AI 驱动科学发现 · 全部实体（第 9 / 100 页）

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• Potential Communication in B5G Networks Through Hybrid Millimeter‐Wave Beamforming and Machine Learning
  B5G and 6G are still in their infancy, but the technological advancement and curiosity about the potential inclusion of ML in today's wireless communication have shown many dimensions to current researchers and academicians. Smartest as proposed and advanced a…
  crossref Snehasis Dey 2026-01-09T21:37:26Z 置信度 0.70
• Fine-tuning of universal machine-learning interatomic potentials for high-entropy alloys with application to 2D (Mo,Ta,Nb,W,V)S2
  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…
  crossref Chun Zhou, Hannu-Pekka Komsa 2026-07-27T15:54:07Z 置信度 0.70
• Thermoelastic anisotropy of lithium niobate from molecular dynamics with a machine-learned interatomic potential
  crossref M.A.N. Dewapriya, J.W. Gillespie 2026-07-22T21:59:40Z 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 2026-07-23T06:18:05Z 置信度 0.70
• Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation
  crossref Kwangnam Kim, Suyue Yuan, Brandon C. Wood, Liwen F. Wan 2026-02-18T17:28:00Z 置信度 0.70
• Potential Energy Surfaces and Their Implications to Chemistry
  crossref Jay R. Walton, Luis A. Rivera-Rivera 2025-10-10T06:08:27Z 置信度 0.70
• Phosphorus-based lubricant additives on iron with Machine Learning Interatomic Potentials
  Phosphorus-based lubricant additives protect metallic contacts under boundary lubrication by forming surface films that reduce wear and friction. However, the molecular mechanisms driving their friction-reducing effects remain unclear, especially for phosphate…
  crossref Paolo Restuccia, Enrico Pedretti, Francesca Benini, Sophie Loehlé 等 2026-03-13T08:46:55Z 置信度 0.70
• How Ready Are Universal Machine-learned Interatomic Potentials for Carbon Capture Simulations?
  Nanoporous materials offer a tunable platform for selective carbon capture through adsorption, and identifying the best candidates requires computational screening built on accurate and transferable models of host–guest interactions. Classical force fields (FF…
  crossref Thiago Reschützegger, Guillaume Maurin, Cíntia Soares, Natan Padoin 等 2026-07-14T05:50:14Z 置信度 0.70
• TurboChIMES: Multi-Layered Machine-Learned Interatomic Models for Enhanced Simulation Efficiency
  Machine-learned interatomic potentials (ML-IAPs) have emerged as a powerful tool for achieving nominally quantum-accurate simulations at reduced computational cost. However, for covalently bonded systems, a fundamental tension exists between the model size req…
  crossref Awwal D. Oladipupo, Benjamin Laubach, Sayed Ahmad Almohri, Rebecca K. Lindsey 2026-08-09T07:29:36Z 置信度 0.70
• Benchmarking Universal Machine-Learning Interatomic Potentials for Accelerated Adsorption Energy Evaluation with DFT Single-Point Calculations
  Abstract Adsorption-energy calculations are essential for understanding molecule–surface interactions, but full DFT optimization remains too expensive for high-throughput screening. Here, we use a DFT single-point workflow as a platform to benchmark universal …
  crossref Sakengali Kazhiyev, Mingfei Zhao, Qiaofu Zhang, Santiago Morandi 等 2026-08-05T19:02:08Z 置信度 0.70
• Tackling complexity in crystal structure determination of metal organic compounds using machine learning interatomic potentials
  Universal machine learning interatomic potentials accelerate ab initio crystal structure prediction to resolve the complex coordination environments of technologically significant organic lithium and sodium salts.
