Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments

Zaverkin, Viktor; Netz, Julia; Zills, Fabian; Köhn, Andreas; Kästner, Johannes

doi:10.1021/acs.jctc.1c00853

Cited by 16 publications

(12 citation statements)

References 73 publications

(160 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After the training stage, ML models can be used to make predictions at very little computational cost, therefore offering a significant speed-up of these simulations. Further adaptation of machine learning schemes to this tasks is needed, but encouraging proof-of-concept applications have recently been presented [56,57,83,85,121,122]. Another interesting strategy involve the use of parametrized Hamiltonians to compute the spin-phonon coupling coefficients.…”

Section: Discussionmentioning

confidence: 99%

Spin-Phonon Relaxation in Magnetic Molecules: Theory, Predictions and Insights

Lunghi¹

2022

Preprint

View full text Add to dashboard Cite

Magnetic molecules have played a central role in the development of magnetism and coordination chemistry and their study keeps leading innovation in cutting-edge scientific fields such as magnetic resonance, magnetism, spintronics, and quantum technologies. Crucially, a long spin lifetime well above cryogenic temperature is a stringent requirement for all these applications. In this chapter we review the foundations of spin relaxation theory and provide a detailed overview of first-principles strategies applied to the problem of spin-phonon relaxation in magnetic molecules. Firstly, we present a rigorous formalism of spin-phonon relaxation based on open quantum systems theory. These results are then used to derive classical phenomenological relations based on the Debye model. Finally, we provide a prescription of how to map the relaxation formalism onto existing electronic structure methods to obtain a quantitative picture of spin-phonon relaxation. Examples from the literature, including both transition metals and lanthanides compounds, will be discussed in order to illustrate how Direct, Orbach and Raman relaxation mechanisms can affect spin dynamics for this class of compounds.

show abstract

Section: Discussionmentioning

confidence: 99%

Spin-Phonon Relaxation in Magnetic Molecules: Theory, Predictions and Insights

Lunghi¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…1). Moreover ensemble properties such as the free energy of solvation 11,[59][60][61][62][63][64][65] , melting points 30,66,124 , magnetic anisotropy tensors 125 , phases of water [126][127][128] have previously been addressed with ML.…”

Section: B Machine Learningmentioning

confidence: 99%

Ab initio machine learning of phase space averages

Weinreich,

Lemm,

von Rudorff

et al. 2022

Preprint

View full text Add to dashboard Cite

Equilibrium structures determine material properties and biochemical functions. We propose to machine learn phase-space averages, conventionally obtained by ab initio or force-field based molecular dynamics (MD) or Monte Carlo simulations. In analogy to ab initio molecular dynamics (AIMD), our ab initio machine learning (AIML) model does not require bond topologies and therefore enables a general machine learning pathway to ensemble properties throughout chemical compound space. We demonstrate AIML for predicting Boltzmann averaged structures after training on hundreds of MD trajectories. AIML output is subsequently used to train machine learning models of free energies of solvation using experimental data, and reaching competitive prediction errors (MAE ∼ 0.8 kcal/mol) for out-of-sample molecules-within milli-seconds. As such, AIML effectively bypasses the need for MD or MC-based phase space sampling, enabling exploration campaigns throughout CCS at a much accelerated pace. We contextualize our findings by comparison to state-of-the-art methods resulting in a Pareto plot for the free energy of solvation predictions in terms of accuracy and time.

show abstract

“…The benefit of PB simulations comes in their access to system observables and properties that are otherwise unavailable experimentally. Whilst the method itself involves the sampling of configuration space under constraints imposed by defined interactions, it is the analysis of these configurations that leads to insights in medicine [1][2][3][4][5], battery technology [6][7][8][9][10][11][12][13][14][15], astrophysics [16], materials engineering [17][18][19][20][21], and much more. When running simulations, one is faced with the choice of either performing On-The-Fly (OTF) analysis, or post-processing their simulation data to extract information.…”

Section: Introductionmentioning

confidence: 99%

MDSuite: comprehensive post-processing tool for particle simulations

et al. 2023

Self Cite

View full text Add to dashboard Cite

Particle-Based (PB) simulations, including Molecular Dynamics (MD), provide access to system observables that are not easily available experimentally. However, in most cases, PB data needs to be processed after a simulation to extract these observables. One of the main challenges in post-processing PB simulations is managing the large amounts of data typically generated without incurring memory or computational capacity limitations. In this work, we introduce the post-processing tool: MDSuite. This software, developed in Python, combines state-of-the-art computing technologies such as TensorFlow, with modern data management tools such as HDF5 and SQL for a fast, scalable, and accurate PB data processing engine. This package, built around the principles of FAIR data, provides a memory safe, parallelized, and GPU accelerated environment for the analysis of particle simulations. The software currently offers 17 calculators for the computation of properties including diffusion coefficients, thermal conductivity, viscosity, radial distribution functions, coordination numbers, and more. Further, the object-oriented framework allows for the rapid implementation of new calculators or file-readers for different simulation software. The Python front-end provides a familiar interface for many users in the scientific community and a mild learning curve for the inexperienced. Future developments will include the introduction of more analysis associated with ab-initio methods, colloidal/macroscopic particle methods, and extension to experimental data.

show abstract

Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments

Cited by 16 publications

References 73 publications

Spin-Phonon Relaxation in Magnetic Molecules: Theory, Predictions and Insights

Spin-Phonon Relaxation in Magnetic Molecules: Theory, Predictions and Insights

Ab initio machine learning of phase space averages

MDSuite: comprehensive post-processing tool for particle simulations

Contact Info

Product

Resources

About