction outcomes. While this structured approach provides a systematic methodology for MNC development, significant challenges persist owing to the high structural and synthetic complexity of MNCs. We then discuss the opportunities brought by recent advances in machine learning and high-throughput experimentation, which have demonstrated potential in addressing these challenges based on their superior computational and data analytical capabilities. We advocate for systematic adoption of this synthesis planning approach enhanced by data-driven methods, addressing inherent limitations in future development to better exploit these integrated approaches for accelerating rational MNC design and synthesis. Nanoclusters ; Metal ; Materials science ; Nanotechnology ; Metallurgy Nanocluster Synthesis and Applications ; Laser-Ablation Synthesis of Nanoparticles ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: P.P. Kobchikova | B. A. Bakirov | R. E. Ryltsev | Xiao He | Ilya A. Khodov
Source: Biophysical Reviews (article, 2025-08-09)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications Membrane biology ; Nanotechnology ; Computer science ; Chemistry ; Materials science ; Biochemistry ; Membrane Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Juda Baikété | Alhadji Malloum | Jeanet Conradie
Source: Artificial Intelligence Chemistry (article, 2025-08-08)
Field: Chemistry — Organic Chemistry

Machine learning prediction of pKa of organic acids The logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to cross the plasma membrane. It affects the chemical properties of absorption, distribution, metabolism, excretion, and toxicity. Thus, accurate prediction of pKa values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. Here, we present four tree-based machine learning models for pKa prediction of organic molecules. The four models, Random Forest (RF), Extra Trees (ExTr), Histogram Gradient Boosting (HGBoost), and Gradient Boosting (GBoost), were trained on an experimental pKa dataset and tested on SAMPL6 and SAMPL7, two external datasets. Structural and organic parameter (SPOC)-based descriptors were introduced to represent the physicochemical properties of molecules. Further molecular descriptors have been generated using density functional theory (DFT) calculations, and RDKit library. The model trained with the ExTr algorithm showed the best prediction performance with an overall mean absolute error (MAE) value of 1.41 pKa units. Our model (ExTr) outperforms selected models on a range of benchmark data, while offering two unique advantages: (1) full transparency (open descriptors and data) in contrast to proprietary black boxes, and (2) reduced computational cost compared to hybrid QM/ML approaches. While specialized tools like QupKake (MAE = 0.67) achieve better accuracy, our framework provides an open-source basis for interpretable pKa predictions, efficiently combining molecular physics and machine learning. This model represents a significant advancement in pKa prediction, offering a powerful tool for various applications in chemistry and beyond. • A machine learning model is built to predict pKa of organic acids. • Several machine learning algorithms are tested among which extra tree is the best. • The model is built based on a dataset of 1268 organic molecules. • The best model reproduces experiment with an error of 1.4 pKa unit. • The built model has been tested successfully on SAMPL6 and SAMPL7. Artificial intelligence ; Chemistry ; Computer science ; Machine learning Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Shengde Zhang | Zihao Wang | Hanyu Gao | Teng Zhou
Source: Industrial & Engineering Chemistry Research (article, 2025-07-14)
Field: Chemistry — Inorganic Chemistry

A Multitask Learning Model for Predicting Various Types of Metal–Organic Framework Stability This study introduced a MOF stability prediction neural network (MOFSNN), a multitask learning architecture to predict various stability metrics of MOFs, including thermal, solvent removal, water, acid, base, and boiling water stabilities. Based on the crystal graph convolutional neural network (CGCNN), MOFSNN incorporates a lattice parameter embedding layer and task-specific attention layers to enhance the prediction accuracy and robustness. Using a data set compiled from literature, the MOFSNN model was demonstrated to outperform the traditional machine learning models and the original CGCNN model, especially in tasks with limited data. Uncertainty analysis using latent space variance (LSV) and latent space entropy (LSE) proved to effectively control prediction reliability. Moreover, the model’s ability to extrapolate to unseen materials further validated its great potential for MOF stability prediction. This study highlights the efficacy of multitask learning in leveraging correlations among different stability metrics, advancing MOF stability prediction and laying a solid foundation for material discovery. Stability (learning theory) ; Computer science ; Multi-task learning ; Machine learning ; Task (project management) ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Cheng-Han Li | Mehmet Cagri Kaymak | Maksim Kulichenko | Nicholas Lubbers | Benjamin Nebgen | Sergei Tretiak | Joshua Finkelstein | Daniel P. Tabor | Anders M. N. Niklasson
Source: Journal of Chemical Theory and Computation (article, 2025-03-14)
Field: Materials Science — Materials Chemistry

Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential We present an extended Lagrangian shadow molecular dynamics scheme with an interatomic Born-Oppenheimer potential determined by the relaxed atomic charges of a second-order charge equilibration model. To parametrize the charge equilibration model, we use machine learning with neural networks to determine the environment-dependent electronegativities and chemical hardness parameters for each atom, in addition to the charge-independent energy and force terms. The approximate shadow molecular dynamics potential in combination with the extended Lagrangian formulation improves the numerical stability and reduces the number of Coulomb potential calculations required to evaluate accurate conservative forces. We demonstrate efficient and accurate simulations with excellent long-term stability of the molecular dynamics trajectories. The significance of choosing fixed or environment-dependent electronegativities and chemical hardness parameters is evaluated. Finally, we compute the infrared spectrum of molecules via the dipole autocorrelation function and compare to experiments to highlight the accuracy of the shadow molecular dynamics scheme with a machine learned flexible charge potential. Charge (physics) ; Computer science ; Molecular dynamics ; Dynamics (music) ; Human–computer interaction ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Laser-Matter Interactions and Applications
Authors: Mads Weile | Sergii Grytsiuk | Aubrey Penn | Daniel G. Chica | Xavier Roy | Kseniia Mosina | Zdeněk Sofer | Jakob Schiøtz | Stig Helveg | Malte Rösner | Frances M. Ross | Julian Klein
Source: Physical Review X (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning Atomic defects underpin the properties of van der Waals materials, and their understanding is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy is a powerful tool for defect identification in atomically thin materials, and extending it to multilayer and beam-sensitive materials would accelerate their exploration. Here, we establish a comprehensive defect library in a bilayer of the magnetic quasi-1D semiconductor CrSBr by combining atomic-resolution imaging, deep learning, and calculations. We apply a custom-developed machine learning work flow to detect, classify, and average point vacancy defects. This classification enables us to uncover several distinct Cr interstitial defect complexes, combined Cr and Br vacancy defect complexes, and lines of vacancy defects that extend over many unit cells. We show that their occurrence is in agreement with our computed structures and binding energy densities, reflecting the intriguing layer interlocked crystal structure of CrSBr. Our calculations show that the interstitial defect complexes give rise to highly localized electronic states. These states are of particular interest due to the reduced electronic dimensionality and magnetic properties of CrSBr and are, furthermore, predicted to be optically active. Our results broaden the scope of defect studies in challenging materials and reveal new defect types in bilayer CrSBr that can be extrapolated to the bulk and to over 20 materials belonging to the same FeOCl structural family. Electron microscope ; Microscopy ; Materials science ; Nanotechnology ; Chemical physics ; Computer science ; Chemistry ; Physics ; Optics Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Electronic and Structural Properties of Oxides
Authors: Duo Zhang | Anyang Peng | Chun Cai | Wentao Li | Yichao Zhou | Jinzhe Zeng | Mingyu Guo | Chengqian Zhang | Bowen Li | Hong Jiang | Tong Zhu | Weile Jia | Linfeng Zhang | Handong Wang
Source: ArXiv.org (preprint, 2025-06-02)
Field: Materials Science — Materials Chemistry

A Graph Neural Network for the Era of Large Atomistic Models Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functional theory (DFT). The scaling law is pivotal in the development of large models, suggesting that their generalizability in downstream tasks consistently improves with increased model size, expanded training datasets, and larger computational budgets. In this study, we present DPA3, a multi-layer graph neural network founded on line graph series (LiGS), designed explicitly for the era of LAMs. We demonstrate that the generalization error of the DPA3 model adheres to the scaling law. The scalability in the number of model parameters is attained by stacking additional layers within DPA3. Additionally, the model employs a dataset encoding mechanism that decouples the scaling of training data size from the model size within its multi-task training framework. When trained as problem-oriented potential energy models, the DPA3 model exhibits superior accuracy in the majority of benchmark cases, encompassing systems with diverse features, including molecules, bulk materials, surface and cluster catalysts, two-dimensional materials, and battery materials. When trained as a LAM on the OpenLAM-v1 dataset, the DPA-3.1-3M model exhibits lowest overall zero-shot generalization error across 12 downstream tasks spanning a diverse array of research domains. This performance suggests superior accuracy as an out-of-the-box potential model, requiring minimal fine-tuning data for downstream scientific applications. Generalizability theory ; Scaling ; Generalization ; Scalability ; Graph ; Artificial neural network ; Benchmark (surveying) ; Stacking Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Graph Neural Networks
Authors: Pankyu Choi | Jin Su Lee | Joonho Bang | Ki‐Hoon Kang | Jong‐Woo Kim | Shraddha Ganorkar | Dongwoo Lee
Source: Journal of Alloys and Compounds (article, 2025-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-driven investigation of magnetostructural phase distributions in Mn-Fe-Ni-Si-Al alloy library Alloy ; Materials science ; Phase (matter) ; Metallurgy ; Condensed matter physics ; Crystallography ; Chemistry ; Physics ; Quantum mechanics Magnetic Properties and Applications ; Shape Memory Alloy Transformations ; Magnetic Properties of Alloys
Authors: Karl Rönnby | Michael Nolan
Source: The Journal of Chemical Physics (article, 2025-06-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting the morphology of cobalt, copper, and ruthenium on TaN for interconnect metal deposition Downscaling of metal interconnects has become a bottleneck in the back-end-of-line processing of CMOS semiconductor devices. The conductivity of the commonly used Cu metal drastically reduces at nanoscale, limiting its use as the devices shrink, as the metal starts forming non-conductive islands. This has led to the requirement for new interconnect metals such as Co and Ru, as they have a much higher tendency to form conductive films with horizontal growth at nanoscale. Understanding how the morphology of interconnects depends on the metals' atomistic properties and their interactions at diffusion barrier layers is necessary to continue the rapid development of interconnects. In this study, we have used first-principles density functional theory (DFT) relaxations, ab initio molecular dynamics, and neural network machine learning potentials (MLPs) to investigate how the morphology of Cu, Co, and Ru differs on TaN substrates. We investigate the binding of single metal atoms and four atom clusters to obtain the metals' substrate binding energy and metal-metal interaction energies. The morphology of the metals was then investigated by 15 ps molecular dynamics simulations with DFT and 5 ns with MLPs using larger metal structures that can display 2D and 3D morphologies. Comparing the binding energies with the obtained morphology allows us to demonstrate how the balance of metal-substrate and metal-metal interactions determines the morphology, while the MLP simulations allow longer timescale processes to be included. These insights help in the development of morphology predictors, allowing a rapid method for screening new interconnect materials with targeted horizontal growth on substrates used in semiconductors. Cobalt ; Copper ; Ruthenium ; Interconnection ; Morphology (biology) ; Deposition (geology) ; Metal ; Materials science ; Metallurgy ; Chemistry ; Computer science ; Geology ; Telecommunications ; Paleontology ; Catalysis Copper Interconnects and Reliability ; Semiconductor materials and devices ; Metal and Thin Film Mechanics
Authors: Rahul Jha | Arzoo Sekhani | Nikita Panchal | Pranav Shah
Source: Journal of Molecular Liquids (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