  crossref Hui Wu, Qiang Zhu, Wei Zhou 2026-05-22T16:00:03Z 置信度 0.70
• Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials
  Machine learning interatomic potentials (MLIPs) have revolutionized the field of atomistic materials simulation, both due to their remarkable accuracy and their computational efficiency compared to established ab initio methods. Very recently, several general …
  crossref Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf 2025-07-09T11:56:28Z 置信度 0.70
• Local lattice dynamics of hcp zinc from EXAFS and machine-learning interatomic potentials
  crossref Vitalijs Dimitrijevs, Pjotrs Žguns, Inga Pudza, Aleksandr Kalinko 等 2026-06-16T15:49:29Z 置信度 0.70
• Artificial Intelligence for CIPS Data Interpretation: From Fundamental Insights into Soil Potential to Automated Machine Learning Applications
  Abstract Close Interval Potential Survey (CIPS) is a critical technique for monitoring the performance of cathodic protection in buried pipelines. As pipeline networks expand, survey programs generate increasingly large datasets, making interpretation both tim…
  crossref Hongbo Ding 2026-03-06T14:14:10Z 置信度 0.70
• DP-EVA: data-efficient fine-tuning framework via maximizing pretrained knowledge of large atomistic models for developing domain-specific machine-learning interatomic potentials
  Abstract Machine-learning interatomic potentials (MLIPs) have greatly extended the temporal and spatial scales of atomistic simulations, enabling the theoretical study of complex processes at affordable computational cost compared with conventional density fun…
  crossref Zhaoqing Liu, Zhe Deng, Huabo Zhao, Han Wang 等 2026-06-10T11:53:07Z 置信度 0.70
• Prediction of new Ti-N phases using machine learned interatomic potential
  crossref Pradeep Kumar Rana, Atharva Vyawahare, Rohit Batra, Satyesh K. Yadav 2026-02-03T17:32:21Z 置信度 0.70
• Vibrationally informed ML potentials: Optimizing interatomic force fields using simulated neutron scattering functions
  Recent advances in ab initio molecular dynamics (AIMD) have enabled precise simulations of vibrational dynamics in molecular systems; however, the high computational cost of AIMD limits its application to small-scale systems and short time spans. Machine learn…
  crossref Gaurav Vishwakarma, Yongqiang Cheng, Christina Hoffmann 2026-05-12T09:59:34Z 置信度 0.70
• How Confinement Tunes Acidity: pKa of Acetic Acid from Machine-Learned Interatomic Potentials
  Nanoconfinement modifies chemistry. One example is acid-base reaction, relevant to many technological and geochemistry processes. Combining ab initio molecular dynamics (AIMD) simulations with free energy (FE) calculations provides avenues to calculate pKa of …
  crossref Anthony Pablo Baldo, Muhammad Saleh, Kevin Leung, Marialore Sulpizi 2026-05-21T16:46:11Z 置信度 0.70
• Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS ₂
  Dopants can tune the performance of MoS 2 in various applications, but the use of molecular dynamics simulations for doped MoS 2 materials discovery is limited by the lack of multidopant interatomic potentials. Universal machine learning interatomic potentials…
  crossref Abrar Faiyad, Ashlie Martini 2026-02-24T15:43:58Z 置信度 0.70
• Atomistic modeling of uranium monocarbide with a machine learning interatomic potential
  crossref Lorena Alzate-Vargas, Kashi N. Subedi, Roxanne M. Tutchton, Michael W.D. Cooper 等 2026-05-22T23:28:44Z 置信度 0.70
• Deciphering physics of point defects in uranium mononitride and its stoichiometry with a machine learning SNAP interatomic potential
  crossref Alexander Antropov, Egor Lobashev, Vladimir Stegailov 2026-08-24T23:31:33Z 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
• Constant Potential Electrochemistry with Multiscale Quantum Mechanical / Machine Learning Simulations
  Abstract Deeper theoretical understandings of electrified interfaces become important for the development of electrochemical devices. While much progress has been in computational electrochemistry, constant-potential-simulations pose fundamental challenges. He…
  crossref Seung-Jae Shin, Aron Walsh, Kara Fong 2026-06-16T10:54:23Z 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
• Comparison of Liquefaction Potential Prediction Results Using Machine Learning Methods Based on CPT Data
  Soil liquefaction is a significant geotechnical hazard that can lead to severe structural damage during seismic events. Traditional liquefaction assessment methods, such as those based on the Standard Penetration Test (SPT) and Cone Penetration Test (CPT), rel…
  crossref Sercan Tekeoğlu, Ender Başarı 2026-04-15T01:42:11Z 置信度 0.70
