AI 驱动科学发现 · 全部实体（第 21 / 100 页）

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• How Generative AI Turns Copyright Law Upside Down
  While courts are litigating many copyright issues involving generative AI, from who owns AI-generated works to the fair use of training to infringement by AI outputs, the most fundamental changes generative AI will bring to copyright law don’t fit in any of th…
  crossref Mark Lemley 2024-06-04T23:00:40Z 置信度 0.70
• What Distinguishes AI-Generated from Human Writing? A Rapid Review of the Literature
  Large language models (LLMs) are now routine writing tools across various domains, intensifying questions about when text should be treated as human-authored, artificial intelligence (AI)-generated, or collaboratively produced. This rapid review aimed to ident…
  crossref Georgios P. Georgiou 2026-01-07T03:45:05Z 置信度 0.70
• Giving AI a ‘case library’ to diagnose battery failures
  Callum Connor, Feng Lin; Giving AI a ‘case library’ to diagnose battery failures, National Science Review, , nwag483, https://doi.org/10.1093/nsr/nwag483
  crossref Callum Connor, Feng Lin 2026-08-12T11:44:59Z 置信度 0.70
• KYYNK AI Review 2026: Is This Uncensored AI Companion Worth Your Money?
  crossref kdsa bnwa 2026-03-05T10:09:45Z 置信度 0.70
• A Comprehensive Review of Large Language Models and Agentic AI Systems for Chemistry and Materials Science
  The emergence of large language models (LLMs) has catalyzed a paradigm shift in computational chemistry and materials science, evolving from passive prediction tools to active autonomous agents capable of orchestrating complex scientific workflows. This review…
  crossref Michal Ezeh, Etinosa Osaro 2026-04-06T10:52:53Z 置信度 0.70
• From Rhetoric About AI to AI as Rhetoric - A Critical Review of the Relation Between AI and Politics
  crossref Pascal König 2024-10-02T18:45:12Z 置信度 0.70
• The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review
  Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data s…
  crossref Jessica Morley 2019-11-16T08:01:04Z 置信度 0.70
• Monitoring, Oversight, and Learning in Medical AI
  When medical AI errs, it often goes unnoticed. If there’s a specific patient injury, and the link to AI is obvious, that problem might be reported to the Food and Drug Administration (FDA), but not always. And many other types of problems, like worse performan…
  crossref W. Nicholson Price 2026-06-08T03:00:26Z 置信度 0.70
• Profit Clones AI Review: A-Complete Review
  crossref Tyler Johnson 2026-03-07T16:22:42Z 置信度 0.70
• AI in Health Care Service Quality: Systematic Review
  Abstract Background Artificial intelligence (AI) is a rapidly evolving technology with the potential to revolutionize the health care industry. In Saudi Arabia, the health care sector has adopted AI technologies over the past decade to enhance service efficien…
  crossref Eman Alghareeb, Najla Aljehani 2025-09-26T21:40:08Z 置信度 0.70
• A Review of Intelligent Self-Powered Sensing Systems Enabling Autonomous AIoT
  The rapid development of the Artificial Intelligence of Things (AIoT) has created unprecedented demands for distributed, long-term, and maintenance-free sensing systems. Conventional battery-powered sensors suffer from inherent drawbacks such as limited lifeti…
  crossref Hangrui Cui, Tianyi Tang, Huicong Liu 2025-12-23T11:30:18Z 置信度 0.70
• Intelligent neurobehavioral analysis: A comprehensive review of multimodal data and integrative AI-based computational techniques for autism spectrum disorder detection
  crossref K. Nithya, C.R. Dhivyaa, A. Devipriya, C. Sharmila 2026-04-30T23:28:09Z 置信度 0.70
• Profit Clones AI Review: A-Complete Review
  crossref Tyler Johnson 2026-03-07T16:21:26Z 置信度 0.70
• Breaking Departmental Silos in Construction Using AI Automation, Chatbots, and Smart Alerts: A Systematic Review
  This study discusses how Artificial Intelligence (AI) technologies can be used to curb the existence of departmental silos within construction organizations. The aim is to determine the effectiveness of AI automation, chatbots, and smart alert systems in enhan…
  crossref Jalal Akram Nasr 2026-04-25T11:41:43Z 置信度 0.70
• AI- and Generative AI-Driven Digital Therapeutics: A Critical Narrative Review of Emerging Evidence
  Background: Artificial intelligence-driven digital therapeutics (AI-DTx) are rapidly emerging as a transformative paradigm in healthcare, integrating machine learning, deep learning, and generative AI into digital interventions across diverse clinical domains.…
  crossref Daniele Giansanti, Andrea Lastrucci 2026-08-03T10:24:07Z 置信度 0.70
• ReviewerAI: Transforming Peer Review Workflows Through AI-Driven Innovation
  The academic publishing landscape continuously evolves as editors and publishers strive for increased efficiency, fairness, and transparency in the peer review process. ReviewerAI is a web-based platform designed to transform the scholarly peer review process …
  crossref Yusuf Sermet, Ibrahim Demir 2025-07-31T20:40:15Z 置信度 0.70
• AI-driven botnet detection in IoT networks: A comprehensive research review
  crossref Azka Wani, Niha Kamal Basha, Masna Mohammed, Iqra Hussain 等 2026-03-05T13:34:48Z 置信度 0.70
• When Industry meets Trustworthy AI: A Systematic Review of AI for Industry 5.0
  We are focusing on analyzing the current industrial evolution paradigm, aiming to make it more sustainable and trustworthy. In Industry 5.0, Artificial Intelligence (AI) is one of the key technologies utilized to develop services with a sustainable, human-cent…
  crossref Nishant Yadav, Anshika Maurya, Dr.Ashima Mehta 2024-04-25T17:34:46Z 置信度 0.70
• Profit Clones AI Review: A-Complete Review
  crossref Tyler Johnson 2026-03-07T16:18:48Z 置信度 0.70
• Profit Clones AI Review: A-Complete Review
  crossref Tyler Johnson 2026-03-07T16:21:08Z 置信度 0.70
• AI in Cybersecurity and Digital Forensics: A Comparative Review of Techniques, Applications, and Research Gaps
  Cybersecurity and digital forensics are experiencing serious challenges as a consequence of a sharp rise in cyber risks and cybercrimes driven on by the rapid growth of digital technologies, cloud computing, and connected services. A great deal of digital evid…
  crossref Hensei Patel, Jay Pathak 2026-05-25T10:57:52Z 置信度 0.70
• AI and Aesthetic Alienation: The Image and Creativity in Contemporary Culture
  In this comment piece, we argue that mass-produced generative AI (GenAI) images, commonly referred to as “AI slop” should be considered a form of aesthetic alienation. Specifically, we focus on GenAI images of fall, arguing that GenAI images alienate not only …
  crossref Naomi Smith, Clare Southerton 2025-07-19T05:05:36Z 置信度 0.70
• AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
  The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are develop…
  crossref Q. Vera Liao, Jennifer Wortman Vaughan 2024-02-29T12:12:57Z 置信度 0.70
• Profit Clones AI Review: A-Complete Review
  crossref Tyler Johnson 2026-03-07T16:21:58Z 置信度 0.70
• Profit Clones AI Review: A-Complete Review
  crossref Tyler Johnson 2026-03-07T16:19:17Z 置信度 0.70
• AI REVHUB Review
  crossref William Johnson 2026-04-14T16:53:22Z 置信度 0.70
• CourseMaster AI Review
  crossref William Johnson 2026-03-08T15:56:16Z 置信度 0.70
• Exploring Linguistic Adaptations in AI-Mediated Communication: A Sociolinguistic Analysis of Human-AI Interaction
  Artificial intelligence is increasingly being integrated into our everyday communication practices and its adoption has led to changes in language production, organization and meaning-making in digital communication. While research on communication with artifi…
  crossref Sajjad Ahmad, Ayesha Bibi, Muhammad Saad Khan 2026-05-17T06:21:52Z 置信度 0.70
• Profit Clones AI Review: A-Complete Review
  crossref Tyler Johnson 2026-03-07T16:18:20Z 置信度 0.70
• Profit Clones AI Review: A-Complete Review
  crossref Tyler Johnson 2026-03-07T16:22:23Z 置信度 0.70
• Scoping Review Media Pembelajaran Digital Generative AI Reenactment dalam Pembelajaran Biologi:
  Integrasi Generative AI (GenAI) dalam pendidikan biologi menawarkan potensi simulasi imersif, namun belum banyak rincian yang jelas mengenai praktik penggunaan dan hasil efektivitasnya.. Penelitian ini bertujuan memetakan pendekatan teknologi, praktik pedagogi…
  crossref Maulana Arsyadani Bintang Pratama Putra, Slamet Hariyadi 2026-06-09T03:37:37Z 置信度 0.70
• AI CEO Review
  crossref William Johnson 2026-05-02T15:18:46Z 置信度 0.70
• SITEGENIUS AI Review
  crossref Taylor Johnson 2026-03-05T17:39:26Z 置信度 0.70
• SITEGENIUS AI Review
  crossref Taylor Johnson 2026-03-05T17:37:42Z 置信度 0.70
• Review for "The Mediation of AI Trust on AI Uncertainties and AI Competence Among Nurses: A Cross‐Sectional Study"
  crossref 2025-09-26T21:06:33Z 置信度 0.70
• Agentic AI: A Review, Applications, and Open Research Challenges
  Agentic Artificial Intelligence (AI) marks a shift from traditional AI systems that simply generate responses to autonomous systems that can independently plan to achieve goals with minimal human intervention. These models can do much more than just respond to…
  crossref Omer Khalid, Ammad Ul Haq Farooqi, Muhammad Bilal 2025-12-10T03:06:26Z 置信度 0.70
• Enhancing Software Quality through AI - Assisted Code Review: Insights from AWS Cloud Infrastructure Development
  Code review is a fundamental component of software development, serving as the primary mechanism for verifying code quality, minimizing defects, and promoting team cooperation. While being effective, the envisioned traditional code review processes can be very…
  crossref Sai Tarun Kaniganti 2024-12-05T12:26:01Z 置信度 0.70
• Review for "The Mediation of AI Trust on AI Uncertainties and AI Competence Among Nurses: A Cross‐Sectional Study"
  crossref 2025-09-26T21:06:33Z 置信度 0.70
• HCB_Montmorillonite_Adsorption
  Density functional theory study of the adsorption of hexachlorobenzene (C6Cl6, HCB) on a montmorillonite clay surface and the effect of partial hydration, with explicit co-adsorbed water molecules. Each configuration is a VASP geometry optimization of an HCB m…
  datacite Tunega, Daniel, Grančič, Peter, Gerzabek, Martin H., Böhm, Leonard 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• CoPt_Dry_Reforming_Methane
  Density functional theory dataset for cobalt, platinum, and CoPt bimetallic catalysts investigated for the dry reforming of methane (DRM). It comprises bulk metals and alloys (Co, Pt, CoPt L1_0 and fcc), (111) surface slab models, and minimum-energy reaction p…
  datacite Niedbalka, David, Prats, Hector, Estefanía Díaz López, Janák, Marcel 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Massive_Atomic_Diversity_MAD-1.5_r2SCAN_Val
  Validation split of the MAD-1.5 (Massive Atomic Diversity version 1.5) dataset, a highly curated collection designed for training broadly applicable atomistic machine-learning models across the full periodic table. MAD-1.5 extends the original MAD dataset with…
  datacite Malosso, Cesare, Bigi, Filippo, Pegolo, Paolo, Abbott, Joseph W. 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Massive_Atomic_Diversity_MAD-1.5_r2SCAN_Test
  Test split of the MAD-1.5 (Massive Atomic Diversity version 1.5) dataset, a highly curated collection designed for training broadly applicable atomistic machine-learning models across the full periodic table. MAD-1.5 extends the original MAD dataset with targe…
  datacite Malosso, Cesare, Bigi, Filippo, Pegolo, Paolo, Abbott, Joseph W. 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• MSR-ACC-TAE25_Train
  MSR-ACC/TAE25 (Microsoft Research Accurate Chemistry Collection, Total Atomization Energies 2025) provides 73,040 total atomization energies (TAEs) at the CCSD(T)/CBS level obtained with the W1-F12 composite wavefunction protocol implemented in Molpro 2024.1. …
  datacite Ehlert, Sebastian, Hermann, Jan, Vogels, Thijs, Satorras, Victor Garcia 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• MSR-ACC-TAE25_Val
  MSR-ACC/TAE25 (Microsoft Research Accurate Chemistry Collection, Total Atomization Energies 2025) provides 73,040 total atomization energies (TAEs) at the CCSD(T)/CBS level obtained with the W1-F12 composite wavefunction protocol implemented in Molpro 2024.1. …
  datacite Ehlert, Sebastian, Hermann, Jan, Vogels, Thijs, Satorras, Victor Garcia 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• MSR-ACC-TAE25_All
  MSR-ACC/TAE25 (Microsoft Research Accurate Chemistry Collection, Total Atomization Energies 2025) provides 73,040 total atomization energies (TAEs) at the CCSD(T)/CBS level obtained with the W1-F12 composite wavefunction protocol implemented in Molpro 2024.1. …
  datacite Ehlert, Sebastian, Hermann, Jan, Vogels, Thijs, Satorras, Victor Garcia 等 2026 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• Open_Direct_Air_Capture_ODAC2025_Val_Full
  The full (unfiltered) validation split of ODAC25.Open Direct Air Capture 2025 (ODAC25) is the largest high-quality DFT dataset for Direct Air Capture, containing over 15,000 Metal-Organic Frameworks (MOFs), including experimental, defective, synthetic, and ami…
  datacite Anuroop Sriram, Brabson, Logan M., Xiaohan Yu, Sihoon Choi 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• FeAl-mMTP-Training
  Training set for magnetic Moment Tensor Potentials (mMTPs) for the bcc Fe-Al system. Contains 2012 configurations of 16-atom Fe-Al supercells with collinear atomic magnetic moments. Configurations were generated using constrained DFT (cDFT) with ABINIT and PAW…
  datacite Kotykhov, Alexey S., Gubaev, Konstantin, Hodapp, Max, Tantardini, Christian 等 2023 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• MatPES-RSCAN-2025.1
  MatPES (Materials Potential Energy Surface) is a foundational PES dataset developed collaboratively by the Materials Virtual Lab and Materials Project. The v2025.1 r2SCAN release contains structures sampled via the DIRECT method from 300 K NpT molecular dynami…
  datacite Kaplan, Aaron D., Runze Liu, Qi, Ji, Tsz Wai Ko 等 2025 置信度 0.66 ColabFitDatasetAgPd_NPJ_2021Materials Sciencehttps://id.loc.gov/authorities/subjects/sh85082094.html
• From semiconductors to superconductors: A data-driven pipeline for exploring complex energy
  The rational design of advanced functional materials—from semiconductors to superconductors—demands efficient exploration of complex, high-dimensional energy landscapes, a task that remains intractable for conventional first-principles methods alone. In this w…
  datacite Wang, Meiyan 2026 置信度 0.66
• Transferable machine learning of excited-state dynamics with extremal pooling
  Photochemical processes govern phenomena ranging from solar energy conversion and atmospheric chemistry to vision and photosynthesis. Accurate simulation of these processes requires modeling excited-state potential energy surfaces, often involving chemical rea…
  datacite Malosso, Cesare, How, Wei Bin, Mirón, Gonzalo Díaz, Hassanali, Ali 等 2026 置信度 0.66 Chemical Physics (physics.chem-ph)FOS: Physical sciencesFOS: Physical sciences
• Computational Modelling Workflows for Metal-Organic Polyhedra in The World Avatar
  Metal-Organic Polyhedra (MOPs) have exceptional potential for host-guest chemistry, but their discovery is hindered by the large combinatorial space of their building units. Computational screening offers a powerful workflow for efficiently screening large set…
  datacite Butler, Patrick, Rihm, Simon, Mosbach, Sebastian, Akroyd, Jethro 等 2026 置信度 0.66
• Approaching the Limit of Intrinsic Crystalline Thermal Insulation
  Crystalline materials with ultralow thermal conductivity ($κ$) are potential thermal barrier coatings or thermoelectrics, yet the discovery of ultralow-$κ$ materials remains inefficient due to the limitations of trial-and-error approaches. Herein, we propose a…
  datacite Cheng, Ruihuan, Cui, Zhiqiang, Jayaraman, Mani, Ji, Lincong 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciencesFOS: Physical sciences
• Robust AI-Driven Discovery of Electronic Metal Phosphide Semiconductors
  Metal phosphides have diverse bonding motifs and coordination environments, making them promising for optoelectronic and thermoelectric applications, but their chemical space remains underexplored. Here we report an AI-driven high-throughput discovery workflow…
  datacite Zhu, Benhao, Faizan, Muhammad, Li, Zewei, Li, Wenshuo 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciencesFOS: Physical sciences
• Minimization of disorder as a key design principle for natural sizes of light harvesting 2 complexes
  The light harvesting 2 (LH2) complex of purple bacteria has excellent energy conversion efficiency. Clarifying the design principle behind such efficiency at the atomistic level is crucial for understanding its structure-function relationship, and can be utili…
  datacite Cho, Kwang Hyun, Jang, Seogjoo J., Rhee, Young Min 2026 置信度 0.66 Chemical Physics (physics.chem-ph)FOS: Physical sciencesFOS: Physical sciences
• Development of a general-purpose machine-learning interatomic potential for aluminum by the physically informed neural network method
  Interatomic potentials are the key components of large-scale atomistic simulations of materials. The recently proposed physically-informed neural network (PINN) method combines a high-dimensional regression implemented by an artificial neural network with a ph…
  crossref G. P. Purja Pun, V. Yamakov, J. Hickman, E. H. Glaessgen 等 2020-11-20T15:40:54Z 置信度 0.70
• Training machine learning interatomic potentials for accurate phonon properties
  Abstract One of the major challenges in the development of universal machine learning interatomic potentials is accurately reproducing phonon properties. This issue appears to arise from the limitations of available datasets rather than the models themselves. …
  crossref Antoine Loew, Hai-Chen Wang, Tiago F T Cerqueira, Miguel A L Marques 2024-10-14T22:57:01Z 置信度 0.70
• In operando active learning of interatomic interaction during large-scale simulations
  Abstract A well-known drawback of state-of-the-art machine-learning interatomic potentials is their poor ability to extrapolate beyond the training domain. For small-scale problems with tens to hundreds of atoms this can be solved by using active learning whic…
  crossref M Hodapp, A Shapeev 2020-07-07T22:15:46Z 置信度 0.70
• Potential Application of Machine Learning in Forensic Ballistics
  Machine-learning forensics (MLF) is an emerging field within forensic science that leverages machine learning to identify criminal patterns, predict criminal activities (e.g., predict the location and timing of crimes) and automate investigative processes. For…
  crossref Pooja Ahuja, Kanica Chugh, Niha Ansari 2025-07-24T17:14:33Z 置信度 0.70
• A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles
  A Gaussian approximation machine learning interatomic potential for platinum is presented. It has been trained on density-functional theory (DFT) data computed for bulk, surfaces, and nanostructured platinum, in particular nanoparticles. Across the range of te…
  crossref Jan Kloppenburg, Livia B. Pártay, Hannes Jónsson, Miguel A. Caro 2023-03-10T20:05:10Z 置信度 0.70
• Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts
  Lithium-based disordered rocksalts (LDRs), which are an important class of positive electrode materials that can increase the energy density of current Li-ion batteries, represent a significantly complex chemical and configurational space for conventional dens…
  crossref 2024-05-24T09:40:12Z 置信度 0.70
• Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
  Abstract Reliable uncertainty quantification (UQ) is essential for developing machine-learned interatomic potentials (MLIPs) in predictive atomistic simulations. Conformal prediction (CP) is a statistical framework that constructs prediction intervals with gua…
  crossref Cheuk Hin Ho, Christoph Ortner, Yangshuai Wang 2025-11-14T16:01:51Z 置信度 0.70
• Efficient and Accurate pKa Prediction Enabled by Pre-Trained Machine-Learned Interatomic Potentials
  Quickly and accurately predicting the pKa of small molecules is an important unsolved challenge in computational chemistry: while approaches based on electronic structure theory have shown great promise, the utility of these methods is limited by the considera…
  crossref Corin Wagen, Arien Wagen 2024-03-08T07:44:13Z 置信度 0.70
• Exploring the Potential of Machine Learning in Educational Research
  crossref Myint Swe Khine 2024-02-24T19:01:58Z 置信度 0.70
• Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials
  Abstract Ga 2 O 3 is a wide-band gap semiconductor of emergent importance for applications in electronics and optoelectronics. However, vital information of the properties of complex coexisting Ga 2 O 3 polymorphs and low-symmetry disordered structures is miss…
  crossref Junlei Zhao, Jesper Byggmästar, Huan He, Kai Nordlund 等 2023-09-01T12:04:27Z 置信度 0.70
• High-Accuracy Machine-Learned Interatomic Potentials for the Phase Change Material Ge3Sb6Te5
  Ge3Sb6Te5 with an emerging\noff-stoichiometric\ncomposition has been proven to have characteristic properties of phase\nchange materials (PCMs) by experiments. However, the detailed mechanism\nof the phase transition and the highly temperature-dependent kineti…
  crossref 2023-08-14T21:10:20Z 置信度 0.70
• Small-data-based machine learning interatomic potentials for graphene grain boundaries enabled by structural unit model
  Machine learning interatomic potentials (MLIPs) are emerging as a powerful tool to achieve efficient atomistic simulations with DFT-level accuracy, which can greatly enhance the capability of modeling more realistic systems. However, training high-quality MLIP…
  crossref Ruiqiang Guo, Guotai Li, Jialin Tang, Yinglei Wang 等 2023-03-24T03:57:19Z 置信度 0.70
• The diffusion-driven orthorhombic to tetragonal transition in YBa2Cu3O7 derived with a machine learning interatomic potential
  Abstract Defects in high-temperature superconductors such as YBa 2 Cu 3 O 7 (YBCO) critically influence their superconducting behavior, as they substantially degrade or even suppress superconductivity. With the renewed interest in cuprates for next-generation …
  crossref Davide Gambino, Niccolò Di Eugenio, Jesper Byggmästar, Johan Klarbring 等 2026-05-21T02:54:40Z 置信度 0.70
• Performance-Based Selection of Machine Learning Interatomic Potentials for Studying Solid-State Electrolytes
  High-throughput, accurate prediction of solid-state electrolyte (SSE) properties is essential for advancing all-solid-state batteries (ASSBs). While density functional theory (DFT) can achieve high accuracy, the structural complexity of inorganic superionic co…
  crossref Donghee Chang, Amir Taqieddin, Forrest Laskowski 2026-04-02T13:18:03Z 置信度 0.70
• Machine Learning Interatomic Potential to Investigate Fundamentals of Electrolytes for Li-ion Solid-State Batteries
  Machine learning and deep learning are used to construct interatomic potential with superior performance by satisfying the accuracy of density functional theory (DFT) calculations while requiring computational resources comparable to those required for classic…
  crossref Kyoungmin Min 2023-01-03T00:16:33Z 置信度 0.70
• A machine-learning interatomic potential to understand the anisotropic fracture behavior of BaZrO3 material
  crossref Zhaoyang Wang, Yuhang Jing, Chuan Zhang, Yi Sun 等 2023-09-21T06:21:38Z 置信度 0.70
• Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion batteries
  SevenNet-0, pretrained on inorganic data, performs surprisingly well on liquid electrolytes—an out-of-distribution system—showing interpolation ability and improving further with fine-tuning.
  crossref Suyeon Ju, Jinmu You, Gijin Kim, Yutack Park 等 2025-05-06T22:00:52Z 置信度 0.70
• Machine learning-based interatomic potential development and phase transition analysis of ferroelectric hafnium dioxide
  The ferroelectric phase ( P c a 2 1 , which is in orthorhombic symmetry) of hafnium dioxide ( HfO 2 ) has gained much attention due to its potential applications in nanoelectronics and advanced memory devices. However, its complex phase behavior under external…
  crossref Yasantha Hetti Kankanamalage, Yufeng Xi, Shuai Zhang, Sobhit Singh 等 2026-01-29T14:16:06Z 置信度 0.70
• Estimating Potential-Dependent Physicochemical Properties at Metal–Electrolyte Interfaces Using Machine Learning Interatomic Potentials
  Metal–electrolyte interfaces play a central role in electrocatalysis, energy storage, and environmental remediation. Understanding the structure and properties of these interfaces is therefore essential to designing efficient electrochemical systems. Density f…
  crossref Ankit Mathanker, Jiawei Guo, Bryan R. Goldsmith, Joel B. Varley 等 2026-04-13T10:09:22Z 置信度 0.70
• Realistic Interatomic Potential Energy Functions
  Abstract Chapter 3 is devoted to realistic interatomic potential energy functions (PEF) and begins with a discussion of the need for more accurate representations of these functions. Following a historical discussion of the Mie/Lennard-Jones potential energy m…
  crossref Hui Li, Frederick R.W. McCourt 2024-04-30T03:20:44Z 置信度 0.70
• Author response for "Phonon transport and thermoelectric properties of Heusler-like Li2CuSb enabled by machine-learning interatomic potentials"
  crossref Min Li, Lang Chen, Wenjie Yuan, Jingjing Zhang 等 2026-08-11T21:08:09Z 置信度 0.70
• <i>(Invited)</i> Are Pre-Trained Universal Machine Learning Interatomic Potentials Ready for Solid Electrolyte Discovery?
  Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond those accessible to conventional first-principles methods. 1 Specifically, for solid electrolyte materials, MLIPs have bridged the gap between ionic condu…
  crossref Ji Qi, Runze Liu, Shyue Ping Ong 2025-11-24T08:13:58Z 置信度 0.70
• Design of Transition Metal Dichalcogenides Using General-Purpose Machine-Learned Interatomic Potentials
  Exploring the general-purpose interatomic potential for describing multiple compounds is challenging. In this study, we confirm the applicability of machine learning methods. By fitting the density-functional theory (DFT) data set using a feedforward neural ne…
  crossref 2025-12-08T20:30:57Z 置信度 0.70
• The comparison between the Pt-Rh thermodynamic database from machine learning interatomic potential and first-principles calculations
  Platinum–rhodium alloys are one of the prominent alloys used in high-temperature and high-corrosion environments. Pt and Rh maintain a single solid-solution phase up to high temperatures. To develop and design a part for use in the field, many techniques, tool…
  crossref Arkapol Saengdeejing, Ryoji Sahara, Hiori Kino, Yoshiyuki Kawazoe 等 2026-07-09T17:35:15Z 置信度 0.70
• Ligand-Controlled Phonon Dynamics in CsPbBr3 Nanocrystals Revealed by Machine-Learned Interatomic Potentials
  crossref 2026-04-28T02:50:47Z 置信度 0.70
• (<i>Invited</i>) Combining Machine-Learning-Interatomic-Potentials MD and Microkinetic Modeling to Unravel the Mechanism of Photoelectrochemical Water Oxidation on Hematite
  The oxygen evolution reaction (OER) plays a crucial role in (photo)electrochemical devices that use renewable energy to produce synthetic fuels. While the mechanism of this reaction is still debated, recent measurements on semiconducting oxides [1,2] have show…
  crossref Simone Piccinin 2025-07-21T07:52:02Z 置信度 0.70
• Accessing thermal conductivity of complex compounds by machine learning interatomic potentials
  While lattice thermal conductivity is an important parameter for many technological applications, its calculation is a time-consuming task, especially for compounds with a complex crystal structure. In this paper, we solve this problem using machine learning i…
  crossref Pavel Korotaev, Ivan Novoselov, Aleksey Yanilkin, Alexander Shapeev 2019-10-22T10:05:46Z 置信度 0.70
• Large-scale structure prediction of near-stoichiometric magnesium oxide based on a machine-learned interatomic potential: Crystalline phases and oxygen-vacancy ordering
  Using a fast and accurate neural network potential, we are able to systematically explore the energy landscape of large unit cells of bulk magnesium oxide with the minima hopping method. The potential is trained with a focus on the near-stoichiometric composit…
  crossref Hossein Tahmasbi, Stefan Goedecker, S. Alireza Ghasemi 2021-08-30T11:51:40Z 置信度 0.70
• Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
  The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the material's synthesis or …
  pubmed Kyle Noordhoek, Christopher J. Bartel, Noordhoek K, Bartel CJ 2024 Mar 28 置信度 0.82
• Growing Importance of Machine Learning in Healthcare to Determine Potential Risk
  The growing convenience of electronic healthcare data represents a significant opportunity within the healthcare segment, offering the potential for both pioneering discoveries as well as practical applications aimed at improving the overall quality of healthc…
  crossref Suchismita Mishra 2024-10-15T06:29:03Z 置信度 0.70
• Machine Learning for Identifying Potential Photovoltaic Installations on Parking Areas
  crossref Frederick Kistner, Sina Keller 2025-04-21T22:29:21Z 置信度 0.70
• Best practices for fitting machine learning interatomic potentials for molten salts: A case study using NaCl-MgCl2
  crossref Siamak Attarian, Chen Shen, Dane Morgan, Izabela Szlufarska 2024-09-24T07:44:08Z 置信度 0.70
• Recent advances in lattice thermal conductivity calculation using machine-learning interatomic potentials
  The accuracy of the interatomic potential functions employed in molecular dynamics (MD) simulation is one of the most important challenges of this technique. In contrast, the high accuracy ab initio quantum simulation cannot be an alternative to MD due to its …
  crossref Saeed Arabha, Zahra Shokri Aghbolagh, Khashayar Ghorbani, S. Milad Hatam-Lee 等 2021-12-06T10:40:20Z 置信度 0.70
• Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization
  Successful engineering of a microbial host for efficient production of a target product from a given substrate can be viewed as an extensive optimization task. Such a task involves the selection of high activity enzymes as well as their gene expression regulat…
  crossref Wenfa Ng 2021-11-15T12:02:12Z 置信度 0.70
• Benchmarking Universal Machine-learned Interatomic Potentials for Intermolecular and Noncovalent Interactions
  Accurate benchmarking of intermolecular interaction energies is central to evaluating quantum chemical methods and guiding the development of reliable machine-learned interatomic potentials (MLIPs) for chemical and biological applications. In this work, we ben…
  crossref Kamal Singh Nayal, Ilkwon Cho, Olexandr Isayev 2026-02-18T17:27:35Z 置信度 0.70
• AisNet: A Universal Interatomic Potential Neural Network with Encoded Local Environment Features
  This paper proposes a new interatomic potential energy\nneural network,\nAisNet, which can efficiently predict atomic energies and forces covering\ndifferent molecular and crystalline materials by encoding universal\nlocal environment features, such as element…
  crossref 2023-03-10T12:40:23Z 置信度 0.70
• Review for "Parsimonious machine learning for the global mapping of aboveground biomass potential"
  crossref 2025-03-20T18:20:01Z 置信度 0.70
• Redefining atomistic simulations of all-solid-state batteries through machine learning interatomic potentials
  crossref Qian Chen, Siwen Wang, Chen Ling 2025-09-04T00:43:37Z 置信度 0.70
• Bias in Universal Machine-Learned Interatomic Potentials and Its Effects on Fine-Tuning
  Universal machine learned interatomic potentials (uMLIPs) embody a growing area of interest due to their transferability across the periodic table, displaying an error of about 0.6 kcal/mol against the Matbench Discovery test set. However, we show that achievi…
  crossref 2026-06-20T13:00:56Z 置信度 0.70
• Improving Molecular‐Dynamics Simulations for Solid–Liquid Interfaces with Machine‐Learning Interatomic Potentials
  Abstract Emerging developments in artificial intelligence have opened infinite possibilities for material simulation. Depending on the powerful fitting of machine learning algorithms to first‐principles data, machine learning interatomic potentials (MLIPs) can…
  pubmed Pengfei Hou, Yumiao Tian, Xing Meng, Hou P 等 2024 Sep 2 置信度 0.82
• Interatomic Potential Models for Ionic Materials
  crossref Julian D. Gale 2007-11-17T01:38:42Z 置信度 0.70
• Identifying Potential Missteps of Machine Learning in Molecular Chemistry
  Machine learning-based methods are widely used today in chemical tasks, particularly in drug design. Graph Convolutional Neural Networks (GCNNs) compete with one another in predicting chemical properties, achieving errors comparable with those of experimental …
  crossref Anastasiia Smirnova, Artem Mitrofanov 2025-03-07T03:45:26Z 置信度 0.70
• Unified Graph-based Interatomic Potential for Perovskite Structure Optimization
  Halide perovskites hold immense potential for applications such as optoelectronics and catalysis. Their vast compositional space spanning bulk alloys, defects, impurities, surfaces, and surface defects poses significant challenges for efficient exploration and…
  crossref Maitreyo Biswas, Rushik Desai, Gavin Bidna, Arun Mannodi-Kanakkithodi 2025-07-14T06:18:56Z 置信度 0.70
• Interatomic potential for neon; bulk properties from scattering potential
  crossref J.A. Barker 2002-07-25T03:39:09Z 置信度 0.70
• Constant-Potential Machine Learning Molecular Dynamics Simulations Reveal Potential-Regulated Cu Cluster Formation on MoS2
  Electrochemical processes play a crucial role in energy storage and conversion systems, yet their computational modeling remains a significant challenge. Accurately incorporating the effects of electric potential has been a central focus in theoretical electro…
  crossref 2025-03-25T16:13:41Z 置信度 0.70
• Assessing the accuracy of machine learning interatomic potentials in predicting the elemental orderings: A case study of Li-Al alloys
  crossref Yunsheng Liu, Yifei Mo 2024-02-07T04:29:06Z 置信度 0.70