jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shengkun Xi | Jiahui Li | Longke Bao | Rongpei Shi | Haijun Zhang | Xiaoyu Chong | Zhou Li | Cuiping Wang | Xingjun Liu
Source: Journal of Materials Research and Technology (article, 2025-03-14)
Field: Materials Science — Materials Chemistry

Phase diagram construction and prediction method based on machine learning algorithms Phase diagram, which is known as the “compass” and “map” of materials research, plays a guiding role in the material design and development. Conventional CALPHAD method could provide the detailed information on the phase equilibria via the assessment of thermodynamics model parameters. However, CALPHAD assessment for the multi-component systems can be particularly challenging due to the significant time costs involved and lack of experimental data, especially when attempting to predict the phase diagrams of multi-component systems. The fast-growing machine learning technique opens a new pathway to deal with tons of data and parameters. Meanwhile, the CALPHAD method has accumulated abundant high-quality phase diagram data who would be the perfect training data for the machine learning algorithms. In the present work, a phase diagram prediction method which integrates machine learning algorithms with CALPHAD descriptors is proposed. The present study establishes and train machine learning models to predict phase-type and solvus temperature of the materials. Using training datasets obtained from the CALPHAD method, we combine the total Gibbs energy and magnetic descriptor with training set to predict the isothermal sections of Cu–Co–Ni and Fe–Cu–Co ternary systems. The results indicate that the elevated temperatures not only enhance the solubility of Co, Ni, and Cu in intermetallic compounds but also facilitate the formation of eutectic precipitates (γFe+αCo). This methodology can efficiently predict the phase diagram of material system with higher number of components by training the phase diagram data of lower ones, thereby providing a new strategy to complement the CALPHAD with machine learning technique and extend the application of CALPHAD method to the advanced materials including high entropy alloys and functional materials. Phase diagram ; Materials science ; Phase (matter) ; Algorithm ; CALPHAD ; Diagram ; Machine learning ; Artificial intelligence ; Computer science ; Physics Machine Learning in Materials Science
Authors: Ji Gao | Wenbo Peng | Andrés Galindo | Ethan Slaton | Juan Daniel Martínez | Guanghui Lan | Zhaohui Tong
Source: Green Chemistry (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-guided optimization for ionic liquid-based polyethylene terephthalate waste recycling Machine learning-based ionic liquid design and reaction condition optimization towards unified economic and environmental performance indicators. Polyethylene terephthalate ; Ionic liquid ; Materials science ; Chemical engineering ; Polyethylene ; Process engineering ; Chemistry ; Waste management ; Organic chemistry ; Pulp and paper industry ; Composite material ; Engineering ; Catalysis Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Process Optimization and Integration
Authors: Tommi Koivisto | Marcin Mińkowski | Lasse Laurson
Source: APL Machine Learning (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning Machine learning models have proven to be powerful tools to discover links between microstructure and properties of materials, but the black box nature of the models limits the physical insights one might gain from them. Here, we study the relationship between the atomic structure and the elastic and plastic properties of polycrystalline tantalum nanopillars under uniaxial compression by means of interpretable machine learning. We first train a convolutional neural network using data from molecular dynamics simulations to learn the mapping from the sample-specific initial atomic structure to features of the stress–strain curve. The model is able to predict the values of the Young’s moduli of the pillars very well, resulting in slightly better predictions than those obtained from classical models (Voigt, Reuss, and Hill), as well as from a newly developed volume integral method. It also has some success in learning the yield stress values. Interpreting the network by employing the gradient-weighted class activation mapping method reveals a correlation between the resulting attention map and the local, grain orientation dependent Young’s moduli, showing that the predicted value of the global Young’s modulus is mainly determined by grains whose local Young’s moduli deviate significantly from the average. We also analyze the attention maps for the yield stress prediction and conclude that for the nanocrystalline samples at hand without pre-existing dislocations, grain boundaries tend to be responsible for reducing the yield stress. Nanopillar ; Crystallite ; Materials science ; Artificial intelligence ; Composite material ; Computer science ; Nanotechnology ; Metallurgy ; Nanostructure Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Diamond and Carbon-based Materials Research
Authors: Pablo M. Piaggi | Julian D. Gale | Paolo Raiteri
Source: Proceedings of the National Academy of Sciences (article, 2025-10-06)
Field: Materials Science — Biomaterials

