jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yirui Hu | Jian Wang | Z. X. A. X. Gao | Hongxing Xiao | Hongsheng Chen | Jie Xiong
Source: Nuclear Engineering and Technology (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Physics-guided multi-task learning for predicting thermophysical properties of Ag-In-Cd absorber alloys with extremely small data Machine learning (ML) techniques have recently shown great promise in advancing nuclear materials research. However, the application of ML in this field is often limited by the scarcity of experimental data due to the high cost and complexity of irradiation experiments. In this study, we propose a physics-guided multi-task neural network (PGMTNN) framework to accurately predict the thermophysical properties of Ag-In-Cd control rod alloys under neutron irradiation, using extremely limited datasets. The PGMTNN model incorporates fundamental physical laws as soft constraints within a multi-task learning architecture, enabling the simultaneous prediction of density, specific heat capacity, thermal diffusivity, and thermal conductivity. This approach effectively captures the intrinsic correlations among different material properties and ensures physical consistency of the predictions. Compared with traditional ML methods and the transformer-based foundation model, the PGMTNN demonstrates superior performance, achieving an average coefficient of determination exceeding 0.92 on the testing set. These results underscore the potential of physics-guided, multi-task ML frameworks for accurate property prediction and reliable design of advanced absorber materials under small-data conditions. Consistency (knowledge bases) ; Thermal ; Artificial neural network ; Experimental data ; Field (mathematics) ; Physical property Machine Learning in Materials Science ; Nuclear reactor physics and engineering ; Nuclear Materials and Properties
Authors: Wai Yuet Chiu (13617039) | Chongzhi Zhang (21368874) | Rongzhi Gao (14664221) | Ziyang Hu (2589370) | GuanHua Chen (1330029)
Source: Figshare (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Accurate Predictionof Open-Circuit Voltages of Lithium-IonBatteries via Delta Learning Accurate prediction of lithium-ion battery capacity before material synthesis is crucial for accelerating battery material discovery. The capacity can be theoretically determined by integrating open-circuit voltage vs state of charge (OCV-SoC) curves of electrode materials. OCV-SoC curves are traditionally computed using first-principles methods, either through geometry optimization (GO) with density functional theory (DFT) or molecular dynamics (MD) simulations of lithiation/delithiation processes using DFT or force fields. While MD simulations incorporate temperature effects that GO lacks, even DFT-based MD simulated OCV-SoC curves show systematic deviations from experimental results due to inherent approximations in DFT functionals. In this study, we performed MD simulations on 43 cathode materials to obtain their OCV-SoC curves. Initial results showed only moderate agreement with experimental data, yielding a coefficient of determination (<i>R</i><sup>2</sup>) of 0.249 and a mean absolute error (MAE) of 1.561 V. Considering the scarcity of data, we implemented a delta learning approach to calibrate the MD results without substantial computational overhead, achieving an improved <i>R</i><sup>2</sup> of 0.933 and an MAE of 0.131 V on the testing set. This calibration method significantly enhanced the accuracy of energy density predictions, reducing the MAE from 106.0 to 10.7 Wh/kg. We also developed an automated delta learning platform to make this approach accessible to researchers without machine learning expertise. Battery (electricity) ; Calibration ; Voltage ; Molecular dynamics ; State of charge ; Density functional theory ; Energy (signal processing) ; Cathode Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Zhendong Cao | Shigang Ou | Lei P Wang
Source: ArXiv.org (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction Crystal structure prediction is a fundamental problem in materials science. We present CrystalFormer-CSP, an efficient framework that unifies data-driven heuristic and physics-driven optimization approaches to predict stable crystal structures for given chemical compositions. The approach combines pretrained generative models for space-group-informed structure generation and a universal machine learning force field for energy minimization. Reinforcement fine-tuning can be employed to further boost the accuracy of the framework. We demonstrate the effectiveness of CrystalFormer-CSP on benchmark problems and showcase its usage via web interface and language model integration. Crystal structure prediction ; Heuristic ; Benchmark (surveying) ; Interface (matter) ; Field (mathematics) ; Reinforcement learning ; Generative grammar Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Chongmyung Kwon | Yu‐Jin Kim | Seoeun Park | Yunji Lee | Charmgil Hong
Source: ArXiv.org (preprint, 2025-10-09)
Field: Materials Science — Materials Chemistry