• Philanthropy Through Regulated Markets – Evaluating the Adoption Potential of India’s Social Stock Exchange
  crossref 2026-06-15T05:23:10Z 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
• Multiscale insights into the diffusion of SF6/N2 mixtures via machine-learning interatomic potentials
  Sulfur hexafluoride (SF 6 ) is widely used as an insulating and arc-extinguishing medium in high-voltage electrical equipment due to its excellent dielectric properties and insulation performance. However, SF 6 is also a potent greenhouse gas, so mixing SF 6 w…
  crossref Ke Zhao, Hanyan Xiao, Tianxin Zhuang, Jinggang Yang 等 2026-08-30T08:53:14Z 置信度 0.70
• Federated learning-based collaborative assessment of distributed photovoltaic generation potential in multi-park systems
  This paper proposes a federated learning (FL) framework for the privacy-preserving, collaborative assessment of distributed photovoltaic (PV) generation potential across multiple industrial parks. The approach enables model training on decentralized datasets, …
  crossref Meng Chao Zhang 2026-08-14T14:55:28Z 置信度 0.70
• Review of Modern Interpolation and Fitting Methods to Generate Potential Energy Surfaces
  crossref Jay R. Walton, Luis A. Rivera-Rivera 2025-10-10T06:08:27Z 置信度 0.70
• On-the-fly machine learning of interatomic potentials for elastic property modeling in Al–Mg–Zr solid solutions
  The development of resilient and lightweight Aluminum alloys is central to advancing structural materials for energy-efficient engineering applications. To address this challenge, in this study, we explore the elastic properties of Al-Mg-Zr solid solutions by …
  crossref Lukas Volkmer, Leonardo Medrano Sandonas, Philip Grimm, Julia Kristin Hufenbach 等 2025-12-19T05:31:12Z 置信度 0.70
• Machine learning interatomic potentials for lithium battery electrolyte design
  Abstract Lithium‐ion batteries have dominated the energy storage landscape for decades, driven by their high energy density and operating voltage. However, increasing demands for wide operating temperature ranges and fast charging push current technologies to …
  crossref Gunwook Nam, Junyoung Choi, Kunik Jang, Yousung Jung 2026-01-19T05:52:38Z 置信度 0.70
• Potential Applications of Artificial Intelligence and Machine Learning in Pain Medications in Saudia Arabia: A Narrative Review
  Background: Pain management in Saudi Arabia is a complex clinical challenge due to variability in drug response, increasing opioid-related risks, and the demand for personalised treatment strategies. This narrative review evaluates the potential applications o…
  crossref Safaa M. Alsanosi 2026-06-15T05:37:46Z 置信度 0.70
• Influence of exchange–correlation functional on machine-learnt interatomic potentials accuracy: a systematic study in borosilicate glasses
  Machine-learnt interatomic potential (MLIP) has emerged as a strategy to accelerate molecular simulations, offering the promise of quantum chemical accuracy at a cost close to that of classical force fields. They are commonly trained on reference data obtained…
  crossref Fengming Shi, Luca Brugnoli, François-Xavier Coudert 2026-05-07T17:49:42Z 置信度 0.70
• Mapping “cold spots” of potential hidden alpine permafrost using semi-supervised machine learning
  Geophysical techniques revealed frozen ground within relict periglacial landforms in which the presence of ice was excluded by traditional geomorphological and topographic approaches. These unexpected frozen bodies, referred to here as cold spots, suggest that…
  crossref Yaniv Goldschmidt, Jacopo Boaga, Francesco Marra 2026-03-13T20:22:36Z 置信度 0.70
• Explainable Machine Learning for Source-Resolved PM2.5 Oxidative Potential: Implications for Global Mortality Burden
  Fine particulate matter (PM2.5) oxidative potential (OP) is an important indicator of health risk, and it varies substantially across different emission sources. Although concentration–response functions (CRFs) exist that relate PM2.5 mass to its OP, the absen…
  crossref Charles O. Esu, Kuk Cho 2026-03-13T20:22:36Z 置信度 0.70
• Pushing the limits of unconstrained machine-learned interatomic potentials
  Abstract Machine-learned interatomic potentials (MLIPs) are increasingly used to replace computationally demanding electronic-structure calculations to model matter at the atomic scale. The most commonly used model architectures are constrained to fulfill a nu…
  crossref Filippo Bigi, Paolo Pegolo, Arslan Mazitov, Jonathan Schmidt 等 2026-04-23T22:53:06Z 置信度 0.70
• On the Potential of Machine Learning-Based Optimal Noise Control