In silico cocrystal screening using COSMO-RS: A rapid and efficient approach for solubility prediction and crystal engineering Cocrystal ; COSMO-RS ; Solubility ; In silico ; Crystal engineering ; Crystal (programming language) ; Computer science ; Chemistry ; Biochemical engineering ; Materials science ; Biological system ; Computational chemistry ; Molecule ; Organic chemistry ; Engineering ; Biochemistry ; Hydrogen bond Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition ; X-ray Diffraction in Crystallography
Authors: Bence Balázs Mészáros | András Szabó | János Daru
Source: Journal of Chemical Theory and Computation (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Short-Range Δ-Machine Learning: A Cost-Efficient Strategy to Transfer Chemical Accuracy to Condensed Phase Systems DFT-based machine-learning potentials (MLPs) are now routinely trained for condensed-phase systems, but surpassing DFT accuracy remains challenging due to the cost or unavailability of periodic reference calculations. Our previous work ( <i>Phys. Rev. Lett.</i> 2022, 129, 226001) demonstrated that high-accuracy periodic MLPs can be trained within the CCMD framework using extended yet finite reference calculations. Here, we introduce <i>short-range</i> Δ<i>-Machine Learning</i> (srΔML), a method that starts from a baseline MLP trained on low-level periodic data and adds a Δ-MLP correction based on high-level cluster calculations at the CC level. Applied to liquid water, srΔML reduces the required cluster size from (H<sub>2</sub>O)<sub>64</sub> to (H<sub>2</sub>O)<sub>15</sub> and significantly lowers the number of clusters needed, resulting in a 50-200× reduction in computational cost. The resulting potential closely reproduces the target CC potential and accurately captures both two- and three-body structural descriptors. Computer science ; Range (aeronautics) ; Phase (matter) ; Transfer of learning ; Artificial intelligence ; Machine learning ; Chemistry ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Xu Zhang | Bei Li | Meng Wang | Bo Liu | Weimiao Lv | Hanxing Liu | Kai Liu
Source: Ceramics International (article, 2025-08-06)
Field: Materials Science — Materials Chemistry

Deep-learning potential with anisotropic effective charge tensors for ab initio ferro-piezoelectricity of lead-free (K, Na)NbO3 perovskites Ab initio ; Materials science ; Piezoelectricity ; Anisotropy ; Charge (physics) ; Lead (geology) ; Condensed matter physics ; Ab initio quantum chemistry methods ; Chemical physics ; Molecule ; Physics ; Quantum mechanics ; Composite material Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Electronic and Structural Properties of Oxides
Authors: Guiping Yang | Shu Zhang | Zhao Pei | Chuanhao Li | Lei Tang | Jun Jiang | Chong Zhao
Source: Journal of Chemical Theory and Computation (article, 2025-01-10)
Field: Materials Science — Materials Chemistry

Prediction of Thermodynamic Properties of C<sub>60</sub>-Based Fullerenols Using Machine Learning Traditional machine learning methods face significant challenges in predicting the properties of highly symmetric molecules. In this study, we developed a machine learning model based on graph neural networks (GNNs) to accurately and swiftly predict the thermodynamic and photochemical properties of fullerenols, such as C<sub>60</sub>(OH)<i><sub>n</sub></i> (<i>n</i> = 1 to 30). First, we established a global method for generating fullerenol isomers through isomer fingerprinting, which can generate all possible isomers or produce diverse structural types on demand. Significantly, by incorporating interpretable descriptors such as atomic labels, bond lengths, and bond angles from highly symmetric isomers, our multilayer GNN model achieved over 90% accuracy in predicting the thermodynamic stability of fullerenols. The model also performed excellently in predicting electronic properties, including the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and the energy gap. Overall, this work demonstrates a new strategy using interpretable descriptors for accurately predicting the properties of highly symmetric structures, offering theoretical chemists a valuable tool for studying these materials. Artificial neural network ; Computer science ; Graph ; Machine learning ; Artificial intelligence ; Thermodynamics ; Physics ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fullerene Chemistry and Applications