AI 驱动科学发现 · 全部实体（第 6 / 100 页）

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• Protein Structure Prediction
  The primary resource for protein structure is PDB. This database stores the coordinates of protein structures that have been solved either using X-rays or NMR. There exists a huge literature on techniques for predicting protein secondary structure from sequenc…
  crossref Andy Brass 2023-06-07T17:37:53Z 置信度 0.70
• Protein Active Site Structure Prediction Strategy and Algorithm
  Computational Drug Design is important as it reduces conventional research deadline and cost.Proper Drug Design is a challenge till date for complex diseases in reduced time and cost.Few days back we were completely dependent on wet chemistry lab but now with …
  crossref 2017-06-05T07:21:24Z 置信度 0.70
• Algorithms for Multiple Protein Structure Alignment and Structure-Derived Multiple Sequence Alignment
  crossref Maxim Shatsky, Ruth Nussinov, Haim J. Wolfson 2007-12-08T18:23:36Z 置信度 0.70
• Supplemental Information 2: Description of protein structure prediction on Swiss-Model
  crossref 2023-10-13T03:28:21Z 置信度 0.70
• Review for "Protein structure prediction using the evolutionary algorithm USPEX"
  crossref 2023-02-14T16:11:16Z 置信度 0.70
• Review for "Protein Oligomer Structure Prediction using GALAXY in CASP14"
  crossref 2021-08-09T12:43:52Z 置信度 0.70
• Review for "Protein structure prediction using the evolutionary algorithm USPEX"
  crossref 2023-02-14T16:11:16Z 置信度 0.70
• Review for "Protein structure prediction using the evolutionary algorithm USPEX"
  crossref 2023-02-14T16:11:16Z 置信度 0.70
• Molecular blueprints: Guiding drug discovery through protein structure analysis
  The structural landscape of proteins serves as a molecular blueprint for drug discovery, offering critical insights into target interactions, binding mechanisms, and rational drug design. Advances in structural biology, including X-ray crystallography, cryo-el…
  crossref 2025-05-08T17:33:51Z 置信度 0.70
• Identification of Domains from Protein Sequences
  The fundamental unit of protein structure is the domain, defined as a region or regions of a polypeptide that fold independently and possesses a hydrophobic core ( see Note 1 ). Domains, particularly those with enzymatic activities, may possess functions indep…
  crossref Chris P. Ponting, Ewan Birney 2003-11-15T01:38:03Z 置信度 0.70
• Prediction of Protein Structure and the Principles of Protein Conformation
  crossref 2011-10-01T04:40:23Z 置信度 0.70
• Hybrid Metaheuristic Approach for Protein Structure Prediction
  crossref Nanda Dulal Jana, Swagatam Das, Jaya Sil 2018-03-02T09:04:10Z 置信度 0.70
• Discovering Patterns Conserved in Sets of Unaligned Protein Sequences
  A protein family is a set of proteins that are homologous (have a common ancestor) and have similar function and structure. If one discovers that a pattern of residues is common to the sequences in a family, it is possible that the presence of these residues i…
  crossref Inge Jonassen 2003-11-15T01:38:03Z 置信度 0.70
• Chou-Fasman Prediction of the Secondary Structure of Proteins
  crossref Peter Prevelige, Gerald D. Fasman 2011-10-01T00:40:23Z 置信度 0.70
• Molecular Dynamics Simulations of Protein Folding
  crossref Angel E. Garcia 2007-12-08T18:23:36Z 置信度 0.70
• De Novo Protein Structure Prediction
  crossref Ling-Hong Hung, Shing-Chung Ngan, Ram Samudrala 2010-05-01T07:58:37Z 置信度 0.70
• Backgrounds on Protein Structure Prediction and Metaheuristics
  crossref Nanda Dulal Jana, Swagatam Das, Jaya Sil 2018-03-02T09:04:10Z 置信度 0.70
• Algorithms for Multiple Protein Structure Alignment and Structure-Derived Multiple Sequence Alignment
  Primary amino acid content and the geometry of the folded protein 3D structure are major parameters of protein function. During the course of evolution the protein 3D structure is more preserved than its primary sequence. Thus, analysis of protein structures i…
  crossref Maxim Shatsky, Ruth Nussinov, Haim J. Wolfson 2008-01-10T06:13:01Z 置信度 0.70
• The prediction challenge
  Abstract Genome projects will soon be producing hundreds of kilobases of raw sequence a day. A major effort in biocomputing will be the analysis of sequence data in terms of functional and structural characterization. Homology-based predictions have proved to …
  crossref Mansoor a. S Saqi 2023-11-03T20:06:06Z 置信度 0.70
• Prediction of Protein-Protein Interface Sequence Diversity Using Flexible Backbone Computational Protein Design
  A major challenge in computational protein design is to identify functional sequences as top predictions. One reason for design failures is conformational plasticity, as proteins frequently change their conformation in response to mutations. To advance protein…
  crossref Elisabeth L. Humphris, Tanja Kortemme 2008-12-10T11:11:32Z 置信度 0.70
• Protein Structure Prediction
  Determination of the three-dimensional structure of a protein can be considered as one of the most important goals in biochemistry. A variety of experimental methods exist that can be used for analysis of protein structures (described in part II of this book).…
  crossref 2013-06-19T20:47:24Z 置信度 0.70
• Deep Template-based Protein Structure Prediction
  Abstract Motivation TBM (template-based modeling) is a popular method for protein structure prediction. When very good templates are not available, it is challenging to identify the best templates, build accurate sequence-template alignments and construct 3D m…
  preprints Fandi Wu, Jinbo Xu 2020 置信度 0.74
• Protein Structure Prediction, Second Edition
  crossref Zaki Mohammed, Bystroff Chris 2007-12-08T18:23:36Z 置信度 0.70
• Protein Structure Prediction
  TABLE OF CONTENTS Preface Contributors Overview of Protein Structure Prediction A historical perspective of template-based protein structure prediction, Jun-tao Guo, Kyle Ellrott, and Ying Xu The assessment of methods for protein structure prediction, Anna Tra…
  crossref 2008-01-10T06:13:01Z 置信度 0.70
• Protein Structure Prediction: Biological Basis, Processing Methods and Deep Learning
  crossref Jingkai Wen 2022-08-24T08:10:41Z 置信度 0.70
• Composite Approaches to Protein Tertiary Structure Prediction: A Case‐Study by I‐Tasser
  This chapter contains sections titled: Introduction I-Tasser: A Composite Method for Protein Structure Prediction AB Initio Prediction of I-Tasser on Small Proteins Blind Test of I-Tasser in CASP Experiments Concluding Remarks References
  crossref Ambrish Roy, Sitao Wu, Yang Zhang 2011-06-25T14:40:03Z 置信度 0.70
• Review for "CASP16 Protein Monomer Structure Prediction Assessment"
  crossref 2025-10-01T00:06:13Z 置信度 0.70
• Challenging the state of the art in protein structure prediction: Highlights of experimental target structures for the 10th critical assessment of techniques for protein structure prediction experiment CASP10
  For the last two decades, CASP has assessed the state of the art in techniques for protein structure prediction and identified areas which required further development. CASP would not have been possible without the prediction targets provided by
  crossref 2015-05-23T08:30:42Z 置信度 0.70
• Protein structure prediction with machine learning
  Makigaki and Ishida have reviewed and refined their model.Chains of amino acids form the primary structure of proteins.
  crossref 2020-01-07T09:16:42Z 置信度 0.70
• Protein Secondary Structure Prediction
  Protein secondary structures are stable local conformations of a polypeptide chain. They are critically important in maintaining a protein three-dimensional structure. The highly regular and repeated structural elements include α-helices and β-sheets. It has b…
  crossref 2012-06-19T16:59:28Z 置信度 0.70
• Protein Tertiary Structure Prediction
  One of the most important scientific achievements of the twentieth century was the discovery of the DNA double helical structure by Watson and Crick in 1953. Strictly speaking, the work was the result of a three-dimensional modeling conducted partly based on d…
  crossref 2012-06-19T16:59:28Z 置信度 0.70
• A meta-heuristic optimization tool for simplified protein structure prediction
  Meta-heuristic algorithms give a satisfactory solution of complex optimization problems in a reasonable time. They are among the most promising and successful optimization techniques. However, some problems are highly complex and require improved techniques. A…
  crossref Gurpreet Lakha 2019-05-13T21:32:26Z 置信度 0.70
• Advanced machine learning method for prediction of protein secondary structure
  A new method based on Markov process to encode the protein sequences has been introduced. With this simple method, input vectors that contain the essential features of protein sequence can be extracted and efficiently used to train SVM classifiers. Our method …
  crossref Kasemsant Kuphanumat 2025-03-19T02:27:38Z 置信度 0.70
• Exploring the viability of protein structure prediction using sequence entropy
  crossref Shalini Potluri 2019-05-03T16:51:48Z 置信度 0.70
• Novel computational developments for protein structure analysis and prediction
  Développements algorithmiques pour l'analyse et la prédiction de la structure des protéines Les protéines sont omniprésentes dans les processus biologiques. Identifier leurs fonctions aide à comprendre et éventuellement à contrôler ces processus. Cependant, si…
  crossref Guillaume Pages 2026-04-03T16:51:04Z 置信度 0.70
• Protein Structure Prediction
  crossref 2012-12-05T23:00:21Z 置信度 0.70
• Workflow for Protein Structure Prediction, Refinement, and Validation v1
  This protocol provides a reproducible computational workflow for protein structure prediction using a combination of freely available software, open-source pipelines, and specialized protein structure prediction services. The protocol covers sequence preparati…
  crossref Marco Palma 2025-08-27T08:17:41Z 置信度 0.70
• Ab Initio Protein Structure Prediction
  crossref Jooyoung Lee, Peter L. Freddolino, Yang Zhang 2017-04-12T10:15:43Z 置信度 0.70
• Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data
  In\nrecent years mass spectrometry-based covalent labeling techniques\nsuch as hydroxyl radical footprinting (HRF) have emerged as valuable\nstructural biology techniques, yielding information on protein tertiary\nstructure. These data, however, are not suffic…
  crossref 2020-04-08T12:14:06Z 置信度 0.70
• Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data
  In\nrecent years mass spectrometry-based covalent labeling techniques\nsuch as hydroxyl radical footprinting (HRF) have emerged as valuable\nstructural biology techniques, yielding information on protein tertiary\nstructure. These data, however, are not suffic…
  crossref 2020-04-08T12:14:06Z 置信度 0.70
• Molecular Dynamics Simulations of Protein Folding
  I illustrate the use of the replica exchange molecular dynamics (REMD) algorithm to study the folding of a small (57 amino acids) protein that folds into a three-helix bundle, protein A. The REMD is a trivially parallel method that uses multiple copies of the …
  crossref Angel E. Garcia 2008-01-10T11:13:01Z 置信度 0.70
• Homology-Based Modeling of Protein Structure
  crossref Zhexin Xiang 2007-08-24T08:51:05Z 置信度 0.70
• Study of Real-Valued Distance Prediction For Protein Structure Prediction with Deep Learning
  Abstract Inter-residue distance prediction by deep ResNet (convolutional residual neural network) has greatly advanced protein structure prediction. Currently the most successful structure prediction methods predict distance by discretizing it into dozens of b…
  preprints Jin Li, Jinbo Xu 2020 置信度 0.74
• Protein Structure Prediction Using Improved Variants of Metaheuristic Algorithms
  crossref Nanda Dulal Jana, Swagatam Das, Jaya Sil 2018-03-02T09:04:10Z 置信度 0.70
• Improving Protein Secondary Structure Prediction by Deep Language Models and Transformer Networks
  Protein secondary structure prediction is useful for many applications. It can be considered a language translation problem, that is, translating a sequence of 20 different amino acids into a sequence of secondary structure symbols (e.g., alpha helix, beta str…
  crossref Tianqi Wu, Weihang Cheng, Jianlin Cheng 2024-11-22T10:44:04Z 置信度 0.70
• Local Structure Prediction of Proteins
  crossref Victo A. Simossis, Jaap Heringa 2007-08-24T08:51:05Z 置信度 0.70
• Review for "Protein Oligomer Structure Prediction using GALAXY in CASP14"
  crossref 2021-08-09T12:43:52Z 置信度 0.70
• Decision letter for "CASP16 Protein Monomer Structure Prediction Assessment"
  crossref 2025-10-01T00:06:13Z 置信度 0.70
• Decision letter for "CASP16 Protein Monomer Structure Prediction Assessment"
  crossref 2025-10-01T00:06:13Z 置信度 0.70
• Protein tertiary structure prediction and refinement using deep learning
  [EMBARGOED UNTIL 8/1/2023] Building the high-quality structure of a protein from its amino acid sequence has important applications in protein engineering and drug design. The problem of accurate protein three-dimensional structure prediction from its amino ac…
  crossref Tianqi Wu 2023-02-07T18:43:35Z 置信度 0.70
• The MULTICOM Protein Tertiary Structure Prediction System
  With the expansion of genomics and proteomics data aided by the rapid progress of next-generation sequencing technologies, computational prediction of protein three-dimensional structure is an essential part of modern structural genomics initiatives. Predictio…
  crossref Jilong Li, Debswapna Bhattacharya, Renzhi Cao, Badri Adhikari 等 2014-02-26T12:52:04Z 置信度 0.70
• New consensus-based algorithms for quality assessment in protein structure prediction
  Two of the essential tasks in protein tertiary structure prediction are predicting quality and selecting the best quality model from given model structures.Finding solutions to these problems are fundamental to understanding the nature of proteins and advancin…
  crossref Kittinun Vantasin 2018-11-08T00:47:27Z 置信度 0.70
• Review for "Automated benchmarking of combined protein structure and ligand conformation prediction"
  crossref 2023-10-28T17:03:25Z 置信度 0.70
• Review for "Automated benchmarking of combined protein structure and ligand conformation prediction"
  crossref 2023-10-28T17:03:25Z 置信度 0.70
• Applications of deep neural networks to protein structure prediction
  Protein secondary structure, backbone torsion angle and other secondary structure features can provide useful information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accu…
  crossref Chao Fang 2024-09-24T16:07:02Z 置信度 0.70
• Protein structure prediction and design on near-term quantum computers
  In the convergence of quantum computing and life science, we explore protein structure prediction and design on near-term intermediate-scale quantum devices. We investigate the algorithmic and resource constraints of today’s quantum computers, aiming to assess…
  crossref Hanna Linn 2025-10-15T20:00:05Z 置信度 0.70
• PROTEIN STRUCTURE PREDICTION
  crossref David S. Wishart, Alastair K. Muir 2013-12-03T20:33:22Z 置信度 0.70
• Decision letter for "Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14"
  crossref 2021-09-10T07:23:05Z 置信度 0.70
• Review for "Automated benchmarking of combined protein structure and ligand conformation prediction"
  crossref 2023-10-28T17:03:25Z 置信度 0.70
• Single-sequence protein structure prediction using supervised transformer protein language models
  Abstract It remains challenging for single-sequence protein structure prediction with AlphaFold2 and other deep learning methods. In this work, we introduce trRosettaX-Single, a novel algorithm for singlesequence protein structure prediction. It is built on se…
  preprints Wenkai Wang, Zhenling Peng, Jianyi Yang 2022 置信度 0.74
• Indexing Protein Structures Using Suffix Trees
  crossref Feng Gao, Mohammed J. Zaki 2007-12-08T13:23:36Z 置信度 0.70
• Foldify: Web Application for Protein Structure Prediction
  crossref 2026-06-26T12:00:24Z 置信度 0.70
• Protein sequence alignment and database scanning
  Abstract In the context of protein structure prediction, there are two principle reasons for comparing and aligning protein sequences: (a) To obtain an accurate alignment. This may be for protein modelling by comparison to proteins of known three-dimensional s…
  crossref Geoffreyj Barton 2023-11-03T20:06:06Z 置信度 0.70
• Review for "Automated benchmarking of combined protein structure and ligand conformation prediction"
  crossref 2023-10-28T17:03:25Z 置信度 0.70
• Scatter search metaheuristic for homology based protein structure prediction. (c2015)
  crossref Mouses Hrag Stamboulian 2017-09-19T13:16:58Z 置信度 0.70
• Foldify: Web Application for Protein Structure Prediction
  crossref 2026-06-30T03:20:04Z 置信度 0.70
• A threading approach to protein structure prediction: studies on TNF-like molecules, Rev proteins, and protein kinases
  The main focus of this dissertation is the application of the threading approach to specific biological problems. The threading scheme developed in our group targets incorporating important structural features necessary for detecting structural similarity betw…
  crossref Yungok Ihm 2018-08-13T15:02:19Z 置信度 0.70
• Copyright
  crossref 2025-09-17T12:14:57Z 置信度 0.70
• Decision letter for "Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14"
  crossref 2021-09-10T07:23:05Z 置信度 0.70
• Decision letter for "Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14"
  crossref 2021-09-10T07:23:05Z 置信度 0.70
• An algorithm for protein secondary structure prediction based on class prediction
  An algorithm has been developed to improve the success rate in the prediction of the secondary structure of proteins by taking into account the predicted class of the proteins. This method has been called the 'double prediction method' and consists of a first …
  crossref G. Deléage, B. Roux 2007-01-04T23:15:27Z 置信度 0.70
• Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling
  Proteins perform essential roles across nearly all cellular processes, and accurate three-dimensional structures remain critical for elucidating structure–function relationships and studies on drug discovery. Cryo-electron microscopy (cryo-EM), X-ray crystallo…
  europepmc 2026 置信度 0.80
• Latest Advancements and Mechanistic Insights into High-Entropy Alloys: Design, Properties and Applications.
  High-entropy alloys (HEAs) are a class of multi-principal element materials composed of five or more elements in near-equimolar ratios. This unique compositional design generates high configurational entropy, which stabilizes simple solid solution phases and r…
  europepmc 2025 置信度 0.80
• Grain boundary amorphization as a strategy to mitigate lithium dendrite growth in solid-state batteries.
  Solid-state lithium metal batteries using garnet-type Li 7 La 3 Zr 2 O 12 electrolytes hold immense promise for next-generation energy storage, but grain boundary defects promote lithium redistribution and dendrite formation, compromising performance and safet…
  europepmc 2025 置信度 0.80
• Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations
  Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional quantum-mechanics based methods. At the same time, the construction of new m…
  datacite Miksch, April M., Morawietz, Tobias, Kästner, Johannes, Urban, Alexander 等 2021 置信度 0.66 540
• A Review of AI-Driven Approaches for Nanoscale Heat Conduction and Radiation
  Heat conduction and radiation are two of the three fundamental modes of heat transfer, playing a critical role in a wide range of scientific and engineering applications ranging from energy systems to materials science. However, traditional physics-based simul…
  datacite Guo, Ziqi, Carne, Daniel, Khot, Krutarth, Feng, Dudong 等 2025 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Mesoscale and Nanoscale Physics (cond-mat.mes-hall)FOS: Physical sciencesFOS: Physical sciences
• Machine Learning Potentials for Ti-V-Ta-W Alloys (Linear ML and Kernel ML)
  This repository provides machine learning interatomic potentials for Ti-V-Ta-W high-entropy alloys, along with example input files for LAMMPS simulations. Two potential models are included: Linear machine learning potential: lammps_bso4_snap1_params.pot Kernel…
  datacite Wróbel, Jan 2025 置信度 0.66 Molecular Dynamics SimulationMachine LearningAlloysDensity Functional Theory
• Machine Learning Potentials for Ti-V-Ta-W Alloys (Linear ML and Kernel ML)
  This repository provides machine learning interatomic potentials for Ti-V-Ta-W high-entropy alloys, along with example input files for LAMMPS simulations. Two potential models are included: Linear machine learning potential: lammps_bso4_snap1_params.pot Kernel…
  datacite Wróbel, Jan 2025 置信度 0.66 Molecular Dynamics SimulationMachine LearningAlloysDensity Functional Theory
• A practical guide to machine learning interatomic potentials -- Status and future
  The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such resear…
  datacite Jacobs, Ryan, Morgan, Dane, Attarian, Siamak, Meng, Jun 等 2025 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Machine Learning (cs.LG)FOS: Physical sciencesFOS: Physical sciencesFOS: Computer and information sciences
• Gaussian Process Regression for Materials and Molecules.
  We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of inter…
  datacite Deringer, Volker L, Bartók, Albert P, Bernstein, Noam, Wilkins, David M 等 2021 置信度 0.66 3403 Macromolecular and Materials Chemistry34 Chemical SciencesMachine Learning and Artificial IntelligenceGeneric health relevance
• From GAP to ACE to MACE
  The Gaussian approximation potential (GAP) machine-learning-inspired functional form was the first to be used for a general-purpose interatomic potential. The atomic cluster expansion (ACE), previously the subject of a KIM Review, and its multilayer neural-net…
  datacite Bernstein, Noam 2024 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciencesFOS: Physical sciences
• Systematic assessment of various universal machine-learning interatomic potentials
  Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of \abinitio quality over very large time and length scales. More recently, various universal …
  datacite Yu, Haochen, Giantomassi, Matteo, Materzanini, Giuliana, Wang, Junjie 等 2024 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciencesFOS: Physical sciences
• Large-scale atomistic study of plasticity in amorphous gallium oxide with a machine-learning potential
  Compared to the widely investigated crystalline polymorphs of gallium oxide (Ga2O3), knowledge about its amorphous state is still limited. With the help of a machine-learning interatomic potential, we conducted large-scale atomistic simulations to investigate …
  datacite Zhang, Jiahui, Zhao, Junlei, Byggmästar, Jesper, Frankberg, Erkka J. 等 2024 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Statistical Mechanics (cond-mat.stat-mech)FOS: Physical sciencesFOS: Physical sciences
• Deep Potentials for Materials Science
  To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learnin…
  datacite Wen, Tongqi, Zhang, Linfeng, Wang, Han, E, Weinan 等 2022 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Computational Physics (physics.comp-ph)FOS: Physical sciencesFOS: Physical sciences
• OUTCAR dataset for machine learning potential about a h-BN growth on Pt(111) surface
  The growth of monolayer h-BN from boron and nitrogen atoms on Pt(111) is investigated using molecular dynamics combined with machine-learning potentials trained based on first-principles data. The MD simulation can be performed to investigate the h-BN growth o…
  datacite Yeo, Kangmo, Jeong, Sukmin 2022 置信度 0.66 DFTmachine learning potentialhexagonal boron nitride growthPt(111) surface
• OUTCAR dataset for machine learning potential about a h-BN growth on Pt(111) surface
  The growth of monolayer h-BN from boron and nitrogen atoms on Pt(111) is investigated using molecular dynamics combined with machine-learning potentials trained based on first-principles data. The MD simulation can be performed to investigate the h-BN growth o…
  datacite Yeo, Kangmo, Jeong, Sukmin 2022 置信度 0.66 DFTmachine learning potentialhexagonal boron nitride growthPt(111) surface
• Continuous and Optimally Complete Description of Chemical Environments Using Spherical Bessel Descriptors
  Recently, machine learning potentials have been advanced as candidates to combine the high-accuracy of quantum mechanical simulations with the speed of classical interatomic potentials. A crucial component of a machine learning potential is the description of …
  datacite Kocer, Emir, Mason, Jeremy K., Erturk, Hakan 2019 置信度 0.66 Computational Physics (physics.comp-ph)Chemical Physics (physics.chem-ph)FOS: Physical sciencesFOS: Physical sciences
• Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations
  Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional quantum mechanics based methods. At the same time, the construction of new m…
  datacite Miksch, April M., Morawietz, Tobias, Kästner, Johannes, Urban, Alexander 等 2021 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciencesFOS: Physical sciences
• Machine-learning interatomic potentials for materials science
  Large-scale atomistic computer simulations of materials rely on interatomic potentials providing computationally efficient predictions of energy and Newtonian forces. Traditional potentials have served in this capacity for over three decades. Recently, a new c…
  datacite Mishin, Y. 2021 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciencesFOS: Physical sciences
• Gaussian Process Regression for Materials and Molecules.
  We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of inter…
  datacite Deringer, Volker L, Bartók, Albert P, Bernstein, Noam, Wilkins, David M 等 2021 置信度 0.66
• Representation learning of crystal materials using Graph Neural Networks: Passive symmetry challenges and advances.
  Crystal structures are naturally represented as graphs, making Graph Neural Networks (GNNs) a powerful tool for capturing complex atomic interactions and geometric relationships. This review summarizes recent advances in GNNs-based representation learning for …
  europepmc 2026 置信度 0.80
• Predictive machine learning models for rational permeability design in de novo macrocycle engineering: a review.
  Small molecule drug discovery has been highly successful across many therapeutic areas over decades of progress; however, many disease-relevant proteins remain difficult to target. In particular, intracellular proteins with large, shallow, or flexible interact…
  europepmc 2026 置信度 0.80
• Harnessing confinement effect and interpretable machine learning to predict alkane diffusion in zeolite catalysts.
  Diffusion within zeolites is a critical determinant of their performance in catalysis and separations. Nevertheless, mechanistic insights remain largely system-specific, and a universal framework connecting topological features to diffusion properties is still…
  europepmc 2026 置信度 0.80
• Examining Genetic Variants Associated with FOXP1 Syndrome through Molecular Dynamics of Its DNA-Binding Domain and Self-Organizing Maps.
  Genetic mutations in the transcription factor FOXP1 (forkhead box protein P1) cause an autosomal dominant neurodevelopmental disorder called FOXP1 syndrome. To understand the structural impact of pathogenic variants associated with FOXP1 syndrome, we investiga…
  europepmc 2026 置信度 0.80
• Mechanochemical disassembly pathways of self-assembled polymer-decorated Pd<sub>n</sub>L<sub>2n</sub> supramolecular architectures.
  Supramolecular Pd n L 2n architectures are versatile molecular platforms with applications spanning catalysis, sensing, and therapeutic delivery. Whereas their thermodynamically driven assembly has been extensively studied, controlled strategies for disassembl…
  europepmc 2026 置信度 0.80
• Interstitial cation defect chemistry and correlated disorder in melilite solid electrolytes.
  Solid electrolytes are fundamental to fuel cells, batteries, sensors and electrolysers. Among them, melilite oxides are promising oxide-ion solid electrolytes due to their unique layered tetrahedral networks. However, the defect chemistry in acceptor-doped mel…
  europepmc 2026 置信度 0.80
• Multiscale Computational Enzymology of CYP450 Biotransformation of Organic Halogenated Pollutants: Methods and Environmental Perspectives.
  Halogenated organic pollutants (HOPs) represent a persistent class of environmental contaminants with high stability, bioaccumulation potential, and toxicity. Cytochrome P450 enzymes (CYP450s) play a pivotal role in their oxidative biotransformation, yet mecha…
  europepmc 2026 置信度 0.80
• The onset of collective diffusion in hcp-iron: a combined theory of soft-matter and solid-state physics.
  Lately, scientists have discovered the existence of collective diffusion in hcp-iron via machine-learning algorithms. This result has opened a promising avenue for geophysical studies on planetary cores. However, the crossover between collective and non-collec…
  europepmc 2026 置信度 0.80
• A conformational benchmark for optical property prediction with solvent-aware graph neural networks.
  Accurately predicting optical spectra of molecules is essential for creating better OLED emitters, solar-cell dyes, and fluorescent probes. Traditional methods, such as time-dependent density-functional theory, are computationally expensive and often inaccurat…
  europepmc 2026 置信度 0.80
• Machine learning potentials for modeling alloys across compositions.
  Materials properties depend strongly on chemical composition, i.e., the relative amounts of each chemical element. Changes in composition lead to entirely different chemical arrangements, which vary in complexity from perfectly ordered (i.e., stoichiometric co…
  europepmc 2026 置信度 0.80