  Active noise control is a challenging problem, as the required optimal corrections vary with the environmental settings. Existing approaches, such as filtered Least Mean Square, can suffer from secondary path modeling errors, suboptimal solutions, and slow con…
  crossref Levent Ugur, Maks J. Groom, Beckett Zhou 2026-05-20T18:06:58Z 置信度 0.70
• Named Entity Recognition Models for Machine Learning Interatomic Potentials: A User‐Centric Approach to Knowledge Extraction from Scientific Literature
  Machine learning interatomic potential (MLIP) is an emerging technique that has helped achieve molecular dynamics simulations with unprecedented balance between efficiency and accuracy. Recently, the body of MLIP literature has been growing rapidly, which prop…
  crossref Bowen Zheng, Grace X. Gu 2025-06-09T05:20:06Z 置信度 0.70
• Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
  Abstract Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near first-principles accuracy at substantially reduced computational cost, making them powerful tools for large-scale materials modeling. The accuracy of MLIPs is typic…
  crossref Yonatan Kurniawan, Mingjian Wen, Ellad Tadmor, Mark K Transtrum 2026-08-27T22:54:17Z 置信度 0.70
• Atomistic modeling of polymorphism in iron using an explicitly magnetic machine-learned interatomic potential
  crossref Mashroor S. Nitol, Saryu J. Fensin, Hala Ben Messaoud, Christopher D. Barrett 等 2026-06-01T15:27:22Z 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
• Benchmarking universal machine learning interatomic potentials on elemental systems
  Abstract The rapid emergence of universal machine learning interatomic potentials (uMLIPs) has transformed materials modeling. Nevertheless, a comprehensive understanding of their generalization behavior across configurational space remains an open challenge. …
  crossref Hossein Tahmasbi, Andreas Knüpfer, Thomas D. Kühne, Hossein Mirhosseini 2026-07-28T16:25:26Z 置信度 0.70
• Machine Learning Prediction of Ionic Conductivity in Solid-State Electrolytes Using Compositional, Structural, and Neural Potential Features
  Identifying solid-state electrolyte (SSE) materials with high ionic conductivity is critical for next-generation lithium-ion batteries, yet experimental discovery remains slow and resource-intensive. Here we present a machine learning pipeline that combines co…
  crossref Ethan I, Ethan Im 2026-07-09T13:13:16Z 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
• Angular relational knowledge distillation of machine learning interatomic potentials for scalable catalyst exploration
  State-of-the-art equivariant Graph Neural Networks (GNNs) achieve DFT-level accuracy for molecular simulations but remain computationally prohibitive for high-throughput screening and long-timescale dynamics. Knowledge distillation (KD) offers a promising solu…
  crossref Hyukjun Lim, Seokhyun Choung, Jinuk Moon, Jeong Woo Han 2026-04-03T14:37:47Z 置信度 0.70
• High-Resolution Mapping of Stem Water Potential in Walnut Orchards via Stacked Ensemble Machine Learning
  Accurate and spatially explicit monitoring of tree water status is essential for optimizing irrigation in high-value perennial crops, particularly in water-limited environments. Stem Water Potential (SWP) is a direct and widely accepted measure of plant water …
  crossref Srinivasa Rao Peddinti, Isaya Kisekka 2026-06-30T03:38:52Z 置信度 0.70
• Corrosion Potential Prediction of Marine Engineering Steel Based on Machine Learning
  Abstract Corrosion is a major cause of failure in marine engineering steels, resulting in large economic losses worldwide. This study combines marine corrosion knowledge with machine learning techniques to predict corrosion potential. Using experimental data c…
  crossref Bin Wu, Yicong Luo, Shiwei Yu, Endian Fan 2026-02-06T21:48:18Z 置信度 0.70
• Predicting Pitting Potential of Additively Manufactured Stainless Steel using Machine Learning
  Abstract The heterogeneous corrosion response of metal additive manufacturing (AM) parts caused by the variability in the printed parts hinders their broad adoption and implementation. Existing corrosion response characterization protocols rely on experimental…
  crossref David Montes Oca Zapiain, Michael Melia, Ryan Katona 2026-03-31T09:58:30Z 置信度 0.70
• A general-purpose machine-learning interatomic potential for FeCr steel: Atomistic insights into high-temperature mechanical behavior
  FeCr alloys are promising for cladding due to their thermal stability and radiation resistance, but their atomic-scale mechanical behaviors under varying temperatures is not yet well understood. Traditional empirical potentials are unreliable at high temperatu…
