Papers citing On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations
A generalizable, uncertainty-aware neural network potential for GeSbTe with Monte Carlo dropout
Solid-State Electronics · 2022
Sung-Ho Lee, Valério Olevano, B. Sklénard
Read original paper ↗ ·Conical Intersection in Chemiluminescence of Cyclic Peroxides
The Journal of Physical Chemistry Letters · 2022
Ling Yue, Ya‐Jun Liu
Read original paper ↗ ·Adaptive regularized Gaussian process regression for application in the context of hydrogen adsorption on graphene sheets
Journal of Computational Chemistry · 2022
Gunnar Alexander Schmitz, Bastian Schnieder
Read original paper ↗ ·Deep coarse-grained potentials via relative entropy minimization
The Journal of Chemical Physics · 2022
Stephan Thaler, Maximilian Stupp, Julija Zavadlav
Read original paper ↗ ·Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials
Chemical Science · 2023
Benjamin W. J. Chen, Xinglong Zhang, Jia Zhang
Read original paper ↗ ·Allegro-Legato: Scalable, Fast, and Robust Neural-Network Quantum Molecular Dynamics via Sharpness-Aware Minimization
Lecture notes in computer science · 2023
Hikaru Ibayashi, Taufeq Mohammed Razakh, Liqiu Yang, Thomas Linker, Marco Olguin, Shinnosuke Hattori, Ye Luo, Rajiv K. Kalia, Aiichiro Nakano, Ken‐ichi Nomura, Priya D. Vashishta
Read original paper ↗ ·An Introduction to Machine Learning in Molecular Sciences
Challenges and advances in computational chemistry and physics · 2023
Hanchao Liu, Chen Qu
Read original paper ↗ ·Efficient, Accurate, and Transferable Machine Learning Potentials: Application to Dislocations and Cracks in Iron
SSRN Electronic Journal · 2023
Lei Zhang, Gábor Cśanyi, Erik Van der Giessen, Francesco Maresca
Read original paper ↗ ·Systematic Collapse of Transfer Learning in Small-Data Materials Regimes: Why Fine-Tuning Fails
Journal of Computational and Data-Driven Materials Engineering · 2023
Chen Hao, Liu Fang, Zhao Lin
Read original paper ↗ ·Adsorption at Nanoconfined Solid–Water Interfaces
Annual Review of Physical Chemistry · 2023
Anastasia Ilgen, Kevin Leung, Louise Criscenti, Jeffery A. Greathouse
Read original paper ↗ ·A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions
Mathematics · 2023
Alaa Tharwat, Wolfram Schenck
Read original paper ↗ ·Counterintuitive Oxidation of Alcohols at Air–Water Interfaces
Journal of the American Chemical Society · 2023
Deming Xia, Jingwen Chen, Hong‐Bin Xie, Jie Ping Zhong, Joseph S. Francisco
Read original paper ↗ ·Restricted multicanonical sampling for machine learning potential construction
Physical review. B./Physical review. B · 2023
Kazutoshi Miwa
Read original paper ↗ ·Molecular Views on Mechanisms of Brønsted Acid-Catalyzed Reactions in Zeolites
Chemical Reviews · 2023
Céline Chizallet, Christophe Bouchy, Kim Larmier, Gerhard D. Pirngruber
Read original paper ↗ ·Defect modeling and control in structurally and compositionally complex materials
Nature Computational Science · 2023
Xie Zhang, Jun Yong Kang, Su‐Huai Wei
Read original paper ↗ ·Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential
The Journal of Physical Chemistry Letters · 2023
Ryosuke Jinnouchi, Saori Minami, Ferenc Karsai, Carla Verdi, Georg Kresse
Read original paper ↗ ·Challenges for fluoride superionic conductors: fundamentals, design, and applications
Journal of Physics Condensed Matter · 2023
Tsuyoshi Takami, Chanachai Pattanathummasid, Alex Kutana, Ryoji Asahi
Read original paper ↗ ·Anharmonic Correction to Free Energy Barriers from DFT-Based Molecular Dynamics Using Constrained Thermodynamic Integration
Journal of Chemical Theory and Computation · 2023
Jonas Amsler, Philipp N. Pleßow, Felix Studt, Tomáš Bučko
Read original paper ↗ ·Site-Averaged Ab Initio Kinetics: Importance Learning for Multistep Reactions on Amorphous Supports
Journal of Chemical Theory and Computation · 2023
Armin Shayesteh Zadeh, Salman A. Khan, Craig A. Vandervelden, Baron Peters
Read original paper ↗ ·Neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia
Physical review. B./Physical review. B · 2023
Sebastian Bichelmaier, Jesús Carrete, Ralf Wanzenböck, Florian Buchner, Georg K. H. Madsen
Read original paper ↗ ·Challenges in modelling dynamic processes in realistic nanostructured materials at operating conditions
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2023
Véronique Van Speybroeck
Read original paper ↗ ·Computing Surface Reaction Rates by Adaptive Multilevel Splitting Combined with Machine Learning and Ab Initio Molecular Dynamics
Journal of Chemical Theory and Computation · 2023
Thomas Pigeon, Gabriel Stoltz, Manuel Corral Valero, Ani Anciaux‐Sedrakian, Maxime Moreaud, Tony Lelièvre A, Pascal Raybaud
Read original paper ↗ ·Evaluation of Machine Learning Interatomic Potentials for Gold Nanoparticles—Transferability towards Bulk
Nanomaterials · 2023
Marco Fronzi, Roger D. Amos, Rika Kobayashi
Read original paper ↗ ·Challenges for ab initio molecular dynamics simulations of electrochemical interfaces
Current Opinion in Electrochemistry · 2023
Axel Groß
Read original paper ↗ ·Machine-learning driven global optimization of surface adsorbate geometries
npj Computational Materials · 2023
Hyunwook Jung, Lena Sauerland, Sina Stocker, Karsten Reuter, Johannes T. Margraf
Read original paper ↗ ·Machine-learned acceleration for molecular dynamics in CASTEP
The Journal of Chemical Physics · 2023
Tamás K. Stenczel, Zakariya El‐Machachi, Guoda Liepuoniute, Joe D. Morrow, Albert P. Bartók, Matt I. J. Probert, Gábor Cśanyi, Volker L. Deringer
Read original paper ↗ ·On-the-fly machine learning force field study of liquid-Al/α-Al2O3 interface
Applied Surface Science · 2023
Zhang Guicheng, Wenting Liu, Tao Hu, Sansan Shuai, Chaoyue Chen, Songzhe Xu, Wei Li Ren, Jiang Wang, Zhongming Ren
Read original paper ↗ ·Melting of atomic materials under high pressures using computer simulations
Advances in Physics X · 2023
Diana Yu, Elke Pahl
Read original paper ↗ ·Creation of crystal structure reproducing X-ray diffraction pattern without using database
npj Computational Materials · 2023
Joohwi Lee, Junpei Oba, Nobuko Ohba, Seiji Kajita
Read original paper ↗ ·Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations
ACS Catalysis · 2023
Véronique Van Speybroeck, Massimo Bocus, Pieter Cnudde, Louis Vanduyfhuys
Read original paper ↗ ·Staged Training of Machine-Learning Potentials from Small to Large Surface Unit Cells: Efficient Global Structure Determination of the RuO2(100)-c(2 × 2) Reconstruction and (410) Vicinal
The Journal of Physical Chemistry C · 2023
Yonghyuk Lee, Jakob Timmermann, Chiara Panosetti, Christoph Scheurer, Karsten Reuter
Read original paper ↗ ·Structural Dynamics Descriptors for Metal Halide Perovskites
The Journal of Physical Chemistry C · 2023
Xia Liang, Johan Klarbring, William J. Baldwin, Zhenzhu Li, Gábor Cśanyi, Aron Walsh
Read original paper ↗ ·Molecular dynamics simulations using machine learning potential for a-Si:H/c-Si interface: Effects of oxygen and hydrogen on interfacial defect states
Journal of materials research/Pratt's guide to venture capital sources · 2023
Takayuki Semba, Jacob McKibbin, Ryosuke Jinnouchi, Ryoji Asahi
Read original paper ↗ ·Machine-learning surrogate model for accelerating the search of stable ternary alloys
Physical Review Materials · 2023
Michail Minotakis, Hugo Rossignol, Matteo Cobelli, Stefano Sanvito
Read original paper ↗ ·Applications and Advances in Machine Learning Force Fields
Journal of Chemical Information and Modeling · 2023
Shiru Wu, Xiaowei Yang, Xun Zhao, Zhipu Li, Min Lü, Xiaoji Xie, Jiaxu Yan
Read original paper ↗ ·2023 Roadmap on molecular modelling of electrochemical energy materials
Journal of Physics Energy · 2023
Chao Zhang, Jun Jie Cheng, Yiming Chen, Maria K. Y. Chan, Qiong Cai, Rodrigo Pereira de Carvalho, Cleber F. N. Marchiori, Daniel Brandell, Carlos Moyses Araujo, Ming Chen, Xiangyu Ji, Guang Feng, Kateryna Goloviznina, Alessandra Serva, Mathieu Salanne, Toshihiko Mandai, Tomooki Hosaka, Mirna Alhanash, Patrik Johansson, Yunze Qiu, Hai Yan Xiao, Michael Hermann Eikerling, Ryosuke Jinnouchi, Marko Melander, Georg Kastlunger, Assil Bouzid, Alfredo Pasquarello, Seung‐Jae Shin, Minho M. Kim, Hyungjun Kim, Kathleen Schwarz, Ravishankar Sundararaman
Read original paper ↗ ·Molecular dynamics-driven global potential energy surfaces: Application to the AlF dimer
The Journal of Chemical Physics · 2023
Xiangyue Liu, Weiqi Wang, Jesús Pérez‐Ríos
Read original paper ↗ ·Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning
Journal of Chemical Theory and Computation · 2023
Xiangyu Chen, William Shao, Nam Q. Le, Paulette Clancy
Read original paper ↗ ·Stress and heat flux via automatic differentiation
The Journal of Chemical Physics · 2023
Marcel F. Langer, J. Thorben Frank, Florian Knoop
Read original paper ↗ ·Accurate melting point prediction through autonomous physics-informed learning
Physical review. B./Physical review. B · 2023
Olga Klimanova, Timofei Miryashkin, Alexander V. Shapeev
Read original paper ↗ ·Phase Transitions, Dielectric Response, and Nonlinear Optical Properties of Aziridinium Lead Halide Perovskites
Chemistry of Materials · 2023
Mirosław Mączka, Maciej Ptak, Anna Gągor, Jan K. Zaręba, Xia Liang, Sergejus Balčiūnas, Oleksandr A. Semenikhin, Olesia I. Kucheriv, Il’ya A. Gural’skiy, Sergiu G. Shova, Aron Walsh, J. Banys, Mantas Šimėnas
Read original paper ↗ ·Machine-learned interatomic potentials: Recent developments and prospective applications
Journal of materials research/Pratt's guide to venture capital sources · 2023
Volker Eyert, Jonathan L. Wormald, W.A. Curtin, E. Wimmer
Read original paper ↗ ·Low-Cost Vibrational Free Energies in Solid Solutions with Machine Learning Force Fields
The Journal of Physical Chemistry Letters · 2023
Kasper Tolborg, Aron Walsh
Read original paper ↗ ·High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks
Digital Discovery · 2024
Tim Rensmeyer, Ben Craig, Denis Kramer, Oliver Niggemann
Read original paper ↗ ·Self-Consistency Is Not Enough: A Framework for Error-Correcting ML Potentials with On-the-Fly Residual Learning
Journal of Computational and Data-Driven Materials Engineering · 2024
Siti Rahman, Ahmad Zaki, Nurul Huda, Amir Faisal, Lim Wei
Read original paper ↗ ·Active machine learning model for the dynamic simulation and growth mechanisms of carbon on metal surface
Nature Communications · 2024
Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li
Read original paper ↗ ·Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks
npj Computational Materials · 2024
Sandro Wieser, Egbert Zojer
Read original paper ↗ ·Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment
Journal of Chemical Theory and Computation · 2024
Sakib Matin, Alice E. A. Allen, Justin S. Smith, Nicholas Lubbers, Ryan B. Jadrich, Richard A. Messerly, Benjamin T. Nebgen, Ying Wai Li, Sergei Tretiak, Kipton M. Barros
Read original paper ↗ ·Atomistic simulations of nuclear fuel UO 2 with machine learning interatomic potentials
Physical Review Materials · 2024
E.T. Dubois, Julien Tranchida, Johann Bouchet, Jean‐Bernard Maillet
Read original paper ↗ ·Harmonizing mechanical and thermal properties in Al/SiC superlattices: Ab initio machine-learning-potential study
Physical review. B./Physical review. B · 2024
Chol‐Jun Yu, Jong-Hyok Jang, Kum-Chol Ri, Song-Mu Kim, Chol-Hyok Pak, Ryong-Jin Kim
Read original paper ↗ ·