jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ashok K. Verma | Junaid Jami | Amrita Bhattacharya
Source: Computational Materials Science (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Accelerating magnetic materials discovery using interaction matrix-based machine learning descriptors Ferrimagnetism ; Magnetism ; Magnetic moment ; Moment (physics) ; Support vector machine ; Ferromagnetism ; Atom (system on chip) ; Magnet ; Binary number Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Jingjing Shi | Xiangmin Feng | Zhenming Xu | Xiaogang Zhang
Source: The Journal of Physical Chemistry Letters (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Simulation and Artificial Intelligence-Driven Development of New Lithium Electrolytes with High Ionic Conductivity and Understanding Ion Transport High ionic conductivity electrolytes are vital for ensuring robust lithium-ion battery performance, especially in low-temperature environments. In this study, we systematically investigated a novel chemical space comprising 2604 electrolyte formulations using high-throughput molecular dynamics (MD) simulations, integrating the OPLS-AA force field with the RESP2 charge model. This methodology accurately replicated Li<sup>+</sup> solvation shell structures and identified numerous innovative electrolytes exhibiting high room-temperature ionic conductivities more than 10 mS/cm, and many of which were experimentally validated for the first time. By leveraging MD data sets and the machine learning method, the composition-property relationships governing Li<sup>+</sup> solvation shell structure and ion transport in electrolytes were elucidated. Li<sup>+</sup> solvation shell structures are primarily influenced by solvent concentration, molecular topology, and surface charge distribution, with the higher solvent concentrations enhancing Li<sup>+</sup>-molecule coordination numbers. Ionic conductivity of electrolyte is predominantly determined by viscosity, and the low-viscosity components such as PF<sub>6</sub><sup>-</sup>, DOL, DME, EA, and DMC boost ionic conductivity, while TFSI<sup>-</sup>, DEC, and EMC tend to reduce it. Additionally, the high coordination numbers with weakly coordinating solvents leading to the larger localized Li<sup>+</sup> interactions further enhance ion transport in electrolyte. Molecular descriptors, including HallKierAlpha and MaxPartialCharge, exhibit strong correlations with ionic conductivity, serving as the effective metrics for the large-scale screening tasks. Consequently, the optimal high-conductivity electrolytes should incorporate low-viscosity solvents with high coordination numbers, strong Li<sup>+</sup> binding energies, elevated HallKierAlpha values, and reduced MaxPartialCharges. This synergistic integration of high-throughput simulations and machine learning offers a powerful approach for the discovery of advanced electrolytes. Electrolyte ; Solvation ; Ionic conductivity ; Solvation shell ; Molecular dynamics ; Ionic bonding ; Ion ; Conductivity ; Solvent Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: P. V. Chirkov | G. S. Eltsov | R. M. Kichigin | A. A. Mirzoev | A. V. Karavaev | V. V. Dremov
Source: Physical review. B./Physical review. B (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

Machine learning enhanced quantum molecular dynamics assessment of the phase diagram of uranium Phase diagram ; Uranium ; Molecular dynamics ; Phase (matter) ; Dynamics (music) ; Computer science ; Statistical physics ; Chemical physics ; Physics ; Nuclear physics ; Quantum mechanics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Abhishek Kumar Thakur | Sanyam Nitin Totade | Krishna Muralidharan | Monojit Dutta
Source: The Philosophical Magazine A Journal of Theoretical Experimental and Applied Physics (review, 2025-09-17)
Field: Materials Science — Materials Chemistry

Phase diagram determination using machine learning methods in materials science and engineering: a review Phase diagram ; Phase (matter) ; Diagram ; Feature (linguistics) ; Stability (learning theory) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Microstructure and Mechanical Properties of Steels
Authors: Yang Xiao | Yuqi Liu | Zhongqiang Zhang | Ke Xu | Zheyong Fan | Shunda Chen | Shiyun Xiong | Haikuan Dong
Source: ArXiv.org (preprint, 2025-04-24)
Field: Materials Science — Materials Chemistry