  crossref ChengYi Hou, RuiXuan Zhao, HuiJun Zhang, ChuBin Wan 等 2025-10-30T07:52:19Z 置信度 0.70
• Interatomic Potential Prediction Based on Charge Equilibration and Equivariant Transformer
  Conventional local machine-learning interatomic potentials describe atomic environments with a finite cutoff radius and therefore have difficulty capturing long-range electrostatic coupling in polar, charged, or charge-transfer systems. This paper proposes CE-…
  crossref Yijun Shi, Tao Luo 2026-08-24T10:59:58Z 置信度 0.70
• Dual-Coordinate Relative Free Energy Simulations Using Machine-Learned Interatomic Potentials
  Abstract The integration of machine-learned interatomic potentials (MLIPs) into free energy simulations (FES) offers the promise of near-quantum mechanical accuracy at a reduced computational cost. However, employing MLIPs for alchemical relative free energy c…
  crossref Anna Katharina Picha, Stefan Boresch 2026-07-23T02:04:34Z 置信度 0.70
• Hierarchical transfer learning: an agile and equitable strategy for machine-learning interatomic models
  Machine-learned interatomic models are growing in popularity due to their ability to afford near quantum-accurate predictions for complex phenomena with orders-of-magnitude greater computational efficiency. However, these models struggle when applied to system…
  crossref Rebecca K. Lindsey, Awwal D. Oladipupo, Sorin Bastea, Bradley A. Steele 等 2026-01-09T13:47:22Z 置信度 0.70
• Canonical Approaches to Interatomic Interactions
  crossref 2025-10-10T09:39:30Z 置信度 0.70
• Machine learning potential for DGEBA/MTHPA epoxy under water ingress
  crossref Zhenhua Feng, Ju Tang 2026-07-08T22:46:53Z 置信度 0.70
• Stoichiometry dependent properties of cerium hydride: An active learning developed interatomic potential study
  Cerium hydride has a variety of interesting properties, including a known lattice contraction and densification with increasing hydrogen content. However, precise stoichiometric control is not experimentally straightforward and ab initio approaches are not com…
  crossref Brenden W. Hamilton, Travis E. Jones, Timothy C. Germann, Benjamin T. Nebgen 2026-07-20T12:33:14Z 置信度 0.70
• Canonical Approaches to Pairwise Interatomic Interactions
  crossref Jay R. Walton, Luis A. Rivera-Rivera 2025-10-10T06:08:27Z 置信度 0.70
• MACHINE LEARNING–BASED PREDICTION AND POTENTIAL HAZARD ASSESSMENT OF SOIL LIQUEFACTION
  This study develops a machine-learning-based framework for predicting soil liquefaction occurrence and assessing liquefaction hazard after a large earthquake. Using data from the 1995 Southern Hyogo Prefecture Earthquake, the model is constructed from measured…
  crossref Airi TADA, Yeboon YUN, Tetsuo TOBITA 2026-06-26T22:11:38Z 置信度 0.70
• CUSTOMER CHURN PREDICTION: MACHINE LEARNING CLASSIFICATION FOR IDENTIFYING POTENTIAL CHURN
  Explore the article titled CUSTOMER CHURN PREDICTION: MACHINE LEARNING CLASSIFICATION FOR IDENTIFYING POTENTIAL CHURN from IJIRT Volume 12, Issue 10. This study evaluates the effectiveness of teaching programs on waste management knowledge among women.
  crossref 2026-04-07T12:08:50Z 置信度 0.70
• Multispectral Infrared and Machine Learning Methods for Assessing Critical Raw Material Potential in Mining Residuals
  Critical Raw Materials (CRMs) are vital to modern technologies and key sectors such as renewable energy, electronics, and aerospace. Growing geopolitical, environmental, and market risks make supply diversification essential. Mining residuals, including tailin…
  crossref Tianqi Li, Feven Desta, Mike Buxton 2026-03-14T00:39:53Z 置信度 0.70
• Selective differential attention enhanced cartesian atomic moment machine learning interatomic potentials with cross-system transferability
  crossref Xuan Li, Pandi Teng, Yunna Ou, Zhao Niu 等 2026-03-06T08:26:48Z 置信度 0.70
• Role of spin-crossover phenomena in DFT training data for robust machine-learning interatomic potentials
  crossref Zhenyu Zhu, Museng Li, Rong Fu, Shunbo Hu 等 2026-07-21T12:31:47Z 置信度 0.70
• Graph neural networks and machine learning interatomic potentials for voltage prediction and screening high energy density sodium-ion cathode materials
  Sodium-ion batteries are gaining increasing attention, driven by sodium’s natural abundance and promising performance for large-scale energy storage. In this work, we evaluate the performance of machine-learning interatomic potentials and Materials Project-tra…
  crossref Muhammed Thameem, Obaid Khaleifah Alhmoudi, Nirpendra Singh, Ali Elkamel 等 2026-06-26T00:03:02Z 置信度 0.70
• Interatomic Potential Investigation of the Structural and Thermodynamic Properties of Mixed Plutonium-Americium Oxides
  Minor actinides (MA)-bearing mixed oxides are considered for the transmutation of long-lived radioactive isotopes that result from the lifecycle and cooling of nuclear reactors. Accurate knowledge of the U – Pu – Am – Np – O thermodynamic system is necessary f…
  crossref Unai Aizpurua Tome, Baptiste Labonne, Zoé Lambert, Christine Gueneau 等 2026-02-24T20:31:22Z 置信度 0.70
• Spatial Mapping of Groundwater Potential (GWP) Identification Using Random Forest Machine Learning in Kedah, Malaysia
  Groundwater is a critical source of freshwater in Malaysia, where rapid urban growth, agricultural expansion, and recurrent shortages of surface water have increased relianced on aquifers. Effective groundwater potential (GWP) mapping is therefore essential to…
  crossref S.R.S. Zabidi 2026-03-30T02:54:07Z 置信度 0.70
• A machine learning approach for chronic kidney disease detection and prediction
  crossref Denis R 2026-03-07T07:33:47Z 置信度 0.70
• Mass Identification of Potential Perforation Targets and Workover Prioritization Through Machine Learning
  Abstract The developed methodology allows for the generation of an objective criterion to rank workovers and repair operations or to detect opportunities in unperforated layers, as demonstrated through an application to well logs from a field in Argentina. Thi…
  crossref M. S. Fraguío, M. L. Maestri 2026-06-02T00:02:31Z 置信度 0.70
• Uso de machine learning para identificar nuevos fármacos y nutracéuticos con potencial contra las epilepsias
  crossref Alan Talevi 2026-07-06T08:41:24Z 置信度 0.70
• Constructing machine learning interatomic potentials with minimum amount of ab initio data
  Machine learning interatomic potentials (MLIP) are powerful tools for using large-scale molecular dynamics (MD) to evaluate material properties, including the performance of solid-state electrolytes (SSEs). While there are many efforts for constructing univers…
  crossref Wentao Zhang, Xingxing Wu, Chen Wang, Siyu Hu 等 2026-03-17T02:40:21Z 置信度 0.70
• Vibrational power spectra as a tool to benchmark universal machine-learning interatomic potentials for molecular systems: the OMOL-1k-MD data set
  Abstract We present OMOL-1k-MD, a new dataset for the benchmark and training of universal machine-learning interatomic potentials (uMLIPs). It contains three independent ab initio molecular dynamics (AIMD) trajectories of 10 ps each for 1000 arbitrarily chosen…
  crossref Maximilian A. Bechtel, Julien Steffen 2026-08-15T16:43:29Z 置信度 0.70
• A simple and efficient non-DFT-based machine learning interatomic potential to simulate titanium MXenes
  crossref Luis F. V. Thomazini, Alexandre F. Fonseca 2026-05-21T18:02:13Z 置信度 0.70
• Machine learning inversion of interatomic force constants from single-crystal inelastic neutron scattering
  Machine learning bridges synthetic and experimental inelastic neutron scattering data to recover interatomic force constants under realistic measurement conditions.
  crossref Aiden Sable, Bander Linjawi, Kyle Bradbury, Jordan Malof 等 2026-03-17T14:06:17Z 置信度 0.70
• High-Performance Computing in the Age of Machine Learning Interatomic Potentials: A Review of Optimization Strategies for Training and Inference
  crossref Si-Yu Hu, Er-Lin Yao, Guang-Ming Tan, Wei-Le Jia 2026-04-10T07:22:06Z 置信度 0.70
• Potential Evapotranspiration (PET) Prediction using Machine Learning Algorithms
  The principle of Petro-Evaporative Rainfall. With the use of AI, a system may be built to predict the PIC and establish the range. Climate, humidity, wind, and sunlight are the causes of transpiration and evaporation. Using PET is crucial in hydrology, agronom…
  crossref Praveen Kumar Khandappa 2026-01-07T07:25:14Z 置信度 0.70
• Machine Learning Identifies Potential Accessory Resistance-Associated Mutations in HIV-1 Integrase
  Abstract Background Although integrase strand transfer inhibitors (INSTIs) have a high genetic barrier to resistance, cases of virological failure continue to emerge, sometimes in the absence of major resistance-associated mutations. Conventional genotypic and…
  crossref Alfred Ssekagiri, Deogratius Ssemwanga, David Patrick Kateete, Daudi Jjingo 2026-05-29T07:27:38Z 置信度 0.70
• Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance
  Understanding hydrogen diffusion is critical for improving the reliability and performance of oxide thin-film transistors (TFTs), where hydrogen plays a key role in carrier modulation and bias instability.
  crossref Hyunsung Cho, Minseok Moon, Jaehoon Kim, Eunkyung Koh 等 2025-11-10T12:29:27Z 置信度 0.70
• Review for "The interlocking process in molecular machines explained by a combined approach: the nudged elastic band method and a machine learning potential"
  crossref 2026-01-13T21:07:54Z 置信度 0.70
• Machine Learning-Based Spatiotemporal Prediction of Corrosion Potential in Cathodically Protected Reinforced Concrete under Chloride Exposure
  Abstract The durability and service life of reinforced concrete structures are substantially impacted by steel corrosion, particularly in the presence of chloride and under varying environmental conditions. This study predicts half-cell potential (HCP) in chlo…
  crossref Durgesh Aankre, Yogesh Iyer Murthy 2026-03-26T12:36:28Z 置信度 0.70
• Boosting Workforce Potential with Human Augmentation Using Brain‐Computer Interface
  The profound effects of emerging technologies can enhance productivity while fostering a culture of excellence, community, and societal contribution; conversely, some may amplify disadvantages like injury, health disruptions, and loss of autonomy. In this cont…
  crossref Rajesh Singh, Fraiz Parveen, Praveen Kumar Malik 2026-08-14T21:23:06Z 置信度 0.70
• Machine Learning
  crossref 2026-02-11T04:58:46Z 置信度 0.70
• A Genetic Algorithm Trained Machine-Learned Interatomic Potential for the Silicon–Carbon System
  A linear regression-based machine learned interatomic potential (MLIP) was developed for the silicon–carbon system. The MLIP was predominantly trained on structures discovered through a genetic algorithm, encompassing the entire silicon–carbon composition spac…
  crossref Michael MacIsaac, Salil Bavdekar, Douglas Spearot, Ghatu Subhash 2024-07-16T18:20:33Z 置信度 0.70
• Systematic assessment of various universal machine‐learning interatomic potentials
  Abstract Machine‐learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of ab initio quality over very large time and length scales. More recently, various u…
  crossref Haochen Yu, Matteo Giantomassi, Giuliana Materzanini, Junjie Wang 等 2024-08-01T03:07:05Z 置信度 0.70
• Fine-Tuned Machine Learning Interatomic Potentials for Predicting Radiation Damage Tolerance in V-Cr-Ti Alloys
  crossref Myles Stapelberg, James Damewood, James Xu, Dennis Whyte 等 2026-06-05T23:44:37Z 置信度 0.70
• Using machine learning to explore the full potential of porous materials
  Advanced tools can help sift through nearly endless combinations to find materials with desired properties.
  crossref Avery Thompson 2024-05-17T17:10:43Z 置信度 0.70
• Construction of Machine Learning Interatomic Potentials for Metals
  crossref S. V. Dmitriev, A. A. Kistanov, I. V. Kosarev, S. A. Scherbinin 等 2024-09-20T12:02:53Z 置信度 0.70
• PREDICTIVE MACHINE LEARNING FRAMEWORK FOR POTENTIAL DELAY
  Delays relating to order deliveries in elastic manufacturing can significantly disrupt production timelines, affect supply chain efficiency, and diminish customer satisfaction. This research presents a machine learning-based framework to address these challeng…
  crossref J. Kariyawasam 2026-03-11T08:00:41Z 置信度 0.70
• Standards for Meaningful Evaluations of Machine Learning Interatomic Potentials Software
  crossref Rika Kobayashi, Emily Kahl, Roger Amos 2025-03-02T17:42:55Z 置信度 0.70
• Dynamic Metrics to Validate the Use of Machine-Learned Interatomic Potential for Dynamic Sampling
  Machine learning and data-driven approaches are increasingly being used to predict the energies and forces of chemical compounds and materials, enabling in silico design and the construction of digital twins. Machine-learning interatomic potentials (MLIPs), ty…
  crossref Bourgeois Biova Irénée Gadjagboui, Md Sharif Khan, Oliviero Andreussi 2026-02-16T17:50:01Z 置信度 0.70
• Benchmarking Universal Machine Learning Interatomic Potentials for Alkali-Ion Battery Kinetics
  Abstract Accelerating alkali-ion battery discovery requires accurate modeling of atomic-scale kinetics, yet the reliability of universal machine learning interatomic potentials (uMLIPs) in capturing these high-energy landscapes remains uncertain. Here, we syst…
