AI 驱动科学发现 · 全部实体（第 8 / 100 页）

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• Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks
  Accurate microscopic models of amorphous metal–organic frameworks (MOFs) are difficult to create. Machine learning potentials based on data from ab initio molecular dynamics offer a novel way to achieve this goal.
  crossref Nicolas Castel, Dune André, Connor Edwards, Jack D. Evans 等 2024-01-08T04:41:40Z 置信度 0.70
• Unlocking Potential Catalysts: A Machine Learning Approach with Bayesian and Regression Models
  Abstract Due to their excellent catalytic efficiency, endurance, adaptability, and unusual structure, single‐atom alloys are an important category of materials with huge potential for efficiently utilising rare and costly metals in catalytic applications. Sinc…
  crossref Chandra Chowdhury 2024-10-01T10:49:24Z 置信度 0.70
• Machine Learning in Spill Regulation
  crossref Aakaash Narayanan 2024-05-26T02:05:37Z 置信度 0.70
• eLife assessment: Machine learning and biological validation identify sphingolipids as potential mediators of paclitaxel-induced neuropathy in cancer patients
  crossref Pramod K Mistry 2024-09-30T09:51:12Z 置信度 0.70
• Applying Machine learning to Performance Management: Obstacles and Potential
  Technology and big data have changed organizational management in many ways. Machine learning can improve performance management. This article examines how machine learning affects performance management accuracy, efficiency, and decision-making. This paper re…
  crossref Pankaj Kumar 2024-03-01T09:02:32Z 置信度 0.70
• Advanced Production Diagnostics and Optimized Drilling Strategy: Unlocking the True Potential of Production Data Using Physics and Machine Learning
  Abstract Reliable well survival statistics forecast for shale wells is crucial for investment decisions, optimized drilling rates, and energy security policies. At present, the industry lacks a standard analytical or data-driven solution for survival probabili…
  crossref S. Haider 2024-02-27T21:56:19Z 置信度 0.70
• Supervised Machine Learning and Molecular Docking Modeling to Identify Potential Anti-Parkinson's Agents
  crossref adib ghaleb, adnane aouidate, mohamed AARJANE, anane hafid 2024-11-21T12:38:53Z 置信度 0.70
• Product potential user prediction based on machine learning
  In today's ever-changing business environment, it is crucial for businesses to market their products with the needs of users in mind to increase profitability. With consumers increasingly receiving services through digital platforms, analyzing potential users …
  crossref Jingyi Yu 2024-06-18T18:05:02Z 置信度 0.70
• Exploration of Potential Biomarkers for Diagnosing Dementia through Machine Learning Techniques
  Dementia, marked by cognitive decline and neuropsychiatric symptoms, significantly impacts individuals and society, especially with an aging global population. Despite the need for early diagnosis and intervention, current diagnostic methods are costly and inv…
  crossref Muchen Xu 2024-09-04T17:28:12Z 置信度 0.70
• Integration of Metaverse and Machine Learning in the Education Sector
  E-Tutor is revolutionizing education through technology, offering personalized learning with integrated study materials, video lectures, interactive quizzes, and metaverse features. Research indicates a 35% increase in student engagement and a 20% improvement …
  crossref J. Shanthalakshmi Revathy, J. Mangaiyarkkarasi 2024-08-09T18:15:01Z 置信度 0.70
• Development of Machine Learned Interatomic Potentials for Modeling Plasma-Material Interactions
  crossref Mary Alice Cusentino, Ember Salas, Megan McCarthy, Mitchell Wood 等 2025-05-07T02:13:43Z 置信度 0.70
• Studies of Ni-Cr complexation in FLiBe molten salt using machine learning interatomic potentials
  crossref Siamak Attarian, Dane Morgan, Izabela Szlufarska 2024-03-19T17:21:36Z 置信度 0.70
• Review for "Automated detection of potential artifacts in machine learning based bio-image segmentation"
  crossref 2025-10-21T21:16:14Z 置信度 0.70
• DPχ: Constant-Potential Machine-Learning Potentials for Large-Scale Electrocatalysis
