MLJ: A Julia package for composable machine learning

Blaom, Anthony D.; Király, Franz J.; Lienart, Thibaut; Simillides, Yiannis; Arenas, Diego; Vollmer, Sebastian J.

doi:10.21105/joss.02704

Cited by 56 publications

(44 citation statements)

References 7 publications

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“…88,89 More recently, ML models of excited-state properties and whole Hamiltonians have become available [90][91][92] There has been substantial recent progress in machine learning with Julia. [93][94][95] For example the ACE.jl package 96,97 provides for the parametrization of interatomic potentials based on the Atomic Cluster Expansion. Nevertheless, most existing atomistic ML models are developed and trained using other languages.…”

Section: B Performing Dynamicsmentioning

confidence: 99%

NQCDynamics.jl: A Julia Package for Nonadiabatic Quantum Classical Molecular Dynamics in the Condensed Phase

Gardner,

Douglas-Gallardo,

Stark

et al. 2022

Preprint

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Accurate and efficient methods to simulate nonadiabatic and quantum nuclear effects in high-dimensional and dissipative systems are crucial for the prediction of chemical dynamics in condensed phase. To facilitate effective development, code sharing and uptake of newly developed dynamics methods, it is important that software implementations can be easily accessed and built upon. Using the Julia programming language, we have developed the NQCDynamics.jl package which provides a framework for established and emerging methods for performing semiclassical and mixed quantum-classical dynamics in condensed phase. The code provides several interfaces to existing atomistic simulation frameworks, electronic structure codes, and machine learning representations. In addition to the existing methods, the package provides infrastructure for developing and deploying new dynamics methods which we hope will benefit reproducibility and code sharing in the field of condensed phase quantum dynamics. Herein, we present our code design choices and the specific Julia programming features from which they benefit. We further demonstrate the capabilities of the package on two examples of chemical dynamics in condensed phase: the population dynamics of the spin-boson model as described by a wide variety of semi-classical and mixed quantum-classical nonadiabatic methods and the reactive scattering of H 2 on Ag(111) using the Molecular Dynamics with Electronic Friction method. Together, they exemplify the broad scope of the package to study effective model Hamiltonians and realistic atomistic systems. Methods FSSH NRPMD Ehrenfest MDEF Classical Langevin eCMM Simulation Parameters Atoms Elements Masses Models Analytic model ASE calculator ML model Keywords Simulation cell Temperature Method extras Simulation Method (atoms , model; kwargs¡ ) { } FIG. 1. The user inputs required to define the parameters for a simulation. This diagram along with Figs. 2 and 3 was created using the Excalidraw 65 online tool.

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Section: B Performing Dynamicsmentioning

confidence: 99%

NQCDynamics.jl: A Julia Package for Nonadiabatic Quantum Classical Molecular Dynamics in the Condensed Phase

Gardner,

Douglas-Gallardo,

Stark

et al. 2022

Preprint

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“…caret [Kuhn, 2015]), Julia (e.g. MLJ [Blaom et al, 2020]) etc. allow you to try out multiple models with only small changes to your code, so there's no reason not to try out multiple models and find out for yourself which one works best.…”

Section: Do Try Out a Range Of Different Modelsmentioning

confidence: 99%

How to avoid machine learning pitfalls: a guide for academic researchers

Lones¹

2021

Preprint

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This document gives a concise outline of some of the common mistakes that occur when using machine learning techniques, and what can be done to avoid them. It is intended primarily as a guide for research students, and focuses on issues that are of particular concern within academic research, such as the need to do rigorous comparisons and reach valid conclusions. It covers five stages of the machine learning process: what to do before model building, how to reliably build models, how to robustly evaluate models, how to compare models fairly, and how to report results.

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“…All training data can therefore be used to fit one model instead of an individual models for each k. This approach will therefore be less dependent on having a large training data set compared to linear regression. The regression has been performed with MLJ.jl (0.16.2) [37] and ScikitLearn (0.24.0) [38] using the default settings. Deterministic point forecasts are generated using a least square regressor, while scenarios are generated using quantile regression.…”

Section: Quantile Regression and Transition Probabilitymentioning

confidence: 99%

Stochastic Optimization of Microgrid Operation With Renewable Generation and Energy Storage

Aaslid¹,

Korpås²,

Belsnes³

et al. 2021

Preprint

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The operation of electric energy storages (EES) in power systems where variable renewable energy sources (VRES) and EES must contribute to securing the supply can be considered as an arbitrage against scarcity. The value of using stored energy instantly must be balanced against its potential future value and future risk of scarcity. This paper proposes a multi-stage stochastic programming model for the operation of microgrids with VRES, EES and thermal generation that is divided into a short- and a long-term model. The short-term model utilizes information from forecasts updated every six hours, while the long-term model considers the value of stored energy beyond the forecast horizon. The model is solved using stochastic dual dynamic programming and Markov chains, and the results show that the significance of accounting for short- and long-term uncertainty increases for systems with a high degree of variable renewable generation and EES and decreasing dispatchable generation capacity.<br>

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MLJ: A Julia package for composable machine learning

Cited by 56 publications

References 7 publications

NQCDynamics.jl: A Julia Package for Nonadiabatic Quantum Classical Molecular Dynamics in the Condensed Phase

NQCDynamics.jl: A Julia Package for Nonadiabatic Quantum Classical Molecular Dynamics in the Condensed Phase

How to avoid machine learning pitfalls: a guide for academic researchers

Stochastic Optimization of Microgrid Operation With Renewable Generation and Energy Storage

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