  crossref Xingyu Guo, Cheng Gui, Zhenbin Wang 2026-05-28T13:58:38Z 置信度 0.70
• DPχ: A Charge-Based Machine-Learning Potential Validated on the Pt(111)-Water Electrochemical Interface
  crossref 2026-05-12T07:22:56Z 置信度 0.70
• Basics, constraints, and future potential of machine learning in human–machine interaction
  crossref Kapil Gupta, Shresth Gupta, Varun Bajaj 2026-04-24T08:28:17Z 置信度 0.70
• Agentic, autonomous design evolution of machine-learned interatomic potentials
  Autonomous agents can write code, run experiments and propose follow-up trials, but systematic autoresearch requires more than isolated candidate generation. AutoResearch-MLIP tests this boundary in machine-learned interatomic potentials (MLIPs), treating deve…
  crossref Muyu Lu, Danyang Chen, Fan Yu, Jun Jiang 等 2026-05-25T10:57:08Z 置信度 0.70
• How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
  Foundational machine-learning interatomic potentials (MLIPs) are being developed at a rapid pace, promising closer and closer approximation to ab initio accuracy. This unlocks the possibility to simulate much larger length and time scales. However, benchmarks …
  crossref Luuk H. E. Kempen, Raffaele Cheula, Mie Andersen 2026-05-21T11:41:26Z 置信度 0.70
• Canonical Approaches to Multidimensional Potential Energy Surfaces
  crossref Jay R. Walton, Luis A. Rivera-Rivera 2025-10-10T06:08:27Z 置信度 0.70
• Scalable data-driven basis selection for linear machine learning interatomic potentials
  Abstract Machine learning interatomic potentials provide an effective approach for accurately and efficiently modeling atomic interactions, expanding the capabilities of atomistic simulations to complex systems. However, a priori feature selection leads to hig…
  crossref Tina Torabi, Matthias Militzer, Michael P Friedlander, Christoph Ortner 2026-02-24T22:54:31Z 置信度 0.70
• AtomProNet: data flow to and from machine learning interatomic potentials in materials science
  Abstract As the atomistic simulations of materials science move from traditional potentials to machine learning interatomic potential (MLIP), the field is entering the second phase, focused on discovering and explaining new material phenomena. While MLIP devel…
  crossref Musanna Galib, Mewael Isiet, Mauricio Ponga 2026-05-26T22:55:36Z 置信度 0.70
• Machine learning methods to fit interatomic potentials for plasma–surface interactions: A C–H–O–Ar example
  At the core of molecular dynamics (MD) 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…
  crossref Jack S. Draney, Athanassios Z. Panagiotopoulos, David B. Graves 2026-08-11T13:25:29Z 置信度 0.70
• Assessing Universal Machine Learning Interatomic Potentials for Aluminosilicate Glasses
  ABSTRACT Machine learning interatomic potentials (MLIPs) offer a promising route for accurate and transferable modeling of oxide glasses; however, their performance for complex aluminosilicate compositions remains insufficiently benchmarked. In this work, we s…
  crossref Matilde Benassi, Annalisa Pallini, Marco Bertani, Sofia C. Sarnataro 等 2026-07-03T05:55:26Z 置信度 0.70
• Remarkably high tensile strength and lattice thermal conductivity in wide band gap oxidized holey graphene C(2)O nanosheet.
  Recently, the synthesis of oxidized holey graphene with the chemical formula C 2 O has been reported (J. Am. Chem. Soc. 2024, 146, 4532). We herein employed a combination of density functional theory (DFT) and machine learning interatomic potential (MLIP) calc…
  pubmed Shojaei F, Zhang Q, Zhuang X, Mortazavi B 2024 Jun 11 置信度 0.82
• Extra Tree Classifier Predicts an Interactome Hub Gene as HSPB1 in Oral Cancer: A Bioinformatics Analysis.
  Introduction Oral cancer is a significant global health issue that is mainly caused by factors, such as smoking, alcohol consumption, poor oral hygiene, age, and the human papillomavirus. Unfortunately, delayed diagnosis contributes to high rates of illness an…
  pubmed Arumuganainar D 2024 May 置信度 0.82