Ab initio machine-learning simulation of calcium carbonate from aqueous solutions to the solid state A first-principles machine-learning model has been developed aimed at studying the formation of calcium carbonate from aqueous solution using molecular dynamics simulations. The model, dubbed strongly constrained and appropriately normed-machine learning (SCAN-ML), reproduces accurately the potential energy surface derived from ab initio density-functional theory within the SCAN approximation for the exchange and correlation functional. A broad range of properties have been calculated relevant to ions in solution, solid phases, and the calcite/water interface. Careful comparison with results from experiments and semiempirical force fields shows that SCAN-ML provides an excellent description of this system, surpassing state-of-the-art force fields for many properties, while providing a benchmark for many quantities that are currently beyond the reach of direct ab initio molecular dynamics. A key feature of SCAN-ML is its ability to capture chemical reactions, which reveals that calcium carbonate ion pair formation occurs predominantly via binding of calcium to bicarbonate, with the subsequent loss of a proton to water, rather than by direct association. Our model thus paves the way for the ab initio study of reactive crystallization pathways in biominerals, which are currently poorly understood. Ab initio ; Aqueous solution ; Molecular dynamics ; Ion ; Ab initio quantum chemistry methods ; Calcium carbonate ; Field (mathematics) ; Force field (fiction) Calcium Carbonate Crystallization and Inhibition ; Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Masato Ohnishi | Tianqi Deng | Pol Torres | Zhihao Xu | Terumasa Tadano | Haoming Zhang | Wei Nong | Masatoshi Hanai | Zhiting Tian | Ming Hu | Xiulin Ruan | Ryo Yoshida | Toyotaro Suzumura | Lucas Lindsay | Alan J. H. McGaughey | Tengfei Luo | Kedar Hippalgaonkar | Junichiro Shiomi
Source: npj Computational Materials (preprint, 2025-05-14)
Field: Materials Science — Materials Chemistry

Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations <title>Abstract</title> Understanding the anharmonic phonon properties of crystal compounds—such as phonon lifetimes and thermal conductivities—is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6,000 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enable the identification of materials exhibiting extreme thermal conductivities—both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials. Anharmonicity ; Scalability ; Brute force ; Computer science ; Phonon ; Database ; Physics ; Condensed matter physics ; Computer security Thermal properties of materials ; Machine Learning in Materials Science ; Semiconductor materials and devices
Authors: Jiancheng Song | Xinyu Tong | Jingjing Guo | Bo Qian | Jianbin Lin | Pavlo O. Dral | Hui‐Jun Zhang
Source: Organic Letters (article, 2025-03-27)
Field: Chemistry — Organic Chemistry

