Publications
2024
Wanzenböck, Ralf; Buchner, Florian; Kovács, Péter; Madsen, Georg K. H.; Carrete, Jesús
Clinamen2: Functional-style evolutionary optimization in Python for atomistic structure searches
Journal ArticleForthcomingOpen AccessIn: Computer Physics Communications, vol. 297, no. 109065, Forthcoming.
Abstract | Links | BibTeX | Tags: P09
@article{wanzenboeck2024,
title = {Clinamen2: Functional-style evolutionary optimization in Python for atomistic structure searches},
author = {Ralf Wanzenböck and Florian Buchner and Péter Kovács and Georg K. H. Madsen and Jesús Carrete},
doi = {10.1016/j.cpc.2023.109065},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-01},
journal = {Computer Physics Communications},
volume = {297},
number = {109065},
abstract = {Clinamen2 is a versatile functional-style Python implementation of the covariance matrix adaptation evolution strategy (CMA-ES) utilizing Cholesky decomposition. On top of a problem-agnostic core algorithm, the software package offers a suite of utilities and library code enabling applications to important atomistic structure searches. Features include massively distributed computation and the BI-Population restart scheme. This article details the general code structure and introduces examples that illustrate some relevant applications for the materials science and chemistry worlds, including interfacing to density-functional-theory codes and machine-learned surrogate models. The functional design renders the code modular and adaptable, and makes the creation of interfaces to other atomistic software straightforward.},
keywords = {P09},
pubstate = {forthcoming},
tppubtype = {article}
}
2023
Carrete, Jesús; Montes-Campos, Hadrián; Wanzenböck, Ralf; Heid, Esther; Madsen, Georg K. H.
Journal ArticleOpen AccessIn: The Journal of Chemical Physics, vol. 158, no. 20, pp. 204801-1–204801-18, 2023.
Abstract | Links | BibTeX | Tags: P09
@article{Carrete2023,
title = {Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning},
author = {Jesús Carrete and Hadrián Montes-Campos and Ralf Wanzenböck and Esther Heid and Georg K. H. Madsen},
doi = {10.1063/5.0146905},
year = {2023},
date = {2023-05-22},
journal = {The Journal of Chemical Physics},
volume = {158},
number = {20},
pages = {204801-1--204801-18},
publisher = {AIP Publishing},
abstract = {A reliable uncertainty estimator is a key ingredient in the successful use of machine-learning force fields for predictive calculations. Important considerations are correlation with error, overhead during training and inference, and efficient workflows to systematically improve the force field. However, in the case of neural-network force fields, simple committees are often the only option considered due to their easy implementation. Here, we present a generalization of the deep-ensemble design based on multiheaded neural networks and a heteroscedastic loss. It can efficiently deal with uncertainties in both energy and forces and take sources of aleatoric uncertainty affecting the training data into account. We compare uncertainty metrics based on deep ensembles, committees, and bootstrap-aggregation ensembles using data for an ionic liquid and a perovskite surface. We demonstrate an adversarial approach to active learning to efficiently and progressively refine the force fields. That active learning workflow is realistically possible thanks to exceptionally fast training enabled by residual learning and a nonlinear learned optimizer.},
keywords = {P09},
pubstate = {published},
tppubtype = {article}
}
2022
Wanzenböck, Ralf; Arrigoni, Marco; Bichelmaier, Sebastian; Buchner, Florian; Carrete, Jesús; Madsen, Georg K. H.
Neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions
Journal ArticleOpen AccessIn: Digital Discovery, vol. 1, no. 5, pp. 703–710, 2022.
Abstract | Links | BibTeX | Tags: P09
@article{Wanzenboeck2022,
title = {Neural-network-backed evolutionary search for SrTiO_{3}(110) surface reconstructions},
author = {Ralf Wanzenböck and Marco Arrigoni and Sebastian Bichelmaier and Florian Buchner and Jesús Carrete and Georg K. H. Madsen},
doi = {10.1039/d2dd00072e},
year = {2022},
date = {2022-08-26},
journal = {Digital Discovery},
volume = {1},
number = {5},
pages = {703--710},
publisher = {Royal Society of Chemistry (RSC)},
abstract = {The determination of atomic structures in surface reconstructions has typically relied on structural models derived from intuition and domain knowledge. Evolutionary algorithms have emerged as powerful tools for such structure searches. However, when density functional theory is used to evaluate the energy the computational cost of a thorough exploration of the potential energy landscape is prohibitive. Here, we drive the exploration of the rich phase diagram of TiO_{x} overlayer structures on SrTiO_{3}(110) by combining the covariance matrix adaptation evolution strategy (CMA-ES) and a neural-network force field (NNFF) as a surrogate energy model. By training solely on SrTiO_{3}(110) 4×1 overlayer structures and performing CMA-ES runs on 3×1, 4×1 and 5×1 overlayers, we verify the transferability of the NNFF. The speedup due to the surrogate model allows taking advantage of the stochastic nature of the CMA-ES to perform exhaustive sets of explorations and identify both known and new low-energy reconstructions.},
keywords = {P09},
pubstate = {published},
tppubtype = {article}
}
Montes-Campos, Hadrián; Carrete, Jesús; Bichelmaier, Sebastian; Varela, Luis M; Madsen, Georg K. H.
A Differentiable Neural-Network Force Field for Ionic Liquids
Journal ArticleOpen AccessIn: Journal of Chemical Information and Modeling, vol. 62, no. 1, pp. 88–101, 2022.
Abstract | Links | BibTeX | Tags: P09
@article{MontesCampos2021,
title = {A Differentiable Neural-Network Force Field for Ionic Liquids},
author = {Hadrián Montes-Campos and Jesús Carrete and Sebastian Bichelmaier and Luis M Varela and Georg K. H. Madsen},
doi = {10.1021/acs.jcim.1c01380},
year = {2022},
date = {2022-01-03},
urldate = {2022-01-03},
journal = {Journal of Chemical Information and Modeling},
volume = {62},
number = {1},
pages = {88--101},
abstract = {We present NeuralIL, a model for the potential energy of an ionic liquid that accurately reproduces first-principles results with orders-of-magnitude savings in computational cost. Based on a multilayer perceptron and spherical Bessel descriptors of the atomic environments, NeuralIL is implemented in such a way as to be fully automatically differentiable. It can thus be trained on ab-initio forces instead of just energies, to make the most out of the available data, and can efficiently predict arbitrary derivatives of the potential energy. We parametrize the model for the case of ethylammonium nitrate. We discuss the best way to include chemical information in the atom-centered descriptors for a many-component system. Furthermore, we demonstrate an ensemble-learning approach to the detection of extrapolation. With out-of-sample accuracies better than 0.1 kcal/mol in the energies and 100 meV/Å in the forces, our potential model considerably outperforms molecular-mechanics force fields and opens the door to large-scale thermodynamical calculations with ab-initio-like accuracy for ionic liquids. Including the forces does away with the idea that vast amounts of atomic configurations are required to train a neural network force field based on atom-centered descriptors. We also find that a separate treatment of long-range interactions is not required to achieve a high-quality representation of the potential
energy surface of these dense ionic systems.},
keywords = {P09},
pubstate = {published},
tppubtype = {article}
}
energy surface of these dense ionic systems.
2021
Arrigoni, Marco; Madsen, Georg K. H.
Evolutionary computing and machine learning for discovering of low-energy defect configurations
Journal ArticleOpen AccessIn: npj Computational Materials, vol. 7, no. 1, 2021.
Abstract | Links | BibTeX | Tags: P09, pre-TACO
@article{Arrigoni2021,
title = {Evolutionary computing and machine learning for discovering of low-energy defect configurations},
author = {Marco Arrigoni and Georg K. H. Madsen},
doi = {10.1038/s41524-021-00537-1},
year = {2021},
date = {2021-05-20},
urldate = {2021-05-20},
journal = {npj Computational Materials},
volume = {7},
number = {1},
publisher = {Springer Science and Business Media LLC},
abstract = {Density functional theory (DFT) has become a standard tool for the study of point defects in materials. However, finding the most stable defective structures remains a very challenging task as it involves the solution of a multimodal optimization problem with a high-dimensional objective function. Hitherto, the approaches most commonly used to tackle this problem have been mostly empirical, heuristic, and/or based on domain knowledge. In this contribution, we describe an approach for exploring the potential energy surface (PES) based on the covariance matrix adaptation evolution strategy (CMA-ES) and supervised and unsupervised machine learning models. The resulting algorithm depends only on a limited set of physically interpretable hyperparameters and the approach offers a systematic way for finding low-energy configurations of isolated point defects in solids. We demonstrate its applicability on different systems and show its ability to find known low-energy structures and discover additional ones as well.},
keywords = {P09, pre-TACO},
pubstate = {published},
tppubtype = {article}
}
2020
van Roekeghem, Ambroise; Carrete, Jesús; Curtarolo, Stefano; Mingo, Natalio
Journal ArticleIn: Physical Review Materials, vol. 4, no. 11, pp. 113804, 2020.
Abstract | Links | BibTeX | Tags: P09, pre-TACO
@article{Roekeghem2020,
title = {High-throughput study of the static dielectric constant at high temperatures in oxide and fluoride cubic perovskites},
author = {Ambroise van Roekeghem and Jesús Carrete and Stefano Curtarolo and Natalio Mingo},
doi = {10.1103/physrevmaterials.4.113804},
year = {2020},
date = {2020-11-13},
journal = {Physical Review Materials},
volume = {4},
number = {11},
pages = {113804},
publisher = {American Physical Society (APS)},
abstract = {Using finite-temperature phonon calculations and the Lyddane-Sachs-Teller relations, we calculate ab initio the static dielectric constants of 78 semiconducting oxides and fluorides with cubic perovskite structures at 1000 K. We first compare our method with experimental measurements, and we find that it succeeds in describing the temperature dependence and the relative ordering of the static dielectric constant ε_{DC} in the series of oxides BaTiO_{3}, SrTiO_{3}, KTaO_{3}. We show that the effects of anharmonicity on the ion-clamped dielectric constant, on Born charges, and on phonon lifetimes, can be neglected in the framework of our high-throughput study. Based on the high-temperature phonon spectra, we find that the dispersion of ε_{DC} is one order of magnitude larger among oxides than fluorides at 1000 K. We display the correlograms of the dielectric constants with simple structural descriptors, and we point out that ε_{DC} is actually well correlated with the infinite-frequency dielectric constant ε_{∞}, even in those materials with phase transitions in which ε_{DC} is strongly temperature dependent.},
keywords = {P09, pre-TACO},
pubstate = {published},
tppubtype = {article}
}
2017
Legrain, Fleur; Carrete, Jesús; van Roekeghem, Ambroise; Curtarolo, Stefano; Mingo, Natalio
How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids
Journal ArticleIn: Chemistry of Materials, vol. 29, no. 15, pp. 6220–6227, 2017.
Abstract | Links | BibTeX | Tags: P09, pre-TACO
@article{Legrain2017,
title = {How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids},
author = {Fleur Legrain and Jesús Carrete and Ambroise van Roekeghem and Stefano Curtarolo and Natalio Mingo},
doi = {10.1021/acs.chemmater.7b00789},
year = {2017},
date = {2017-06-22},
journal = {Chemistry of Materials},
volume = {29},
number = {15},
pages = {6220--6227},
publisher = {American Chemical Society (ACS)},
abstract = {Computing vibrational free energies (F_{vib}) and entropies (S_{vib}) has posed a long-standing challenge to the high-throughput ab initio investigation of finite temperature properties of solids. Here, we use machine-learning techniques to efficiently predict F_{vib} and S_{vib} of crystalline compounds in the Inorganic Crystal Structure Database. Using descriptors based simply on the chemical formula and using a training set of only 300 compounds, mean absolute errors of less than 0.04 meV/K/atom (15 meV/atom) are achieved for S_{vib} (F_{vib}), whose values are distributed within a range of 0.9 meV/K/atom (300 meV/atom.) In addition, for training sets containing fewer than 2000 compounds, the chemical formula alone is shown to perform as well as, if not better than, four other more complex descriptors previously used in the literature. The accuracy and simplicity of the approach means that it can be advantageously used for fast screening of chemical reactions at finite temperatures.},
keywords = {P09, pre-TACO},
pubstate = {published},
tppubtype = {article}
}
2016
van Roekeghem, Ambroise; Carrete, Jesús; Oses, Corey; Curtarolo, Stefano; Mingo, Natalio
Journal ArticleOpen AccessIn: Physical Review X, vol. 6, no. 4, pp. 041061, 2016.
Abstract | Links | BibTeX | Tags: P09, pre-TACO
@article{Roekeghem2016,
title = {High-Throughput Computation of Thermal Conductivity of High-Temperature Solid Phases: The Case of Oxide and Fluoride Perovskites},
author = {Ambroise van Roekeghem and Jesús Carrete and Corey Oses and Stefano Curtarolo and Natalio Mingo},
doi = {10.1103/physrevx.6.041061},
year = {2016},
date = {2016-06-13},
urldate = {2016-06-13},
journal = {Physical Review X},
volume = {6},
number = {4},
pages = {041061},
publisher = {American Physical Society (APS)},
abstract = {Using finite-temperature phonon calculations and machine-learning methods, we assess the mechanical stability of about 400 semiconducting oxides and fluorides with cubic perovskite structures at 0, 300, and 1000 K. We find 92 mechanically stable compounds at high temperatures—including 36 not mentioned in the literature so far—for which we calculate the thermal conductivity. We show that the thermal conductivity is generally smaller in fluorides than in oxides, largely due to a lower ionic charge, and describe simple structural descriptors that are correlated with its magnitude. Furthermore, we show that the thermal conductivities of most cubic perovskites decrease more slowly than the usual T^{−1} behavior. Within this set, we also screen for materials exhibiting negative thermal expansion. Finally, we describe a strategy to accelerate the discovery of mechanically stable compounds at high temperatures.},
keywords = {P09, pre-TACO},
pubstate = {published},
tppubtype = {article}
}