Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.

The molecular structure of water is dynamic, with intermolecular (H)-bond interactions being modified by both electronic charge transfer and nuclear quantum effects (NQEs). Electronic charge transfer and NQEs potentially change under acidic / basic conditions, but such details have not been measured. Here, we developed correlated vibrational spectroscopy, a symmetry-based method that distinctively separates interacting from non-interacting molecules in self- and cross-correlation spectra, giving access to previously inaccessible information. We found that OH − donated ~8% more negative charge to the H-bond network of water and H 3 O + accepted ~4% less negative charge from the H-bond network of water. D 2 O had ~9% more H-bonds compared to H 2 O, and acidic solutions displayed more dominant NQEs than basic ones. 

Dissecting the hydrogen bond network of water: Charge transfer and nuclear quantum effects

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2× to 10× over previous, nonoptimized, iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net.

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations.

Interphase boundaries are essential in the deformation and phase transformations in titanium (Ti) alloys. While static structures of semicoherent α/β interfaces in various Ti alloys have been carefully examined, their migration behavior at atomic scales is far less clear. In this study, we employed molecular dynamics simulations to investigate the migration of the semicoherent α/β interface in pure Ti. The interface migration behavior shows a shear-coupled feature with the interface dislocation glide and a macroscopic shear. The simulation reveals that both the glide direction of the dislocations with respect to the interface and the dislocation spacing strongly influence the migration rate, and the low-index glide plane of the interface dislocation plays a minor role. The dependence of interface mobility on temperatures confirms the critical role of thermal activation during the interface migration, especially for activating the interface dislocation glide. Furthermore, the shear-coupled interface migration driven by element partition is simulated using a newly developed Ti-Mo potential, consistent with the displacive-diffusional features previously observed in the surface precipitates. The simulated interface migration mode is validated by comparing it with the crystallography features of surface precipitates in a Ti-Cr alloy. The interface energy and mobility obtained from simulations further explain why the distinctive crystallographic features of the surface precipitates observed experimentally are favored over other candidate interfaces. The present study has explored an approach for systematically examining thermodynamic and kinetic factors governing the development of phase transformation crystallography at different temperatures and chemical driving forces. 

Dislocation-mediated migration of the α/β interfaces in titanium

An accurate and transferable machine learning (ML) potential for the simulation of binary sodium silicate glasses over a wide range of compositions (from 0 to 50% Na2O) was developed. The potential energy surface is approximated by the sum of atomic energy contributions mapped by a neural network algorithm from the local geometry comprising information on atomic distances and angles with neighboring atoms using the DeePMD code [Wang, H. Comput. Phys. Commun. 2018, 228, 178-184]. Our model was trained on a large data set of total energies and atomic forces computed at the density functional theory level on structures extracted from classical molecular dynamics (MD) simulations performed at several temperatures from 300 to 3000 K. This allows for the generation of a robust and transferable ML potential applicable over the full compositional range of glass formability at different temperatures that outperforms the empirical potentials available in the literature in reproducing structures and properties such as bond angle distribution, total distribution functions, and vibrational density of state. The generality of the approach enables the future training of a potential with other or more elements allowing for simulations of structures, properties, and behavior of ternary and multicomponent oxide glasses with nearly ab initio accuracy at a fraction of the computational cost.

Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses.

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.

pdf/deepmd-kit-v2-a-software-package-for-deep-potential-models-11b27oys.pdf

DeePMD-kit v2: A software package for Deep Potential models

Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties of Sn are sensitive to pressures. We develop machine-learning-based deep potentials for Sn with pressures ranging from 0 to 50 GPa and temperatures ranging from 0 to 2000 K. In particular, we find the deep potential, which is obtained by training the ab initio data from density functional theory calculations with the state-of-the-art SCAN exchange-correlation functional, is suitable to characterize high-pressure phases of Sn. We systematically validate several structural and elastic properties of the {\alpha} (diamond structure), {\beta}, bct, and bcc structures of Sn, as well as the structural and dynamic properties of liquid Sn. The thermodynamics integration method is further utilized to compute the free energies of the {\alpha}, {\beta}, bct, and liquid phases, from which the deep potential successfully predicts the phase diagram of Sn including the existence of the triple-point that qualitatively agrees with the experiment.

Modeling the high-pressure solid and liquid phases of tin from deep potentials with 
ab initio
 accuracy

Initial Damage Analysis in Bone Cement-Stem Debonding Procession of Cemented Hip Arthroplasty

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DeePMD-kit v2: A software package for Deep Potential models

Modeling the high-pressure solid and liquid phases of tin from deep potentials with ab initio accuracy

Initial Damage Analysis in Bone Cement-Stem Debonding Procession of Cemented Hip Arthroplasty