Alternating Donor–Acceptor Thienoacenes Featuring Up to 23 Linearly Fused Rings A group of ladder-type imide-functionalized thienoacenes including <b>[9]ITAa</b>, <b>[9]ITAb</b>, <b>[16]ITA</b>, and <b>[23]ITA</b>, featuring 9, 16, and 23 linearly fused rings, respectively, were readily synthesized through stitching thienannulation of polyalkyne precursors composed of alternating naphthalene diimide (NDI) and <i>m</i>-phenylenediyne units. A single crystal of <b>[16]ITA</b>, with a molecular length of 33.8 Å, was obtained, representing the longest reported single crystal of a linear thienoacene. The optical and electronic properties of these soluble alternating donor-acceptor (D-A) type thienoacenes with varying fused lengths were elucidated using UV-vis-NIR spectroscopy, cyclic voltammetry, and machine learning-enhanced quantum chemical and density functional theory calculations, revealing their low energy gap characteristics. This work shows that the stitching thienannulation strategy, when applied with rationally designed precursors, can precisely synthesize fully π-conjugated molecules with previously unachievable topologies. Chemistry ; Acceptor ; Stereochemistry ; Combinatorial chemistry ; Physics ; Quantum mechanics Synthesis and Properties of Aromatic Compounds ; Porphyrin and Phthalocyanine Chemistry ; Organic Chemistry Cycloaddition Reactions
Authors: Yinjie Shen | Jia Fu | Fuyi Han | Dongbo Li | Bing Yang | Yunqing Tang
Source: Nanomaterials (review, 2025-08-13)
Field: Materials Science — Materials Chemistry

Interface Thermal Resistance in Heterostructures of Micro–Nano Power Devices: Current Status and Future Challenges As micro-nano power devices have evolved towards high frequency, high voltage, and a high level of integration, the issue of thermal resistance at heterointerfaces has become increasingly prominent, posing a key bottleneck that limits device performance and reliability. This paper presents a systematic review of the current state of research and future challenges related to interface thermal resistance in heterostructures within micro and nano power devices. First, based on phonon transport theory, we conducted an in-depth analysis of the heat transfer mechanisms at typical heterointerfaces, such as metal-semiconductor and semiconductor-semiconductor, and novel low-dimensional materials. Secondly, a comprehensive review of current interface thermal resistance characterization techniques is provided, including the application and limitations of advanced methods such as time domain thermal reflection and Raman thermal measurement in micro- and nano-scale thermal characterization. Finally, in response to the application requirements of semiconductor power devices, future research directions such as atomic-level interface engineering, machine learning-assisted material design, and multi-physics field collaborative optimization are proposed to provide new insights for overcoming the thermal management bottlenecks of micro-nano power devices. Bottleneck ; Interface (matter) ; Power semiconductor device ; Materials science ; Characterization (materials science) ; Thermal resistance ; Engineering physics ; Semiconductor device ; Semiconductor ; Nanotechnology ; Computer science ; Nano- ; Heterojunction ; Reliability (semiconductor) ; Thermal conductivity ; Electronic engineering ; Optoelectronics ; Thermal ; Power (physics) ; Electrical engineering ; Voltage ; Engineering ; Physics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Graphene research and applications
Authors: Feng Wang | Delano P. Chong
Source: Molecules (article, 2025-07-07)
Field: Materials Science — Surfaces, Coatings and Films

The Role of Exchange Energy in Modeling Core-Electron Binding Energies of Strongly Polar Bonds Accurate determination of carbon core-electron binding energies (C1s CEBEs) is crucial for X-ray photoelectron spectroscopy (XPS) assignments and predictive computational modeling. This study evaluates density functional theory (DFT)-based methods for calculating C1s core-electron binding energies (CEBEs), comparing three functionals-PW86x-PW91c (DFTpw), mPW1PW, and PBE50-across 68 C1s cases in small hydrocarbons and halogenated molecules (alkyl halides), using the delta self-consistent field ΔSCF (or ΔDFT) method developed by one of the authors over the past decade. The PW86x-PW91c functional achieves a root mean square deviation (RMSD) of 0.1735 eV, with improved accuracy for polar C-X bonds (X=O, F) using mPW1PW and PBE50, reducing the average absolute deviation (AAD) to ~0.132 eV. The study emphasizes the role of Hartree-Fock (HF) exchange in refining CEBE predictions and highlights the synergy between theoretical and experimental approaches. These insights lay the groundwork for machine learning (ML)-driven spectral analysis, advancing materials characterization, and catalysis through more reliable automated XPS assignments. Binding energy ; Polar ; Core (optical fiber) ; Energy exchange ; Core electron ; Chemistry ; Electron exchange ; Chemical physics ; Chemical polarity ; Electron ; Atomic physics ; Physics ; Atmospheric sciences ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques
Authors: Y.-P. Ji | Jiuyang Liang | Zhenli Xu
Source: Physical Review Letters (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

Machine-Learning Interatomic Potentials for Long-Range Systems Machine-learning interatomic potentials have emerged as a revolutionary class of force-field models in molecular simulations, delivering quantum-mechanical accuracy at a fraction of the computational cost and enabling the simulation of large-scale systems over extended timescales. However, they often focus on modeling local environments, neglecting crucial long-range interactions. We propose a sum-of-Gaussians neural network (SOG-Net), a lightweight and versatile framework for integrating long-range interactions into a machine-learning force field. The SOG-Net employs a latent-variable learning network that seamlessly bridges short-range and long-range components, coupled with an efficient Fourier convolution layer that incorporates long-range effects. By learning sum-of-Gaussians multipliers across different convolution layers, the SOG-Net adaptively captures diverse long-range decay behaviors while maintaining close-to-linear computational complexity during training and simulation via nonuniform fast Fourier transforms. The method is demonstrated effective for a broad range of long-range systems. Convolution (computer science) ; Focus (optics) ; Fourier transform ; Interatomic potential ; Class (philosophy) ; Range (aeronautics) ; Computational complexity theory ; Fast Fourier transform Machine Learning in Materials Science ; Topic Modeling ; Neural Networks and Applications
Authors: Tao Zhou | Ping Gui | Chao Hu | Weihua Xiao | Dong Liu | Zhiping Wang
Source: Journal of environmental chemical engineering (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

AI driven electrochemical interface design: Development and economic evaluation of low-cost electrode materials based on the 'structure-activity consumption' model Interface (matter) ; Core (optical fiber) ; Scale (ratio) ; Characterization (materials science) ; Electrochemical energy storage ; Efficient energy use ; Design strategy ; Nonlinear system Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Ammonia Synthesis and Nitrogen Reduction
Authors: Yuzhi Chen | Hongyu Liu | Xu Fang | Yuanhua Li | Jing Chen | Lin Peng | Xiaolin Liu | Jia Lin
Source: Solar Energy Materials and Solar Cells (article, 2025-01-04)
Field: Materials Science — Materials Chemistry

A machine learning workflow for large-scale discovery of direct bandgap double perovskites Workflow ; Scale (ratio) ; Band gap ; Materials science ; Computer science ; Nanotechnology ; Optoelectronics ; Physics ; Database Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Shimelis Kebede Kassahun | Sintayehu Nibret Tiruneh | Shegaw Ahmed Mohammed | Hern Kim
Source: Journal of Water Process Engineering (article, 2025-04-01)
Field: Chemistry — Organic Chemistry

Core shell Fe3O4/SiO2/N-TiO2 for photodegradation of 4-nitrophenol under sunlight: A machine learning process optimization Photodegradation ; Shell (structure) ; Core (optical fiber) ; Process (computing) ; Nitrophenol ; Sunlight ; Photochemistry ; 4-Nitrophenol ; Computer science ; Materials science ; Chemistry ; Photocatalysis ; Nanotechnology ; Optics ; Telecommunications ; Nanoparticle ; Physics ; Catalysis ; Composite material ; Organic chemistry Nanomaterials for catalytic reactions ; TiO2 Photocatalysis and Solar Cells ; Advanced Photocatalysis Techniques
Authors: Sakib Matin | Alice E. A. Allen | Emily Shinkle | Aleksandra Pachalieva | Galen T. Craven | Benjamin Nebgen | Justin S. Smith | Richard A. Messerly | Ying Wai Li | Sergei Tretiak | Kipton Barros | Nicholas Lubbers
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Teacher-student training improves the accuracy and efficiency of machine learning interatomic potentials Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Training (meteorology) ; Computer science ; Training set ; Mathematics education ; Machine learning ; Artificial intelligence ; Psychology ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Rongrong Zheng | Wenjia Huo | Boyang Liang | Xiang Wu
Source: ACS Applied Polymer Materials (article, 2025-07-30)
Field: Materials Science — Polymers and Plastics

