AI 驱动科学发现 · 全部实体（第 7 / 100 页）

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• Computational discovery of high-temperature superconducting ternary hydrides via deep learning.
  The discovery of novel high-temperature, or even room-temperature, superconducting materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast chemical and configurational search space poses a sign…
  europepmc 2026 置信度 0.80
• Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials.
  Excited-state nonadiabatic simulations with quantum mechanics/molecular mechanics (QM/MM) are essential to understand photoinduced processes in explicit environments. However, the high computational cost of the underlying quantum chemical calculations limits i…
  europepmc 2025 置信度 0.80
• Strong crystalline thermal insulation induced by extended antibonding states.
  Crystalline solids with extreme insulation often exhibit a plateau or even an upward-sloping tail in thermal conductivity above room temperature. Herein, we synthesize a crystalline material AgTl 2 I 3 with an exceptionally low thermal conductivity of 0.21 Wm …
  europepmc 2025 置信度 0.80
• Modeling the potential energy surface by force fields for heterogeneous catalysis: classification, applications, and challenges.
  The concept of the potential energy surface (PES) in computational simulations is essential for studying material properties and heterogeneous catalytic processes. However, constructing the PES using quantum mechanical methods is computationally expensive and …
  europepmc 2025 置信度 0.80
• Methods in PES-Learn: Direct-Fit Machine Learning of Born-Oppenheimer Potential Energy Surfaces.
  The release of PES-Learn version 1.0 as an open-source software package for the automatic construction of machine learning models of semi-global molecular potential energy surfaces (PESs) is presented. Improvements to PES-Learn's interoperability are stressed …
  europepmc 2025 置信度 0.80
• Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential.
  Computer simulations have long been key to understanding and designing phase-change materials (PCMs) for memory technologies. Machine learning is now increasingly being used to accelerate the modelling of PCMs, and yet it remains challenging to simultaneously …
  europepmc 2025 置信度 0.80
• Editorial for Future Trends in Ultra-Precision Machining.
  Ultra-precision machining (UPM) is an advanced manufacturing technology used to produce components with exceptionally high dimensional accuracy, surface integrity, and form precision [...].
  europepmc 2026 置信度 0.80
• Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries.
  Solid-state batteries (SSBs) are pivotal for sustainable energy storage, delivering extended life span, low-temperature resilience, and enhanced safety. However, designing stable solid electrolytes and interfaces in SSBs remains a formidable challenge. As a di…
  europepmc 2025 置信度 0.80
• Bridging electron microscopy and materials analysis with an autonomous agentic platform.
  Electron microscopy (EM) reveals atomic-scale structures that underpin catalysis, energy storage, and semiconductor reliability, yet current workflows remain fragmented across segmentation, crystallographic reconstruction, property modeling, and literature rev…
  europepmc 2026 置信度 0.80
• Active learning for nonparametric multiscale modeling of boundary lubrication.
  Lubricated friction is a multiscale problem where molecular processes dictate the macroscopic response of the system. Traditional lubrication models rely on semiempirical constitutive relations, which become unreliable under extreme conditions. Here, we presen…
  europepmc 2025 置信度 0.80
• Unprecedented robustness of physics-informed atomic energy models at and beyond room temperature.
  Machine-learned potentials (MLPs) have become widely adopted alternatives to traditional electronic structure and molecular mechanics methods. However, despite excelling on fixed test sets, most MLPs remain prone to instability when deployed in molecular dynam…
  europepmc 2026 置信度 0.80
• Accurate prediction of tensorial spectra using equivariant graph neural network.
  Optical spectroscopies provide a powerful means to probe light-matter interactions and complex electronic features that are crucial for the development and optimization of optoelectronic devices, where performance is closely tied to the underlying electronic s…
  europepmc 2026 置信度 0.80
• Caveat emptor: predicting and modeling protein-DNA recognition and binding via machine-learning computational approaches.
  The recent development of AI-based predictive tools, such as AlphaFold3, for the prediction of the structures of biological molecules and their complexes has transformed modern molecular and cellular biology. While it displays exceptional accuracy in the model…
  europepmc 2026 置信度 0.80
• Deep learning insights into β-lactamase dynamics and resistance evolution.
  The rapid global expansion of β-lactamase-mediated antimicrobial resistance demands mechanistic approaches capable of resolving the dynamics of enzyme adaptation. Although β-lactamase evolution often involves subtle rearrangements rather than large structural …
  europepmc 2026 置信度 0.80
• Impact of the Nano-Precipitation Size Distribution on the Mechanical Behavior of Nickel-Based Alloys by Experiment and Simulation.
  The outstanding mechanical properties of nickel-based alloys are predominantly governed by the characteristics of their nano-precipitation. Traditional optimization approaches, however, have focused almost exclusively on average precipitation size, neglecting …
  europepmc 2025 置信度 0.80
• Accelerating Lattice Thermal Conductivity Calculations in MXenes: A Machine Learning Force Field Approach.
  Traditionally, lattice thermal conductivity is evaluated using the phonon Boltzmann transport equation (PBTE) in combination with density functional theory (DFT) calculations. However, this approach is computationally intensive. In this study, we predicted lat…
  europepmc 2025 置信度 0.80
• Ultrafast solvent-modulated roaming mechanism in bromoform revealed by femtosecond X-ray solution scattering.
  Roaming-mediated isomerization is a universal reaction mechanism in photochemistry, yet solvent-dependent pathways of roaming intermediates remain poorly understood, particularly for environmentally relevant halogen compounds involved in ozone depletion. Here,…
  europepmc 2026 置信度 0.80
• Physics-Informed Gaussian Process Inference of Liquid Structure from Scattering Data.
  We present a nonparametric Bayesian framework to infer radial distribution functions from experimental scattering measurements with uncertainty quantification using nonstationary Gaussian processes. The Gaussian process prior mean and kernel functions are desi…
  europepmc 2025 置信度 0.80
• Scalable Boltzmann generators for equilibrium sampling of large-scale materials.
  Generating equilibrium ensembles of structures is essential for modeling molecules and materials, yet traditional simulators like molecular dynamics suffer from limited sampling efficiency. Boltzmann Generators introduced the concept of one-shot deep learning …
  europepmc 2026 置信度 0.80
• CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning.
  Predicting crystal properties is essential for understanding structure-property relationships and accelerating material discovery. However, conventional approaches like experimental measurements or density functional theory calculations are resource-intensive,…
  europepmc 2026 置信度 0.80
• High-Performance Fe-MoS<sub>2</sub> Electrocatalyst for Efficient Nitrate Reduction to Ammonia: Synergistic Design for Sustainable Ammonia Production.
  The electrochemical reduction of nitrate (NO 3 - ) to ammonia (NH 3 ) offers a promising route for both environmental remediation and sustainable ammonia production, yet current catalysts are limited by low efficiency and selectivity. Inspired by the active si…
  europepmc 2025 置信度 0.80
• Integrating multi-omics and machine learning to decipher the molecular pathways of bisphenol a-associated lactylation-related genes driving bladder cancer.
  In this study, we systematically investigated bladder cancer-related gene signatures using a toxicogenomics-informed framework, with particular attention to genes associated with lactylation-related pathways. Multi-omics data from the Gene Expression Omnibus (…
  europepmc 2026 置信度 0.80
• Physically Consistent Self-Diffusion Coefficient Calculation with Molecular Dynamics and Symbolic Regression.
  Machine Learning methods are exploited to extract a universal approach for self-diffusion coefficient calculation in molecular fluids. Analytical expressions are derived through symbolic regression for fluids both in bulk and confined nanochannels. The symboli…
  europepmc 2025 置信度 0.80
• Nonequilibrium quantum dynamics in SrTiO<sub>3</sub> under impulsive THz radiation with machine learning.
  Ultrafast spectroscopy paved the way for probing transient states of matter produced through photoexcitation. The microscopic processes governing the formation of these states remain largely unknown, due to the inherent challenges in accessing the microscopic …
  europepmc 2025 置信度 0.80
• Adaptive Transition-State Refinement with Learned Equilibrium Flows.
  Identifying transition states (TSs), the high-energy configurations that molecules pass through during chemical reactions, is essential for understanding and designing chemical processes. However, accurately and efficiently identifying these states remains one…
  europepmc 2026 置信度 0.80
• Hybrid Graph-Machine Learning Framework for Accurate and Interpretable Band Gap Prediction.
  Accurate prediction of the electronic band gap is essential for accelerating the discovery and design of semiconducting and energy materials. Conventional density functional theory (DFT) methods, while physically rigorous, remain computationally expensive and …
  europepmc 2026 置信度 0.80
• Developing Interface Force Fields for Water and Oxygen on Pt, and <i>Pt</i> <sub><i>3</i></sub> <i>Ni</i>, <i>Pt</i> <sub><i>3</i></sub> <i>Co</i> Alloy Surfaces for Proton-Exchange Membrane Fuel Cell (PEMFC) Applications.
  Interface force fields (IFFs) are crucial for the atomistic modeling of PEMFC interfaces. We benchmark ten optimization algorithms and four force-field forms against DFT adsorption energies and forces and present a framework for selecting the algorithm/force-f…
  europepmc 2026 置信度 0.80
• Short-range order in high entropy carbides.
  High-entropy carbides (HECs) are a new class of materials with properties that are promising for applications in extreme environments, involving high temperature, corrosion, and high ion-flux. In HECs, multiple principal cations form solid solutions, similar t…
  europepmc 2026 置信度 0.80
• A Review of Computational Modeling of Polymer Composites and Nanocomposites.
  Polymer composites and nanocomposites have become indispensable in aerospace, automotive, energy, electronics, soft robotics, and biomedical applications due to their high specific stiffness, strength, and manufacturability with highly tailorable multifunction…
  europepmc 2026 置信度 0.80
• Electrochemical potential-driven water dynamics control CO<sub>2</sub> electroreduction at the Ag/H<sub>2</sub>O interface.
  A molecular-level understanding of the catalyst-electrolyte interface under realistic operating conditions remains a central challenge in electrocatalysis. In particular, the role of the electrochemical potential in modulating interfacial solvation, and its co…
  europepmc 2025 置信度 0.80
• An equivariant pretrained transformer for unified 3D molecular representation learning.
  Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications. However, prior efforts typically focus on pretraining models in a specific domain, missing the opportunity to leverage cross-domain knowledge.…
  europepmc 2026 置信度 0.80
• Inverse Design of Metal-Organic Polyhedra through Molecular Fragmentation and Evolutionary Optimisation.
  Reticular materials have come to the fore of chemistry with exceptional potential in applications ranging from CO 2 capture and chemical separations to catalysis and drug delivery. However, due to the vast combinatorial space of molecular building blocks that …
  europepmc 2026 置信度 0.80
• Generative AI-Driven Discovery of Next-Generation Electrolytes for Alkali Metal Batteries.
  Recent advances in artificial intelligence (AI) are revolutionizing materials science by unlocking unprecedented capabilities in designing novel compounds and accurately predicting their properties. Among these, graph-based machine learning (ML) algorithms hav…
  europepmc 2026 置信度 0.80
• Integrating Machine Learning into Free Energy Perturbation Workflows.
  Free energy perturbation (FEP) methods are among the most accurate tools in structure-based drug design for predicting protein-ligand binding affinities. However, their adoption remains limited due to high computational demands and complex setup procedures. Th…
  europepmc 2025 置信度 0.80
• Artificial Intelligence for Perovskite Additive Engineering: From Molecular Screening to Autonomous Discovery.
  Additive engineering plays a crucial role in enhancing the performance of perovskite solar cells (PSCs), yet identifying suitable additives within the vast chemical space remains a significant challenge. This paper describes a paradigm shift in additive discov…
  europepmc 2026 置信度 0.80
• Nanoconfined superionic water is a molecular superionic.
  Superionic ice, where water molecules dissociate into a lattice of oxygen ions and a rapidly diffusing "gas" of protons, represents a state of matter with broad implications for planetary interiors and energy applications. Recently, a nanoconfined superionic s…
  europepmc 2026 置信度 0.80
• Achieving Exceptionally Enhanced Thermal Conductivity and Bulk Modulus in Polar Insulators Via Modification of Chemical Bonding.
  Materials capable of altering their physical properties in response to external stimuli are highly desirable for a wide range of applications. In particular, materials that exhibit substantial changes in thermal conductivity hold promise for advanced thermal m…
  europepmc 2025 置信度 0.80
• <i>Ab initio</i>path-integral simulations of hydrogen-isotope diffusion in face-centred cubic metals.
  Lighter isotopes typically diffuse faster than heavier isotopes; however, the case is not necessarily true for H. Predicting the kinetics of H isotope transport and reactions in substances remains a fundamental challenge in material and condensed matter physic…
  europepmc 2025 置信度 0.80
• Triggering dynamically disordered lithium sublattice in superionic conductors.
  The design of superionic conductors has been largely focused on static structural features, with the dynamic ion transport mechanism less explored. Here, we explore a paradigm that harnesses the polyanion rotations to trigger the dynamically disordered Li subl…
  europepmc 2026 置信度 0.80
• Rational design of machine-learned interatomic potentials for molecular dynamics
  crossref Megan McCarthy 2025-05-07T02:13:55Z 置信度 0.70
• Machine Learning Uncovers Potential Inconsistencies in NZQA External Evaluation and Review Ratings
  All tertiary institutions in New Zealand, except universities, undergo External Evaluation and Review (EER) quality assurance assessments carried out by NZQA.The outcome of an EER assessment and the quality category assigned to an institution can have a substa…
  crossref Michael Watts 2024-10-02T21:55:11Z 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
• The Power of Personalized Healthcare: Harnessing the Potential of Machine Learning in Precision Medicine
  Integrating machine learning in precision medicine represents a transformative development in healthcare [1]. Precision medicine aims to personalize medical treatments based on individual patient characteristics, including their genetic makeup, lifestyle, and …
  crossref Bharath Srinivasaiah 2024-06-03T12:18:01Z 置信度 0.70
• Building a DFT+U machine learning interatomic potential for uranium dioxide
  Despite uranium dioxide (UO2) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging UO2 historical operating experience. Mechanistic models that consider an atomistic understanding…
  crossref Elizabeth Stippell, Lorena Alzate-Vargas, Kashi N. Subedi, Roxanne M. Tutchton 等 2023-12-26T11:48:48Z 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
• Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review
  Abstract The emergence of artificial intelligence has provided efficient methodologies to pursue innovative findings in material science. Over the past two decades, machine‐learning potential (MLP) has emerged as an alternative technology to density functional…
  crossref Shingo Urata, Marco Bertani, Alfonso Pedone 2024-06-09T23:49:26Z 置信度 0.70
• Unlocking the Potential of Machine Learning and Deep Learning for Screening of Geriatric Depression
  crossref Arkaprabha Sau, Santanu Phadikar, Ishita Bhakta 2025-01-02T19:08:42Z 置信度 0.70
• Atomic-scale modelling of boundaries characterized lath martensite microstructure for hydrogen-induced crack initiation: A machine learning interatomic potentials study
  In this study, we attempted to elucidate the hydrogen embrittlement mechanism in lath martensite steels by multiscale approach across fracture test, microbeam analysis and atomic simulation. It was confirmed that the microcracks initiated not only along prior …
  crossref Kazuki Matsubara, Shusaku Takagi, Fan-Shun Meng, Shigenobu Ogata 等 2024-11-10T22:11:46Z 置信度 0.70
• Machine learning interatomic potentials in engineering perspective for developing cathode materials
  Machine learning interatomic potentials (MLIPs) predict thermodynamic phase stability and structural parameters like density functional theory (DFT) but are much faster, making them valuable for engineering applications.
  crossref Dohyeong Kwon, Duho Kim 2024-07-29T02:01:28Z 置信度 0.70
• Machine Learning Potential for Serpentines
  Abstract Serpentines are layered hydrous magnesium silicates (MgOO) formed through serpentinization, a geochemical process that significantly alters the physical property of the mantle. They are hard to investigate experimentally and computationally due to the…
  crossref Hongjin Wang, Chenxing Luo, Renata M. Wentzcovitch 2024-12-08T18:50:03Z 置信度 0.70
• Machine learning reveals huge potential benefits of sustainable fertilizer use
  Agricultural fertilizers are the main global source of ammonia emissions, which harm human health and reduce farmers’ profits. An analysis using big data and machine learning reveals that locally optimized fertilizer-management and tillage practices could slas…
  crossref 2024-02-23T18:03:16Z 置信度 0.70
• Modeling of Non-invasive Cell Membrane Potential Measurement: LTSpice Simulation and Machine Learning Analysis
  Abstract This paper presents a novel simulation approach using electric pulses to measure cell membrane potential. The primary objective was to develop a non-invasive method for accurately assessing membrane potential without altering the cell membrane or its …
  crossref Xiaofeng Ma 2024-09-08T02:25:17Z 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
• Exploring potential biomarkers for acne based on WGCNA and machine learning algorithms (Preprint)
  BACKGROUND Acne stands as a prevalent chronic inflammatory dermatological condition, typically characterized by concurrent skin inflammation and scarring. OBJECTIVE Currently, although many studies have been conducted on the pathogenesis of acne, a comprehensi…
  crossref Changhui Wen 2025-01-03T16:04:12Z 置信度 0.70
• Adaptive loss weighting for machine learning interatomic potentials
  crossref Daniel Ocampo, Daniela Posso, Reza Namakian, Wei Gao 2024-06-20T14:06:42Z 置信度 0.70
• Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
  Abstract Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the same time, …
  crossref Wojciech G Stark, Cas van der Oord, Ilyes Batatia, Yaolong Zhang 等 2024-07-03T23:22:34Z 置信度 0.70
• Classification Methods for Identifying Potential Clients in Banking Using Machine Learning Models
  crossref Jizhi Jin 2026-06-29T12:34:57Z 置信度 0.70
• [Poster Board #2139] BalanceNet: An uncertainty-based mixture-of-experts model architecture for machine learning potential
  crossref Shuhao Zhang 2024-03-25T00:12:54Z 置信度 0.70
• Development of machine learning and empirical interatomic potentials for the binary Zr-Sn system
  crossref Haojie Mei, Liang Chen, Feifei Wang, Guisen Liu 等 2023-10-22T09:38:52Z 置信度 0.70
• Unlocking Potential: A Holistic Approach to Predicting Employee Performance with Business Analytics and Machine Learning
  This paper presents a comprehensive framework for predicting employee performance by integrating business analytics and machine learning techniques. In today's dynamic organizational landscape, accurately forecasting employee performance is crucial for optimiz…
  crossref Ibrahim Adeoye 2024-03-14T15:35:32Z 置信度 0.70
• Unlocking predictive potential
  Artificial intelligence (AI) plays a powerful role to increase the reliability, performance, and accuracy of software development. Accurate software work estimation serves as essential for successful project planning. Traditional and Agile techniques give base…
  crossref Meenakshi, Meenakshi Pareek 2024-12-19T15:48:22Z 置信度 0.70
• Detection of Potential Specific Learning Disabilities in Children through Handwriting Analysis Using Machine Learning
  Specific learning disability refers to a variety of learning difficulties that impact an individual's capacity to acquire and apply specific academic skills, including reading, writing, mathematics, and reasoning. Learning difficulties, if not identified, can …
  crossref Dinusha P, Subha Sreekumar, Lijiya A 2024-11-19T18:38:53Z 置信度 0.70
• Lightweight equivariant model for efficient interatomic potential predictions
  Abstract In modern computational materials science, deep learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional simulations. However, existing models typically sacrifice either accuracy or efficien…
  crossref Lei Shen, Ziduo Yang, Xian Wang, Yifan Li 等 2024-03-01T06:49:31Z 置信度 0.70
• Polymers simulation using machine learning interatomic potentials
  crossref Teng Long, Jia Li, Chenlu Wang, Hua Wang 等 2024-07-17T02:44:25Z 置信度 0.70
• On application of machine learning classifiers in evaluating liquefaction potential of civil infrastructure
  crossref Eman F. Saleh, Ahmad N. Tarawneh 2024-01-21T05:37:52Z 置信度 0.70
• Deep learning interatomic potential for thermal and defect behaviour of aluminum nitride with quantum accuracy
  crossref Tao Li, Qing Hou, Jie-chao Cui, Jia-hui Yang 等 2023-11-27T04:46:03Z 置信度 0.70
• Unveiling the Veil: Predicting Potential Shell Firms Using Machine Learning Approach
  crossref Zijian Cheng, Tianze Li, Zhangxin (Frank) Liu 2024-09-04T17:21:46Z 置信度 0.70
• DLK1 as a Potential Biomarker and shows NOTCH signaling could be the potential target for Endometriosis: A Machine Learning Approach
  Abstract Purpose: The objective of this research is to pinpoint potential diagnostic markers for endometriosis and explore the immune infiltration patterns linked with this condition through the utilization of machine learning techniques. Methods: A total of f…
  crossref Liting Liao, Zhijian Pan 2024-03-07T19:30:56Z 置信度 0.70
• Exploring the Molecular Potential of Natural Products Against Covid-19 Through Molecular Modeling and Machine Learning
  crossref Rafael Vieira 2024-04-11T14:46:40Z 置信度 0.70
• Prediction of Potential Evapotranspiration via Machine Learning and Deep Learning for the Murat River Basin
  Potential evapotranspiration (PET) is a significant factor contributing to water loss in hydrological systems, making it a critical area of research. However, accurately calculating and measuring PET remains challenging due to the limited availability of compr…
  crossref Ibrahim Abbas Hasan, Mehmet Ishak Yuce 2024-12-04T00:57:32Z 置信度 0.70
• An Interatomic Potential for Describing Martensitic Transformation in Ternary Nitizr Shape Memory Alloys
  crossref Shan Liu, Xinkai Wang, Xin Zhang, Yidan Pang 等 2024-06-18T02:18:17Z 置信度 0.70
• A New Accurate, Efficient and Transferable Deep Learning Interatomic Potential for Carbon with Advanced Applications
  crossref Songyou Wang, Kai Chen, Riyi Yang, Zhefeng Wang 等 2024-05-20T15:17:49Z 置信度 0.70
• Thermal transport in disordered wurtzite ScAlN alloys using machine learning interatomic potentials
  crossref Haoyu Dong, Zhiqiang Li, Baole Sun, Yanguang Zhou 等 2024-05-16T17:48:31Z 置信度 0.70
• Review for "Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations"
  crossref 2024-12-24T16:13:33Z 置信度 0.70
• Geothermal Potential of a Petroleum Mature Field Using Machine Learning Methods: Ecuadorian Amazon Region
  crossref Franklin Vinicio Gomez, Marianna Vadászi 2024-09-17T16:29:11Z 置信度 0.70
• Machine Learning Combined with Molecular Docking for Screening Potential Anti-Tumor Components of Ganoderma Lucidum
  crossref Qi Yang, Lili Fan 2024-03-26T19:50:16Z 置信度 0.70
• Designing Potential Inhibitors of Acetylcholinesterase: A Chemoinformatics Approach with Machine Learning Models
  crossref Uppula Purushotham, Karunakar Tanneeru, Stephen Abhishek Raj 2024-05-06T16:59:26Z 置信度 0.70
• Review for "Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations"
  crossref 2024-12-24T16:13:33Z 置信度 0.70
• Assessing the Payment Potential of Customers in the Electricity Sector: A Machine Learning Approach
  crossref Emine Ceren Özay, Erman Çakıt 2024-02-29T19:23:19Z 置信度 0.70
• Review for "Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations"
  crossref 2024-12-24T16:13:33Z 置信度 0.70
• Unlocking Potential: A Machine Learning Approach to Job Category Prediction
  India's high unemployment rate (53.4%, ILO Department of Statistics, n.d.) despite a reasonable literacy rate (76.32%, Statista, 2024) suggests a mismatch between skills and job availability. This research addresses this using machine learning to identify suit…
  crossref Swati Garg, Chandra Sekhar, Lov Kumar 2024-11-19T18:38:53Z 置信度 0.70
• Using Machine Learning to Investigate Potential Image Bias in News Articles
  Media bias refers to the deviation from objective reporting in media, where journalists introduce external beliefs or agendas into the journalistic process, altering the perception of an event or issue. A newspaper article may introduce bias in numerous ways, …
  crossref Gabriel Hili, Dylan Seychell 2024-07-30T17:52:38Z 置信度 0.70
• Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning
  Abstract The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverage…
  crossref Alice E. A. Allen, Nicholas Lubbers, Sakib Matin, Justin Smith 等 2024-07-17T10:02:22Z 置信度 0.70
• First-principles-based machine learning interatomic potential for molecular dynamics simulations of 2D lateral MoS2/WS2 heterostructures
  Understanding the mechanical and thermodynamic properties of transition-metal dichalcogenides (TMDs) and their heterostructures is pivotal for advancing the development of flexible semiconductor devices, and molecular dynamics (MD) simulation is widely applied…
  crossref Xiangjun Liu, Baolong Wang, Kun Jia, Quanjie Wang 等 2024-05-28T09:35:39Z 置信度 0.70
• Reviewer #1 (Public Review): Machine learning and biological validation identify sphingolipids as potential mediators of paclitaxel-induced neuropathy in cancer patients
  crossref 2024-09-30T09:51:12Z 置信度 0.70
• Machine Learning and Molecular Docking Studies to Identify Potential Anti-Parkinson's Agents
  crossref adib ghaleb, adnane aouidate, mohammed aarjane, anane hafid 2024-11-19T17:13:35Z 置信度 0.70
• Reviewer #1 (Public Review): Machine learning and biological validation identify sphingolipids as potential mediators of paclitaxel-induced neuropathy in cancer patients
  Chemotherapy-induced peripheral neuropathy (CIPN) is a serious therapy-limiting side effect of commonly used anticancer drugs. Previous studies suggest that lipids may play a role in CIPN. Therefore, the present study aimed to identify the particular types of …
  crossref 2024-08-22T12:33:54Z 置信度 0.70
• eLife Assessment: Machine learning and biological validation identify sphingolipids as potential mediators of paclitaxel-induced neuropathy in cancer patients
  Chemotherapy-induced peripheral neuropathy (CIPN) is a serious therapy-limiting side effect of commonly used anticancer drugs. Previous studies suggest that lipids may play a role in CIPN. Therefore, the present study aimed to identify the particular types of …
  crossref Pramod Mistry 2024-08-22T12:33:54Z 置信度 0.70
• Kinetic Consequences of Quasi-Harmonic Entropies Calculated with Machine Learning Interatomic Potentials for Microkinetic Modeling
  Microporous catalysts are ubiquitous in chemical processes including sustainable transformations of biobased feedstocks into fuels and fine chemicals. The mechanistic insights needed to design next-generation microporous catalysts can be obtained with ab initi…
  crossref Geet Gupta, Brandon C. Bukowski 2024-11-14T10:35:52Z 置信度 0.70
• JCO Precision Oncology Commentary Provides Valuable Guidance for Authors Using Machine Learning Models
  crossref 2024-12-05T15:34:23Z 置信度 0.70
• Machine Learning Techniques with Potential Applications in Risk Management at Small and Mid-Size Business
  Small and mid-sized enterprises (SMBs) encounter distinct obstacles in risk management because of their resource constraints and the difficulty of recognizing and averting possible risks. By providing creative ways to improve risk management procedures, machin…
  crossref 2024-09-03T07:03:57Z 置信度 0.70
• Machine learning approaches for estimating cross-neutralization potential among FMD serotype O viruses
  Abstract In this study, we aimed to develop an algorithm that uses sequence data to estimate cross-neutralization between serotype O foot-and-mouth disease viruses (FMDV) based on r1 values, while identifying key genomic sites associated with high or low r1 va…
  crossref Dennis N Makau, Jonathan Arzt, Kimberly VanderWaal 2024-05-24T20:20:10Z 置信度 0.70
• Exploring the Potential of Machine Learning and Deep Learning for COVID‐19 Detection
  This book chapter presents an extensive review of the application of machine learning and deep learning methods in detecting COVID-19. It emphasizes the significance of early and accurate diagnosis in effectively managing the disease during the COVID-19 pandem…
  crossref Saimul Bashir, Faisal Firdous, Syed Zoofa Rufai 2024-06-21T21:36:51Z 置信度 0.70
• Synthetic pre-training for neural-network interatomic potentials
  Abstract Machine learning (ML) based interatomic potentials have transformed the field of atomistic materials modelling. However, ML potentials depend critically on the quality and quantity of quantum-mechanical reference data with which they are trained, and …
  crossref John L A Gardner, Kathryn T Baker, Volker L Deringer 2023-12-15T17:30:15Z 置信度 0.70
• Silicon nitride crystallization study via molecular dynamics and the ultra-fast 3-body (UF3) machine-learned interatomic potential
  crossref Tesia Janicki, Jason Gibson, Carlos Chacon, Edwin Chiu 等 2025-04-02T02:20:45Z 置信度 0.70
• Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping monte carlo and active learning
  Nanoparticles, distinguished by their unique chemical and physical properties, have emerged as focal points within the realm of materials science. Traditional theoretical approaches for atomic simulations mainly include empirical force field and ab initio simu…
  crossref Fu-Qiang Gong, Ke Xiong, Jun Cheng 2024-03-20T02:01:15Z 置信度 0.70
• Modeling martensitic transformation temperatures in Zirconia–Ceria solid solutions using machine learning interatomic potentials
  Abstract Shape memory ceramics (SMCs), while exhibiting high strength, sizeable recoverable strain, and substantial energy damping, tend to shatter under load and have low reversibility. Recent developments in SMCs have shown significant promise in enhancing t…
  crossref Owen T Rettenmaier, Joshua J Gabriel, Srikanth Patala 2024-09-26T22:53:57Z 置信度 0.70
• Relativistic Effects in Interatomic Ionization Processes and Formation of Antimatter Ions in Interatomic Attachment Reactions
  In this book interatomic energy transfer by electron correlations is studied. Focus is on relativistic effects and applications in antimatter physics.
  crossref Andreas Jacob 2024-03-28T20:01:30Z 置信度 0.70
• The Dark Side of Language Models: Exploring the Potential of LLMs in Multimedia Disinformation Generation and Dissemination
  Disinformation - the deliberate spread of false or misleading information poses a significant threat to our society by undermining trust, exacerbating polarization, and manipulating public opinion. With the rapid advancement of artificial intelligence and the …
  crossref Dipto Barman, Ziyi Guo, Owen Conlan 2024-03-11T10:18:14Z 置信度 0.70
• Revolutionizing Agriculture in Artificial Intelligence Through Exploring the Potential of Machine Learning and Chat GPT
  crossref Vijay S, Aarthi M, Ana Raj 2025-09-29T10:59:30Z 置信度 0.70