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| 1 | Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials显示文摘Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with firstprinciples accuracy.It is not yet clear,however,how accurately they describe anharmonic properties,which are crucial for predicting the lattice thermal conductivity and phase transitions in solids and,thus,shape their technological applications.Here we employ a recently developed on-the-fly learning technique based on molecular dynamics and Bayesian inference in order to generate an interatomic potential capable to describe the thermodynamic properties of zirconia,an important transition metal oxide.This machine-learned potential accurately captures the temperature-induced phase transitions below the melting point.We further showcase the predictive power of the potential by calculating the heat transport on the basis of Green–Kubo theory,which allows to account for anharmonic effects to all orders.This study indicates that machine-learned potentials trained on the fly offer a routine solution for accurate and efficient simulations of the thermodynamic properties of a vast class of anharmonic materials. | Carla Verdi Ferenc Karsai Peitao Liu Ryosuke Jinnouchi Georg Kresse | 2021 | npj Computational Materials2021，，1： | 5 |
| 2 | 基于机器学习势函数的材料力热性质多尺度模拟研究进展显示文摘随着人工智能技术的发展,采用机器学习方法进行势函数的构建和拟合,成为目前解决经验势函数精度问题的主要技术途径。机器学习方法解决了传统势函数拟合中的试错低效问题,已成为材料设计和物性研究不可或缺的有力工具。本文围绕当前机器学习势函数的特点,及其在相变研究、本征性质研究和界面研究等方面的应用,全面总结介绍势函数及其拟合策略,以及其在特定物性研究中的应用,推动机器学习势函数在材料力热性质的多尺度模拟研究。最后,展望了机器学习势函数所面临的挑战和未来发展前景。 | 吴静 黄安 谢涵鹏 魏东海 李奥南 彭博 王慧敏 秦真真 刘德欢 秦光照 | 2023 | 硅酸盐学报2023，51，2： | 1 |
| 3 | Hyperactive learning for data-driven interatomic potentials显示文摘Data-driven interatomic potentials have emerged as a powerful tool for approximating ab initio potential energy surfaces.The most time-consuming step in creating these interatomic potentials is typically the generation of a suitable training database.To aid this process hyperactive learning(HAL),an accelerated active learning scheme,is presented as a method for rapid automated training database assembly.HAL adds a biasing term to a physically motivated sampler(e.g.molecular dynamics)driving atomic structures towards uncertainty in turn generating unseen or valuable training configurations.The proposed HAL framework is used to develop atomic cluster expansion(ACE)interatomic potentials for the AlSi10 alloy and polyethylene glycol(PEG)polymer starting from roughly a dozen initial configurations.The HAL generated ACE potentials are shown to be able to determine macroscopic properties,such as melting temperature and density,with close to experimental accuracy. | Cas van der Oord Matthias Sachs Dávid Péter Kovács Christoph Ortner Gábor Csányi | 2023 | npj Computational Materials2023，，1： | 1 |
| 4 | A systematic approach to generating accurate neural network potentials:the case of carbon显示文摘Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling.Artificial neural network-based approaches for generating potentials are promising;however,neural network training requires large amounts of data,sampled adequately from an often unknown potential energy surface.Here we propose a selfconsistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis,to construct an accurate,inexpensive,and transferable artificial neural network potential.Using this approach,we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond,graphite,and graphene,as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases. | Yusuf Shaidu Emine Küçükbenli Ruggero Lot Franco Pellegrini Efthimios Kaxiras Stefano de Gironcoli | 2021 | npj Computational Materials2021，，1： | 1 |
| 5 | Crystal structure prediction at finite temperatures显示文摘Crystal structure prediction is a central problem of crystallography and materials science, which until mid-2000s was consideredintractable. Several methods, based on either energy landscape exploration or, more commonly, global optimization, largely solvedthis problem and enabled fully non-empirical computational materials discovery. A major shortcoming is that, to avoid expensivecalculations of the entropy, crystal structure prediction was done at zero Kelvin, reducing to the search for the global minimum ofthe enthalpy rather than the free energy. As a consequence, high-temperature phases (especially those which are not quenchableto zero temperature) could be missed. Here we develop an accurate and affordable solution, enabling crystal structure prediction atfinite temperatures. Structure relaxation and fully anharmonic free energy calculations are done by molecular dynamics with aforcefield (which can be anything from a parametric forcefield for simpler cases to a trained on-the-fly machine learning interatomicpotential), the errors of which are corrected using thermodynamic perturbation theory to yield accurate results with full ab initioaccuracy. We illustrate this method by applications to metals (probing the P–T phase diagram of Al and Fe), a refractory covalentsolid (WB), an Earth-forming silicate MgSiO_(3) (at pressures and temperatures of the Earth’s lower mantle), and ceramic oxide HfO_(2). | Ivan A.Kruglov Alexey V.Yanilkin Yana Propad Arslan B.Mazitov Pavel Rachitskii Artem R.Oganov | 2023 | npj Computational Materials2023，，1： | 0 |
| 6 | Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields显示文摘We introduce a training protocol for developing machine learning force fields(MLFFs),capable of accurately determining energy barriers in catalytic reaction pathways.The protocol is validated on the extensively explored hydrogenation of carbon dioxide to methanol over indium oxide.With the help of active learning,the final force field obtains energy barriers within 0.05 eV of Density Functional Theory.Thanks to the computational speedup,not only do we reduce the cost of routine in-silico catalytic tasks,but also find an alternative path for the previously established rate-limiting step,with a 40%reduction in activation energy.Furthermore,we illustrate the importance of finite temperature effects and compute free energy barriers.The transferability of the protocol is demonstrated on the experimentally relevant,yet unexplored,top-layer reduced indium oxide surface.The ability of MLFFs to enhance our understanding of extensively studied catalysts underscores the need for fast and accurate alternatives to direct ab-initio simulations. | Lars L.Schaaf Edvin Fako Sandip De Ansgar Schäfer Gábor Csányi | 2023 | npj Computational Materials2023，，1： | 0 |
| 7 | Complex Ga_(2)O_(3) polymorphs explored by accurate and general-purpose machine-learning interatomic potentials显示文摘Ga_(2)O_(3) is a wide-band gap semiconductor of emergent importance for applications in electronics and optoelectronics.However,vital information of the properties of complex coexisting Ga_(2)O_(3) polymorphs and low-symmetry disordered structures is missing.We develop two types of machine-learning Gaussian approximation potentials(ML-GAPs)for Ga_(2)O_(3) with high accuracy forβ/κ/α/δ/γpolymorphs and generality for disordered stoichiometric structures.We release two versions of interatomic potentials in parallel,namely soapGAP and tabGAP,for high accuracy and exceeding speedup,respectively.Both potentials can reproduce the structural properties of all the five polymorphs in an exceptional agreement with ab initio results,meanwhile boost the computational efficiency with 5×102 and 2×105 computing speed increases compared to density functional theory,respectively.Moreover,the Ga_(2)O_(3) liquid-solid phase transition proceeds in three different stages.This experimentally unrevealed complex dynamics can be understood in terms of distinctly different mobilities of O and Ga sublattices in the interfacial layer. | Junlei Zhao Jesper Byggmästar Huan He Kai Nordlund Flyura Djurabekova Mengyuan Hua | 2023 | npj Computational Materials2023，，1： | 0 |
| 8 | Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent显示文摘Highly accurate ab initio molecular dynamics(MD)methods are the gold standard for studying molecular mechanisms in the condensed phase,however,they are too expensive to capture many key properties that converge slowly with respect to simulation length and time scales.Machine learning(ML)approaches which reach the accuracy of ab initio simulation,and which are,at the same time,sufficiently affordable hold the key to bridging this gap.In this work we present a robust ML potential for the EC:EMC binary solvent,a key component of liquid electrolytes in rechargeable Li-ion batteries.We identify the necessary ingredients needed to successfully model this liquid mixture of organic molecules.In particular,we address the challenge posed by the separation of scale between intra-and inter-molecular interactions,which is a general issue in all condensed phase molecular systems. | Ioan-Bogdan Magdău Daniel JArismendi-Arrieta Holly ESmith Clare PGrey Kersti Hermansson Gábor Csányi | 2023 | npj Computational Materials2023，，1： | 0 |
| 9 | Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials显示文摘The universal mathematical form of machine-learning potentials(MLPs)shifts the core of development of interatomic potentials to collecting proper training data.Ideally,the training set should encompass diverse local atomic environments but conventional approaches are prone to sampling similar configurations repeatedly,mainly due to the Boltzmann statistics.As such,practitioners handpick a large pool of distinct configurations manually,stretching the development period significantly.To overcome this hurdle,methods are being proposed that automatically generate training data.Herein,we suggest a sampling method optimized for gathering diverse yet relevant configurations semi-automatically.This is achieved by applying the metadynamics with the descriptor for the local atomic environment as a collective variable.As a result,the simulation is automatically steered toward unvisited local environment space such that each atom experiences diverse chemical environments without redundancy.We apply the proposed metadynamics sampling to H:Pt(111),GeTe,and Si systems.Throughout these examples,a small number of metadynamics trajectories can provide reference structures necessary for training high-fidelity MLPs.By proposing a semiautomatic sampling method tuned for MLPs,the present work paves the way to wider applications of MLPs to many challenging applications. | Dongsun Yoo Jisu Jung Wonseok Jeong Seungwu Han | 2021 | npj Computational Materials2021，，1： | 0 |
| 10 | Machine learning potentials for metal-organic frameworks using an incremental learning approach显示文摘Computational modeling of physical processes in metal-organic frameworks(MOFs)is highly challenging due to the presence of spatial heterogeneities and complex operating conditions which affect their behavior.Density functional theory(DFT)may describe interatomic interactions at the quantum mechanical level,but is computationally too expensive for systems beyond the nanometer and picosecond range.Herein,we propose an incremental learning scheme to construct accurate and data-efficient machine learning potentials for MOFs.The scheme builds on the power of equivariant neural network potentials in combination with parallelized enhanced sampling and on-the-fly training to simultaneously explore and learn the phase space in an iterative manner.With only a few hundred single-point DFT evaluations per material,accurate and transferable potentials are obtained,even for flexible frameworks with multiple structurally different phases.The incremental learning scheme is universally applicable and may pave the way to model framework materials in larger spatiotemporal windows with higher accuracy. | Sander Vandenhaute Maarten Cools-Ceuppens Simon DeKeyser Toon Verstraelen Veronique Van Speybroeck | 2023 | npj Computational Materials2023，，1： | 0 |
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