Accelerating the Discovery of Low-Dielectric Polyimides Based on Interpretable Machine Learning Rapidly advancing computer technology has demonstrated great potential in the development of promising materials. Polyimide (PI), an important dielectric material, is widely applied in modern industries. The demand for devices with low dielectric and low energy consumption in the microelectronics industry has led to a growing need for low dielectric constant (Dk) PI. Developing Dk prediction models using machine learning (ML) algorithms can help construct quantitative structure–property relationships and deeply understand the molecular nature that affects the dielectric properties of PI. In this study, we assembled a data set comprising 970 PIs and extracted key structural features using RDKit. Among the 12 popular ML algorithms, the Extra Trees-based model yielded the most accurate results, achieving a coefficient of determination of 0.897 for the test set, a mean absolute error of 0.194, and a root-mean-square error of 0.267. SHapley Additive exPlanation analysis was then employed to explain the optimal model for Dk prediction from a physicochemical point of view and structural aspects. The study identified the BCUT2D_LOGPHI descriptor as being particularly influential on Dk, showing a negative correlation. More importantly, eight potential PI candidates with low Dk values were designed according to the chemical insights of the key descriptors, which were verified through all-atom molecular dynamics (MD) simulation. By comparison of the calculated and predicted Dk values, the lowest prediction deviation was found to be approximately 1.06%. The proposed methodology achieved a prediction accuracy comparable to that of traditional MD simulation, but the computational time and resource consumption were dramatically reduced. And Schuffenhauer’s synthetic accessibility scores were used to evaluate the ease of synthesis of each PI before the experiment. This research demonstrates the feasibility of using ML methods to accelerate the property prediction and molecular design and provides a powerful tool for discovering high-performance dielectric materials in the future. Dielectric ; Artificial intelligence ; Machine learning ; Materials science ; Computer science ; Optoelectronics Synthesis and properties of polymers
Authors: Thorben Prein | Elton Pan | Janik Jehkul | Steffen Weinmann | Elsa Olivetti | Jennifer L. M. Rupp
Source: ACS Applied Materials & Interfaces (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials Inorganic synthesis planning currently relies primarily on heuristic approaches or machine learning models trained on limited data sets, which constrains its generality. We demonstrate that language models (LMs) without task-specific fine-tuning can recall synthesis conditions reported in the scientific literature. Off-the-shelf models, such as GPT-4.1, Gemini 2.0 Flash, and Llama 4 Maverick achieve a Top-1 precursor prediction accuracy of up to 53.8% and a Top-5 performance of 66.8% on a held-out set of 1000 reactions. They also predict calcination and sintering temperatures with mean absolute errors of <126 °C, matching or surpassing specialized regression models. Ensembling these LMs further enhances predictive accuracy and reduces inference cost per prediction by up to 70%. Given the broad, cross-domain knowledge of LMs, we evaluate whether they enable knowledge transfer by training a transformer, SyntMTE, on 28,548 LM-generated reaction recipes. Compared to a model trained on literature-reported data, we find that a model trained solely on LM-generated data exhibits competitive performance (only 6% worse). Conversely, a model trained on both the LM-generated and literature-reported data improves performance by up to 4%. In a case study on Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> solid-state electrolytes, we demonstrate that SyntMTE reproduces the experimentally observed dopant-dependent sintering trends. Our hybrid workflow enables scalable and data-efficient inorganic synthesis planning. Workflow ; Scalability ; Inference ; Heuristic ; Predictive modelling ; Training set ; Set (abstract data type) ; Matching (statistics) ; Performance prediction Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies