BetaCitation Atlas

RESEARCH PAPER · Nature Computational Science · 2023

Accelerating the prediction of stable materials with machine learning

Sean D. Griesemer, Yi Xia, Chris M. Wolverton

Read original paper ↗

See where this research leads.

104 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Accelerating the prediction of stable materials with machine learningOriginalAlgorithmic Novelty vs Chemical Novelty: Rethinking Innovation Metrics0Algorithmic Settling Time: A Conceptual Framework for When Materials AI Outputs Stabilize0Computational and Data-Driven Materials Engineering: Multimodal Materials Datasets, Integration Frameworks, and Discovery Potential0Enhancing crystal structure prediction by combining computational and experimental data via graph networks0Development and application of Few-shot learning methods in materials science under data scarcity47Efficient first principles based modeling via machine learning: from simple representations to high entropy materials23Inverse design of experimentally synthesizable crystal structures by leveraging computational and experimental data7Probabilistic prediction of material stability: integrating convex hulls into active learning10Machine learning a universal harmonic interatomic potential for predicting phonons in crystalline solids13Predicting thermal transport properties in phononic crystals via machine learning2Engineering and Design of Halide Perovskite Photoelectrochemical Cells for Solar‐Driven Water Splitting54Boosting–Crystal Graph Convolutional Neural Network for Predicting Highly Imbalanced Data: A Case Study for Metal–Insulator Transition Materials5Computational design of energy‐related materials: From first‐principles calculations to machine learning9Revealing the Origin of Anisotropic Rashba Spin‐Orbital Splitting and Four‐Phonon Scattering in Strontium‐Tin‐Selenium Thermoelectrics41Physical studies of spintronics-based Heusler alloys63Efficient Exploratory Synthesis of Quaternary Cesium Chlorides Guided by In Silico Predictions5PerCNet: Periodic complete representation for crystal graphs6Machine Learning Assisted Prediction of Donor Numbers: Guiding Rational Fabrication of Polymer Electrolytes for Lithium‐Ion Batteries2Machine Learning Assisted Prediction of Donor Numbers: Guiding Rational Fabrication of Polymer Electrolytes for Lithium‐Ion Batteries34Synthesis and characterization of machine learning designed TADF molecules7Eco-friendly synthesis coupled with predictive analytics: Developing hierarchical lignin-derived ordered mesoporous carbon for advanced supercapacitors20Machine learning prediction of materials properties from chemical composition: Status and prospects29Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction35Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials188Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials87Bandgap prediction of hybrid metal halide perovskites by transfer machine learning: From three to two dimensionality3Algorithmic Discovery vs. Scientific Discovery: A Conceptual Boundary for AI-Driven Materials Research0Applications of machine learning in high-entropy alloys: a review of recent advances in design, discovery, and characterization41Artificial intelligence-driven revolution in nanozyme design: from serendipity to rational engineering59Historical experimental data and theoretical volcano map-accelerated cross-scale design of a highly active and durable ternary alloy electrocatalyst for formic acid oxidation2Wide-ranging predictions of new stable compounds powered by recommendation engines12Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials10PINK: physical-informed machine learning for lattice thermal conductivity8Advances in Starch-Based Nanocomposites for Functional Food Systems: Harnessing AI and Nuclear Magnetic Resonance Technologies for Tailored Stability and Bioactivity23Accelerating high-throughput phonon calculations via machine learning universal potentials19Deep potential-driven structure exploration of ice polymorphs7Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions2Theoretical Calculations in Separation Science for Analytical Chemistry: Applications and Insights3Accelerated Design of Fenton‐Like Copper Single‐Atom Catalysts by Adaptive Learning with Genetic Programming0Accelerated Design of Fenton‐Like Copper Single‐Atom Catalysts by Adaptive Learning with Genetic Programming12Accurate reconstruction and prediction of T55511 titanium alloy microstructure using DDPM model and quantitative evaluation3Design Approaches of High‐Entropy Alloys Using Artificial Intelligence: A Review27Predicting and Understanding Work Functions of Double Transition Metal MXenes via Interpretable Machine Learning Methods4An innovative semi-surrogate model approach for numerical simulation of CO2 bubble reaction in Alkanolamine solutions: Balancing computational efficiency and accuracy1Artificial Intelligence‐Driven Approaches in Semiconductor Research44Transforming Computational Nanotechnology: Accelerating Material Discovery, Design, and Property Prediction through Soft Computing Techniques24A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials12Machine learning-assisted high-throughput prediction and experimental validation of high-responsivity extreme ultraviolet detectors10Compositional Design Guides Property Control in A 2 M 2+ x Ti 1–( x /4) Q 4 Semiconductors6Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models16Transition metal single-atom doped MoS2 for gas adsorption: A combined density functional theory and machine learning study10Interpretable machine learning framework: Predicting catalytic activities of complex photocatalysts towards CO2 conversion4High entropic engineering for sustainable energy and environmental applications2Review: machine learning in high-entropy alloys-transformative potential and innovative application7Breaking the Ion Ordering in the Perovskite Anode for Enhanced High-Temperature Oxygen Evolution Reaction Activity15Streamlining Coding Assignments and Grading on the Cloud: A Preconfigured JupyterHub Image for Chemistry Education0First-Principles Insights into Pressure-Induced Structural Transformations in Molecular Solids1Quantifying morphology for dynamic grain evolution modeling and property correlation analysis in Ni-rich cathode materials0Artificial intelligence for post-moore field-effect transistors: a review and perspective1Exploring novel Ni-Mn-X based magnetocaloric materials via machine learning with physical descriptors0Data-Driven Optimization of Discontinuous and Continuous Fiber Composite Processes Using Machine Learning: A Review36Interpretable machine learning for solvent prediction and mechanistic insights in multi-component crystal screening5Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency44Bandgap prediction and design of halide double perovskites via ensemble machine learning6Rescaled Schwarz-Christoffel Transformations for Isotropic, Polygon, and Multiphysics Metamaterials7Convergence of Computational Materials Science and AI for Next-Generation Energy Storage Materials6An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials8Machine learning-guided design of confinement space carbon materials for enhanced oxygen reduction catalysis5Machine Learning Approaches for Factor-Based Stock Price Prediction0AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology8AI for battery-accelerated discovery of high-voltage electrolytes for advanced lithium batteries0High-performance interpretable model for exfoliation energy prediction of 2D nanomaterials0Identifying Water-Containing Genes of Electric Vehicle Cell Using Machine Learning Algorithm0Identifying Water-Containing Genes of Electric Vehicle Cell Using Machine Learning Algorithm0Informatics for thermodynamics: Stability to synthesizability0The synergy of geometric tolerance factor and machine learning in discovering stable materials0Artificial intelligence films for dynamic and sustainable preservation of high-value fruits5Combining Compositional Screening via Hydride Route, Machine Learning, and In Situ Diffraction to Discover Antimonides in the K–Cd–Sb System3Electrocatalysis Informatics Driven Design of Formic Acid‐Producing Alloy Catalysts for CO 2 Electroreduction1Leveraging transfer learning for accurate estimation of ionic migration barriers in solids3Simulation-guided AI-driven digital twin for plasma etching in semiconductor fabrication0Can Intermediate Temperatures be a “Goldilocks Zone” for Green Hydrogen Production?4Machine learning acceleration of granular and solid-fluid flow simulations: A review1Systematically Verified Experimental Thermoelectric Dataset For Data-driven Approaches1Tailoring Materials Design for Aqueous Energy Storage and Conversion through Electrochemical Reconstruction20Predicting sliding ferroelectricity in heterobilayers via physical descriptors without explicit bilayer modeling0Chemistry-wise transfer active learning accelerated investigation of high-entropy alloys towards CO2 reduction0Multiscale computational design of hydrogen-compatible integrated structural-functional alloys5Multimodal fusion and transfer learning enable cross-material life prediction of defect dominated fatigue in additively manufactured metals2Application of Machine Learning in Predicting the Properties of Two-Dimensional Semiconductor Materials3Recent advances in electronic structure learning0Improving crystal material property prediction with multi-view geometric graph transformer1Non-negative matrix factorization analysis of spatially-resolved photoemission spectra for epitaxially grown graphene on SiC0Composition-dependent electronic, optical, and thermoelectric properties of CsIn1-xGaxBr3 from DFT and machine learning1Machine learning-enabled performance prediction and design for flexible strain sensors0A Machine-Learning-Accelerated Approach for Room-Temperature Phase Diagram Predictions0Engineered interfaces in electronic materials for energy-efficient computing0Generative Models in Inorganic Crystals Discovery and Inverse Design0A comprehensive review of perovskite materials for electronic device applications0High-entropy alloy catalysts: from foundational thermodynamics to machine learning-enabled discovery and closed-loop optimization for electrocatalytic applications0Global minimum search for transition metal nanoclusters with an embedded atom method potential in a DRL framework0Unraveling the discrete nature of structural, energetic, and kinetic coupling of nano-voids in body-centered cubic metals through combined atomistic simulation and evolutionary algorithms0Tailoring charge dynamics: The multifunctional role of MOFs and their derivatives in next-generation photovoltaics0Data-augmented crystal graph convolutional neural network for uncertainty-aware thermodynamic stability prediction of two-dimensional MXene/MoS2 materials0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

104 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Algorithmic Novelty vs Chemical Novelty: Rethinking Innovation Metrics

    Journal of Computational and Data-Driven Materials Engineering · 2023-01-01

    Lucas Andrade, Mariana Lopes

    Read original paper ↗ ·

  2. Algorithmic Settling Time: A Conceptual Framework for When Materials AI Outputs Stabilize

    Journal of Artificial Intelligence for Materials Science · 2023-01-01

    Maria Gonzalez, Javier Ruiz, Lucia Torres, Elena Ruiz

    Read original paper ↗ ·

  3. Computational and Data-Driven Materials Engineering: Multimodal Materials Datasets, Integration Frameworks, and Discovery Potential

    Journal of Computational and Data-Driven Materials Engineering · 2023-01-01

    Maria Gonzalez, Javier Ruiz, Lucia Torres

    Read original paper ↗ ·

  4. Enhancing crystal structure prediction by combining computational and experimental data via graph networks

    arXiv (Cornell University) · 2023-11-20

    Chenglong Qin, Jinde Liu, Shiyin Ma, Jiguang Du, Gang Jiang, Liang L. Zhao

    Read original paper ↗ ·

  5. Development and application of Few-shot learning methods in materials science under data scarcity

    Journal of Materials Chemistry A · 2024-01-01

    Yongxing Chen, Long Peng, Bin Liu, Yi Wang, Junlong Wang, Tian Ma, Huilin Wei, Yue Kang, Haining Ji

    Read original paper ↗ ·

  6. Efficient first principles based modeling via machine learning: from simple representations to high entropy materials

    Journal of Materials Chemistry A · 2024-01-01

    Kangming Li, Kamal Kumar Choudhary, Brian DeCost, Michael Greenwood, Jason R. Hattrick-Simpers

    Read original paper ↗ ·

  7. Inverse design of experimentally synthesizable crystal structures by leveraging computational and experimental data

    Journal of Materials Chemistry A · 2024-01-01

    Chenglong Qin, Jinde Liu, Shiyin Ma, Jiguang Du, Gang Jiang, Liang L. Zhao

    Read original paper ↗ ·

  8. Probabilistic prediction of material stability: integrating convex hulls into active learning

    Materials Horizons · 2024-01-01

    Andrew C. Novick, Diana Cai, Quan Nguyen, Roman Garnett, Ryan Prescott Adams, Eric S. Toberer

    Read original paper ↗ ·

  9. Machine learning a universal harmonic interatomic potential for predicting phonons in crystalline solids

    Applied Physics Letters · 2024-03-04

    Huiju Lee, Yi Xia

    Read original paper ↗ ·

  10. Predicting thermal transport properties in phononic crystals via machine learning

    Applied Physics Letters · 2024-04-15

    Liyuan Dong, Wei Li, Xian‐He Bu

    Read original paper ↗ ·

  11. Engineering and Design of Halide Perovskite Photoelectrochemical Cells for Solar‐Driven Water Splitting

    Advanced Functional Materials · 2024-06-25

    Saikiran Sunil Khamgaonkar, Anny Leudjo Taka, Vivek Maheshwari

    Read original paper ↗ ·

  12. Boosting–Crystal Graph Convolutional Neural Network for Predicting Highly Imbalanced Data: A Case Study for Metal–Insulator Transition Materials

    ACS Applied Materials & Interfaces · 2024-08-09

    Eun Ho Kim, Jun Hyeong Gu, Jun Ho Lee, Seong Hun Kim, Jaeseon Kim, Hyo Gyeong Shin, Shin Hyun Kim, Donghwa Lee

    Read original paper ↗ ·

  13. Computational design of energy‐related materials: From first‐principles calculations to machine learning

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2024-09-01

    Haibo Xue, Guanjian Cheng, Wan‐Jian Yin

    Read original paper ↗ ·

  14. Revealing the Origin of Anisotropic Rashba Spin‐Orbital Splitting and Four‐Phonon Scattering in Strontium‐Tin‐Selenium Thermoelectrics

    Advanced Functional Materials · 2024-09-27

    Shulin Bai, Dongrui Liu, Haonan Shi, Yongxin Qin, Bingchao Qin, Tao Hong, Tian Hong Gao, Yuting Qiu, Yu Liu, Zhen Li, Peng Kang, Lei Zheng, Li‐Dong Zhao

    Read original paper ↗ ·

  15. Physical studies of spintronics-based Heusler alloys

    Critical reviews in solid state and materials sciences/CRC critical reviews in solid state and materials sciences · 2024-10-09

    Yarub Al-Douri, M. Ameri

    Read original paper ↗ ·

  16. Efficient Exploratory Synthesis of Quaternary Cesium Chlorides Guided by In Silico Predictions

    Journal of the American Chemical Society · 2024-10-16

    AKIRA B. MIURA, Muratahan Aykol, Shumma Kozaki, Chikako Moriyoshi, Shintaro Kobayashi, Shogo Kawaguchi, Chul‐Ho Lee, Yongming Wang, Amil Merchant, Simon Batzner, Hiroshi Kageyama, Kiyoharu Tadanaga, Pushmeet Kohli, Ekin D. Cubuk

    Read original paper ↗ ·

  17. PerCNet: Periodic complete representation for crystal graphs

    Neural Networks · 2024-10-31

    Jiao Huang, Qianli Xing, Jinglong Ji, Bo Xiong Yang

    Read original paper ↗ ·

  18. Machine Learning Assisted Prediction of Donor Numbers: Guiding Rational Fabrication of Polymer Electrolytes for Lithium‐Ion Batteries

    Angewandte Chemie · 2024-11-28

    Yuqing Gao, Shengguang Qi, Mianrui Li, Tongmei Ma, Huiyu Song, Zhiming Cui, Zhenxing Liang, Li Du

    Read original paper ↗ ·

  19. Machine Learning Assisted Prediction of Donor Numbers: Guiding Rational Fabrication of Polymer Electrolytes for Lithium‐Ion Batteries

    Angewandte Chemie International Edition · 2024-11-28

    Yuqing Gao, Shengguang Qi, Mianrui Li, Tongmei Ma, Huiyu Song, Zhiming Cui, Zhenxing Liang, Li Du

    Read original paper ↗ ·

  20. Synthesis and characterization of machine learning designed TADF molecules

    Heliyon · 2024-11-28

    Weimei Shi, Yan Li, Zi‐Ying Zhang, Zheng Tan, Shiqing Yang

    Read original paper ↗ ·

  21. Eco-friendly synthesis coupled with predictive analytics: Developing hierarchical lignin-derived ordered mesoporous carbon for advanced supercapacitors

    Green Energy & Environment · 2024-11-29

    Xiaolan Zhao, Xiaoqi Wang, Pei Gao, Peng Zhao, Jingjing Wang, Yingna Li, Zhibin Han, Boxiong Shen

    Read original paper ↗ ·

  22. Machine learning prediction of materials properties from chemical composition: Status and prospects

    Chemical Physics Reviews · 2024-12-01

    Mohammed Alghadeer, Nufida D. Aisyah, Mahmoud Hezam, Saad M. Alqahtani, Ahmer A.B. Baloch, Fahhad H. Alharbi

    Read original paper ↗ ·

  23. Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction

    Energy and AI · 2024-12-06

    James Daniell, Kazuma Kobayashi, Ayodeji Babatunde Alajo, Syed Bahauddin Alam

    Read original paper ↗ ·

  24. Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

    Advanced Energy Materials · 2024-12-10

    Bohayra Mortazavi

    Read original paper ↗ ·

  25. Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials

    Journal of the American Chemical Society · 2024-12-17

    Jaesun Kim, Jisu Kim, Jaehoon Kim, Jiho Lee, Yutack Park, Youngho Kang, Seungwu Han

    Read original paper ↗ ·

  26. Bandgap prediction of hybrid metal halide perovskites by transfer machine learning: From three to two dimensionality

    Inorganic Chemistry Communications · 2024-12-31

    Liyuan Dong, Xiaolong Xu, Wei Li

    Read original paper ↗ ·

  27. Algorithmic Discovery vs. Scientific Discovery: A Conceptual Boundary for AI-Driven Materials Research

    Journal of Artificial Intelligence for Materials Science · 2025-01-01

    Lucas Meyer, Stefan Braun, Anna Schmid

    Read original paper ↗ ·

  28. Applications of machine learning in high-entropy alloys: a review of recent advances in design, discovery, and characterization

    Nanoscale · 2025-01-01

    Mohammad Hossein Golbabaei, Mohammad Zohrevand, Ning Zhang

    Read original paper ↗ ·

  29. Artificial intelligence-driven revolution in nanozyme design: from serendipity to rational engineering

    Materials Horizons · 2025-01-01

    Yixin Yu, Mingzhen Zhang, Kelong Fan

    Read original paper ↗ ·

  30. Historical experimental data and theoretical volcano map-accelerated cross-scale design of a highly active and durable ternary alloy electrocatalyst for formic acid oxidation

    EES Catalysis · 2025-01-01

    Pengcheng Liu, Dezhi Su, Xiao Chen, Yanyi Liu, Kai‐Li Wang, Da Chen, Xijun Liu, Jia He

    Read original paper ↗ ·

  31. Wide-ranging predictions of new stable compounds powered by recommendation engines

    Science Advances · 2025-01-03

    Sean D. Griesemer, Bianca Baldassarri, Ruijie Zhu, Jiahong Shen, Koushik Pal, Cheol Woo Park, Chris M. Wolverton

    Read original paper ↗ ·

  32. Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

    Advanced Engineering Materials · 2025-01-18

    Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi

    Read original paper ↗ ·

  33. PINK: physical-informed machine learning for lattice thermal conductivity

    Journal of Materials Informatics · 2025-02-20

    Yujie Liu, Xiaoying Wang, Yuzhou Hao, Xuejie Li, Jun Sun, Turab Lookman, Xiangdong Ding, Zhibin Gao

    Read original paper ↗ ·

  34. Advances in Starch-Based Nanocomposites for Functional Food Systems: Harnessing AI and Nuclear Magnetic Resonance Technologies for Tailored Stability and Bioactivity

    Foods · 2025-02-24

    Yue Sun, Ziyu Wang, Jian Ye, Yinta Li, Lili Wang, Ruge Cao

    Read original paper ↗ ·

  35. Accelerating high-throughput phonon calculations via machine learning universal potentials

    Materials Today Physics · 2025-03-07

    Huiju Lee, Vinay I. Hegde, Chris M. Wolverton, Yi Xia

    Read original paper ↗ ·

  36. Deep potential-driven structure exploration of ice polymorphs

    The Innovation · 2025-03-13

    Yajie Lei, Xiangyang Liu, Yaochen Yu, Haiyang Niu

    Read original paper ↗ ·

  37. Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions

    International Journal of Quantum Chemistry · 2025-03-19

    Guo‐Jin Cao

    Read original paper ↗ ·

  38. Theoretical Calculations in Separation Science for Analytical Chemistry: Applications and Insights

    Chemistry - An Asian Journal · 2025-04-09

    Dongdong Wang, Yüting Xiong, Qianying Sheng, Yi Huang, Guangyan Qing

    Read original paper ↗ ·

  39. Accelerated Design of Fenton‐Like Copper Single‐Atom Catalysts by Adaptive Learning with Genetic Programming

    Angewandte Chemie · 2025-04-16

    Haoyang Fu, Ke Li, Qingze Chen, Bijun Tang, Zhongyi Deng, Ziyang Toh, Runliang Zhu, Shuzhou Li