  We introduce DPχ, a machine-learning potential that enables electrocatalysis simulations at spatiotemporal scales 10^3-10^4 beyond AIMD while retaining near first-principles accuracy. DPχ adopts a Bader-basin-centroid (BC) representation and explicitly decompo…
  crossref Junxiang Chen, Tao Wang 2025-08-31T03:42:42Z 置信度 0.70
• Review for "Automated detection of potential artifacts in machine learning based bio-image segmentation"
  crossref 2025-10-21T21:16:14Z 置信度 0.70
• Review for "Automated detection of potential artifacts in machine learning based bio-image segmentation"
  crossref 2025-10-21T21:16:14Z 置信度 0.70
• Review for "Automated detection of potential artifacts in machine learning based bio-image segmentation"
  crossref 2025-10-21T21:16:14Z 置信度 0.70
• Unified Graph-based Interatomic Potential for Perovskite Structure Optimization
  Halide perovskites (HaPs) 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 explor…
  crossref Maitreyo Biswas, Rushik Desai, Gavin Bidna, Arun Mannodi-Kanakkithodi 2025-11-14T10:44:38Z 置信度 0.70
• Decision letter for "Modelling Silica using MACE-MP Machine Learnt Interatomic Potentials"
  crossref 2025-07-21T21:05:55Z 置信度 0.70
• Decision letter for "Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials"
  crossref 2025-04-25T17:14:06Z 置信度 0.70
• Mechanical Properties of Boron Nitride Nanosheets Using Machine-Learned Interatomic Potential
  crossref Mahesh, Vijay Choyal, Nitin Luhadiya, S. I. Kundalwal 2025-10-10T16:39:34Z 置信度 0.70
• Decision letter for "Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials"
  crossref 2025-04-25T17:14:06Z 置信度 0.70
• Kanad: An HPC Framework for Large–Scale Machine–Learned Interatomic Potentials
  Machine-learned interatomic potentials (MLIPs) offer near-DFT accuracy at classical molecular dynamics computational cost, yet developing accurate, robust potentials for largescale simulations remains challenging. We present Kanad, an integrated HPC framework …
  crossref Utkarsh Bhardwaj, Vinayak Mishra, Manoj Warrier 2026-01-19T20:52:47Z 置信度 0.70
• Potential energy surfaces: Δ-machine learning from analytical functional forms
  Delta-machine learning (Δ-ML) is a highly cost-effective approach to developing high-level potential energy surfaces (PES) from a large number of low-level configurations. In particular, the high flexibility of the analytical PES-2008 is exploited to efficient…
  crossref Cipriano Rangel, Joaquin Espinosa-Garcia 2025-04-16T08:29:32Z 置信度 0.70
• A robust machine learned interatomic potential for Nb: collision cascade simulations with accurate non-equilibrium properties
  Abstract Niobium (Nb) and its alloys are extensively used in various technological applications owing to their favorable mechanical, thermal and irradiation properties. Accurately modeling Nb under irradiation is essential for predicting microstructural change…
  crossref Utkarsh Bhardwaj, Vinayak Mishra, Suman Mondal, Manoj Warrier 2025-09-09T22:49:28Z 置信度 0.70
• Unifying the Description of Hydrocarbons and Hydrogenated Carbon Materials with a Chemically Reactive Machine Learning Interatomic Potential
  High Resolution Image Download MS PowerPoint Slide We present a general-purpose machine learning (ML) interatomic potential for carbon and hydrogen which is capable of simulating various materials and molecules composed of these elements. This ML interatomic p…
  crossref Rina Ibragimova, Mikhail S. Kuklin, Tigany Zarrouk, Miguel A. Caro 2025-01-22T17:09:58Z 置信度 0.70
• Review for "Automated detection of potential artifacts in machine learning based bio-image segmentation"
  crossref 2025-10-21T21:16:14Z 置信度 0.70
• Automated detection of potential artifacts in machine learning based bio-image segmentation
  Abstract Image segmentation algorithms, while powerful, are inherently prone to artifacts, making perfect segmentation theoretically and practically impossible. We propose an automated artifact identification scheme for posterior rapid manual re-correction to …
  crossref Saiyam B Jain, Zongru Shao, Michael Hecht 2025-10-20T22:52:26Z 置信度 0.70
• Trapping mechanisms in potential subsurface carbon storage reservoirs and their prediction by machine learning
  crossref David A. Wood 2025-02-21T09:38:40Z 置信度 0.70
• Learning Long-Range Interactions in Equivariant Machine Learning Interatomic Potentials via Electronic Degrees of Freedom
  Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties. Message-passing graph neural networks, commonly used in these MLIPs, rely on local descriptor-…
  crossref Moin Uddin Maruf, Sungmin Kim, Zeeshan Ahmad 2025-08-26T11:09:31Z 置信度 0.70
• Significant increase in thermal conductivity of cathode material LiFePO₄ by Na substitution: A machine learning interatomic potential-assisted investigation
  Abstract LiFePO 4 is a cathode material with good thermal stability, but low thermal conductivity is a critical problem. In this study, we employ a machine learning potential approach based on first-principles methods combined with the Boltzmann transport theo…
  crossref Shi-Yi 诗怡 Li 李, Qian 骞 Liu 刘, Yu-Jia 育佳 Zeng 曾, Guofeng 国锋 Xie 谢 等 2024-12-13T08:48:37Z 置信度 0.70
• Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data
  We present a new method for fingerprint- ing atomic configurations relevant to ML-IAM training and application, utilizing the ChIMES descriptor. These fingerprints enable rigor- ous analysis of statistical distinguishability be- tween configurations. Sample ap…
  crossref Benjamin Laubach, Rebecca Lindsey 2025-04-08T07:46:42Z 置信度 0.70
• FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
  We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine‐learning force fields (MLFFs) with 3D potential‐energy‐surface sampling and interpolation. Our method suppresses periodic self‐interacti…
  crossref Hanwen Kang, Tenglong Lu, Zhanbin Qi, Jiandong Guo 等 2025-09-17T22:50:42Z 置信度 0.70
• A machine-learning interatomic potential for iron under high pressure and its application to shock response
  crossref Xin Zeng, Shifang Xiao, Yangchun Chen, Xiaofan Li 等 2025-06-13T02:43:29Z 置信度 0.70
• Interplay of water film dewetting and hydrogen evolution on Pt(111): Insights from a machine-generated interatomic potential
  The processes that determine the kinetics of hydrogen evolution reaction (HER) on metal surfaces remain a topic of discussion despite their long-standing importance in improving the efficiency of hydrogen generation. A major cause of this uncertainty is the ex…
  crossref Michael E. Foster, Norman C. Bartelt, Reese E. Jones 2025-10-22T10:35:32Z 置信度 0.70
• Systematic softening in universal machine learning interatomic potentials
  Abstract Machine learning interatomic potentials (MLIPs) have introduced a new paradigm for atomic simulations. Recent advancements have led to universal MLIPs (uMLIPs) that are pre-trained on diverse datasets, providing opportunities for universal force field…
  crossref Bowen Deng, Yunyeong Choi, Peichen Zhong, Janosh Riebesell 等 2025-01-10T12:44:24Z 置信度 0.70
• Predicting hydrogen diffusion in nickel–manganese random alloys using machine learning interatomic potentials
  Abstract To advance carbon neutrality, structural materials for high-pressure hydrogen environments must be designed based on fundamental principles. However, the atomic-scale complexity of random alloys hinders the development of interatomic potentials that c…
  crossref Kazuma Ito, Naoki Matsumura, Yuto Iwasaki, Yasufumi Sakai 等 2025-08-23T11:59:04Z 置信度 0.70
• utils4VASP: Setup and Evaluation of Electronic Structure and Machine-Learned Interatomic Potential Simulations with VASP
  We present an open-source collection of scripts and programs for the setup, management and evaluation of calculations with the Vienna ab initio simulation package (VASP), called utils4VASP. It contains 14 independent Python scripts and Fortran programs, all wi…
  crossref Julien Steffen, Andreas Mölkner, Maximilian A. Bechtel 2025-11-06T18:19:38Z 置信度 0.70
• Decision letter for "Parsimonious machine learning for the global mapping of aboveground biomass potential"
  crossref 2025-03-20T18:20:01Z 置信度 0.70
• Optimizing machine learning interatomic potentials for hydroxide transport: Surprising efficiency of single-concentration training
  We investigate the transferability of machine learning interatomic potentials across concentration variations in chemically similar systems, using aqueous potassium hydroxide solutions as a case study. Despite containing identical chemical species (K+, OH−, an…
  crossref Jonas Hänseroth, Christian Dreßler 2025-08-28T10:11:44Z 置信度 0.70
• Decision letter for "Automated detection of potential artifacts in machine learning based bio-image segmentation"
  crossref 2025-10-21T21:16:14Z 置信度 0.70
• Decision letter for "Automated detection of potential artifacts in machine learning based bio-image segmentation"
  crossref 2025-10-21T21:16:14Z 置信度 0.70
• Decision letter for "Automated detection of potential artifacts in machine learning based bio-image segmentation"
  crossref 2025-10-21T21:16:14Z 置信度 0.70
• Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations
  Abstract Active learning (AL) requires massive time for comprehensive sampling of complex potential energy surfaces to achieve desirable accuracy and stability of machine learning (ML) potentials. Here, we develop an active delta-learning (ADL) protocol for sp…
  crossref Yaohuang Huang, Yi-Fan Hou, Pavlo O Dral 2025-07-02T19:01:36Z 置信度 0.70
• Decision letter for "Parsimonious machine learning for the global mapping of aboveground biomass potential"
  crossref 2025-03-20T18:20:01Z 置信度 0.70
• Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials
  Abstract Understanding the thermal transport properties of CALF-20, a recent addition to the metal-organic framework family, is crucial for its effective utilization in greenhouse gas capture. Here, we report the thermal transport study of CALF-20 using artifi…
  crossref Soham Mandal, Prabal K. Maiti 2025-02-02T11:08:07Z 置信度 0.70
• Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential
  Abstract Using first‐principles‐based machine‐learning potential, molecular dynamics (MD) simulations are performed to investigate the micro‐mechanism in phase transition of . Treating the DFT results of the low‐ and intermediate‐temperature phases of as train…
  crossref Xinhang Li, Yongqiang Wang, Tianyu Jiao, Zhaoxin Liu 等 2025-05-06T09:34:09Z 置信度 0.70
• TiAlNb alloy interatomic potentials: comparing passive and active machine learning techniques with MTP and DeePMD
  Intermetallic titanium aluminides are interesting for aerospace and automotive applications due to their superior high-temperature mechanical properties. In particular, γ -TiAl-based alloys containing 5–10 at.% Niobium (Nb) have attracted significant attention…
  crossref Anju Chandran, Archa Santhosh, Claudio Pistidda, Paul Jerabek 等 2025-07-30T09:57:48Z 置信度 0.70
• Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals
  We fine-tune machine learning interatomic potentials to accurately model molecular crystals at finite temperature with the inclusion of nuclear quantum effects.
  europepmc Flaviano Della Pia, Benjamin X. Shi, Venkat Kapil, Andrea Zen 等 2025 置信度 0.80
• A Machine Learning Potential for the in silico Design of Functional Organic Molecular Crystals
  The design and discovery of functional molecular materials can be greatly accelerated through in silico approaches. Machine learning (ML) models, in particular, demonstrate significant promise for the rapid analysis and manipulation (and re-analysis) of both m…
  crossref Vinayak Bhat, Chad Risko 2025-06-11T03:31:36Z 置信度 0.70
• Potential of Quantum Machine Learning for Processing Multispectral Earth Observation Data
  Quantum computers with hundreds of noisy qubits are already available for the research community. They have the potential to run complex quantum computations well beyond the computational capacity of any classical device. It is natural to ask the question, wha…
  crossref Manish Kumar Gupta, Michał Romaszewski, Piotr Gawron 2025-06-02T16:32:21Z 置信度 0.70
• Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data
  We present a new method for fingerprint- ing atomic configurations relevant to ML-IAM training and application, utilizing the ChIMES descriptor. These fingerprints enable rigor- ous analysis of statistical distinguishability be- tween configurations. Sample ap…
  crossref Benjamin Laubach, Vincenzo Lordi, Rebecca Lindsey 2025-12-23T13:12:39Z 置信度 0.70
• Ml4solar: Continental Solar Potential Evaluation Using Machine Learning 
  crossref Chika Calistus Maduabuchi, Kingsley Okoli, Chigozie Udoh 2025-03-26T16:34:04Z 置信度 0.70
• Constant Potential Electrochemistry with Multiscale Quantum Mechanical / Machine Learning Simulations
  A deeper theoretical understanding of electrified interfaces becomes important for the development and control of electrochemical devices. While much progress has been in computational electrochemistry, constant potential simulations pose fundamental challenge…
  crossref Seung-Jae Shin, Kara D. Fong, Aron Walsh 2025-10-30T05:51:15Z 置信度 0.70
• Potential of Employing a Machine Learning Model for Glacier Motion Monitoring
  Monitoring marine-terminating glaciers and their dynamics in the light of advancing climate change is a critical concern for many scientists. Observing marine-terminating glaciers in Greenland is especially significant because glacier calving and melting influ…
  crossref Magdalena Łucka, Ryszard Hejmanowski, Wojciech Witkowski 2024-03-11T11:16:49Z 置信度 0.70
• Atomistic insights into room-temperature ion conduction mechanisms in Li10GeP2S12 via machine learning interatomic potentials
  Solid-state electrolytes (SSEs), as a key component of all-solid-state lithium-ion batteries, face significant hurdles for commercialization due to their inherently lower ionic conductivity compared to conventional liquid electrolytes. Among various SSE candid…
  crossref Seonhye Park, Joonhee Kang 2025-10-01T12:01:11Z 置信度 0.70
• TENSORMD: Accelerating Molecular Dynamics with a High-Performance Machine Learning Interatomic Potential
  AI has been integrated into HPC across various scientific fields, significantly enhancing performance. In molecular dynamics simulations, HPC+AI facilitates the investigation of atomic-scale physical properties using machine-learning interatomic potentials (ML…
  crossref Yucheng Ouyang, Xin Chen, Ying Liu, Xin Chen 等 2025-11-12T16:05:39Z 置信度 0.70
• High-Throughput Screening of Ru-Based MOF-Supported Single-Atom Catalysts for Hydrogen Evolution Reaction Via Machine Learning Interatomic Potential
  Single-atom catalysts supported on metal–organic frameworks (SAC-MOFs) offer high metal utilization and tunable coordination environments. However, identifying optimal metal-MOF combinations from the vast chemical space remains a formidable challenge. Here, we…
  crossref Hwayeon Kum, Jihan Kim 2025-11-13T20:10:53Z 置信度 0.70
• Machine learning reveals potential consequences of cuts to US research
  crossref 2025-09-24T16:48:19Z 置信度 0.70
• A machine-learned interatomic potential for defects investigation in lead-free double perovskite Cs ₂ NaYbCl ₆
  Abstract We derive and validate a machine-learned interatomic potential, based on the Chebyshev Interaction Model for Efficient Simulation, for the lead-free double perovskite Cs 2 NaYbCl 6 , with special emphasis on native defect behavior. Starting from Densi…
  crossref Riccardo Dettori, Antonio Cappai, Claudio Melis, Luciano Colombo 2025-10-07T22:49:16Z 置信度 0.70
• Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress–Strain and Vibrational Properties
  High Resolution Image Download MS PowerPoint Slide Machine learning interatomic potentials (MLIPs) offer an efficient and accurate framework for large-scale molecular dynamics (MD) simulations, effectively bridging the gap between classical force fields and ab…
  crossref Felipe Hawthorne, Paulo R. E. Raulino, Ronaldo Rodrigues Pelá, Cristiano F. Woellner 2025-07-12T13:14:49Z 置信度 0.70
• Machine Learning Assessment of Soil Carbon Sequestration Potential: Integrating Land Use, Pedology, and Machine Learning in Croatia
  Spatially quantifying the soil carbon sequestration potential (SCSP) is crucial for targeting climate change mitigation strategies like carbon farming. However, static mapping approaches often fail by assuming that the drivers of soil organic carbon (SOC) are …
  crossref Lucija Galić, Mladen Jurišić, Ivan Plaščak, Dorijan Radočaj 2025-12-22T07:01:33Z 置信度 0.70
• A Study to Discuss the Potential of Mobile Network Optimization Using Machine Learning
  5G networks promise a surge in data traffic, stricter service demands, and cost efficiency. However, accurately predicting complex and dynamic user behavior such as cyber, physical, and social systems remains a challenge. This chapter explores these challenges…
  crossref Geeta Arora, Jaya Gupta, Shubham Mishra 2025-03-27T08:24:07Z 置信度 0.70
• An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
  Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio methods. Fine-tuning pretrained MLIPs offers a practical app…
  crossref Jisu Kim, Jiho Lee, Sangmin Oh, Yutack Park 等 2025-12-17T08:14:26Z 置信度 0.70
• Universal interatomic potentials shine in finding crystal structures
  crossref Ju Li 2025-06-26T05:03:32Z 置信度 0.70
• Potential Applications of Machine Learning in Forensic Questioned Document Examination
  The chapter discusses the potential applications of artificial intelligence and machine learning (AI–ML) techniques in forensic document analysis. The field of forensic document examination has been dominated by manual methods of examination worldwide in the a…
  crossref Surbhi Mathur, Sumit Kumar Choudhary, Parvesh Sharma, Kritika Sood 等 2025-07-24T17:14:33Z 置信度 0.70
• Landslide modeling in the age of AI: A review of physically based and machine learning approaches and their potential integration
  crossref Husam Al-Najjar, Biswajeet Pradhan, Ghassan Beydoun 2025-07-23T09:10:54Z 置信度 0.70
• Physics-Informed Machine Learning for Mapping the Heat Mitigation Potential of Vegetation in Singapore
  This study develops a Physics-Informed Neural Network (PINN) model using a multi-layer perceptron (MLP) to estimate vegetation cooling effects, specifically spatially averaged air temperature reduction (ΔT) and vegetation-surrounding UTCI reduction (ΔUTCI), ac…
  crossref Liqing Zhang, Chao Yuan 2025-05-21T15:10:32Z 置信度 0.70
• Estimating Soil Carbon Sequestration Potential with Mid-Ir Spectroscopy and Explainable Machine Learning
  crossref Yang Hu, Raphael Viscarra Rossel 2025-02-11T18:36:58Z 置信度 0.70
• Review for "A Simplified Machine Learning Workflow for Identifying Potential Singlet Fission Candidates: Benzannulated Biphenylenes as a Case Study"
  crossref 2026-03-10T21:13:06Z 置信度 0.70
• Exploring the energy landscape of aluminas through machine learning interatomic potential
  Despite the widespread applications of alumina due to its rich polymorphism, the structures of many transitional aluminas remain unresolved. This work employs the neuroevolution potential (NEP) approach to accurately describe polymorphic aluminas. Its accuracy…
  crossref Lei Zhang, Wenhao Luo, Renxi Liu, Mohan Chen 等 2025-02-07T11:36:29Z 置信度 0.70
• High temperature melting of dense molecular hydrogen from machine-learning interatomic potentials trained on quantum Monte Carlo
  We present results and discuss methods for computing the melting temperature of dense molecular hydrogen using a machine learned model trained on quantum Monte Carlo data. In this newly trained model, we emphasize the importance of accurate total energies in t…
  crossref Shubhang Goswami, Scott Jensen, Yubo Yang, Markus Holzmann 等 2025-02-05T10:49:46Z 置信度 0.70
• Small-cell-based fast active learning of machine learning interatomic potentials
  Machine learning interatomic potentials (MLIPs) are often trained with on-the-fly active learning, where sampled configurations from atomistic simulations are added to the training set. However, this approach is limited by the high computational cost of ab ini…
  crossref Zijian Meng, Hao Sun, Edmanuel Torres, Christopher Maxwell 等 2025-05-10T12:25:14Z 置信度 0.70
• High-Throughput Screening of Metal Organic Frameworks for Zn-Ion Conductors Using Machine Learning Interatomic Potentials
  Metal organic frameworks (MOFs) are a relatively new class of materials with metal-complex based cations connected with organic linker chains. They offer a diverse range of properties due to combination of different cation complexes and linkers. Moreover, they…
  crossref Venkata Surya Chaitanya Kolluru, Yiming Chen, Saifeldeen Abed Alrhman, Harshan Reddy Gopidi 等 2025-11-24T08:25:01Z 置信度 0.70
• Unveiling the Potential Benefits of Machine Learning Integration in Security Support Mechanisms for Cloud Computing
  crossref Samatha K, A. Mohan 2025-01-09T17:38:22Z 置信度 0.70
• Identification Of Potential Sglt2 Inhibitors for Antidiabetic Drug Repositioning Using Machine Learning
  crossref Julius Mulindwa, Frank Kalungi, Eric Kisakye Kyambadde 2025-06-19T01:38:24Z 置信度 0.70
• Integrative Machine Learning Reveals Potential Signature Genes Using Transcriptomics in Colon Cancer
  Abstract Background Colon cancer is a significant health burden in the world and the second leading cause of cancer-related deaths. Despite advancements in diagnosis and treatment, identifying robust biomarkers for early detection and therapeutic targets remai…
  crossref Mostafa Amir Hamza, Md. Saiful Islam 2025-03-07T06:50:14Z 置信度 0.70
• WITHDRAWN: Decoding Innovation Potential of Japanese Firms with A Machine Learning Approach
  Abstract This study proposes an efficiency technique for predicting firm-level innovation capabilities utilizing machine learning models for improving the forecast accuracy. The study employed boosted trees and neural boosting models and compared them with tra…
  crossref Niyata Kawewong, Napitiporn Manoli, Yoshiyuki Matsuura 2025-01-24T03:14:47Z 置信度 0.70
• Unified approach to generating a training set for machine learning interatomic potentials: The case of BCC tungsten
  crossref Andrey A. Kistanov, Igor V. Kosarev, Stepan A. Shcherbinin, Alexander V. Shapeev 等 2025-01-05T07:36:52Z 置信度 0.70
• Interpretable Machine Learning and Comparative Genomics Reveal Microbial Plastic-Degrading (Microbeyt) Potential
  Abstract Plastic pollution poses a critical environmental threat, and microbial enzymes represent a sustainable strategy for polymer degradation. We present a computational pipeline that integrates orthogroup-based genomic analysis with machine learning and in…
  crossref Lokendra S. Thakur, Gurpreet Bharj, Manish Saroya 2025-09-19T13:40:14Z 置信度 0.70
• WITHDRAWN: Decoding Innovation Potential of Japanese Firms with A Machine Learning Approach
  Abstract The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
  crossref Niyata Kawewong, Napitiporn Manoli, Yoshiyuki Matsuura 2025-05-22T15:25:53Z 置信度 0.70
• Text Mining and Machine Learning-Based Data Extraction for Environmental Pollutants (PPDs and PPDs-Q): Machine Learning-Enabled Global Aqueous Concentration Compilation and Potential Biotic Risk Implications
  p-Phenylenediamine antioxidants (ppDs) and their quinone derivatives (ppDs-Q) are key additives in rubber products with strong toxicity, persistence, and increasing aquatic concentrations, though global data scarcity hinders risk assessment. Thisstudy addresse…
  crossref Yaolin Zhang, Menghui Li 2025-08-28T02:05:58Z 置信度 0.70
• Simulating the Effects of the Native Oxide Layer During Kinetic Spraying of Tantalum Particles Using a Deep Learning Interatomic Potential
  crossref Stephen G. Bierschenk, Michael F. Becker, Desiderio Kovar 2025-04-11T14:41:57Z 置信度 0.70
• Exploring potential circRNA biomarkers for cancers based on double-lineheterogeneous graph representation learning
  crossref ZhenMei Wang 2025-11-26T05:54:48Z 置信度 0.70
• The Structure and Migration of Twin Boundaries in Tetragonal β-Sn: An Application of Machine Learning Based Interatomic Potentials
  crossref Ian Chesser, Mashroor Nitol, Esther C. Hessong, Himanshu Joshi 等 2025-06-17T19:40:31Z 置信度 0.70
• 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 a computationally efficient mach…
  crossref Ali Rajabpour, Bohayra Mortazavi, Pedram Mirchi, Julien El Hajj 等 2025-03-18T15:30:21Z 置信度 0.70
• Accelerating the training and improving the reliability of machine-learned interatomic potentials for strongly anharmonic materials through active learning
  Molecular dynamics (MD) employing machine-learned interatomic potentials (MLIPs) serve as an efficient, urgently needed complement to molecular dynamics. By training these potentials on data generated from methods, their averaged predictions can exhibit compar…
  crossref Kisung Kang, Thomas A. R. Purcell, Christian Carbogno, Matthias Scheffler 2025-06-06T11:49:22Z 置信度 0.70
• Machine Learning-Driven Screening of g-C3N4 Supported Single-Atom-Catalysts for CO2 Conversion and Potential Syngas Production
  crossref Yifu Li 2025-06-13T18:10:32Z 置信度 0.70
• Global Modeling of Fluorescent Aerosol Particles with Machine Learning Reveals Potential Regional Anthropogenic Impacts
  Primary biological aerosol particles (PBAPs) significantly affect human health and aerosol-cloud-climate interactions. Fluorescent aerosol particles (FAPs), detected using light/laser-induced fluorescence (LIF) instruments, serve as a crucial proxy for underst…
  crossref Yanhao Miao, Patrick K. H. Lee 2025-03-15T04:22:26Z 置信度 0.70
• Integrating machine learning-driven virtual screening and molecular dynamics simulations to identify potential TNF-α inhibitors for bipolar disorder
  crossref Subathra Selvam 2025-08-19T15:22:29Z 置信度 0.70
• Forest ecosystem restoration potential unravelled through machine learning algorithms
  Decades of scientific development have led to a specialisation of environmental analyses, hampering the recognition of forest ecosystem restoration. However, an integrated approach is required to better understand the trajectory of forest recovery. In this stu…
  crossref Jenny Vivian, Robin L. Chazdon, Alison Shapcott, David J Lee 2025-12-02T14:40:40Z 置信度 0.70
• Author response for "Automated detection of potential artifacts in machine learning based bio-image segmentation"
  crossref Saiyam Jain, Zongru Shao, Michael Hecht 2025-10-21T21:16:14Z 置信度 0.70
• Understanding Dynamics of Nanocluster-Organic Frameworks and Gas Diffusion from Machine Learning Potential-based Simulations
  Nanocluster-organic frameworks (NOFs) have emerged as unique materials with broad applications in sensing, photocatalysis, and optoelectronics. These photofunctional materials have an excellent luminescence switching response to gases such as oxygen (\ce{O2}) …
  crossref Animesh Karmakar, Dhananjay Gupta, Tarak Karmakar 2025-04-16T03:31:31Z 置信度 0.70
• Modeling Potential Earthquake Risk Zones in Myanmar Due to the March 2025 Earthquake and Geological Factors Using Machine Learning
  The magnitude of an earthquake is related to many variables. Among those variables are the geological rocks. It is hypothesized that a sedimentary rock is one of the geological types that can magnify an earthquake wave. Recently an earthquake with a strong mag…
  crossref Andri Wibowo 2025-04-01T01:29:14Z 置信度 0.70
• Author response for "Automated detection of potential artifacts in machine learning based bio-image segmentation"
  crossref Saiyam Jain, Zongru Shao, Michael Hecht 2025-10-21T21:16:14Z 置信度 0.70
• Review for "<b>Machine learning potential for modelling dynamic hydrogen bond networks in MOF MIL-120</b>"
  crossref 2026-04-02T21:13:15Z 置信度 0.70
• Empowering a sustainable future: unleashing the potential of machine learning for energy efficiency and conservation
  crossref Tarik Ikbal 2024-11-15T04:56:00Z 置信度 0.70
• Label-Free, Quantum-Mechanically Informed Scoring of Protein–Ligand Complexes with Machine-Learned Interatomic Potentials
  Virtual chemical libraries now exceed billions of compounds, placing joint demands on the scoring tools used to prioritize candidates for accuracy and scalability. Supervised affinity models meet scalability demands but remain vulnerable to dataset memorizatio…
  crossref ilkwon cho, Hatice Gokcan, Olexandr Isayev 2026-07-27T06:05:07Z 置信度 0.70
• Enhanced mechanical and frictional performance of Ta-MoS2-Ta sandwich films: A machine learning interatomic potential-based molecular dynamics study
  crossref Shujia Wan, Ruiting Tong, Bing Han, Ning Dong 等 2026-01-20T07:40:35Z 置信度 0.70
• Predicting Crystal Structures and Ionic Conductivities in Li ₃ YCl _6−x Br _x Halide Solid Electrolytes Using a Fine‐Tuned Machine Learning Interatomic Potential
  Understanding ionic transport in halide solid electrolytes (SEs) is essential for advancing next‐generation solid‐state batteries. This work demonstrates the effectiveness of fine‐tuning the Crystal Hamiltonian Graph Network universal machine learning interato…
  crossref Jonas Böhm, Aurélie Champagne 2026-04-19T18:29:52Z 置信度 0.70