jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qingchen Wu | Linfeng Dong | Lei Kang | Zheshuai Lin
Source: Advanced Optical Materials (article, 2025-01-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction and Evaluation of Li <sub>2</sub> ZnS <sub>2</sub> Crystals as Mid‐Infrared Nonlinear Optical Material with High Thermal Conductivity Abstract Infrared (IR) nonlinear optical (NLO) crystals are essential for laser applications in mid‐ or far‐IR regions. The common mid‐IR NLO crystals are inadequate under high‐power laser mainly due to low laser‐induced damage threshold which is closely related to the lattice thermal conductivity ( κ L ). Here a combined method involving automatic structure prediction algorithms, first‐principles calculations and machine learning model is used to predict and evaluate a new IR NLO material family with the chemical formula of Li 2 ZnS 2 . Totally, three Li 2 ZnS 2 structures (or phases) are designed, where one phase is expected to meet the stringent requirements for mid‐IR NLO materials pumped by high‐power laser, with the high thermal conductivity, decent NLO coefficient, wide bandgap and phase‐matching ability in the mid‐IR region. This study presents an efficient approach to exploring mid‐IR NLO materials with high κ L based on accumulated knowledge concerning the structure‐property relationship in the NLO field and provides inspiration to improve κ L prediction. Materials science ; Infrared ; Thermal conductivity ; Nonlinear optical ; Optical materials ; Conductivity ; Thermal ; Optoelectronics ; Nonlinear optics ; Nonlinear system ; Analytical Chemistry (journal) ; Optics ; Composite material ; Physical chemistry ; Thermodynamics ; Laser Crystal Structures and Properties ; Solid-state spectroscopy and crystallography ; Chalcogenide Semiconductor Thin Films
Authors: Vivin Vinod | Peter Zaspel
Source: Scientific Data (article, 2025-02-03)
Field: Materials Science — Materials Chemistry

QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules Progress in both Machine Learning (ML) and Quantum Chemistry (QC) methods have resulted in high accuracy ML models for QC properties. Datasets such as MD17 and WS22 have been used to benchmark these models at a given level of QC method, or fidelity, which refers to the accuracy of the chosen QC method. Multifidelity ML (MFML) methods, where models are trained on data from more than one fidelity, have shown to be effective over single fidelity methods. Much research is progressing in this direction for diverse applications ranging from energy band gaps to excitation energies. One hurdle for effective research here is the lack of a diverse multifidelity dataset for benchmarking. We provide the Quantum chemistry MultiFidelity (QeMFi) dataset consisting of five fidelities calculated with the TD-DFT formalism. The fidelities differ in their basis set choice: STO-3G, 3-21G, 6-31G, def2-SVP, and def2-TZVP. QeMFi offers to the community a variety of QC properties such as vertical excitation properties and molecular dipole moments. Further QeMFi offers QC computation times allowing for a time benefit benchmark of multifidelity models for ML-QC. Quantum chemical ; Molecule ; Chemistry ; Chemical physics ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Talal M. Althagafi | Mudassir Hussain Tahir | A. Badshah | Fatimah Mohammed A. Alzahrani | M.S. Al-Buriahi
Source: Journal of Physics and Chemistry of Solids (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

Designing of a library of polymers and prediction of thermal conductivity: a machine assisted framework Thermal conductivity ; Polymer ; Materials science ; Thermal ; Computer science ; Engineering physics ; Nanotechnology ; Engineering ; Composite material ; Thermodynamics ; Physics Thermal properties of materials ; Machine Learning in Materials Science ; Heat Transfer and Optimization
Authors: Jinyuan Shi | Qinghua Ren | Yi Gao
Source: The Journal of Physical Chemistry A (article, 2025-02-25)
Field: Materials Science — Materials Chemistry