Optimizing thermoelectric performance of graphene antidot lattices via quantum transport and machine-learning molecular dynamics simulations Thermoelectric materials, which can convert waste heat to electricity or be utilized as solid-state coolers, hold promise for sustainable energy applications. However, optimizing thermoelectric performance remains a significant challenge due to the complex interplay between electronic and thermal transport properties. In this work, we systematically optimize $ZT$ in graphene antidot lattices (GALs), nanostructured graphene sheets with periodic nanopores characterized by two geometric parameters: the hexagonal unit cell side length $L$ and the antidot radius $R$. The lattice thermal conductivity is determined through machine-learned potential-driven molecular dynamics (MD) simulations, while electronic transport properties are computed using linear-scaling quantum transport in combination with MD trajectories based on a bond-length-dependent tight-binding model. This method is able to account for electron-phonon scattering, allowing access to diffusive transport in large-scale systems, overcoming limitations of previous methods based on nonequilibrium Green function formalism. Our results show that the introduction of the antidots effectively decouples lattice and electronic transport and lead to a favorable and significant violation of the Wiedemann-Franz law. We find that optimal $ZT$ values occur in GALs with intermediate $L$ and $R$, closely correlated with peak power factor values. Notably, thermoelectric performance peaks near room temperature, with maximal $ZT$ values approaching 2, highlighting GALs as promising candidates for high-performance thermoelectric energy conversion. Thermoelectric effect ; Graphene ; Thermal conductivity ; Thermoelectric materials ; Seebeck coefficient ; Nanopore ; Molecular dynamics ; Non-equilibrium thermodynamics ; Ballistic conduction ; Thermoelectric generator Machine Learning in Materials Science ; Graphene research and applications ; Advanced Thermoelectric Materials and Devices
Authors: Md Tohidul Islam | Scott Broderick
Source: Crystals (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

A Data-Driven Framework for Accelerated Modeling of Stacking Fault Energy from Density of States Spectra Stacking fault energy (SFE) is a critical property governing deformation mechanisms and influencing the mechanical behavior of materials. This work presents a unified framework for understanding and predicting SFE based solely on an electronic structure representation. By integrating density of states (DOS) spectral data, dimensionality reduction techniques, and machine learning models, it was found that the SFE behavior is indeed represented within the electronic structure and that this information can be used to accelerate the prediction of SFE. In the first part of this study, we established quantitative relationships between electronic structure and microstructural features, linking chemistry to mechanical properties. Using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP), we identified key features from high-resolution vector representation of DOS data and explored their correlation with SFE. The second part of this work focuses on the predictive modeling of SFE, where a machine learning model trained on UMAP-reduced features achieved high accuracy (R2 = 0.86, MAE = 15.46 mJ/m2). To bridge length scales, we extended this methodology to predict SFE in alloy systems, leveraging single-element data to inform multi-element alloy design. We illustrate this approach with Cu-Zn alloys, where the framework enabled rapid screening of compositional space while capturing complex electronic structure interactions. The proposed framework accelerates alloy design by reducing reliance on costly experiments and ab initio calculations. Spectral line ; Fault (geology) ; Stacking ; Energy (signal processing) ; Computer science ; Statistical physics ; Computational physics ; Physics ; Geology ; Seismology ; Nuclear magnetic resonance ; Quantum mechanics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Divyansh Adha | Avinash Anand | Abhishek Roy | Vijayanand S. Moholkar
Source: ACS Sustainable Resource Management (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Valorization of Polycarbonate Waste by Ultrasound-Assisted Methanolysis: Optimization Using an Artificial Neural Network and Kinetic Investigation This study presents an integrated approach combining experimental optimization, machine learning (using ANN-GA), and kinetic modeling to enhance ultrasound-assisted methanolysis of polycarbonate (PC) waste. Central composite design (CCD) under response surface methodology (RSM) was employed to optimize methanolysis reaction parameters of solvent ratio, catalyst loading, temperature, reaction time, and lagging time. The optimized process achieved 98.56% PC conversion under 0.138 g of the KOH catalyst, 3.63:1 THF: methanol ratio, 47.7 °C, 26.8 min reaction time, and 7 min lagging time. Product characterization confirmed the formation of high-purity bisphenol A (BPA) and dimethyl carbonate (DMC). To enhance predictive capability, an artificial neural network optimized with genetic algorithm (ANN-GA) was developed. The model with 12 hidden neurons improved the predictions of RSM-CCD with 99.95% PC conversion and identified the solvent ratio and catalyst dosage as the most influential variables through sensitivity analysis. A kinetic model based on four reversible reaction steps was solved using the 4th-order Runge–Kutta method with GA-optimized constants, showing strong agreement with experimental data (R2 > 0.94). The kinetic constants provided mechanistic insight into the role of ultrasound, which enhanced depolymerization by improving mass transfer and reducing activation barriers. This work demonstrates an efficient, sustainable pathway for polycarbonate waste valorization and green synthesis of BPA and DMC. Polycarbonate ; Methanol ; Catalysis ; Artificial neural network ; Depolymerization ; Mass transfer ; Batch reactor ; Dimethyl carbonate Ultrasound and Cavitation Phenomena ; Catalysis for Biomass Conversion ; Polymer Foaming and Composites
Authors: Junchi Liu | Ying Tang | Sergei Tretiak | Wenhui Duan | Zhou Liujiang
Source: ArXiv.org (preprint, 2025-05-01)
Field: Materials Science — Materials Chemistry

SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm, Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR), that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 times acceleration compared to conventional SR implementations that heavily rely on first principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior. Property (philosophy) ; Fidelity ; Graph ; Regression ; High fidelity ; Computer science ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Statistics ; Engineering ; Telecommunications ; Electrical engineering ; Philosophy Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Jie Feng | Tao Sun | Jianmei Yuan
Source: Computational Materials Science (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

SSFormer: Self-supervised transformer model for predicting the properties of crystalline materials Transformer ; Generalization ; Convolutional neural network ; Representation (politics) ; String (physics) ; Graph ; Deep learning ; Performance prediction Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Material Selection and Properties
Authors: Peihong Teng | Chengyu Fu | Shangkun Shen | Huaijuan Zang | Jing Fang | Yongsheng Ren | Shu Zhan
Source: Materials Today Communications (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

MatGNet: A graph neural network for crystal property prediction as an alternative to first-principles calculations Deep learning techniques are increasingly prevalent in predicting the properties of periodic crystalline materials, gradually emerging as a powerful alternative to time-consuming and computationally intensive first-principles calculations. To this end, we innovatively propose a new model, the Graph Convolutional Neural Network , based on Mat2vec Atomic Embeddings for Crystals (MatGNet). Our model represents crystalline structures as graph structures, where node features are encoded using the Mat2vec atomic embedding method and integrated with atomic coordinate information. On the other hand, edge features comprehensively reflect interatomic interactions by incorporating Gaussian distances between atoms expanded using radial basis functions . Additionally, we have optimized the model better to characterize the angular features of crystals' structural embeddings and enhance crystalline structures' representational capacity. The network architecture of the MatGNet model is an improved gated convolutional network with a self-attention mechanism, enabling efficient updating and propagation of node and edge information on graph structures. Experiments on the JARVIS-DFT dataset demonstrate that our method excels in predicting the properties of crystalline materials, with accuracy surpassing previous models. This approach provides an efficient and accurate solution for predicting the properties of crystalline materials, potentially accelerating the rapid development of crystalline material research. Materials science ; Artificial neural network ; Property (philosophy) ; Crystal structure prediction ; Crystal (programming language) ; Crystal structure ; Machine learning ; Computer science ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: An-Jun Liu | Bryan K. Clark
Source: Physical review. B./Physical review. B (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

Efficient optimization of neural network backflow for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>a</mml:mi> <mml:mi>b</mml:mi> </mml:mrow> <mml:mo> </mml:mo> <mml:mrow> <mml:mi>i</mml:mi> <mml:mi>n</mml:mi> <mml:mi>i</mml:mi> <mml:mi>t</mml:mi> <mml:mi>i</mml:mi> <mml:mi>o</mml:mi> </mml:mrow> </mml:math> quantum chemistry The ground state of second-quantized quantum chemistry Hamiltonians is key to determining molecular properties. Neural quantum states (NQSs) offer flexible and expressive wave-function Ans\"atze for this task but face two main challenges: highly peaked ground-state wave functions hinder efficient sampling, and local energy evaluations scale quartically with system size, incurring significant computational costs. In this work, we overcome these challenges by introducing a suite of algorithmic enhancements, which includes efficient periodic compact subspace construction, truncated local energy evaluations, improved stochastic sampling, and physics-informed modifications. Applying these techniques to the neural network backflow Ansatz, we demonstrate significant gains in both accuracy and scalability. Our enhanced method surpasses traditional quantum chemistry methods like coupled cluster singles and doubles (CCSD) and CCSD and perturbative triples [CCSD(T)], outperforms other NQS approaches, and achieves competitive energies with state-of-the-art ab initio techniques such as heat-bath configuration interaction, adaptive sampling configuration interaction, full configuration interaction quantum Monte Carlo, and density matrix renormalization group. A series of ablation and comparative studies quantifies the contribution of each enhancement to the observed improvements in accuracy and efficiency. Furthermore, we investigate the representational capacity of the Ansatz, finding that its performance correlates with the inverse participation ratio, with more delocalized states being more challenging to approximate. Quantum ; Density matrix ; Ab initio ; Artificial neural network ; Wave function ; Coupled cluster ; Density matrix renormalization group ; Renormalization Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Wanying Liang | Guangyue Xu
Source: Chemistry (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Advancements and Challenges of Cobalt–Zeolite Composite Catalysts in Heterogeneous Catalysis Cobalt–zeolite composite catalysts (Co–zeolite) and their heterogeneous catalytic systems have garnered significant research attention owing to their superior catalytic activity and cost-effectiveness. The speciation of cobalt within these catalysts—either through impregnation onto the zeolite framework or structural incorporation within the aluminosilicate matrix—is critically governed by the employed synthesis methodology, which subsequently dictates their distinct catalytic advantages in targeted reaction systems. Compared to homogeneous catalytic systems, heterogeneous Co–zeolite configurations demonstrate enhanced structural integrity that effectively mitigates cobalt leaching, thereby improving catalyst recyclability while minimizing environmental contamination. This review systematically examines recent advancements in Co–zeolite fabrication techniques and their catalytic performance across diverse applications, including Fischer–Tropsch synthesis, nitrogen oxide abatement, hydrogenation processes, and oxidative transformations. Particular emphasis is placed on elucidating the metal-framework interactions, with analysis of synergistic effects arising from multi-valent cobalt speciation and bimetallic cooperativity between cobalt and secondary transition metals. This work critically evaluates current challenges in Co–zeolite catalyst design. Finally, we propose future research directions focusing on a precise identification of active species and mechanistic elucidation, innovative synthesis strategies for cobalt speciation control, machine learning-guided catalyst optimization, and the advancement of eco-friendly catalysts. Catalysis ; Zeolite ; Cobalt ; Composite number ; Materials science ; Chemical engineering ; Business ; Chemistry ; Composite material ; Organic chemistry ; Metallurgy ; Engineering Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Vir Karan | Max C. Gallant | Yuxing Fei | Gerbrand Ceder | Kristin A. Persson
Source: ArXiv.org (preprint, 2025-01-15)
Field: Materials Science — Materials Chemistry

Ion correlations explain kinetic selectivity in diffusion-limited solid state synthesis reactions Establishing viable solid-state synthesis pathways for novel inorganic materials remains a major challenge in materials science. Previous pathway design methods using pair-wise reaction approaches have navigated the thermodynamic landscape with first-principles data but lack kinetic information, limiting their effectiveness. This gap leads to suboptimal precursor selection and predictions, especially for reactions forming competing phases with similar formation energies, where ion diffusion is a critical influence. Here, we demonstrate an inorganic synthesis framework by incorporating machine learning-derived transport properties through "liquid-like" product layers into a thermodynamic cellular reaction model. In the Ba-Ti-O system, known for its competitive polymorphism, we obtain accurate predictions of phase formation with varying BaO:TiO2 ratios as a function of time and temperature. We find that diffusion-thermodynamic interplay governs phase compositions, with cross-ion transport coefficients critical for predicting diffusion-limited selectivity. This work bridges length and time scales by integrating solid-state reaction kinetics with first-principles thermodynamics and spatial reactivity. Kinetic energy ; Selectivity ; Ion ; Diffusion ; Solid-state ; Chemical physics ; Chemistry ; Thermodynamics ; Materials science ; Physical chemistry ; Physics ; Catalysis ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Materials Characterization Techniques