Recent advancements in machine learning potentials (MLPs) have significantly impacted the fields of chemistry, physics, and biology by enabling large-scale first-principles simulations. Among different machine learning approaches, kernel-based MLPs distinguish themselves through their ability to handle small datasets, quantify uncertainties, and minimize over-fitting. Nevertheless, their extensive computational requirements present considerable challenges. To alleviate these, sparsification methods have been developed, aiming to reduce computational scaling without compromising accuracy. In the context of isothermal and isobaric ML molecular dynamics (MD) simulations, achieving precise pressure estimation is crucial for reproducing reliable system behavior under constant pressure. Despite progress, sparse kernel MLPs struggle with precise pressure prediction. Here, we introduce a virial kernel function that significantly enhances pressure estimation accuracy of MLPs. Additionally, we propose the active sparse Bayesian committee machine (BCM) potential, an on-the-fly MLP architecture that aggregates local sparse Gaussian process regression (SGPR) MLPs. The sparse BCM potential overcomes the steep computational scaling with the kernel size, and a predefined restriction on the size of kernel allows for a fast and efficient on-the-fly training. Our advancements facilitate accurate and computationally efficient machine learning-enhanced MD (MLMD) simulations across diverse systems, including ice-liquid coexisting phases, \ce{Li10Ge(PS6)2} lithium solid electrolyte, and high-pressure liquid boron nitride.
Pressure effects in the properties of simple monohydric alcohols. Lessons from molecular dynamics simulations of united atom type UAM-EW model
Instituto de de Química, Universidad Nacional Autónoma de México（墨西哥国立自治大学化学研究所）；Wigner Research Centre for Physics（维格纳物理研究中心）；Faculty for Advanced Science and Technology, Kumamoto University（熊本大学先进科学技术学院）
Effects of ion concentration and solvent composition on the properties of water-methanol solutions of NaCl. NPT molecular dynamics computer simulation results
On the composition dependence of the microscopic structure, thermodynamic, dynamic and dielectric properties of water-dimethyl formamide model mixtures. Molecular dynamics simulation results
Structural, Electronic, and Dynamical Properties of Liquid Water by ab initio Molecular Dynamics based on SCAN Functional within the Canonical Ensemble
采用SCAN元-GGA泛函近似，对正则系综中的液态水进行了从头算分子动力学（AIMD）模拟，并与GGA水平的PBE泛函和包含PBE泛函的Tkatchenko-Scheffler van der Waals（vdW）色散修正的结果进行了系统的比较.我们分析了液态水的各种性质，包括径向分布函数，氧-氧-氧三重态角分布，四面体，氢键，扩散系数，环统计，态密度，带隙和偶极矩。我们发现，扫描功能通常是更准确的比其他两个功能的液态水，不仅捕获的中程VDW相互作用，但也减轻了PBE模拟中规定的过于强大的氢键。我们还比较了基于扫描的AIMD模拟的结果在典型的等温等压合奏。我们的研究结果表明，SCAN提供了一个可靠的描述大多数结构，电子和动力学性质的液态水。
On the properties of a single OPLS-UA model curcumin molecule in water, methanol and dimethyl sulfoxide. Molecular dynamics computer simulation results
II. Temperature trends in the properties of simple monohydric alcohols. Molecular dynamics simulations of united atom UAMI-EW model
Instituto de de Química, Universidad Nacional Autónoma de México（墨西哥国立自治大学化学研究所）；Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa（墨西哥自治大学伊斯塔帕拉帕分校化学系）
On the temperature, pressure and composition effects in the properties of water-methanol mixtures. I. Density, excess mixing volume and enthalpy, and self-diffusion coefficients from molecular dynamics simulations