Accelerating Global Optimization of Cerium Oxide Nanocluster Structures with High-Dimensional Neural Network Potential CeO<sub>2</sub>, characterized by its unique 4f electronic structure and high oxygen storage capacity, is widely recognized as an important catalyst and support material in energy and catalytic applications. Despite its importance, the complexity of CeO<sub>2</sub> nanoclusters poses challenges for structural characterization. Herein, we present a machine learning approach to accelerate the global optimization of cerium oxide nanocluster structures using a high-dimensional neural network potential (HDNNP). Our methodology integrates active learning to construct a versatile HDNNP that enables the exploration of the vast configurational space of small to medium cerium oxide clusters (Ce<sub><i>n</i></sub>O<sub>2<i>n</i>+<i>x</i></sub>, <i>n</i> = 2-18, <i>x</i> = -1, 0, +1). The HDNNP, refined through iterative active learning, achieves an accuracy comparable to first-principles calculations. Results indicate that the configuration of the lowest energy structures varies across different intervals. At <i>n</i> = 9 and <i>n</i> = 14, there is a transition from compact structures to multilayered ordered structures, and subsequently to pyramidal structures. When <i>n</i> > 14, almost all structures are derived from the pyramidal structure as the core grows continuously. In addition, the electronic structures of the lowest-energy clusters are analyzed. Our findings provide insights into the size-dependent stability and electronic behavior of cerium oxide nanoclusters. Cerium oxide ; Artificial neural network ; Cerium ; Computer science ; Nanotechnology ; Oxide ; Materials science ; Artificial intelligence ; Metallurgy Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; X-ray Diffraction in Crystallography
Authors: Zhuo Chen | Yuejin Yuan | Wenyang Ding | Shouhang Li | Meng An | Gang Zhang
Source: Chinese Physics B (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Hyperparameter optimization and force error correction of neuroevolution potential for predicting thermal conductivity of wurtzite GaN Abstract As a representative of wide-bandgap semiconductors, wurtzite gallium nitride (GaN) has been widely utilized in high-power devices due to its high breakdown voltage and low specific on-resistance. Accurate prediction of wurtzite GaN’s thermal conductivity is a prerequisite for designing effective thermal management systems for electronic applications. Machine learning-driven molecular dynamics simulation offers a promising approach to predicting the thermal conductivity of large-scale systems without requiring predefined parameters. However, these methods often underestimate the thermal conductivity of materials with inherently high thermal conductivity due to the large predicted force error compared with first-principles calculations, posing a critical challenge for their broader application. In this study, we successfully developed a neuroevolution potential for wurtzite GaN and accurately predicted its thermal conductivity, 259±6 W/(m⋅K) at room temperature, achieving excellent agreement with reported experimental measurements. The hyperparameters of the neuroevolution potential (NEP) were optimized based on a systematic analysis of reproduced energy and force, structural features, and computational efficiency. Furthermore, a force error correction method was implemented, effectively reducing the error caused by the additional force noise in the Langevin thermostat by extrapolating to the zero-force error limit. This study provides valuable insights and holds significant implications for advancing efficient thermal management technologies in wide-bandgap semiconductor devices. Hyperparameter ; Wurtzite crystal structure ; Computer science ; Thermal conductivity ; Neuroevolution ; Coincidence ; Materials science ; Artificial intelligence ; Artificial neural network ; Composite material ; Zinc Thermal properties of materials ; Heat Transfer and Optimization ; GaN-based semiconductor devices and materials
Authors: Fabian Grünewald | Leif Seute | Riccardo Alessandri | Melanie König | P. Kroon
Source: Journal of Chemical Information and Modeling (article, 2025-03-24)
Field: Chemistry — Spectroscopy

