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• Structure-activity modeling and hybrid machine learning-based prediction of bioactivity in pyrazole derivatives for drug discovery applications.
  Background/aim Pyrazole derivatives are of growing interest due to their diverse pharmacological activities. However, their biological activity is often highly sensitive to subtle structural modifications. Existing quantitative structure-activity relationships…
  europepmc 2026 置信度 0.80
• Uncertainty Quantification for <i>In Silico</i> Chemistry.
  The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular propert…
  europepmc 2026 置信度 0.80
• Large-scale atomistic study of plasticity in amorphous gallium oxide with ab-initio accuracy.
  Compared to the widely investigated crystalline polymorphs of gallium oxide ([Formula: see text]), knowledge about its amorphous state is very limited. With the help of a machine-learning interatomic potential, we conducted large-scale atomistic simulations to…
  europepmc 2025 置信度 0.80
• Response Matching for Generating Materials and Molecules.
  Diffusion models have recently emerged as powerful tools for the generation of new molecular and material structures. The key insight is that the noise in these models is related to the response of the atoms to displacement, and the denoising step is thus anal…
  pubmed Cheng B 2024 置信度 0.82
• Atomistic and data-driven modeling of laser-induced graphene formation on sustainable polymer substrates.
  Wood-based substrates—known for their renewability, abundance, and surface functionalization potential—have recently gained attention as polymers for laser-induced graphene (LIG) synthesis because of their environmentally friendly attributes. These environment…
  europepmc 2025 置信度 0.80
• Multistage nucleation pathway in LiF molten salt mirrors the crystal-melt interface structure.
  Despite over a century of studies, fundamental questions remain about the processes governing crystal nucleation from melts or solutions. Research over the past three decades has presented mounting evidence for kinetic pathways of crystal nucleation that are m…
  europepmc 2025 置信度 0.80
• Advances in Computational Modeling and Machine Learning of Cellulosic Biopolymers: A Comprehensive Review.
  The hierarchical structure and multifunctional properties of bio-based cellular materials, particularly cellulose, hemicellulose, and lignin, have attracted increasing attention and interest due to their sustainability and versatility. Recent advances in compu…
  europepmc 2025 置信度 0.80
• Revisiting Machine Learning Potentials for Silicate Glasses: The Missing Role of Dispersion Interactions.
  Machine learning interatomic potentials (MLIPs) offer a promising alternative to traditional force fields and ab initio methods for simulating complex materials such as oxide glasses. In this work, we present the first evaluation of the pretrained MACE (Multi-…
  pubmed Pedone A, Bertani M, Benassi M 2025 置信度 0.82
• AI-Accelerated Discovery of Electrocatalyst Materials.
  The rational exploration and design of high-performance, stable electrocatalysts are crucial for efficient renewable energy storage, conversion, and utilization. Artificial intelligence (AI) is revolutionizing this field by significantly reducing the time and …
  europepmc 2026 置信度 0.80
• Machine Learning-Based Investigation of Atomic Packing Effects: Chemical Pressures at the Extremes of Intermetallic Complexity.
  Intermetallic phases represent a domain of emergent behavior, in which atoms with packing and electronic preferences can combine into complex geometrical arrangements whose long-range order involves repeat patterns containing thousands of atoms or is incompati…
  pubmed Van Buskirk JS, Peterson GGC, Fredrickson DC 2024 置信度 0.82
• Bifunctional Electrocatalysts with High-Entropy Alloys: Bridging Hydrogen Evolution and Oxygen Reduction.
  High-entropy alloys (HEAs) have emerged as a promising class of bifunctional electrocatalysts capable of simultaneously driving the hydrogen evolution reaction (HER) and the oxygen reduction reaction (ORR) with high activity and durability. Their near-equiatom…
  europepmc 2026 置信度 0.80
• Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents.
  A significant challenge in computational chemistry is developing approximations that accelerate ab initio methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potenti…
  europepmc 2025 置信度 0.80
• Direct Observation of Electric-Field-Driven Phase Transitions Associated with Energy Storage in Antiferroelectric Films.
  Antiferroelectric materials are promising candidates for high-energy-density capacitors due to their reversible electric-field-induced phase transitions. However, the atomic-scale mechanism underlying the electric-field-driven antiferroelectric-to-ferroelectri…
  europepmc 2026 置信度 0.80
• Deep docking, part 2: an amplified DDU platform for ultra-large virtual screening.
  The exponential growth of accessible chemical space represents a significant computational challenge for structure-based virtual screening. Hence, active-learning and machine-learning approaches, such as Deep Docking, have been introduced to significantly spee…
  europepmc 2026 置信度 0.80
• Are diffusion models ready for materials discovery in unexplored chemical space?
  While diffusion models are attracting increasing attention for materials discovery, their ability to generate low-energy structures in unexplored chemical spaces has not been systematically assessed. Here, we evaluate the performance of the diffusion models Ma…
  europepmc 2026 置信度 0.80
• TrIP─Transformer Interatomic Potential Predicts Realistic Energy Surface Using Physical Bias.
  Accurate interatomic energies and forces enable high-quality molecular dynamics simulations, torsion scans, potential energy surface mappings, and geometry optimizations. Machine learning algorithms have enabled rapid estimates of the energies and forces with …
  pubmed Hedelius BE, Tingey D, Della Corte D 2024 置信度 0.82
• PerQueue: managing complex and dynamic workflows.
  Workflow managers play a critical role in the efficient planning and execution of complex workloads. A handful of these already exist within the world of computational materials discovery, but their dynamic capabilities are somewhat lacking. The PerQueue workf…
  pubmed Sjølin BH, Hansen WS, Morin-Martinez AA, Petersen MH 等 2024 置信度 0.82
• Gold-Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential.
  Monolayer protected metal clusters comprise a rich class of molecular systems and are promising candidate materials for a variety of applications. While a growing number of protected nanoclusters have been synthesized and characterized in crystalline forms, th…
  europepmc 2024 置信度 0.80
• Revisiting Many-Body Interaction Heat Current and Thermal Conductivity Calculations Using the Moment Tensor Potential/LAMMPS Interface.
  The definition of heat current operator for systems for nonpairwise additive interactions and its impact on related lattice thermal conductivity (κ L ) via molecular dynamics (MD) simulation are ambiguous and controversial when migrating from empirical potenti…
  europepmc 2025 置信度 0.80
• Multicrystalline informatics: a methodology to advance materials science by unraveling complex phenomena.
  Multicrystalline materials play a crucial role in our society. However, their microstructure is complicated, and there is no universal approach to achieving high performance. Therefore, a methodology is necessary to answer the fundamental question of how we sh…
  pubmed Usami N, Kutsukake K, Kojima T, Kudo H 等 2024 置信度 0.82
• A Molecular View of Methane Activation on Ni(111) through Enhanced Sampling and Machine Learning.
  A combination of machine learned interatomic potentials (MLIPs) and enhanced sampling simulations is used to investigate the activation of methane on a Ni(111) surface. The work entails the development and iterative refinement of MLIPs, initially trained on a …
  pubmed Xu Y, Jin Y, García Sánchez JS, Pérez-Lemus GR 等 2024 置信度 0.82
• Application of 3D atom pair map in an attention model for enhanced drug virtual screening.
  Scientific contribution This study demonstrates the utility of a novel molecular representation, 3D APM and a deep learning model based on it for virtual screening, suggesting that many other prediction models would also benefit from adopting APM. An open-sour…
  europepmc 2025 置信度 0.80
• Exploring a bimetallic catalyst family for hydrogen oxidation with insights into superior activity and durability.
  Anion exchange membrane fuel cells are limited by the slow kinetics of the alkaline hydrogen oxidation reaction (HOR). Aided by density functional theory combined with fine-tuned machine learning interatomic potential, we establish a family of bimetallic catal…
  europepmc 2025 置信度 0.80
• Inorganic Sodium Solid-State Electrolytes: Progress, Existing Issues, and Solutions Towards High-Performance all Solid-State Batteries.
  Na-ion batteries (NIBs) have gained attention as a cost-effective option for large-scale energy storage, offering electrochemical properties similar to lithium-ion batteries (LIBs). To improve safety and energy density, solid-state electrolytes (SSEs) are bein…
  europepmc 2026 置信度 0.80
• When can we trust structural models derived from pair distribution function measurements?
  The pair distribution function (PDF) is an important metric for characterising structure in complex materials, but it is well known that meaningfully different structural models can sometimes give rise to equivalent PDFs. In this paper, we discuss the use of m…
  europepmc 2025 置信度 0.80
• Machine-Learning-Driven High-Throughput Screening for High-Energy Density and Stable NASICON Cathodes.
  The Na super ionic conductor (NASICON), which has outstanding structural stability and a high operating voltage, is an appealing material for overcoming the limits of low specific energy and larger volume distortion of sodium-ion batteries. In this study, to d…
  pubmed Jeong J, Kim J, Sun J, Min K 2024 置信度 0.82
• Atomic cluster expansion potential for large scale simulations of hydrocarbons under shock compression.
  We present an Atomic Cluster Expansion (ACE) machine learned potential developed for high-fidelity atomistic simulations of hydrocarbons, targeting pressures and temperatures near and above supercritical fluid regimes for molecular fluids. A diverse set of sto…
  pubmed Willman JT, Perriot R, Ticknor C 2024 置信度 0.82
• Coverage-Dependent Structural Evolution of CoBr<sub>2</sub> at the Au(111) Interface.
  Unraveling the growth mechanism of van der Waals materials is crucial for their device implementation, as this improves the overall film quality, allowing precise control of their electronic and magnetic properties in nanoscale applications. The initial struct…
  europepmc 2025 置信度 0.80
• Surface melting-driven hydrogen absorption for high-pressure polyhydride synthesis.
  The synthesis of new polyhydrides with high superconducting T c is challenging owing to the high pressures and temperatures required. In this study, we used machine-learning potential molecular dynamics simulations to investigate the initial stage of polyhydri…
  europepmc 2025 置信度 0.80
• Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation.
  The development of machine learning interatomic potentials (MLIPs) has revolutionized computational chemistry by enhancing the accuracy of empirical force fields while retaining a large computational speed-up compared to first-principles calculations. Despite …
  europepmc Nils Gönnheimer, Karsten Reuter, Johannes T. Margraf 2025 置信度 0.80
• Octahedral Tilt-Driven Phase Transitions in BaZrS<sub>3</sub> Chalcogenide Perovskite.
  Chalcogenide perovskites are lead-free materials for potential photovoltaic or thermoelectric applications. BaZrS 3 is the most-studied member of this family due to its superior thermal and chemical stability, desirable optoelectronic properties, and low therm…
  europepmc 2025 置信度 0.80
• Probing iron in Earth's core with molecular-spin dynamics.
  Dynamic compression of iron to Earth-core conditions is one of the few ways to gather important elastic and transport properties needed to uncover key mechanisms surrounding the geodynamo effect. Herein, a machine-learned ab initio derived molecular-spin dynam…
  europepmc 2024 置信度 0.80
• Intermediates of forming transition metal dichalcogenide heterostructures revealed by machine learning simulations.
  Two-dimensional (2D) transition metal dichalcogenide (TMD) van der Waals heterostructures (vdWHs) hold promise for high-performance electronics, but their large-scale synthesis remains limited by size constraints and alloying contaminations. Recently, a two-st…
  europepmc 2026 置信度 0.80
• Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.
  Computational methods have revolutionized NMR spectroscopy, driving significant advancements in structural biology and related fields. This review focuses on recent developments in quantum chemical and machine learning approaches for computational NMR, emphasi…
  europepmc 2025 置信度 0.80
• Molecular dynamics of liquid-electrode interface by integrating Coulomb interaction into universal neural network potential.
  Computational understanding of the liquid-electrode interface faces challenges in efficiently incorporating reactive force fields and electrostatic potentials within reasonable computational costs. Although universal neural network potentials (UNNPs), represen…
  pubmed Hisama K, Valadez Huerta G, Koyama M 2024 置信度 0.82
• Generative Models for Crystalline Materials.
  Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materi…
  europepmc 2026 置信度 0.80
• MLTB: Enhancing Transferability and Extensibility of Density Functional Tight-Binding Theory with Many-body Interaction Corrections.
  We present a hybrid semiempirical density functional tight-binding (DFTB) model with a machine learning neural network potential as a correction to the repulsive term. This hybrid model, termed machine learning tight-binding (MLTB), employs the standard self-c…
  europepmc 2025 置信度 0.80
• SuperSalt: equivariant neural network force fields for multicomponent molten salts system.
  Molten salts are crucial for clean energy applications, yet exploring their thermophysical properties across diverse chemical space remains challenging. We present the development of a machine learning interatomic potential (MLIP) called SuperSalt, which targe…
  europepmc 2025 置信度 0.80
• Deep Learning-Driven Intelligent Fluorescent Probes: Advancements in Molecular Design for Accurate Food Safety Detection.
  The complexity of global food supply chains challenges public health, requiring advanced detection technologies beyond traditional lab methods. Fluorescent sensing, known for its sensitivity and quick response, is promising for food safety but hindered by inef…
  europepmc 2025 置信度 0.80
• Unraveling Reactivity Origin of Oxygen Reduction at High-Entropy Alloy Electrocatalysts with a Computational and Data-Driven Approach.
  High-entropy alloys (HEAs), characterized as compositionally complex solid solutions with five or more metal elements, have emerged as a novel class of catalytic materials with unique attributes. Because of the remarkable diversity of multielement sites or sit…
  pubmed Huang Y, Wang SH, Wang X, Omidvar N 等 2024 置信度 0.82
• Exploring new useful phosphors by combining experiments with machine learning.
  New phosphors are consistently in demand for advances in solid-state lighting and displays. Conventional trial-and-error exploration experiments for new phosphors require considerable time. If a phosphor host suitable for the target luminescent property can be…
  europepmc 2024 置信度 0.80
• Multimodal Learning of Protein-Protein Interactions for Accurate Binding Affinity Prediction.
  Protein-protein interactions are central mediators in many biological processes. Accurately predicting the protein-protein binding affinity is crucial for guiding the modulation of these interactions, thereby playing a significant role in therapeutic developme…
  europepmc 2025 置信度 0.80
• From Data to Discovery: Recent Trends of Machine Learning in Metal-Organic Frameworks.
  Renowned for their high porosity and structural diversity, metal-organic frameworks (MOFs) are a promising class of materials for a wide range of applications. In recent decades, with the development of large-scale databases, the MOF community has witnessed in…
  europepmc 2024 置信度 0.80
• BEmXRD-Nets framework for novel machine learning models to predict crystal energy with diversity structures.
  This work proposes the BEmXRD-Nets framework, a novel machine learning framework that integrates fundamental atomic properties with learned embeddings from experimental X-ray diffraction (XRD) patterns to accurately predict the crystal energy in diverse and co…
  europepmc 2026 置信度 0.80
• Machine Learning Force Field for Optimization of Isolated and Supported Transition Metal Particles.
  Computational modeling is an integral part of catalysis research. With it, new methodologies are being developed and implemented to improve the accuracy of simulations while reducing the computational cost. In particular, specific machine-learning techniques h…
  europepmc 2025 置信度 0.80
• A theoretical perspective on solid-state ionic interfaces.
  Solid-state ionic conductors find application across various domains in materials science, particularly showcasing their significance in energy storage and conversion technologies. To effectively utilize these materials in high-performance electrochemical devi…
  europepmc 2024 置信度 0.80
• Recent Applications of Theoretical Calculations and Artificial Intelligence in Solid-State Electrolyte Research: A Review.
  Solid-state electrolytes (SSEs), as key materials for all-solid-state batteries (ASSBs), face challenges such as low ionic conductivity and poor interfacial stability. With the rapid advancement of computational science and artificial intelligence (AI) technol…
  europepmc 2025 置信度 0.80
• A machine learning based classifier for topological quantum materials.
  Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab init…
  europepmc 2024 置信度 0.80
• Bridging Atomistic and Mesoscale Lithium Transport via Machine-Learned Force Fields and Markov State Models.
  Lithium diffusion in silicon battery anodes is governed by thermally activated jumps between (meta)stable sites separated by significant energy barriers, making such events rare on ab initio molecular dynamics (AIMD) time scales. To overcome this limitation, w…
  europepmc 2026 置信度 0.80
• Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics.
  Machine learned potentials (MLPs) have been widely employed in molecular dynamics simulations to study thermal transport. However, the literature results indicate that MLPs generally underestimate the lattice thermal conductivity (LTC) of typical solids. Here,…
  pubmed Wu X, Zhou W, Dong H, Ying P 等 2024 置信度 0.82
• Mix-match synthesis of nanosynbiotics from probiotics and prebiotics to counter gut dysbiosis via AI integrated formulation profiling.
  Antibiotics, improper food, and stress have created a dysbiotic state in the gut and almost 81% of the world's population has been affected due to the pandemic of COVID-19 and the prevalence of dengue virus in the past few years. The main intent of this study …
  europepmc 2024 置信度 0.80
• Deep Learning Prediction of Inflammatory Inducing Protein Coding mRNA in P. gingivalis Released Outer Membrane Vesicles.
  Aim The Insilco study uses deep learning algorithms to predict the protein-coding pg m RNA sequences. Material and methods The NCBI GEO DATA SET GSE218606's GEO R tool discovered P.G's outer membrane vesicles' most differentially expressed mRNA. Genemania anal…
  europepmc 2024 置信度 0.80
• Effect of Interlayer Bonding on Superlubric Sliding of Graphene Contacts: A Machine-Learning Potential Study.
  Surface defects and their mutual interactions are anticipated to affect the superlubric sliding of incommensurate layered material interfaces. Atomistic understanding of this phenomenon is limited due to the high computational cost of ab initio simulations and…
  europepmc 2024 置信度 0.80
• Interfacial Optimization for AlN/Diamond Heterostructures via Machine Learning Potential Molecular Dynamics Investigation of the Mechanical Properties.
  AlN/diamond heterostructures hold tremendous promise for the development of next-generation high-power electronic devices due to their ultrawide band gaps and other exceptional properties. However, the poor adhesion at the AlN/diamond interface is a significan…
  pubmed Qi Z, Sun X, Sun Z, Wang Q 等 2024 置信度 0.82
• Enhanced climbing image nudged elastic band method with Hessian eigenmode alignment.
  Accurate determination of the transition states is central to an understanding of reaction kinetics. Double-endpoint methods where both the initial and final states are specified, such as the climbing image nudged elastic band (CI-NEB), identify the minimum en…
  europepmc 2026 置信度 0.80
• Revealing the structure of the active sites for the electrocatalytic CO₂ reduction to CO over Co single atom catalysts using operando XANES and machine learning.
  Transition-metal nitrogen-doped carbons (TM-N-C) are emerging as a highly promising catalyst class for several important electrocatalytic processes, including the electrocatalytic CO 2 reduction reaction (CO 2 RR). The unique local environment around the singl…
  europepmc 2024 置信度 0.80
• AQuaRef: machine learning accelerated quantum refinement of protein structures.
  Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical data, which, in addition to being limited to known chemical entities, do n…
  europepmc 2025 置信度 0.80
• AI-guided competitive docking for virtual screening and compound efficacy prediction.
  Machine learning has revolutionized protein structure and interaction prediction, yet its full potential for drug discovery is still emerging. In this study, we show that denoise diffusion-based co-folding methods-such as AlphaFold3 and Boltz-1/2-not only achi…
  europepmc 2026 置信度 0.80
• Peering inside the black box by learning the relevance of many-body functions in neural network potentials.
  Machine learned potentials based on artificial neural networks are becoming a popular tool to define an effective energy model for complex systems, either incorporating electronic structure effects at the atomistic resolution, or effectively renormalizing part…
  europepmc 2025 置信度 0.80
• Methodological Frameworks for Computational Electrocatalysis: From Theory to Practice.
  Modeling electrocatalytic reactions at solid-liquid interfaces requires capturing both the quantum-mechanical processes at the electrode surface and the complex response of the surrounding electrochemical environment. This review examines the main theoretical …
  europepmc 2026 置信度 0.80
• Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals.
  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. There hav…
  pubmed Varughese B, Manna S, Loeffler TD, Batra R 等 2024 置信度 0.82
• Spin-informed universal graph neural networks for simulating magnetic ordering.
  The screening and discovery of magnetic materials are hindered by the computational cost of first-principles density-functional theory (DFT) calculations required to find the ground state magnetic ordering. Although universal machine-learning interatomic poten…
  europepmc 2025 置信度 0.80
• Artificial Intelligence and the Discovery of Antibiotics: Reinventing with Opportunities, Challenges, and Clinical Translation.
  Background The outbreak and spreading of antimicrobial resistance (AMR) in a very short time has made most of the old-fashioned antibiotics ineffective, and thus new therapeutic substances have to be developed. The traditional methods of antibiotics discovery …
  europepmc 2026 置信度 0.80
• On the Physical Origins of Reduced Ionic Conductivity in Nanoconfined Electrolytes.
  Ion transport through nanoscale pores is at the heart of numerous energy storage and separation technologies. Despite significant efforts to uncover the complex interplay of ion-ion, ion-water, and ion-pore interactions that give rise to these transport proces…
  europepmc 2025 置信度 0.80
• Decoding GuaB: Machine Learning-Powered Discovery of Enzyme Inhibitors Against the Superbug <i>Acinetobacter baumannii</i>.
  Background/Objectives: GuaB, which is known as inosine 5'-phosphate dehydrogenase (IMPDH), is an enzymatic target involved in the de novo guanine biosynthetic pathway of the multidrug-resistant (MDR) Acinetobacter baumannii . GuaB has emerged as a potential th…
  europepmc 2025 置信度 0.80
• Machine Learning for Designing Perovskites and Perovskite-Inspired Solar Materials: Emerging Opportunities and Challenges.
  The development of perovskites and perovskite-inspired materials (PIMs) is driven by the need for efficient, non-toxic and stable solar energy conversion technologies. While halide perovskites exhibit outstanding optoelectronic properties, their practical depl…
  europepmc 2026 置信度 0.80
• Spectral operator representations.
  Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic geometry, typically decomposed into local atomic environments. This approach, while well-suited for machine-learned interatomic potenti…
  pubmed Zadoks A, Marrazzo A, Marzari N 2024 置信度 0.82
• Data Generation for Machine Learning Interatomic Potentials and Beyond.
  The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of dive…
  pubmed Kulichenko M, Nebgen B, Lubbers N, Smith JS 等 2024 Dec 25 置信度 0.82
• ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials.
  The emergence of artificial intelligence is profoundly impacting computational chemistry, particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional potential energy surface representations, MLIPs overcome the conventional computa…
  pubmed David R, de la Puente M, Gomez A, Anton O 等 2025 Jan 15 置信度 0.82
• Exploring structural variances in monatomic metallic glasses using machine learning and molecular dynamics simulation.
  BCC and FCC metals have different glass-forming abilities (GFA) and exhibit different characteristics during the glass transition. However, the structural origin of their different GFAs is still not clear. Here, we explored the structures of eight monatomic me…
  pubmed Yang C, Sun M 2024 Nov 12 置信度 0.82
• Hyperparameter Optimization for Atomic Cluster Expansion Potentials.
  Machine learning-based interatomic potentials enable accurate materials simulations on extended time- and length scales. ML potentials based on the atomic cluster expansion (ACE) framework have recently shown promising performance for this purpose. Here, we de…
  pubmed Thomas du Toit DF, Zhou Y, Deringer VL 2024 Nov 26 置信度 0.82
• Accurate Crystal Structure Prediction of New 2D Hybrid Organic-Inorganic Perovskites.
  Low-dimensional hybrid organic-inorganic perovskites (HOIPs) are promising electronically active materials for light absorption and emission. The design space of HOIPs is extremely large, as a variety of organic cations can be combined with different inorganic…
  pubmed Karimitari N, Baldwin WJ, Muller EW, Bare ZJL 等 2024 Oct 9 置信度 0.82
• Pursuing Extreme Descriptive Power and Generality in Chemical Bond Theories: A Method to Decipher "Interatomic Genomes" from Interatomic Electron Structures.
  The description and analysis of chemical bonds have been difficult following the popularization of electronic structure calculations. Although many attempts have been made from the perspective of electronic structure, the sheer volume of information in the ele…
  pubmed Zhang X, Wei J, Jia H, Liu J 等 2024 Oct 8 置信度 0.82
• A unified moment tensor potential for silicon, oxygen, and silica.
  Si and its oxides have been extensively explored in theoretical research due to their technological importance. Simultaneously describing interatomic interactions within both Si and SiO 2 without the use of ab initio methods is considered challenging, given th…
  pubmed Zongo K, Sun H, Ouellet-Plamondon C, Béland LK 2024 置信度 0.82
• Revisiting Aspirin Polymorphic Stability Using a Machine Learning Potential.
  In this study, we present a systematic computational investigation to analyze the long-debated free energy stability of two well-known aspirin polymorphs, denoted as Form I and Form II. Specifically, we developed a strategy to collect training configurations c…
  pubmed Hattori S, Zhu Q 2024 Aug 27 置信度 0.82
• Investigating the behavior of diffusion models for accelerating electronic structure calculations.
  We present an investigation of diffusion models for molecular generation, with the aim of better understanding how their predictions compare to the results of physics-based calculations. The investigation into these models is driven by their potential to signi…
  pubmed Rothchild D, Rosen AS, Taw E, Robinson C 等 2024 Aug 22 置信度 0.82
• Advancing Bioactivity Prediction Through Molecular Docking and Self-Attention.
  Bioactivity refers to the ability of a substance to induce biological effects within living systems, often describing the influence of molecules, drugs, or chemicals on organisms. In drug discovery, predicting bioactivity streamlines early-stage candidate scre…
  pubmed Yin Y, Lam HYI, Mu Y, Li HY 等 2024 Dec 置信度 0.82
• Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks.
  The computational cost of accurate quantum chemistry (QC) calculations of large molecular systems can often be unbearably high. Machine learning offers a lower computational cost compared to QC methods while maintaining their accuracy. In this study, we employ…
  pubmed Kubečka J, Ayoubi D, Tang Z, Knattrup Y 等 2024 Oct 2 置信度 0.82
• i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations.
  Atomic-scale simulations have progressed tremendously over the past decade, largely thanks to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach exte…
  pubmed Litman Y, Kapil V, Feldman YMY, Tisi D 等 2024 Aug 14 置信度 0.82
• Density Functional Tight-Binding Models for Band Structures of Transition-Metal Alloys and Surfaces across the d-Block.
  First-principles electronic structure simulations are an invaluable tool for understanding chemical bonding and reactions. While machine-learning models such as interatomic potentials significantly accelerate the exploration of potential energy surfaces, elect…
  pubmed Balzaretti F, Voss J 2024 Aug 8 置信度 0.82
• Machine Learning Interatomic Potentials for Heterogeneous Catalysis.
  Atomistic modeling can provide valuable insights into the design of novel heterogeneous catalysts as needed nowadays in the areas of, e. g., chemistry, materials science, and biology. Classical force fields and ab initio calculations have been wi…
  pubmed Tang D, Ketkaew R, Luber S 2024 Oct 28 置信度 0.82
• FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials.
  Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab initio molecular dynamics simulations. In recent years, numerous advances in…
  pubmed Plé T, Adjoua O, Lagardère L, Piquemal JP 2024 Jul 28 置信度 0.82
• Accelerating NMR Shielding Calculations Through Machine Learning Methods: Application to Magnesium Sodium Silicate Glasses.
  In this work, we have applied the Kernel Ridge Regression (KRR) method using a Least Square Support Vector Regression (LSSVR) approach for the prediction of the NMR isotropic magnetic shielding (σ iso ) of active nuclei ( 17 O, 23 Na, 25 Mg, and 29 Si) i…
  pubmed Bertani M, Pedone A, Faglioni F, Charpentier T 2024 Nov 18 置信度 0.82
• Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials.
  Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as "designer solvents" as they can be mixed to precisely tailor the physiochemical properties. As using ma…
  pubmed Goodwin ZAH, Wenny MB, Yang JH, Cepellotti A 等 2024 Aug 1 置信度 0.82
• Dual-Vacancy-Engineered ZnIn(2)S(4) Nanosheets for Harnessing Low-Frequency Vibration Induced Piezoelectric Polarization Coupled with Static Dipole Field to Enhance Photocatalytic H(2) Evolution.
  This study investigates the impact of In- and S-vacancy concentrations on the photocatalytic activity of non-centrosymmetric zinc indium sulfide (ZIS) nanosheets for the hydrogen evolution reaction (HER). A positive correlation between the concentrations of du…
  pubmed Zhong WJ, Hung MY, Kuo YT, Tian HK 等 2024 Sep 置信度 0.82
• Learning a reactive potential for silica-water through uncertainty attribution.
  The reactivity of silicates in aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reacti…
  pubmed Roy S, Dürholt JP, Asche TS, Zipoli F 等 2024 Jul 17 置信度 0.82
• Virtual node graph neural network for full phonon prediction.
  Understanding the structure-property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, includi…
  pubmed Okabe R, Chotrattanapituk A, Boonkird A, Andrejevic N 等 2024 Jul 置信度 0.82
• Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces.
  Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecule…
  pubmed Focassio B, M Freitas LP, Schleder GR 2025 置信度 0.82
• Unveiling Competitive Adsorption in TiO(2) Photocatalysis through Machine-Learning-Accelerated Molecular Dynamics, DFT, and Experimental Methods.
  The efficient harnessing of solar power for water treatment via photocatalytic processes has long been constrained by the challenge of understanding and optimizing the interactions at the photocatalyst surface, particularly in the presence of nontarget cosolut…
  pubmed Allam O, Maghsoodi M, Jang SS, Snow SD 2024 Jul 17 置信度 0.82
• Goldene: An Anisotropic Metallic Monolayer with Remarkable Stability and Rigidity and Low Lattice Thermal Conductivity.
  In a recent breakthrough in the field of two-dimensional (2D) nanomaterials, the first synthesis of a single-atom-thick gold lattice of goldene has been reported through an innovative wet chemical removal of Ti 3 C 2 from the layered Ti 3 AuC 2 . Inspired by t…
  pubmed Mortazavi B 2024 May 31 置信度 0.82
• Applications of machine learning in high-entropy alloys: a review of recent advances in design, discovery, and characterization.
  High-entropy alloys (HEAs) have attracted considerable attention due to their exceptional properties and outstanding performance across various applications. However, the vast compositional space and complex high-dimensional atomic interactions pose significan…
  europepmc 2025 置信度 0.80
• Antibonding States Drive Anharmonicity and Low Thermal Conductivity in Edge-Sharing Metal Chalcogenides.
  Stereochemically active lone pairs (SCALPs) and orbital hybridization in edge-sharing polyhedra play a crucial role in suppressing lattice thermal conductivity (κ L ) in thermoelectric materials. Strong mixing between pnictogen s - and chalcogen p -orbitals ge…
  europepmc 2025 置信度 0.80
• Microscopic insights into the solvation of polyethylene glycol chains in water: A machine learning potential approach.
  Polyethylene glycol (PEG) is a structurally simple, nontoxic, and water-soluble polymer widely utilized in medical and pharmaceutical applications. Notably, when a PEG chain is immersed in water, the surrounding water molecules play a key role in driving confo…
  europepmc 2025 置信度 0.80
• Learning Pairwise Interaction for Extrapolative and Interpretable Machine Learning Interatomic Potentials with Physics-Informed Neural Network.
  Achieving both robust extrapolation and physical interpretability in machine learning interatomic potentials (ML-IPs) for atomistic simulation remains a significant challenge, particularly in data-scarce areas such as chemical reactions or complex, multicompon…
  europepmc Hoje Chun, Minjoon Hong, Seung Hyo Noh, Byungchan Han 2025 置信度 0.80
• Moiré-Induced Magnetoelectricity in Twisted Bilayer NiI_{2}.
  Twisted magnetic van der Waals materials offer a promising route for multiferroic engineering, yet modeling large-scale moiré superlattices remains challenging. Leveraging a newly developed SpinGNN++ framework that effectively handles spin-lattice coupled syst…
  europepmc 2025 置信度 0.80
• Machine Learning-Guided Multimodal Synchrotron Analysis Workflow for Fuel Cell Electrocatalyst Discovery.
  Synchrotron radiation provides exceptional sensitivity and resolution, enabling the acquisition of highly precise information critical for advancing fuel cell technology. When combined with machine learning-based, data-driven approaches, it offers powerful ins…
  europepmc 2025 置信度 0.80
• PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.
  Machine-learning interatomic potentials have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the cost. Leveraging large quantum mechanical databases and expressive architectures, r…
  europepmc 2025 置信度 0.80
• Dataset exploring the atomic scale structure and ionic dynamics of polyanion sodium cathode materials.
  Polyanionic sodium cathode materials exhibit promising electrochemical properties and high stability, making this chemical space worth exploring to enhance the performance of sodium-ion batteries. Given the vast chemical space, fast and efficient computational…
  europepmc 2025 置信度 0.80
• Global universal scaling and ultrasmall parameterization in machine-learning interatomic potentials with superlinearity.
  Using machine learning (ML) to construct interatomic interactions and thus potential energy surface (PES) has become a common strategy for materials design and simulations. However, those current models of machine-learning interatomic potential (MLIP) consider…
  europepmc 2025 置信度 0.80
• Self-Consistency Error Correction for Accurate Machine Learning Potentials from Variational Monte Carlo.
  Variational Monte Carlo (VMC) can be used to train accurate machine learning interatomic potentials (MLIPs), enabling molecular dynamics (MD) simulations of complex materials on time scales and system sizes previously unattainable. VMC training sets are often …
  pubmed Tenti G, Nakano K, Casula M 2025 置信度 0.82