MMM: Quantum-Chemical Molecular Representation Learning for Combinatorial Drug Recommendation Drug recommendation is an essential task in machine learning-based clinical decision support systems. However, the risk of drug-drug interactions (DDI) between co-prescribed medications remains a significant challenge. Previous studies have used graph neural networks (GNNs) to represent drug structures. Regardless, their simplified discrete forms cannot fully capture the molecular binding affinity and reactivity. Therefore, we propose Multimodal DDI Prediction with Molecular Electron Localization Function (ELF) Maps (MMM), a novel framework that integrates three-dimensional (3D) quantum-chemical information into drug representation learning. It generates 3D electron density maps using the ELF. To capture both therapeutic relevance and interaction risks, MMM combines ELF-derived features that encode global electronic properties with a bipartite graph encoder that models local substructure interactions. This design enables learning complementary characteristics of drug molecules. We evaluate MMM in the MIMIC-III dataset (250 drugs, 442 substructures), comparing it with several baseline models. In particular, a comparison with the GNN-based SafeDrug model demonstrates statistically significant improvements in the F1-score (p = 0.0387), Jaccard (p = 0.0112), and the DDI rate (p = 0.0386). These results demonstrate the potential of ELF-based 3D representations to enhance prediction accuracy and support safer combinatorial drug prescribing in clinical practice. ENCODE ; Jaccard index ; Representation (politics) ; Bipartite graph ; Molecular graph ; Graph ; Task (project management) ; Feature learning ; Relevance (law) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Martin J. Uttendorfer | Daniel Barragán-Yani | Matthias Sperl | M. Landmann
Source: ArXiv.org (preprint, 2025-12-15)
Field: Materials Science — Materials Chemistry

A Joint Quantum Computing, Neural Network and Embedding Theory Approach for the Derivation of the Universal Functional We introduce a novel approach that exploits the intersection of quantum computing, machine learning and reduced density matrix functional theory to leverage the potential of quantum computing to improve simulations of interacting quantum particles. Our method focuses on obtaining the universal functional using a deep neural network trained with quantum algorithms. We also use fragment-bath systems defined by density matrix embedding theory to strengthen our approach by substantially expanding the space of Hamiltonians for which the obtained functional can be applied without the need for additional quantum resources. Given the fact that once obtained, the same universal functional can be reused for any system where the interactions within the embedded fragment are identical, our work demonstrates a way to potentially achieve a cumulative quantum advantage within quantum computing applications for quantum chemistry and condensed matter physics. Embedding ; Density matrix ; Quantum ; Quantum computer ; Density functional theory ; Quantum entanglement ; Quantum network ; Leverage (statistics) ; Artificial neural network Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Daniel Willimetz (22445634) | Lukáš Grajciar (1274313)
Source: Figshare (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

A Simple andScalable Kernel Density Approach forReliable Uncertainty Quantification in Atomistic Machine Learning Machine learning models are increasingly used to predict material properties and accelerate atomistic simulations, but the reliability of their predictions depends on the representativeness of the training data. We present a scalable, GPU-accelerated uncertainty quantification framework based on <i>k</i>-nearest-neighbor kernel density estimation (KDE) in a PCA-reduced descriptor space. This method efficiently detects sparsely sampled regions in large, high-dimensional data sets and provides a transferable, model-agnostic uncertainty metric without requiring retraining costly model ensembles. The framework is validated across diverse case studies varying in (i) chemistry, (ii) prediction models (including foundational neural network), (iii) descriptors used for KDE estimation, and (iv) properties whose uncertainty is sought. In all cases, the KDE-based score reliably flags extrapolative configurations, correlates well with conventional ensemble-based uncertainties, and highlights regions of reduced prediction trustworthiness. The approach offers a practical route for improving the interpretability, robustness, and deployment readiness of ML models in materials science. Uncertainty quantification ; Metric (unit) ; Reliability (semiconductor) ; Kernel density estimation ; Kernel (algebra) ; Probabilistic logic ; Scalability ; Representativeness heuristic ; Artificial neural network ; Density estimation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Model Reduction and Neural Networks
Authors: Hamdi Dağıstanlı
Source: Cumhuriyet Science Journal (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Application on Strain Effect of WSe2 Ab initio density functional theory (DFT) calculations have been used to determine the band gap of the 2D layered material WSe2 under uniaxial strain. This involves finding optimised lattice parameters and calculating the electronic band structure. We also note that by applying strains ranging from -15% to 15%, a wide range of band gaps can be obtained to study the behaviours of the semimetal and metal. We found, WSe2 structure becomes semi-metal at +12% and -13% strains then or also becomes metal after +12% and -13% strains. The ensuing outcomes are then applied to the domain of machine learning. Initially, PR, which is polynomial regression, is a machine learning method that can be studied with numpy, sklearn and scipy modules, and ANN (artificial neural network) is the application with tensorflow module that is applied to the optimised semi-metallic and metallic structures. The application of the latter involves the Least Squares method. The implementation of Leaky Relu (LRelu) and Elu functions is instrumental in facilitating the deployment of ANN. The potential dataset is obtained by using Quantum Espresso, and the calculations are made by using the National Center for High Performance Computing of Turkey (UYBHM). The PR and ANN results are calculated using the existing data set. The primary objective of utilising PR and artificial neural networks is to facilitate the plotting of Valance Band Maximum (VBM) and Conduction Band Minimum (CBM) graphs in the vicinity of the Fermi level. This study based on data which are already available and could therefore be considered to be data mining. on the electronic band structure for WSe2. Artificial neural network ; Ab initio ; Conduction band ; Semimetal ; Band gap ; Lattice (music) ; Electronic band structure ; Density functional theory ; Hybrid functional 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Thermoelectric Materials and Devices
Authors: Zhendong Cao | Shigang Ou | Lei P Wang
Source: arXiv (Cornell University) (preprint, 2025-12-20)
Field: Materials Science — Materials Chemistry

CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction Crystal structure prediction is a fundamental problem in materials science. We present CrystalFormer-CSP, an efficient framework that unifies data-driven heuristic and physics-driven optimization approaches to predict stable crystal structures for given chemical compositions. The approach combines pretrained generative models for space-group-informed structure generation and a universal machine learning force field for energy minimization. Reinforcement fine-tuning can be employed to further boost the accuracy of the framework. We demonstrate the effectiveness of CrystalFormer-CSP on benchmark problems and showcase its usage via web interface and language model integration. Crystal structure prediction ; Heuristic ; Benchmark (surveying) ; Interface (matter) ; Field (mathematics) ; Reinforcement learning ; Generative grammar Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Yoyo Hinuma
Source: ArXiv.org (preprint, 2025-02-04)
Field: Materials Science — Materials Chemistry

Direct derivation of anisotropic atomic displacement parameters from molecular dynamics simulations in extended solids with substitutional disorder using a neural network potential Atomic displacement parameters (ADPs) are crystallographic information that describe the statistical distribution of atoms around an atom site. Anisotropic ADPs by atom were directly derived from classical molecular dynamics (MD) simulations using a universal machine-learned potential. The (co)valences of atom positions were taken over recordings at different time steps in a single MD simulation. The procedure was demonstrated on extended solids, namely rocksalt structure MgO and three thermoelectric materials, Ag8SnSe6, Na2In2Sn4, and BaCu1.14In0.86P2. Unlike the very frequently used lattice dynamics approach, the MD approach can obtain ADPs in crystals with substitutional disorder and explicitly at finite temperature, but not under conditions where atoms migrate in the crystal. The calculated ADP becomes -&gt;0 at temperature -&gt;0 and the ADP is proportional to the temperature when the atom is in a harmonic potential and the sole contribution to the actual non-zero ADP is from the zero-point motion. The zero-point motion contribution can be estimated from the proportionality constant assuming this Einstein model. ADPs from MD simulations would act as a tool complementing experimental efforts to understand the crystal structure including the distribution of atoms around atom sites. Anisotropy ; Molecular dynamics ; Displacement (psychology) ; Thermoelectric effect ; Materials science ; Thermoelectric materials ; Dynamics (music) ; Physics ; Statistical physics ; Classical mechanics ; Condensed matter physics ; Thermodynamics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Finkler, Jonas Alexander | Lin, Yan; id_orcid 0000-0002-2320-9777 | Du, Tao | Hu, Jilin; id_orcid 0000-0002-7739-7769 | Smedskjær, Morten Mattrup
Source: VBN Forskningsportal (Aalborg Universitet) (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

AMDEN:Amorphous Materials DEnoising Network Disordered (amorphous) materials, such as glasses, are emerging as promising candidates for applications within energy storage, nonlinear optics, and catalysis. Inverse design aims to directly predict the composition and structure of new materials with targeted properties using machine learning models. This avoids the time-consuming trial-and-error process of traditional materials design and has the potential to significantly accelerate the discovery of new materials.<br/><br/>In this work, we introduce AMDEN (Amorphous Material DEnoising Network), a diffusion model-based framework that generates structures of amorphous materials and can be conditioned on target properties. We demonstrate inherent challenges for diffusion models to generate relaxed structures. These low-energy configurations are typically obtained through a thermal motion-driven random search-like process that cannot be replicated by standard denoising procedures. We therefore introduce an energy-based AMDEN variant that implements Hamiltonian Monte Carlo refinement for generating these relaxed structures. We further introduce several amorphous material datasets with diverse properties and compositions to evaluate our framework and support future development.<br/> Process (computing) ; Inverse ; Monte Carlo method ; Nonlinear system ; Noise reduction ; Diffusion process ; Amorphous solid Machine Learning in Materials Science ; Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys
Authors: Huichun Xue | Lin Zhu | An Du
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Regulation Mechanisms of Single-Atom Doped Ti2co2 Mxene for Co2 Reduction: A Dft Study Reduction (mathematics) ; Doping ; Atom (system on chip) ; Materials science ; Atomic physics ; Physics ; Computer science ; Optoelectronics ; Mathematics ; Embedded system ; Geometry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Advancements in Battery Materials
Authors: Liangru Yu | Zhe Zhao | X. Wang | Di Wu | Pengkun Wang
Source: ArXiv.org (preprint, 2025-11-10)
Field: Materials Science — Materials Chemistry

Rethinking Crystal Symmetry Prediction: A Decoupled Perspective Efficiently and accurately determining the symmetry is a crucial step in the structural analysis of crystalline materials. Existing methods usually mindlessly apply deep learning models while ignoring the underlying chemical rules. More importantly, experiments show that they face a serious sub-property confusion SPC problem. To address the above challenges, from a decoupled perspective, we introduce the XRDecoupler framework, a problem-solving arsenal specifically designed to tackle the SPC problem. Imitating the thinking process of chemists, we innovatively incorporate multidimensional crystal symmetry information as superclass guidance to ensure that the model's prediction process aligns with chemical intuition. We further design a hierarchical PXRD pattern learning model and a multi-objective optimization approach to achieve high-quality representation and balanced optimization. Comprehensive evaluations on three mainstream databases (e.g., CCDC, CoREMOF, and InorganicData) demonstrate that XRDecoupler excels in performance, interpretability, and generalization. Symmetry (geometry) ; Perspective (graphical) ; Process (computing) ; Representation (politics) ; Confusion ; Face (sociological concept) ; Crystal (programming language) ; Phase (matter) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Priom Das | Plabon Paul | Sakib Javed | Sadia Tasnim | Monjur Morshed AKM
Source:  (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Data-Driven Machine and Deep Learning-Based Prediction of Interfacial Thermal Resistance Using High-Fidelity Molecular Dynamics Simulations The van der Waals heterostructure (vdWH) of Stanene/Hexagonal Boron Nitride (Sn/h-BN) nanosheet has drawn attention due to its promising electro-thermal properties. Understanding the interfacial thermal resistance (ITR) between layers is essential for High fidelity ; Molecular dynamics ; Artificial intelligence ; Dynamics (music) ; Computer science ; Deep learning ; Fidelity ; Machine learning ; Resistance (ecology) ; Chemistry ; Physics ; Engineering ; Biology ; Computational chemistry ; Electrical engineering Machine Learning in Materials Science
Authors: Mengxin Lu | Jianfu Li | Mengyuan Zhu | Yi Liu | Jianan Yuan | Jiani Lin | Xiaoli Wang
Source: Applied Physics Letters (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Mixed ionic–electronic conductor based on B16 framework Mixed ionic–electronic conductors (MIECs) are essential materials for next-generation energy storage and conversion systems. However, several limitations—including the scarcity of intrinsic MIEC materials, low ionic conductivity, and a narrow operational temperature range—restrict their practical use. Based on the experimentally synthesized MgB4 crystal, this study designs a B16 covalent framework with three-dimensionally connected channels by theoretically removing Mg atoms through computational methods. Using first-principles calculations and machine learning molecular dynamics simulations, we systematically explore its compounds' potential as intrinsic MIEC materials. The results indicate that the B16 framework can stably host and transport Li+, Be2+, and Al3+ ions with different valences. The formations of LiB2, BeB4, and AlB8 enter superionic states at 1200, 1700, and 1400 K, respectively, with ionic conductivities reaching 10−2 S/cm, and the optimal electronic conductivities approaching 104 S/cm. The framework shows good structural stability: the volume change after ion insertion is less than 12.4%, and its mechanical properties (for example, a shear modulus of 47.8 GPa) are comparable to those of traditional electrode materials like LiFePO4 and NCM. Through defect engineering, LiB2 with 12.5% crystal defects can lower the superionic transition temperature to 400 K while maintaining a high ionic conductivity of 1.65 × 10−1 S/cm. This study offers a viable approach to designing MIEC materials and demonstrates significant potential for applications such as high-temperature solid-state batteries and electrochemical sensors. Ionic conductivity ; Ionic bonding ; Conductor ; Fast ion conductor ; Ion ; Electrical conductor ; Electrode ; Thermal conduction ; Valence (chemistry) Boron and Carbon Nanomaterials Research ; Superconductivity in MgB2 and Alloys ; Hydrogen Storage and Materials
Authors: Y.Q. Wang | Hao Dong | Yongchun Guo | Yanru Shao | H. Li | Y. Liang | Quangui Guo | Feng Xia | Zhiyu Yang | J.P. Li
Source: Materials Characterization (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Simultaneous enhancement of the room temperature and high-temperature properties of Al-Cu-Mg-Ag alloys via machine learning on small datasets Pattern recognition (psychology) ; Training set ; Artificial neural network ; Microstructure Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Microstructure and mechanical properties