CGsmiles: A Versatile Line Notation for Molecular Representations across Multiple Resolutions Coarse-grained (CG) models simplify molecular representations by grouping multiple atoms into effective particles, enabling faster simulations and reducing the chemical compound space compared to atomistic methods. Additionally, models with chemical specificity, such as Martini, may extrapolate to cases where experimental data is scarce, making CG methods highly promising for high-throughput (HT) screenings and chemical space exploration. Yet no rigorous data formats exist for the crucial aspect of describing how the atoms are grouped (i.e., the mapping). As CG models advance toward true HT capabilities, the lack of mappings and indexing capabilities for the growing number of CG molecules poses a significant barrier. To address this, we introduce CGsmiles, a versatile line notation inspired by the popular Simplified Molecular Input Line Entry System (SMILES) and BigSMILES. CGsmiles encodes the molecular graph and particle (atom) properties independent of their resolution and incorporates a framework that allows seamless conversion between coarse- and fine-grained resolutions. By specifying fragments that describe how each particle is represented at the next finer resolution (e.g., CG particles to atoms), CGsmiles can represent multiple resolutions and their hierarchical relationships in a single string. In this paper, we present the CGSmiles syntax and analyze a benchmark set of 407 molecules from the Martini force field. We highlight key features missing in existing notations that are essential for accurately describing CG models. To demonstrate the utility of CGsmiles beyond simulations, we construct two simple machine-learning models for predicting partition coefficients, both trained on CGsmiles-indexed data and leveraging information from both CG and atomistic resolutions. Finally, we briefly discuss the applicability of CGsmiles to polymers, which particularly benefit from the multiresolution nature of the notation. Notation ; Line (geometry) ; Computer science ; Programming language ; Arithmetic ; Mathematics Molecular spectroscopy and chirality ; Various Chemistry Research Topics ; Analytical Chemistry and Chromatography
Authors: Soohaeng Yoo Willow | Seungwon Kim | D. ChangMo Yang | Miran Ha | Amir Hajibabaei | Jung Woon Yang | Kwang S. Kim | Chang Woo Myung
Source: Chemical Physics Reviews (article, 2025-04-16)
Field: Materials Science — Materials Chemistry

A sparse Bayesian Committee Machine potential for oxygen-containing organic compounds Accurate and scalable interatomic potentials are essential for understanding material properties at the atomic level; however, steep computational demands often limit their application. Although recent advances in machine learning (ML) potentials have been significant, extending kernel-based models to accommodate a broad range of chemical compositions remains a major challenge. Here, we present the active robust Bayesian Committee Machine (RBCM) potential, specifically designed to handle extensive datasets encompassing hydrocarbons (in gas, cluster, liquid, and solid phases) and eight families of oxygen-containing organic compounds. By employing a committee-based approach, the RBCM circumvents the poor scaling inherent to kernel regressors, facilitating straightforward and cost-effective model expansion. Systematic benchmarking demonstrates its robustness in accurately describing complex processes such as the Diels–Alder reaction, structural strain effects, and π–π interactions. These results highlight the RBCM's potential as a powerful tool for developing universal, ab initio-level ML potentials that offer both transferability and scalability across diverse chemical systems. Bayesian probability ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Tsubasa Koyama | Yumika Yokoyama | Naoto Tanibata | Hayami Takeda | Masanobu Nakayama
Source: Journal of the Ceramic Society of Japan (article, 2025-02-12)
Field: Materials Science — Materials Chemistry