    Read original paper ↗ ·

  40. Accelerated Design of Fenton‐Like Copper Single‐Atom Catalysts by Adaptive Learning with Genetic Programming

    Angewandte Chemie International Edition · 2025-04-16

    Haoyang Fu, Ke Li, Qingze Chen, Bijun Tang, Zhongyi Deng, Ziyang Toh, Runliang Zhu, Shuzhou Li

    Read original paper ↗ ·

  41. Accurate reconstruction and prediction of T55511 titanium alloy microstructure using DDPM model and quantitative evaluation

    Progress in Natural Science Materials International · 2025-05-07

    Yongcheng Zhu, Zhaoxin Du, Tianhao Gong, Zhiyong Yue, Shuzhi Zhang, Jun Hua Cheng, Xudong Kang, Qiang Zeng, Jingshun Liu

    Read original paper ↗ ·

  42. Design Approaches of High‐Entropy Alloys Using Artificial Intelligence: A Review

    Advanced Engineering Materials · 2025-05-07

    Nour Mahmoud Eldabah, Ayush Pratap, Atul Pandey, Neha Sardana, Sarabjeet Singh Sidhu, Mohamed Abdel Hady Gepreel

    Read original paper ↗ ·

  43. Predicting and Understanding Work Functions of Double Transition Metal MXenes via Interpretable Machine Learning Methods

    Langmuir · 2025-05-13

    Yihao Zheng, Xiangcui Qiu, Haibo Li, Konggang Qu, Hui Yan, Rui Li

    Read original paper ↗ ·

  44. An innovative semi-surrogate model approach for numerical simulation of CO2 bubble reaction in Alkanolamine solutions: Balancing computational efficiency and accuracy

    Powder Technology · 2025-06-16

    Sihong Cheng, Yali Tong, Guoliang Li, Jiajia Gao, Tao Yue

    Read original paper ↗ ·

  45. Artificial Intelligence‐Driven Approaches in Semiconductor Research

    Advanced Materials · 2025-06-19

    Yanping Zheng, Hao Xu, Zhexin Li, Linlin Li, Yongchao Yu, Pengfei Jiang, Yanmeng Shi, Jing Zhang, Yuqing Huang, Qing Luo, Zheng Lou, Lili Wang

    Read original paper ↗ ·

  46. Transforming Computational Nanotechnology: Accelerating Material Discovery, Design, and Property Prediction through Soft Computing Techniques

    ACS Applied Electronic Materials · 2025-06-26

    Hirendra Das, Mridusmita Sharma

    Read original paper ↗ ·

  47. A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

    International Journal of Plasticity · 2025-07-07

    Benhour Amirian, Abhijit P. Brahme, Ricardo A. Lebensohn, Kaan Inal

    Read original paper ↗ ·

  48. Machine learning-assisted high-throughput prediction and experimental validation of high-responsivity extreme ultraviolet detectors

    Nature Communications · 2025-07-07

    R. A. W. Ayyubi, Mei Xian Low, Salar Salimi, Majid Khorsandi, M. Mosarof Hossain, Hurriyat Arooj, Shoaib Masood, Muhammad Husnain Zeb, Nasir Mahmood, Qiaoliang Bao, Sumeet Walia, Babar Shabbir

    Read original paper ↗ ·

  49. Compositional Design Guides Property Control in A 2 M 2+ x Ti 1–( x /4) Q 4 Semiconductors

    Journal of the American Chemical Society · 2025-07-11

    Michael A. Viti, Zhi Li, Craig C. Laing, Chris M. Wolverton, Mercouri G. Kanatzidis

    Read original paper ↗ ·

  50. Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models

    Advanced Science · 2025-07-13

    Qiyuan Zhao, Yunhong Han, Duo Zhang, Jiaxu Wang, Peichen Zhong, Taoyong Cui, Bangchen Yin, Yirui Cao, Haojun Jia, Chenru Duan

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.