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• Training sets for A unified microscopic picture of cation and anion migration in MAPbI3
  The training sets of the MAPbI3 machine learning potentials used in arXiv.2605.02685 are provided. These training sets, in combination with the training parameters provided in the publication, can be used for generating the force fields. The training sets are …
  datacite Tyagi, Viren 2026 置信度 0.66
• Machine learning interatomic potentials for solid-state precipitation
  Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and data-generation schemes des…
  datacite Piersante, Lorenzo, Anirudh Raju, Natarajan 2026 置信度 0.66 MARVELmachine learningmetallurgy
• Machine learning interatomic potentials for solid-state precipitation
  Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and data-generation schemes des…
  datacite Piersante, Lorenzo, Anirudh Raju, Natarajan 2026 置信度 0.66 MARVELmachine learningmetallurgy
• Phonon-Mediated Thermal Transport in Nanocrystalline Silicon Using Machine-Learning Interatomic Potentials
  Understanding phonon-mediated heat transport in structurally complex materials remains a central challenge for next-generation electronic and nanomechanical devices, where grain boundaries and interfacial disorder strongly limit thermal dissipation. Although c…
  datacite Rezgui, Houssem, Benejam, Catalina Coll, Torres, Clivia M. Sotomayor, Pruneda, Miguel 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Mesoscale and Nanoscale Physics (cond-mat.mes-hall)FOS: Physical sciences
• Multiscale Computational Framework for Corrosion-Resistant Alloy Design: Coupling DFT, ReaxFF Molecular Dynamics, and Phase-Field Modeling of Localized Attack
  The design of corrosion-resistant alloys for demanding applications requires predictive tools that can link molecular-scale phenomena to component-level performance across vastly different length and time scales. This comprehensive review systematically examin…
  datacite Masood Amiri Koshkeki 2026 置信度 0.66
• Multiscale Computational Framework for Corrosion-Resistant Alloy Design: Coupling DFT, ReaxFF Molecular Dynamics, and Phase-Field Modeling of Localized Attack
  The design of corrosion-resistant alloys for demanding applications requires predictive tools that can link molecular-scale phenomena to component-level performance across vastly different length and time scales. This comprehensive review systematically examin…
  datacite Masood Amiri Koshkeki 2026 置信度 0.66
• Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms
  Understanding the mechanisms of hydrogen embrittlement (HE) is essential for advancing next-generation high-strength steels, thereby motivating the development of highly accurate machine-learning interatomic potentials (MLIPs) for the Fe-H binary system. Howev…
  datacite Ito, Kazuma 2025 置信度 0.66 Materials Science (cond-mat.mtrl-sci)FOS: Physical sciences
• A nuclear-quantum-corrected machine-learning potential reveals quantum-enhanced hydrogen segregation at general grain boundaries in alpha-iron
  Atomistic descriptions of hydrogen diffusion and trapping at defects are essential for understanding hydrogen embrittlement. As the lightest solute in metals, hydrogen exhibits nuclear quantum effects that alter these processes even at room temperature. Explic…
  datacite Ito, Kazuma 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Quantum Physics (quant-ph)FOS: Physical sciences
• Unlocking Multi-Component Bulk-Materials Molecular Dynamics with a Small-Footprint Machine Learning Interatomic Potential
  Bulk materials, as opposed to nanomaterials, require molecular dynamics (MD) simulations on a large spatial scale (~10^9 atoms or more) to adequately capture their atomic-scale physical properties. Previously, the introduction of machine-learning interatomic p…
  datacite Ouyang, Yucheng, Chen, Xin, Liu, Ying, Wang, Lifang 等 2026 置信度 0.66 Computational Physics (physics.comp-ph)FOS: Physical sciences
• ALKEMIE Agent: an autonomous platform for computational materials design
  Despite the powerful multi-scale modeling methods and high-throughput infrastructures established in the materials community, real material computation workflows remain fragmented and heavily manual, requiring researchers to constantly bridge software tools, d…
  datacite Huang, Hongfu, Li, Yuzhe, Xu, Ao, Liu, Bo 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Artificial Intelligence (cs.AI)FOS: Physical sciencesFOS: Computer and information sciences
• Estimating potential-dependent physicochemical properties at metal--electrolyte interfaces using machine learning interatomic potentials (Part 1)
  This record is the first part of the dataset supporting our associated publication. It contains the electrolyte–metal interfacial dataset used to train machine-learning interatomic potentials (MLIPs) using the MACE architecture, along with the corresponding te…
  datacite Mathanker, Ankit 2025 置信度 0.66
• Estimating potential-dependent physicochemical properties at metal--electrolyte interfaces using machine learning interatomic potentials (Part 1)
  This record is the first part of the dataset supporting our associated publication. It contains the electrolyte–metal interfacial dataset used to train machine-learning interatomic potentials (MLIPs) using the MACE architecture, along with the corresponding te…
  datacite Mathanker, Ankit 2025 置信度 0.66
• Comparison of Elastic Constants and Surface Energies of β-Sn from Density Functional Theory, Universal Machine Learning Potential, and Empirical Potentials — data and code
  Companion data and code repository for "Comparison of Elastic Constants and Surface Energies of β-Sn from Density Functional Theory, Universal Machine Learning Potential, and Empirical Potentials" (Modelling and Simulation in Materials Science and Engineering …
  datacite Tatsumi, Hiroaki, Ito, Atsushi M., Takayama, Arimichi, Nishikawa, Hiroshi 2026 置信度 0.66 beta-tinelastic constantssurface energyWulff constructiondensity functional theory
• Comparison of Elastic Constants and Surface Energies of β-Sn from Density Functional Theory, Universal Machine Learning Potential, and Empirical Potentials — data and code
  Companion data and code repository for "Comparison of Elastic Constants and Surface Energies of β-Sn from Density Functional Theory, Universal Machine Learning Potential, and Empirical Potentials" (Modelling and Simulation in Materials Science and Engineering …
  datacite Tatsumi, Hiroaki, Ito, Atsushi M., Takayama, Arimichi, Nishikawa, Hiroshi 2026 置信度 0.66 beta-tinelastic constantssurface energyWulff constructiondensity functional theory
• Reference energies and atomic forces for thirteen doped zirconia chemistries
  A balanced collection of 104 technically validated Quantum ESPRESSO reference calculations for 13 doped-zirconia chemistries. Each chemistry contains five finite-temperature configurations and three migration-related configurations. The record includes structu…
  datacite Zhang, Qikai, Jin, Zhihao, Chen, Xianfu, Xiong, Hao 等 2026 置信度 0.66 zirconiadensity functional theoryatomic forcesQuantum ESPRESSOmachine learning interatomic potential
• Study of ordering in (MoCrTi)$_{100-x}$Al$_x$ refractory high-entropy alloys using machine learning interatomic potential
  Refractory high-entropy alloys are promising candidates for high-temperature applications, yet the effects of composition on their chemical-ordering pathways and mechanical properties remain insufficiently understood. Here, a universal MLIP combined with MC an…
  datacite Zhang, Jiyao, Lechner, Klemens, Maßwohl, Markus, Spoerk-Erdely, Petra 等 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Disordered Systems and Neural Networks (cond-mat.dis-nn)FOS: Physical sciences
• Spectral Functionals of the Elastic Stiffness Tensor as a Physically Grounded Basis for Hardness Prediction and Directed Crystal Structure Search
  We propose and systematically compare ten physically motivated hardness descriptors based exclusively on the six eigenvalues (λ₁ ≤ λ₂ ≤ ··· ≤ λ₆) of the elastic stiffness tensor in Voigt notation. The models are trained on a curated 45-material dataset using e…
  datacite Prysyazhnyuk, Pavlo 2026 置信度 0.66 Materials Science/methodsComputational Materials Sciencehardness predictionelastic stiffness tensorspectral functionals
• Spectral Functionals of the Elastic Stiffness Tensor as a Physically Grounded Basis for Hardness Prediction and Directed Crystal Structure Search
  We propose and systematically compare ten physically motivated hardness descriptors based exclusively on the six eigenvalues (λ₁ ≤ λ₂ ≤ ··· ≤ λ₆) of the elastic stiffness tensor in Voigt notation. The models are trained on a curated 45-material dataset using e…
  datacite Prysyazhnyuk, Pavlo 2026 置信度 0.66 Materials Science/methodsComputational Materials Sciencehardness predictionelastic stiffness tensorspectral functionals
• Estimating potential-dependent physicochemical properties at metal--electrolyte interfaces using machine learning interatomic potentials (Part 3)
  This record is the third part of the dataset supporting our associated publication. It contains MACE molecular dynamics trajectories for electrolyte–Rh(111) interfaces. These simulations were used to analyze water structure, ion distributions, and interfacial …
  datacite Mathanker, Ankit, Govindarajan, Nitish 2025 置信度 0.66
• Estimating potential-dependent physicochemical properties at metal--electrolyte interfaces using machine learning interatomic potentials (Part 3)
  This record is the third part of the dataset supporting our associated publication. It contains MACE molecular dynamics trajectories for electrolyte–Rh(111) interfaces. These simulations were used to analyze water structure, ion distributions, and interfacial …
  datacite Mathanker, Ankit, Govindarajan, Nitish 2025 置信度 0.66
• ase-calculator-kit: a unified ASE calculator factory for MLIP and DFT calculators
  A thin, unified ASE calculator factory for machine-learning interatomic potentials (SevenNet, CHGNet, MatterSim, NequIP OAM, UMA/fairchem, MACE) and external DFT calculators (VASP, Quantum ESPRESSO). Every call returns a standard ase.Calculator, so the rest of…
  datacite Wakamiya, Taishiro, Ishikawa, Atsushi 2026 置信度 0.66 ASEmachine-learning interatomic potentialdensity functional theorycomputational chemistrymaterials science
• OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent
  The FAIR principles have transformed how computational materials data and workflows are shared, yet existing repositories can only serve pre-computed entries — their coverage is perpetually incomplete and cannot adapt to new questions on demand. We built OptiM…
  datacite Hu, Yang, Turlo, Vladyslav 2026 置信度 0.66 FAIR principleLLM agentmaterials informaticsmulti-principal element alloysuniversal machine learning potentials
• OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent
  The FAIR principles have transformed how computational materials data and workflows are shared, yet existing repositories can only serve pre-computed entries — their coverage is perpetually incomplete and cannot adapt to new questions on demand. We built OptiM…
  datacite Hu, Yang, Turlo, Vladyslav 2026 置信度 0.66 FAIR principleLLM agentmaterials informaticsmulti-principal element alloysuniversal machine learning potentials
• Training sets for Halide Diffusion in Mixed-Halide Perovskites and Heterojunctions
  The training sets for the machine learning potentials used in '10.1021/acs.chemmater.6c00258' are provided. These training sets, in combination with the training parameters provided in the publication, can be used for generating the force fields. They are divi…
  datacite Tyagi, Viren 2026 置信度 0.66
• Training sets for Halide Diffusion in Mixed-Halide Perovskites and Heterojunctions
  The training sets for the machine learning potentials used in '10.1021/acs.chemmater.6c00258' are provided. These training sets, in combination with the training parameters provided in the publication, can be used for generating the force fields. They are divi…
  datacite Tyagi, Viren 2026 置信度 0.66
• Evaluating Electrostatic Embedding MLIP/MM for Relative Binding Free Energy Calculations
  Alchemical relative binding free energy (RBFE) calculations are limited by the fixed-charge approximation of classical force fields. Hybrid machine learning interatomic potential/molecular mechanics (MLIP/MM) schemes correct ligand strain, but under mechanical…
  datacite Farr, Stephen E., De Fabritiis, Gianni 2026 置信度 0.66 Chemical Physics (physics.chem-ph)Computational Physics (physics.comp-ph)FOS: Physical sciences
• MACE-mpa-0 relaxation trajectories with ASE optimizers
  This dataset contains full geometry-relaxation trajectories generated with the MACE-mpa-0 machine-learning interatomic potential (MLIP) using several ASE optimizers (BFGS, LBFGS, and line-search variants of these, FIRE, and (SciPyFmin) CG).It accompanies the p…
  datacite Greten, David, Jakob, Konstantin Simon, Reuter, Karsten, Margraf, Johannes T. 2026 置信度 0.66 OptimizationMachine Learning Interatomic Potentials
• MACE-mpa-0 relaxation trajectories with ASE optimizers
  This dataset contains full geometry-relaxation trajectories generated with the MACE-mpa-0 machine-learning interatomic potential (MLIP) using several ASE optimizers (BFGS, LBFGS, and line-search variants of these, FIRE, and (SciPyFmin) CG).It accompanies the p…
  datacite Greten, David, Jakob, Konstantin Simon, Reuter, Karsten, Margraf, Johannes T. 2026 置信度 0.66 OptimizationMachine Learning Interatomic Potentials
• Generalized Machine Learning Potential Models for Elemental Nanoclusters
  Abstract Nanoclusters occupy a unique size regime between isolated atoms and bulk materials. Their electronic, structural, and thermodynamic properties are governed by quantum effects and complex many-body interactions. Accurately modeling their potential ener…
  europepmc Subramanian Sankaranarayanan, Suvo Banik, Abhishek Aggarwal, Sukriti Manna 等 2025 置信度 0.80
• Machine-learning potential reveals the origin of hydrogen embrittlement at general grain boundaries in α-Fe
  Abstract Development of high-strength structural alloys is crucial for realizing a carbon-neutral society. A common issue in many alloys is hydrogen embrittlement accompanied by cracking at general grain boundaries (GBs), which is characterized by lack of crys…
  europepmc 2025 置信度 0.80
• Transferable Dispersion-Aware Machine Learning Interatomic Potentials for Multilayer Transition Metal Dichalcogenide Heterostructures
  Abstract Stacking atomically thin layers of transition metal dichalcogenides (TMDs) to form heterostructures provides a powerful and versatile platform for investigating exotic quantum phases. Controlling the twist-angle between the TMDs creates moir\'e superl…
  europepmc Yusuf Shaidu, Mit H. Naik, Steven G. Louie, Jeffrey B. Neaton 2025 置信度 0.80
• MatterChat: A Multi-Modal LLM for Material Science
  Abstract Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material structure data with language-based infor…
  preprints 2025 置信度 0.74
• Hydrogen Diffusion in Magnesium Using Machine Learning Potentials
  Abstract Understanding and accurately predicting hydrogen diffusion in materials is challenging due to the complex interactions between hydrogen defects and the crystal lattice. These interactions span large length and time scales, making them difficult to add…
  europepmc 2024 置信度 0.80
• Local potential energy density-supramolecular energy (LPED-SME) machine learning prediction – a web application to obtain the local SME from simple inputs
  Abstract We developed a Flask web application that uses supervised machine learning (ML) to predict the local potential energy density (LPED) based on intermolecular and intramolecular interactions. The predictions are made from simple inputs, specifically the…
  europepmc 2024 置信度 0.80
• Shear-activated phase transformations of diborides via machine-learning potential molecular dynamics
  Abstract The layered character of transition metal diborides (TMB2:s)---with three structure polymorphs representing different stackings of the metallic sublattice---evokes the possibility of activating phase-transformation plasticity via mechanical shear stra…
  europepmc 2024 置信度 0.80
• SuperSalt: Equivariant Neural Network Force Fields for Multicomponent Molten Salts System
  Abstract Molten salts are crucial for clean energy applications, yet exploring their thermophysical properties across diverse chemical spaces remains challenging. We present the development of a machine learning interatomic potential (MLIP) called SuperSalt, w…
  preprints 2025 置信度 0.74
• From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows
  Abstract We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic …
  europepmc 2024 置信度 0.80
• Online Test-time Adaptation for Interatomic Potentials
  Abstract Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physical science. However, distribution shift between training and test data …
  europepmc 2024 置信度 0.80
• DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings
  ABSTRACT Peptide-activated G protein-coupled receptors (GPCRs) regulate critical physiological processes such as metabolism, neural signalling, and endocrine function through their interaction with neuropeptides and peptide hormones. Despite their importance, …
  preprints 2025 置信度 0.74
• Machine Learned Interatomic Potentials for Ternary Carbides trained on the AFLOW Database
  Abstract Large density functional theory (DFT) databases are a treasure trove of energies, forces and stresses that can be used to train machine learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW data…
  europepmc 2024 置信度 0.80
• Robust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling
  Abstract Machine learning interatomic potentials (MLIPs) that enable accurate simulations of materials at scales beyond conventional first-principles approaches have played increasingly important roles in understanding and design of materials. However, MLIPs a…
  preprints Shyue Ping Ong, Ji Qi, Tsz Wai Ko, Brandon Wood 等 2023 置信度 0.74
• A unified moment tensor potential for silicon, oxygen, and silica
  Abstract Si and its oxides have been extensively explored in theoretical research due to their technological and industrial importance. Simultaneously describing interatomic interactions within both Si and SiO₂ without the use of ab inito methods is considered…
  preprints 2023 置信度 0.74
• Accurate machine learning force fields via experimental and simulation data fusion
  Abstract Machine Learning (ML)-based force fields are attracting ever-increasing interest due to their capacity to span spatiotemporal scales of classical interatomic potentials at quantum-level accuracy. They can be trained based on high-fidelity simulations …
  preprints 2023 置信度 0.74
• Neural Network Interatomic Potential for Laser-Excited Materials
  Abstract The design of new materials and the prediction of their properties by means of computational techniques has reached an unprecedent level of accuracy thanks to the development and use of machine-learning (ML) approaches. For instance, interatomic poten…
  preprints Pascal Plettenberg, Bernd Bauerhenne, M. Garcia 2023 置信度 0.74
• AI-based Nano-scale Material Property Prediction For Li-Ion Batteries
  Molecular dynamics (MD) simulations present a sophisticated nano-scale computational approach that can play a critical role in material design for next generation batteries. One critical piece of information needed for MD simulations is the non-bonded potentia…
  preprints 2023 置信度 0.74
• Spin-Dependent Graph Neural Network Potential for Magnetic Materials
  Abstract The development of machine learning interatomic potentials has immensely contributed to the accuracy of simulations of molecules and crystals. However, creating interatomic potentials for magnetic systems that account for both magnetic moments and str…
  preprints 2023 置信度 0.74
• Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
  Abstract Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive quantum chem…
  preprints 2023 置信度 0.74
• Sliding friction over individual covalent bonds correlates with bond order
  Abstract Unveiling the dynamics and energy dissipation involved in atomic scale motion is key to understanding surface catalysis 1–3 , molecular motors 4,5 , and single molecule manipulation 6,7 . Despite significant progress in nanoscale friction 8–10 studies…
  preprints 2025 置信度 0.74
• Uncertainty Driven Dynamics for Active Learning of Interatomic Potentials
  Abstract Machine learning (ML) models, if trained to datasets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse datasets. In this approach, the M…
  preprints 2022 置信度 0.74
• The Arch from the Stones: Understanding Protein Folding Energy Landscapes via Bio-inspired Collective Variables
  Protein folding remains a formidable challenge despite significant advances, particularly in sequence-to-structure prediction. Accurately capturing thermodynamics and intermediates via simulations demands overcoming timescale limitations, making effective coll…
  preprints 2025 置信度 0.74
• Iterative variational learning of committor-consistent transition pathways using artificial neural networks
  Abstract This contribution introduces a neural-network-based approach to discover meaningful transition pathways underlying complex biomolecular transformations in coherence with the committor function. The proposed path-committor-consistent artificial neural …
  preprints 2025 置信度 0.74
• Confidence-guided cryo-EM map optimisation with LocScale-2.0
  Cryogenic sample electron microscopy (cryo-EM) maps often display uneven quality, with high-resolution features coexisting alongside weak or poorly ordered regions. Such variation complicates structural interpretation, especially for heterogeneous macromolecul…
  preprints 2025 置信度 0.74
• Exploring the limits of machine-learned potentials for chemically complex multicomponent systems
  Abstract Chemically complex multicomponent alloys have garnered widespread interest owing to their exceptional properties coming from a sheer inexhaustible compositional space. The complexity poses severe challenges for atomistic modelling and interatomic pote…
  preprints 2022 置信度 0.74
• Transfer and Active Learning of High Dimensional Neural Network Potentials for Transition Metal Clusters and Bulk
  Abstract Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. …
  preprints 2022 置信度 0.74
• Machine-learning potentials for nanoscale simulations of deformation and fracture: example of TiB2 ceramic
  Abstract Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. Their development for investigation of mechanical properties and fracture, however, is far from trivial since ex…
  preprints 2023 置信度 0.74
• AlphaRING-X: accurate interpretation of missense variant deleteriousness based on protein structural stability
  Background Accurate interpretation of missense variants remains a significant challenge hindering genomic diagnosis. While state-of-the-art machine learning and deep learning predictors offer high accuracy, they often lack the transparency required for clinica…
  preprints 2024 置信度 0.74
• Mapping Active-Site Conformational Ensembles Along Competing Catalytic Pathways of the Hairpin Ribozyme
  The catalytic mechanism of the hairpin ribozyme has remained controversial for more than two decades, with different experimental approaches often supporting distinct mechanistic interpretations. In this work, we investigate the conformational landscape of the…
  preprints 2026 置信度 0.74
• Structure and dynamics of Fe90Si3O7 liquids close to Earth's liquid core conditions
  Abstract Using an artificial neural-network machine learning interatomic potential, we have performed molecular dynamics simulations to study the structure and dynamics of Fe 90 Si 3 O 7 liquid close to the Earth's liquid core conditions. The simulation result…
  preprints 2022 置信度 0.74
• A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets
  ABSTRACT Structure-based generative chemistry aims to explore much bigger chemical space to design a ligand with high binding affinity to the target proteins; it is a critical step in de novo computer-aided drug discovery. Traditional in silico methods suffer …
  preprints 2023 置信度 0.74
• Equivalence of charged and neutral density functional formulations for correcting the many-body self-interaction of polarons
  Abstract The electron self-interaction problem in density functional theory affects the accurate modeling of polarons, particularly their localization and formation energy. Charged and neutral density functional formulations have been developed to address this…
  preprints 2025 置信度 0.74
• Predicting Protein-Ligand Binding Structure Using E(n) Equivariant Graph Neural Networks
  Drug design is a costly and time-consuming process, often taking more than 12 years and costing up to billions of dollars. The COVID-19 pandemic has signified the urgent need for accelerated drug development. The initial stage of drug design involves the ident…
  preprints 2023 置信度 0.74
• Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates
  Abstract High-efficient heat dissipation plays critical role for high-power-density electronics. Experimental synthesis of ultrahigh thermal conductivity boron arsenide (BAs, 1300 W m −1 K −1 ) cooling substrates into the wide-bandgap semiconductor of gallium …
  preprints 2022 置信度 0.74
• Development of a physically-informed neural network interatomic potential for tantalum
  Large-scale atomistic simulations of materials heavily rely on interatomic potentials, which predict the system energy and atomic forces. One of the recent developments in the field is constructing interatomic potentials by machine-learning (ML) methods. ML po…
  preprints 2021 置信度 0.74
• A Transferable Active-Learning Strategy for Reactive Molecular Force Fields
  Predictive simulations of dynamic processes in molecular systems require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct force-field models for large-scale molecular simulation by fitting to high-le…
  preprints 2021 置信度 0.74
• Delta variant with P681R critical mutation revealed by ultra-large atomic-scale <i>ab initio</i> simulation: Implications for the fundamentals of biomolecular interactions
  ABSTRACT SARS-CoV-2 Delta variant is emerging as a globally dominant strain. Its rapid spread and high infection rate are attributed to a mutation in the spike protein of SARS-CoV-2 allowing the virus to invade human cells much faster and with increased effici…
  preprints 2021 置信度 0.74
• Using deep learning predictions reveals a large number of register errors in PDB deposits
  The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, especially …
  preprints 2024 置信度 0.74
• Teaching a Neural Network to Attach and Detach Electrons from Molecules
  Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry. In particular, interatomic potentials derived with Machine Learning algorithms such as Deep Neural Networks (DNNs), achi…
  preprints 2021 置信度 0.74
• Investigating the Volume and Diversity of Data Needed for Generalizable Antibody-Antigen ∆∆G Prediction
  Antibody-antigen binding affinity lies at the heart of therapeutic antibody development: efficacy is guided by specific binding and control of affinity. Here we present Graphinity, an equivariant graph neural network architecture built directly from antibody-a…
  preprints 2023 置信度 0.74
• Rationale design of peptide inhibitors for <i>α</i> -Synuclein liquid condensates and fibrillar aggregates using multiscale modelling approach
  α -Synuclein is an intrinsically disordered protein (IDP) whose aggregation is implicated in Parkinson’s disorder. Herein, we computationally design a α -Synuclein derived potential peptide inhibitor against the protein’s monomeric, fibrillar and liquid conden…
  preprints 2025 置信度 0.74
• Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots
  With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of …
  preprints 2022 置信度 0.74
• Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability
  Design: ing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as “developability”, which reflects an antibody’s ability to progress through development stages based on its physicochemical properties. While na…
  preprints 2023 置信度 0.74
• OneOPES, a combined enhanced sampling method to rule them all
  Enhanced sampling techniques have revolutionised molecular dynamics (MD) simulations, enabling the study of rare events and the calculation of free energy differences in complex systems. One of the main families of enhanced sampling techniques uses physical de…
  preprints 2023 置信度 0.74
• Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression
  The Density-Functional Tight Binding (DFTB) method is a popular semiempirical approximation to Density Functional Theory (DFT). In many cases, DFTB can provide comparable accuracy to DFT at a fraction of the cost, enabling simulations on length- and time-scale…
  preprints 2019 置信度 0.74
• Transposable element products, functions, and regulatory networks in Arabidopsis
  ABSTRACT Transposable elements (TEs) are DNA sequences with the ability to propagate themselves within genomes. Their mobilization is catalyzed by self-encoded factors, yet these factors have been poorly investigated. Here, we leveraged extensive long-and shor…
  preprints 2024 置信度 0.74
• Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology
  Abstract Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from p…
  preprints 2021 置信度 0.74
• Review for "Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins"
  crossref 2026-06-08T15:13:01Z 置信度 0.70
• Review for "Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins"
  crossref 2026-06-08T15:13:01Z 置信度 0.70
• Review for "Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins"
  crossref 2026-06-08T15:13:01Z 置信度 0.70
• Review for "Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins"
  crossref 2026-06-08T15:13:01Z 置信度 0.70
• Review for "Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins"
  crossref 2026-06-08T15:13:01Z 置信度 0.70
• Review for "Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins"
  crossref 2026-06-08T15:13:01Z 置信度 0.70
• Decision letter for "Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins"
  crossref 2026-06-08T15:13:01Z 置信度 0.70
• Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules
  We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications…
  crossref 2023-12-18T08:20:24Z 置信度 0.70
• Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing StressStrain and Vibrational Properties
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