Efficient optimization of atom/ion arrangements in crystalline solids using genetic algorithms and machine-learning regression In crystalline materials composed of multiple elements, such as alloys and solid solutions, degrees of freedom for atomic or ionic arrangements arise, making the determination of reasonable atom/ion configurations an important aspect of simulations. However, even in relatively small simulation cells, the number of possible arrangements is vast, rendering exhaustive evaluation infeasible. Although methodologies such as Monte Carlo and special quasi-random structures method have been proposed, genetic algorithm (GA) optimization is particularly useful for identifying stable arrangements, as it is applicable to bulk systems, surfaces, and interfaces. In this study, we improve the search method by combining GA with machine learning (ML), which we refer to as the GA and ML regression analysis (GAML). Specifically, this approach uses ML to screen and evaluate some of the structures generated by a GA, thereby reducing the computational demand of material simulations. This study provides an overview of the GAML, its computational methods, and optimization examples, demonstrating that the GAML achieves optimized structure more efficiently than the conventional GA. Integrating ML into GA significantly enhances the efficiency of optimizing atomic and ionic arrangements in crystalline solids. By achieving stable structures in fewer generations compared with traditional methods, the GAML offers a powerful tool for addressing complex systems with numerous possible configurations, with broad implications for accelerating materials discovery and design, particularly in fields requiring computationally efficient optimization of large and intricate systems. Ion ; Materials science ; Atom (system on chip) ; Genetic algorithm ; Algorithm ; Regression ; Machine learning ; Computer science ; Biological system ; Artificial intelligence ; Mathematics ; Physics ; Statistics ; Parallel computing ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Peichen Zhong | Dongjin Kim | Daniel S. King | Bingqing Cheng
Source: npj Computational Materials (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential can infer electrical response Abstract Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and scalable alternative to quantum mechanical methods, but do not by themselves incorporate electrical response. Here, we show that polarization and Born effective charge (BEC) tensors can be directly extracted from long-range MLIPs within the Latent Ewald Summation (LES) framework, solely by learning from energy and force data. Using this approach, we predict the infrared spectra of bulk water under zero or finite external electric fields, ionic conductivities of high-pressure superionic ice, and the phase transition and hysteresis in ferroelectric PbTiO 3 perovskite. This work thus extends the capability of MLIPs to predict electrical response –without training on charges or polarization or BECs– and enables accurate modeling of electric-field-driven processes in diverse systems at scale. Ferroelectricity ; Polarization (electrochemistry) ; Interatomic potential ; Hysteresis ; Phase transition ; Electric potential energy ; Electrostatics ; Charge (physics) ; Work (physics) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Electronic and Structural Properties of Oxides
Authors: Wenbin Xu | Rohan Yuri Sanspeur | Adeesh Kolluru | Bowen Deng | Peter Harrington | Steven Farrell | Karsten Reuter | John R. Kitchin
Source: Proceedings of the National Academy of Sciences (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Spin-informed universal graph neural networks for simulating magnetic ordering The screening and discovery of magnetic materials are hindered by the computational cost of first-principles density-functional theory (DFT) calculations required to find the ground state magnetic ordering. Although universal machine-learning interatomic potentials (uMLIPs), also known as atomistic foundation models, offer high-fidelity models of many atomistic systems with significant speedup, they currently lack the inputs required for predicting magnetic ordering. In this work, we present a data-efficient, spin-informed graph neural network framework that incorporates spin degrees of freedom as inputs and preserves physical symmetries, extending the functionality of uMLIPs to simulate magnetic orderings. This framework speeds up DFT calculations through better initial guesses for magnetic moments, determines the ground-state ordering of bulk materials and even generalizes to magnetic ordering in surfaces. Furthermore, we implement a closed-loop anomaly detection approach that effectively addresses the classic "chicken-and-egg" problem of creating a high-quality dataset while developing a uMLIP, unearthing anomalies in large benchmark datasets and boosting model accuracy. Condensed matter physics ; Spin (aerodynamics) ; Artificial neural network ; Computer science ; Statistical physics ; Physics ; Theoretical physics ; Artificial intelligence ; Thermodynamics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Changfeng Yan | Liyun Hu | Ke Wen | Qianqian Yu | Xiangyang Chen | Xinhong Qiu
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2025-05-06)
Field: Materials Science — Materials Chemistry

Predicting and analyzing the immobilization of MgAl and CaAl layered double hydroxides for Cr(VI) in polluted soil using machine learning Layered double hydroxides ; Materials science ; Chemical engineering ; Chemistry ; Inorganic chemistry ; Engineering Layered Double Hydroxides Synthesis and Applications
Authors: Yongsheng Gu | Yanfei Chen | Shengping Shen | Daining Fang
Source: Composites Part A Applied Science and Manufacturing (article, 2025-07-01)
Field: Materials Science — Ceramics and Composites

High-temperature experiments and simulation methods for oxidation behavior research of thermostructural materials Materials science ; Composite material Advanced ceramic materials synthesis ; High-Temperature Coating Behaviors ; Intermetallics and Advanced Alloy Properties
Authors: Pengfei Xu | Kangpeng Jin | Jie Huang | Zhenhua Yan | Liangwei Fu | Biao Xu
Source: Nanoscale (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Solution-synthesized nanostructured materials with high thermoelectric performance Facing the growing scarcity of traditional fossil fuels and the inefficiency of energy utilization, thermoelectric materials have garnered increasing attention due to their ability to convert between electrical and thermal energy. However, the strong coupling between thermoelectric parameters presents a significant challenge for simultaneously reducing thermal conductivity and maintaining electrical performance in bulk materials. The solution-based synthesis of nanostructured materials offers a promising approach for the decoupling regulation of electronic and phonon transport properties by regulating grain size and morphology, second phases, and surface ligands. The strategies for optimizing thermoelectric performance outlined above are founded upon several pivotal elements: the enhancement of grain boundary effects, precise regulation of grain stacking, utilization of heterogeneous interface effects, and generation of metastable phases and novel structural configurations facilitated by ligand management approaches. We have also comprehensively addressed the challenges associated with solution-based synthesis, particularly material oxidation and grain coarsening, along with their corresponding mitigation strategies. In addition, machine learning can effectively accelerate solution synthesis and the exploration of composite materials. This review summarizes and generalizes the research related to these strategies, providing recommendations for future research directions based on observed trends. Thermoelectric effect ; Scarcity ; Materials science ; Fossil fuel ; Thermoelectric materials ; Inefficiency ; Nanotechnology ; Engineering physics ; Process engineering ; Waste management ; Thermal conductivity ; Composite material ; Engineering ; Economics Advanced Thermoelectric Materials and Devices
Authors: Xiaoxiang Han | Jun Li | Lin Yuan | Xin Zhang | Weijie Zhu | Yang Liu | Haiyang Zhang | Baoyi Wang
Source: Annals of Nuclear Energy (article, 2025-04-20)
Field: Materials Science — Materials Chemistry

PINN and KAN temperature prediction of carbon Fiber/Epoxy composite materials irradiated by nuclear environment simulated intense heat fluxes Epoxy ; Materials science ; Composite number ; Composite material ; Carbon fiber composite ; Irradiation ; Carbon fibers ; Fiber ; Nuclear engineering ; Nuclear physics ; Physics Graphite, nuclear technology, radiation studies ; Fusion materials and technologies ; Nuclear Physics and Applications
Authors: Danish Khan | Anouar Benali | Scott Y. H. Kim | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: Scientific Data (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Quantum mechanical dataset of 836k neutral closed-shell molecules with up to 5 heavy atoms from C, N, O, F, Si, P, S, Cl, Br We introduce the Vector-QM24 (VQM24) dataset comprehensively covering all possible neutral closed-shell small organic and inorganic molecules with up to five heavy (p-block) atoms: C, N, O, F, Si, P, S, Cl, Br. All valid stoichiometries, Lewis-rule-consistent graphs, and stable conformers (identified via GFN2-xTB) were enumerated combinatorially, yielding 577k conformational isomers spanning 258k constitutional isomers and 5,599 unique stoichiometries. DFT (ωB97X-D3/cc-pVDZ) optimizations were performed for all, and diffusion quantum Monte Carlo (DMC@PBE0(ccECP/cc-pVQZ)) energies are provided for 10,793 lowest-energy conformers with up to 4 heavy atoms. VQM24 includes structures, vibrational modes, rotational constants, thermodynamic properties (Gibbs free energies, enthalpies, ZPVEs, entropies, heat capacities), and electronic properties such as atomization, electron interaction, exchange-correlation, dispersion energies, multipole moments (dipole to hexadecapole), alchemical potentials, Mulliken charges, and wavefunctions. Machine learning models of atomization energies on this dataset reveal significantly higher complexity than QM9, with none achieving chemical accuracy. VQM24 offers a rigorous, high-fidelity benchmark for evaluating quantum machine learning models. Conformational isomerism ; Quantum Monte Carlo ; Molecule ; Multipole expansion ; Quantum ; Quantum chemistry ; Quantum chemical ; Diffusion Monte Carlo Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies