AI 驱动科学发现 · 全部实体（第 26 / 100 页）

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• Multi-Stakeholder AI Ethics in Radiology – Implications for integrated technology and workplace design
  Artificial intelligence (AI) can increase productivity and work quality, but this requires a seamless interplay of individual and organizational skills.. This study investigates the effects of AI on team problem-solving behavior and derives implications for th…
  crossref 2026-02-16T09:28:57Z 置信度 0.70
• AI/data science driving advancements in professional service operations
  crossref Wen Zhang 2024-10-09T17:40:31Z 置信度 0.70
• AI Adoption Barriers and Organizational Culture in Newsrooms A Comparative Study Across Media Types
  본 연구는 언론사의 AI 도입 장벽을 개인 수준과 조직 수준으로 구분하여 각각이 AI 사용의도에 미치는 상대적 영향력을 비교하고, 언론사 유형에 따른 차이와 혁신 지향적 조직문화의 완충 효과를 실증적으로 검증하였다. 이를 위해 국내 언론인 284명 규모의 서베이 데이터를 수집하고, 일원분산분석, 이원분산분석, 집단별 회귀분석을 수행하였다. 연구결과는 다음과 같다. 첫째, 언론사 유형에 따라 개인 수준 장벽 인식에는 유의한 차이가 나타났으나, 조직 수준 장벽 인식…
  crossref Hyunwoo Lee, YoungHeum Park 2026-03-27T01:51:22Z 置信度 0.70
• Research on Copyright Ownership of AI-Assisted Generative Outputs
  Traditional literary and artistic creation has long been understood as an activity grounded in human intellectual and creative labor. Under the traditional copyright framework, both the recognition of a work and the determination of derivative rights have gene…
  crossref 2026-07-08T03:22:56Z 置信度 0.70
• Can AI simplify the alphabet of life?
  Generative AI design yields functional proteins with only 19 amino acids
  crossref Charles Sanfiorenzo, Kaihang Wang 2026-04-30T18:01:06Z 置信度 0.70
• Global majority countries must embed critical minerals into AI governance
  crossref Chinasa T. Okolo 2026-03-05T19:01:12Z 置信度 0.70
• Child-Friendly Computer Science
  This book offers new insights and critical perspectives on the integration of computational thinking, engineering design process, and AI literacy into early childhood education. The authors draw on research from around the world in early childhood education an…
  crossref Weipeng Yang, Jiahong Su 2026-02-04T11:41:54Z 置信度 0.70
• Post-Quantum Cryptography Migration for Agentic AI Systems
  Agentic AI systems depend on classical public-key cryptography for agent identity, tool invocation, inter-agent communication, model integrity, and persistent state, exposing them to a cryptographically relevant quantum computer (CRQC) along two axes: confiden…
  crossref Robert Campbell 2026-06-18T01:27:54Z 置信度 0.70
• AI-Powered Analytics in Technical Communication: Advancing Information Management Toward Content Data Science
  We introduce a similarity-based ensemble framework for assessing the AI-readiness of topic-based technical content in Retrieval-Augmented Generation (RAG) contexts. Rather than evaluating RAG performance through query-and-answer measurements, the proposed appr…
  crossref Wolfgang Ziegler 2026-07-22T14:13:06Z 置信度 0.70
• AI-driven reliability-centered maintenance in aviation systems
  crossref Idriss Dagal 2026-05-16T22:20:51Z 置信度 0.70
• Industrial Design Students’ Acceptance of AI Tools in Studio Learning: Implications for Human–AI Collaboration
  crossref Wenzhi Chen 2026-07-05T18:15:01Z 置信度 0.70
• Meet Zoom AI Companion
  crossref Catherine Pierce 2026-07-02T00:06:27Z 置信度 0.70
• A Perspective from the Science Newsroom
  crossref Ryoma Komiyama 2026-01-29T09:47:32Z 置信度 0.70
• The Radiology AI Handbook
  crossref 2025-12-05T21:31:59Z 置信度 0.70
• Introduction
  crossref 2026-08-28T20:18:35Z 置信度 0.70
• Best Free AI Porn Generator Platforms in Mar 2026 – Top AI Porn AI Generator Free Tools Reviewed
  crossref ydsak hkwa 2026-03-06T10:08:51Z 置信度 0.70
• Contemporary Developments in Forensic Science and Criminal Investigation
  crossref 2026-04-17T15:09:02Z 置信度 0.70
• A COMPREHENSIVE, EQUITABLE DIAGNOSTIC ERROR CORRECTION SYSTEM USING REVERSE-DICTIONARY ALGORITHMS, PATIENT-AI COLLABORATION, AND SUSTAINABLE AI DESIGN
  Misdiagnosis is a critical issue in global health, leading to delayed treatments, exacerbating conditions, and prolonged suffering. In the United States alone, diagnostic errors impact approximately 12 million people annually, commonly misidentifying condition…
  crossref Jalen Cai, Milin Zhu, David T. Garcia 2026-07-22T10:35:37Z 置信度 0.70
• The Promise of AI in Biomedical Research: Applications to the Study of a Gerozyme
  In recent years, the application of Artificial Intelligence (AI) has profoundly impacted biomedical research (Topol, 2019). AI algorithms, particularly machine learning and deep learning models, can analyze large volumes of complex biological data more efficie…
  crossref Megan Mayerle, Helen M. Blau 2025-12-03T12:01:16Z 置信度 0.70
• Conclusion: shaping the future of Healthcare 5.0
  crossref Olfa Boubaker 2026-01-23T14:38:18Z 置信度 0.70
• Cripping AI: Reimagining AI Through Lived Disability Experiences
  Drawing on crip theory, this paper proposes cripping AI as a guiding framework to center lived disability experiences in AI research and development. Moving beyond calls to make AI “accessible” to people with disabilities, cripping AI seeks to: (1) reveal and …
  crossref Xinru Tang, Ting-an Lin, Jingjin Li, Shaomei Wu 2026-06-23T16:20:39Z 置信度 0.70
• AI and Trust
  crossref Kausik Lakkaraju, Biplav Srivastava 2026-08-08T00:10:44Z 置信度 0.70
• AI-Driven Countermeasures Against Hardware Security Threats
  crossref Khaled Mohamed 2026-05-08T20:27:39Z 置信度 0.70
• When politeness becomes frictionless: AI and the devaluation of renqing
  crossref Yifei Zhao, Bingrong Yu 2026-05-23T07:39:58Z 置信度 0.70
• Geospatial AI for liquefaction hazard and impact forecasting: A demonstrative study in the U.S. pacific northwest
  Recent large-magnitude earthquakes have demonstrated the damaging consequences of soil liquefaction and reinforced the need to understand and plan for liquefaction hazards at a regional scale. In the United States, the Pacific Northwest is uniquely vulnerable …
  crossref Morgan D. Sanger, Brett W. Maurer 2026-03-07T23:34:15Z 置信度 0.70
• Applications of Generative AI for Tackling Cyberbullying
  crossref Muthu Ramachandran 2026-06-12T06:45:18Z 置信度 0.70
• AI technology, AI narrative, and firm value
  crossref Xiaoqiang Xing, Zhu Zhang, Weixuan He 2025-08-29T16:57:49Z 置信度 0.70
• TalentCraft: AI Based Virtual Interview Platform
  crossref Varsha Gola, Saud Zargar, Shivani Singh, Amit Bhola 2026-07-08T19:40:41Z 置信度 0.70
• MockGenAI: An Intelligent Framework for API Endpoints Generation Using AI
  crossref Sanyam Saxena, Avishek Nandi 2026-07-08T19:40:41Z 置信度 0.70
• Jigsaw-like knowledge graph generation: a study on generalization patterns with a LightRAG implementation
  The integration of knowledge graphs (KGs) with retrieval-augmented generation (RAG) has significantly advanced domain-specific question-answering systems. However, a critical limitation persists in existing KG-based RAG frameworks: the inability to efficiently…
  crossref Da Long, Yabo Wang, Tian Li, Lifen Sun 2026-02-25T22:49:28Z 置信度 0.70
• India AI Impact Summit 2026: A Defining Moment for Global AI Leadership An Analytical Assessment of the India AI Impact Summit 2026
  The accelerated global deployment of artificial intelligence has intensified debates on governance, safety, and economic competitiveness. To date, much of this discourse has been shaped by advanced economies and private technology actors, often privileging fro…
  crossref Ramakrishna Semaladhari 2026-07-29T13:11:39Z 置信度 0.70
• AI IN GENOMICS AND PRECISION MEDICINE
  Artificial Intelligence (AI) is transforming genomics and precision medicine by enabling the analysis of vast and complex biological datasets to generate actionable insights for personalized healthcare. Advances in sequencing technologies have produced large-s…
  crossref Sangeethpriya S, Bavya S, Safana Begum S, Thilagavathi M 2026-06-03T07:31:11Z 置信度 0.70
• AI-Enabled Library Science, Open Knowledge Systems, and Digital Repositories
  Abstract AI-enabled library science is emerging as a decisive field for governing knowledge infrastructures in an era of large-scale digitization, open scholarship, algorithmic discovery, and public accountability. This manuscript develops a doctoral-level fra…
  crossref Murali Krishna Pasupuleti 2026-06-16T14:03:04Z 置信度 0.70
• Beyond Paper-centric Science: DGP Multiverse Science as an AI-Native Architecture for Scientific Knowledge
  Science seeks increasingly adequate accounts of how reality generates the phenomena we observe. Theoretical inquiry proposes, compresses, and relates generative structure; empirical inquiry constrains it through observation, measurement, and intervention. We a…
  crossref Xiaonan Fu 2026-08-27T04:39:37Z 置信度 0.70
• AI can be cyberbullying perpetrators: Investigating individuals’ perceptions and attitudes towards AI-generated cyberbullying
  crossref Weiping Pei, Fangzhou Wang, Yi Ting Chua 2025-09-29T15:01:55Z 置信度 0.70
• Query Authorship: A Framework for Systematic AI Use in Social Science
  The intellectual contribution in AI-assisted research is not in the text the AI produces but in the instructions that shaped it. Unsystematic use of AI is unaccountable, with a possible consequence of overflowing the scientific literature with noise. In contra…
  crossref Torbjørn Skardhamar 2026-04-28T20:54:21Z 置信度 0.70
• In 2026, Can AI "Humanizers" Outsmart AI Text Detectors?
  <p><span>Research into AI-generated writing and its detection is growing, yet it struggles to keep pace with rapid technological advancements in AI. The rapid development of large language models (LLMs) and other AI-powered technologies, such as AI…
  crossref Roman Lesnov 2026-07-20T12:18:59Z 置信度 0.70
• Universities must act now to regulate AI-use in science
  Artificial Intelligence (AI) is rapidly gaining ground in science. Whereas the use of algorithms was until recently limited to a few specific fields, the availability of generative AI tools such as ChatGPT offers attractive possibilities for virtually all disc…
  crossref Laurens Hessels 2026-05-21T09:22:28Z 置信度 0.70
• Embedding Learner-Instruction Fit into Adaptive AI: A Design Science Approach
  By incorporating Person-Environment Fit (P-E Fit) theory, this paper develops Learner-Instruction Fit Soft Actor-Critic (LIF-SAC), a variant of the Soft Actor-Critic reinforcement-learning algorithm that adapts instructional delivery to learners' evolving cogn…
  crossref Tahereh Saheb 2026-08-31T02:36:41Z 置信度 0.70
• Estimating potential-dependent physicochemical properties at metal--electrolyte interfaces using machine learning interatomic potentials (Part 2)
  This record is the second part of the dataset supporting our associated publication. It contains MACE molecular dynamics trajectories for electrolyte–Cu(111) interfaces. These simulations were used to analyze water structure, ion distributions, and interfacial…
  datacite Mathanker, Ankit, Govindarajan, Nitish 2025 置信度 0.66
• Estimating potential-dependent physicochemical properties at metal--electrolyte interfaces using machine learning interatomic potentials (Part 2)
  This record is the second part of the dataset supporting our associated publication. It contains MACE molecular dynamics trajectories for electrolyte–Cu(111) interfaces. These simulations were used to analyze water structure, ion distributions, and interfacial…
  datacite Mathanker, Ankit, Govindarajan, Nitish 2025 置信度 0.66
• Machine-learning octet $AB$-type binary compounds across chemical space with domain knowledge of the interatomic bond
  The prediction of the structural stability of octet $AB$-type binary compounds is a classical materials informatics problem. The challenge is to capture the relative stability of 4-fold coordinated atoms in zincblende ($β$-ZnS) structure and 6-fold coordinated…
  datacite Kumar, Rohan, Forti, Mariano D., Naik, Aakash A., Ghiringhelli, Luca M. 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Simulating Ionic Liquid Fragmentation in Electrospray Thrusters with Foundation Models
  Predicting the products of ionic-liquid impacts on extractor surfaces is important for electrospray-thruster lifetime analysis, yet available atomistic methods require a compromise between chemical fidelity and computational cost. Reactive force fields enable …
  datacite Huang, Ziyu 2026 置信度 0.66 Chemical Physics (physics.chem-ph)Applied Physics (physics.app-ph)FOS: Physical sciences
• Data and software for "Fidelity-Aware Machine-Learned Activation Barriers without Saddle Searches"
  Data and software underlying the study “Fidelity-Aware Machine-Learned Activation Barriers without Saddle Searches.” The deposit includes NEB barrier and minimum-energy-path datasets, descriptor vectors and data splits, trained machine-learning models, trainin…
  datacite Luzzatto, Julien, Hadjiconstantinou, Nicolas 2026 置信度 0.66 kinetic Monte Carlomachine learningactivation barriersnudged elastic bandoff-lattice simulations
• Data and software for "Fidelity-Aware Machine-Learned Activation Barriers without Saddle Searches"
  Data and software underlying the study “Fidelity-Aware Machine-Learned Activation Barriers without Saddle Searches.” The deposit includes NEB barrier and minimum-energy-path datasets, descriptor vectors and data splits, trained machine-learning models, trainin…
  datacite Luzzatto, Julien, Hadjiconstantinou, Nicolas 2026 置信度 0.66 kinetic Monte Carlomachine learningactivation barriersnudged elastic bandoff-lattice simulations
• Exploring the chemistry of closed-shell Ge-B compounds by DFT and machine-learning interatomic potential analysis for optical properties
  datacite Kyhoiesh, Hussein Ali Kadhim 2026 置信度 0.66
• Exploring the chemistry of closed-shell Ge-B compounds by DFT and machine-learning interatomic potential analysis for optical properties
  datacite Kyhoiesh, Hussein Ali Kadhim 2026 置信度 0.66
• Towards rationalising the structure-property relationships of energetic materials
  An energetic material is a substance containing a large amount of stored chemical energy that can be released quickly upon initiation, for example, an explosive, propellant or pyrotechnic. This initiation event can occur via several methods, including from imp…
  datacite Quayle, Heather Marie 2026 置信度 0.66 Energetic materialsImpact sensitivityPredictive methodsVibrational up-pumping modelComputational method
• Bi2Te3 Deep Potential Workflow Package for Ultra-Flexibility Merges High Power Density in Single-Crystal Bi2Te3 Film Thermoelectric Generators
  Computational workflow, model metadata, representative inputs, and atomistic simulation outputs for the Bi2Te3 DeepMD/DPGEN/LAMMPS/VASP calculations used in the associated Nature Communications manuscript.
  datacite Niu, Junbo 2026 置信度 0.66 Bi2Te3bismuth telluridethermoelectric materialsDeepMD-kitDPGEN
• Bi2Te3 Deep Potential Workflow Package for Ultra-Flexibility Merges High Power Density in Single-Crystal Bi2Te3 Film Thermoelectric Generators
  Computational workflow, model metadata, representative inputs, and atomistic simulation outputs for the Bi2Te3 DeepMD/DPGEN/LAMMPS/VASP calculations used in the associated Nature Communications manuscript.
  datacite Niu, Junbo 2026 置信度 0.66 Bi2Te3bismuth telluridethermoelectric materialsDeepMD-kitDPGEN
• Accelerated Discovery of Materials with Extreme Work Functions through Uncertainty-Aware Multi-Fidelity Screening
  Work function plays a pivotal role in technologies ranging from energy conversion and electronics to catalysis. In this work, we integrated machine learning (ML) with multi-fidelity screening to develop a data-driven framework for accelerating the discovery of…
  datacite Meng, Jun, Jacobs, Ryan, Kapadia, Rehan, Booske, John 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• ase-calculator-kit: a unified ASE calculator factory for MLIP and DFT calculators
  A thin, unified ASE calculator factory for machine-learning interatomic potentials (SevenNet, CHGNet, MatterSim, NequIP OAM, UMA/fairchem, MACE) and external DFT calculators (VASP, Quantum ESPRESSO). Every call returns a standard ase.Calculator, so the rest of…
  datacite Wakamiya, Taishiro, Ishikawa, Atsushi 2026 置信度 0.66 ASEmachine-learning interatomic potentialdensity functional theorycomputational chemistrymaterials science
• Machine Learning Methods to Fit Interatomic Potentials for Plasma-Surface Interactions: a C-H-O-Ar Example
  At the core of molecular dynamics simulations of plasma-surface interactions is the interatomic potential that predicts the energy and forces of atomic configurations. Recently, machine-learned interatomic potentials (MLIPs) have become popular in related fiel…
  datacite Jack Draney, Athanassios Panagiotopoulos, David Graves 2026 置信度 0.66 Physical sciences
• Machine Learning Methods to Fit Interatomic Potentials for Plasma-Surface Interactions: a C-H-O-Ar Example
  At the core of molecular dynamics simulations of plasma-surface interactions is the interatomic potential that predicts the energy and forces of atomic configurations. Recently, machine-learned interatomic potentials (MLIPs) have become popular in related fiel…
  datacite Jack Draney, Athanassios Panagiotopoulos, David Graves 2026 置信度 0.66 Physical sciences
• ase-calculator-kit: a unified ASE calculator factory for MLIP and DFT calculators
  A thin, unified ASE calculator factory for machine-learning interatomic potentials (SevenNet, CHGNet, MatterSim, NequIP OAM, UMA/fairchem, MACE) and external DFT calculators (VASP, Quantum ESPRESSO). Every call returns a standard ase.Calculator, so the rest of…
  datacite Wakamiya, Taishiro, Ishikawa, Atsushi 2026 置信度 0.66 ASEmachine-learning interatomic potentialdensity functional theorycomputational chemistrymaterials science
• Local lattice dynamics of hcp zinc from EXAFS and machine-learning interatomic potentials
  The lattice dynamics of hexagonal close-packed (hcp) zinc, a prototypical anisotropic metal, is studied using temperature-dependent Zn K-edge extended X-ray absorption fine structure (EXAFS) spectroscopy combined with atomistic simulations. The reverse Monte C…
  datacite Dimitrijevs, Vitalijs, Žguns, Pjotrs, Pudza, Inga, Kalinko, Aleksandr 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Fast Isotropic Li-Ion Diffusion in Zeolitic Imidazolate Framework Glass Electrolytes for Batteries
  All-solid-state lithium batteries require solid electrolytes that combine rapid room-temperature ion transport with mechanical robustness and interfacial compatibility. Zeolitic imidazolate framework (ZIF) glasses, with ZIFs being a sub-set of metal-organic fr…
  datacite Li, Yong, Du, Tao, Jamin, Timothée, Li, Zhencai 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Local Structure Dictates Ionic Transport and Mechanical Properties in Glassy Solid Electrolytes for Lithium Batteries
  Electrolytes composed of sulfide and halide glasses are promising candidates for all-solid-state lithium batteries owing to their processability, lack of grain boundaries, and relatively high ionic conductivity. Nevertheless, their ionic conductivity and mecha…
  datacite Li, Yong, Du, Tao, Christensen, Rasmus, Jamin, Timothée 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• The sqrt-Law of Near-Degenerate Error in Universal Machine-Learning Interatomic Potentials: A 2,992-Polymorph-Pair Quantification and Scale-Correction Protocol
  Universal machine-learning interatomic potentials (uMLIPs) are trained on near-equilibrium DFT data and inherit systematic potential-energy-surface softening (Deng et al., npj Comput. Mater. 2025). Using 2,992 polymorph pairs from QMOF (same-composition multi-…
  datacite Qin, Chao 2026 置信度 0.66 machine learning interatomic potentialspolymorphnear-degeneratepower lawMACE
• The sqrt-Law of Near-Degenerate Error in Universal Machine-Learning Interatomic Potentials: A 2,992-Polymorph-Pair Quantification and Scale-Correction Protocol
  Universal machine-learning interatomic potentials (uMLIPs) are trained on near-equilibrium DFT data and inherit systematic potential-energy-surface softening (Deng et al., npj Comput. Mater. 2025). Using 2,992 polymorph pairs from QMOF (same-composition multi-…
  datacite Qin, Chao 2026 置信度 0.66 machine learning interatomic potentialspolymorphnear-degeneratepower lawMACE
• ase-calculator-kit: a unified ASE calculator factory for MLIP and DFT calculators
  A thin, unified ASE calculator factory for machine-learning interatomic potentials (SevenNet, CHGNet, MatterSim, NequIP OAM, UMA/fairchem) and external DFT calculators (VASP, Quantum ESPRESSO). Every call returns a standard ase.Calculator, so the rest of an AS…
  datacite Wakamiya, Taishiro, Ishikawa, Atsushi 2026 置信度 0.66 ASEmachine-learning interatomic potentialdensity functional theorycomputational chemistrymaterials science
• train for ORION-NEP
  This is the training set for ORION-NEP. Empirical force fields remain the primary tool for large-scale molecular simulation, yet their limited flexibility and transferability often hinder predictive modeling in chemically complex condensed-phase systems. Here,…
  datacite Chen, Zherui 2026 置信度 0.66
• train for ORION-NEP
  This is the training set for ORION-NEP. Empirical force fields remain the primary tool for large-scale molecular simulation, yet their limited flexibility and transferability often hinder predictive modeling in chemically complex condensed-phase systems. Here,…
  datacite Chen, Zherui 2026 置信度 0.66
• Custom-trained Machine-learning Interatomic Potentials: ZnCl2 Aqueous Solution
  This dataset was generated using an iterative active-learning strategy implemented in the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials for aqueous ZnCl2 solutions. Each active-learnin…
  datacite Dinpajooh, Mohammadhasan, Chen, Junhan, Gibson, Luke 2026 置信度 0.66
• Δ-Learning for Transferable Machine Learning Interatomic Potentials
  Machine-learning interatomic potentials (MLIPs) trained by directly learning the total interatomic interaction energies can suffer from limited transferability, unphysical behavior beyond a finite cutoff, and large errors for out-of-distribution geometries suc…
  datacite Nguyen Thien Phuc Tu, Christopher Rowley 2026 置信度 0.66 Chemical sciences
• Δ-Learning for Transferable Machine Learning Interatomic Potentials
  Machine-learning interatomic potentials (MLIPs) trained by directly learning the total interatomic interaction energies can suffer from limited transferability, unphysical behavior beyond a finite cutoff, and large errors for out-of-distribution geometries suc…
  datacite Nguyen Thien Phuc Tu, Christopher Rowley 2026 置信度 0.66 Chemical sciences
• ase-calculator-kit: a unified ASE calculator factory for MLIP and DFT calculators
  A thin, unified ASE calculator factory for machine-learning interatomic potentials (SevenNet, CHGNet, MatterSim, NequIP OAM, UMA/fairchem) and external DFT calculators (VASP, Quantum ESPRESSO). Every call returns a standard ase.Calculator, so the rest of an AS…
  datacite Wakamiya, Taishiro, Ishikawa, Atsushi 2026 置信度 0.66 ASEmachine-learning interatomic potentialdensity functional theorycomputational chemistrymaterials science
• ase-calculator-kit: a unified ASE calculator factory for MLIP and DFT calculators
  A thin, unified ASE calculator factory for machine-learning interatomic potentials (SevenNet, CHGNet, MatterSim, NequIP OAM, UMA/fairchem) and external DFT calculators (VASP, Quantum ESPRESSO). Every call returns a standard ase.Calculator, so the rest of an AS…
  datacite Wakamiya, Taishiro, Ishikawa, Atsushi 2026 置信度 0.66 ASEmachine-learning interatomic potentialdensity functional theorycomputational chemistrymaterials science
• Isurwars/Correlation: Correlation 3.8.6
  Changelog All notable changes to this project will be documented in this file. [3.8.6] - 2026-08-04 Added & Refactored Application Settings Persistence: Implemented SettingsManager for JSON-backed user settings persistence across sessions, integrated into AppC…
  datacite Isaías Rodríguez Aguirre, Mineralwater Xu 2026 置信度 0.66
• Isurwars/Correlation: Correlation 3.8.6
  Changelog All notable changes to this project will be documented in this file. [3.8.6] - 2026-08-04 Added & Refactored Application Settings Persistence: Implemented SettingsManager for JSON-backed user settings persistence across sessions, integrated into AppC…
  datacite Isaías Rodríguez Aguirre, Mineralwater Xu 2026 置信度 0.66
• GDB-9-Ex: Quantum chemical prediction of UV/Vis absorption spectra for GDB-9 molecules
  GDB-9-Ex: Quantum chemical prediction of UV/Vis absorption spectra for GDB-9 molecules Massimiliano Lupo Pasini, Pilsun Yoo, Kshitij Mehta, Stephan Irle Python, GDB-9, Time-Dependent Density-Functional Tight-Binding (TD-DFTB), Predicting Excited States Molecul…
  datacite Lupo Pasini, Massimiliano, Yoo, Pilsun, Mehta, Kshitij, Irle, Stephan 2022 置信度 0.66 36 MATERIALS SCIENCE37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS74 ATOMIC AND MOLECULAR PHYSICSPython
• MLIPs Ontology
  MLIPs Ontology: An Ontology for Machine Learning Interatomic Potentials <br> More information can be found in the <a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=633527">README.md</a>.
  datacite Hernández, Daniel, Jung, Jong Hyun, Ikeda, Yuji, Ou, Yongliang 等 2026 置信度 0.66 ChemistryComputer and Information ScienceMachine LearningMaterials ScienceMolecular Dynamics Simulation
• Fine-tuning of universal machine-learning interatomic potentials for high-entropy alloys with application to 2D (Mo,Ta,Nb,W,V)S$_2$
  High-entropy alloy (HEA) materials and their two-dimensional counterparts (2D-HEAs) have recently attracted attention due to their tunable properties and catalytic potential, yet their chemical complexity makes direct density functional theory (DFT) calculatio…
  datacite Zhou, Chun, Komsa, Hannu-Pekka 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Computational Physics (physics.comp-ph)FOS: Physical sciences
• Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential
  Thermal management at silicon-diamond interface is critical for advancing high-performance electronic and optoelectronic devices. In this study, we calculate the interfacial thermal conductance between silicon and diamond using machine learning (ML) interatomi…
  datacite Rajabpour, Ali, Mortazavi, Bohayra, Mirchi, Pedram, Hajj, Julien El 等 2024 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Mesoscale and Nanoscale Physics (cond-mat.mes-hall)FOS: Physical sciences
• From oligomers to entangled polymers: How to train a transferable machine learning interatomic potential
  Over the past decade, Machine Learning Interatomic Potentials (MLIPs) have emerged as a powerful technique for performing molecular dynamics (MD) simulations with nearly ab initio accuracy. Alongside the development of new descriptors and advanced machine lear…
  datacite Fischer, Mirko, Heuer, Andreas 2026 置信度 0.66 Soft Condensed Matter (cond-mat.soft)FOS: Physical sciences
• Development of a machine learning potential for graphene
  We present an accurate interatomic potential for graphene, constructed using the Gaussian approximation potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT) potential energy surface…
  datacite Rowe, Patrick, Csányi, Gábor, Alfè, Dario, Michaelides, Angelos 2018 置信度 0.66 51 Physical Sciences34 Chemical Sciences5104 Condensed Matter Physics3407 Theoretical and Computational ChemistryNetworking and Information Technology R&D (NITRD)
• High-throughput determination of threshold displacement energies in AlGaN alloys
  Accompanying data and analysis from molecular dynamics simulations (machine learning interatomic potential and Stillinger–Weber potential) for AlGaN threshold displacement energies. Five compositions of AlGaN are considered (0%, 25%, 50%, 75%, and 100% Al cont…
  datacite Hauck, Alexander, Gonzalez, Aiden, Fennell, Marley, Jin, Miaomiao 2026 置信度 0.66
• High-throughput determination of threshold displacement energies in AlGaN alloys
  Accompanying data and analysis from molecular dynamics simulations (machine learning interatomic potential and Stillinger–Weber potential) for AlGaN threshold displacement energies. Five compositions of AlGaN are considered (0%, 25%, 50%, 75%, and 100% Al cont…
  datacite Hauck, Alexander, Gonzalez, Aiden, Fennell, Marley, Jin, Miaomiao 2026 置信度 0.66
• PASS: Perturbation augmented space group structure sampling for transferable Fe-O machine learning interatomic potential
  Accurate atomistic modelling of iron (Fe) oxidation requires a reliable interatomic potential, which necessitates an extensive and representative first-principles dataset for training the interatomic potential. However, Fe-oxygen (O) system is known for its st…
  datacite Wei, Zixiong, Shuang, Fei, Dey, Poulumi 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Computational Physics (physics.comp-ph)FOS: Physical sciences
• Quantum machine learning interatomic potential: Application of variational quantum algorithm
  This study applied quantum circuit learning, a commonly used hybrid quantum-classical machine learning algorithm, to a machine learning interatomic potential (MLIP) for predicting the energies of molecules in molecular datasets. We retrained the ANI model usin…
  datacite Numata, Kohei, Mizukami, Wataru, Mitarai, Kosuke, Fujii, Keisuke 等 2026 置信度 0.66 Quantum Physics (quant-ph)FOS: Physical sciences
• Girsanov Reweighting for Uncertainty Propagation in Rare-Event Kinetics
  Machine-learning interatomic potentials (MLIPs) have become a powerful tool for rare event sampling in molecular dynamics, offering near ab initio accuracy at a fraction of the computational cost. However, the uncertainty associated with these models remains a…
  datacite Moracchini, Leonard, Pigeon, Thomas, Menz, Morgane, Faney, Thibault 等 2026 置信度 0.66 Chemical Physics (physics.chem-ph)FOS: Physical sciences
• Transformer Atomic Cluster Expansion: TRACE
  Designing machine-learning interatomic potentials involves achieving the precise representation of complex many-body interactions alongside the efficiency required for scalable molecular dynamics. We introduce Transformer Atomic Cluster Expansion (TRACE), an e…
  datacite Ahlawat, Paramvir 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• MANDALA: An E(3)-Equivariant Graph Neural Network Framework for Learning Electronic-Structure Operators with Observable Guidance
  Electronic-structure calculations based on Kohn-Sham density functional theory remain indispensable in computational materials science and chemistry. Their computational cost, however, limits accessible system sizes and simulation times. At the same time, conv…
  datacite Brzoza, Bartosz, Szopa, Wiktoria, Elabid, Zakaria, Martinetto, Vincent 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Chemical Physics (physics.chem-ph)FOS: Physical sciences
• A Combined Tight Binding with Machine Learning Potential Model for Magnesium Compounds
  We present a model for magnesium-based systems that combines density functional tight binding (DFTB) with MACE, a machine learning interatomic potential (DFTB+MACE). In this model, the conventional repulsive potential, pair potential, is replaced by a many-bod…
  datacite Yu, Jiwen, Mostofi, Arash A., Horsfield, Andrew 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Molecular dynamics with a first-principles-validated universal machine-learning potential reveals dynamic elementary processes of growth-related adspecies on GaN(0001)
  Atomic-scale understanding of the surface elementary processes in metalorganic vapor phase epitaxy (MOVPE) of GaN has so far relied on static density-functional-theory (DFT) energetics and on first-principles molecular dynamics (FPMD) limited to a few tens of …
  datacite Takaesu, Yoshito, Kusaba, Akira, Ishii, Junko, Matsushima, Shigenori 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Computational Physics (physics.comp-ph)FOS: Physical sciences
• Engineering Super-Ionic Diffusion in Solid-State Electrolytes via Machine Learning
  Leading research teams are actively pursuing solid-state electrolytes (SSEs) for next-generation lithium (Li) batteries. A primary requirement a SSE must have is a high room-temperature ionic conductivity. Since super-ionic SSEs exhibit highly connected pathwa…
  datacite Tucci, David 2026 置信度 0.66 Mining and Materials
• Accelerating Solid-State Hydrogen Storage Discovery: Machine Learning, DFT, and Phonon Calculations of Equimolar NaCa(BH4)3
  Solid-state hydrogen storage is pivotal for the transition to zero-emission energy infrastructure, yet high-capacity complex metal hydrides are chronically hindered by unfavorable thermodynamics. While heterovalent mixed-metal borohydrides, such as equimolar N…
  datacite Ismail, Sanaa, Amaral, Ricardo, Gadallah, Attia, Azzazy, Hassan 等 2026 置信度 0.66 Hydrogen Storagecomplex metal HydridesMACE-MPPhonon Calculations
• Accelerating Solid-State Hydrogen Storage Discovery: Machine Learning, DFT, and Phonon Calculations of Equimolar NaCa(BH4)3
  Solid-state hydrogen storage is pivotal for the transition to zero-emission energy infrastructure, yet high-capacity complex metal hydrides are chronically hindered by unfavorable thermodynamics. While heterovalent mixed-metal borohydrides, such as equimolar N…
  datacite Ismail, Sanaa, Amaral, Ricardo, Gadallah, Attia, Azzazy, Hassan 等 2026 置信度 0.66 Hydrogen Storagecomplex metal HydridesMACE-MPPhonon Calculations
• First-principles investigation of novel stable, strong, and highly attractive semiconducting nanoporous C₃N and CN monolayers
  In recent breakthroughs in the field of nanoporous carbon-nitride two-dimensional (2D) nanomaterials, two novel covalent organic frameworks (COFs) with a C 3 N stoichiometry ( J. Am. Chem. Soc. 2024, 146, 18151 & Angew. Chem. 2024, 136, e202415624 ) have b…
  datacite Mortazavi, Bohayra, Karlický, František, Zhuang, Xiaoying, Shahrokhi, Masoud 2026 置信度 0.66 540Carbon nitrideCovalent organic frameworks, 2D materialsDensity functional theorySemiconductors
• Charge-Density-Wave Phase Transitions in Monolayer 1T-TaS2 from Universal Machine Learning Molecular Dynamics
  Charge-density-wave (CDW) phases in 1T transition-metal dichalcogenides arise from strong electron-phonon coupling and accompanying lattice instabilities. Capturing their temperature-dependent structural evolution using conventional first-principles molecular …
  datacite Nesterova, Valentina, Pandey, Tribhuwan, Berlijn, Tom, Kargar, Fariborz 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Property-Guided Diffusion for Inverse Design of Crystalline Materials
  Diffusion-based generative models with property guidance have emerged as a promising paradigm for inverse materials design by enabling the generation of crystalline materials with user-specified target properties. However, despite recent advances, the effectiv…
  datacite Mal, Sourav, Mishra, Subhankar, Sen, Prasenjit 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Isurwars/Correlation: Correlation 3.7.2
  [3.7.2] - 2026-07-26 Fixed Native File Dialog Linkage: Enforced static library compilation (BUILD_SHARED_LIBS=OFF) for nativefiledialog-extended (nfd) on Linux to resolve runtime libnfd.so.1 shared library loading errors. [3.7.0] - 2026-07-24 Added Multi-Vendo…
  datacite Isaías Rodríguez Aguirre, Mineralwater Xu 2026 置信度 0.66
• Isurwars/Correlation: Correlation 3.7.0
  [3.7.0] - 2026-07-24 Added Multi-Vendor GPU Acceleration Framework: Implemented SYCL-based GPU acceleration for distance, $S(Q)$, and Steinhardt parameter calculators (GPUDistanceCalculator, GPUSQCalculator, GPUSteinhardtCalculator) with runtime fallback and d…
  datacite Isaías Rodríguez Aguirre, Mineralwater Xu 2026 置信度 0.66
• Charge-Density-Wave Phase Transitions in Monolayer 1<i>T</i>-TaS₂ - Supplemental Materials
  The files support the work " Charge-Density-Wave Phase Transitions in Monolayer 1 T -TaS 2 from Universal Machine Learning Molecular Dynamics ". In this study, phase transitions in monolayer 1 T -TaS 2 were investigated using Molecular Dynamics (MD) simulation…
  datacite Valentina Nesterova, Tribhuwan Pandey, Tom Berlijn, Fariborz Kargar 等 2026 置信度 0.66 Condensed matter modelling and density functional theoryComputational chemistryPhysical properties of materialsInorganic materials (incl. nanomaterials)
• Charge-Density-Wave Phase Transitions in Monolayer 1<i>T</i>-TaS₂ - Supplemental Materials
  The files support the work " Charge-Density-Wave Phase Transitions in Monolayer 1 T -TaS 2 from Universal Machine Learning Molecular Dynamics ". In this study, phase transitions in monolayer 1 T -TaS 2 were investigated using Molecular Dynamics (MD) simulation…
  datacite Valentina Nesterova, Tribhuwan Pandey, Tom Berlijn, Fariborz Kargar 等 2026 置信度 0.66 Condensed matter modelling and density functional theoryComputational chemistryPhysical properties of materialsInorganic materials (incl. nanomaterials)
• Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys
  Machine learning interatomic potentials (MLIPs) with broad chemical flexibility are essential for atomistic simulations of compositionally complex alloys, but their deployment in large-scale molecular dynamics requires a balance among accuracy, efficiency, sta…
  datacite Shuang, Fei, Ying, Penghua, Liu, Kai, Wei, Zixiong 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems
  We introduce OrbitAll, a geometry- and physics-informed deep learning framework that encodes any molecular system with arbitrary charges, spins, and environmental effects using electronic structure information. It utilizes spin-polarized orbital features from …
  datacite Kang, Beom Seok, Bhethanabotla, Vignesh C., Tavakoli, Amin, Hanisch, Maurice D. 等 2025 置信度 0.66 Machine Learning (cs.LG)Chemical Physics (physics.chem-ph)FOS: Computer and information sciencesFOS: Physical sciences
• From MLIPs to Microstructure: A High-Throughput Computational Framework to Design Spinodal Alloys in High-Dimensional Composition Spaces via Analytic Derivatives of CALPHAD Model Predictions
  Identifying regions of design space subject to spinodal decomposition is a critical component of alloy design in high-dimensional composition spaces. In cases where designers are seeking to exploit spinodal microstructures to tailor alloy properties, predictio…
  datacite Kunselman, Courtney, Sariturk, Doguhan, Zhu, Siya, Attari, Vahid 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Role of Wadsley Defects and Cation Disorder to Enhance MoNb12O33 Diffusion
  Wadsley-Roth (WR) niobates have emerged as high-rate anode materials that can combine rapid ionic diffusion with good electronic conductivity. WR compounds have been defect-enhanced by limited annealing, however, such materials often contain multiple types of …
  datacite Sturgill, CJ, Kumar, Manish, Karimitari, Nima, Milisavljevic, Iva 等 2025 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• Data-driven Design of Metal-Organic Frameworks with Tunable Negative Thermal Expansion
  Materials with negative thermal expansion (NTE) are essential for applications requiring precise control of thermal expansion. Owing to their exceptional chemical tunability, flexible architectures, and low-energy lattice vibrations, metal-organic frameworks (…
  datacite Kamath, Prathami Divakar, Tavani, Francesco, Elena, Alin Marin, Inizan, Théo Jaffrelot 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences