Huziel E. Sauceda

5.3k total citations · 4 hit papers
24 papers, 3.0k citations indexed

About

Huziel E. Sauceda is a scholar working on Materials Chemistry, Molecular Biology and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Huziel E. Sauceda has authored 24 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Materials Chemistry, 9 papers in Molecular Biology and 7 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Huziel E. Sauceda's work include Machine Learning in Materials Science (15 papers), Protein Structure and Dynamics (8 papers) and Computational Drug Discovery Methods (5 papers). Huziel E. Sauceda is often cited by papers focused on Machine Learning in Materials Science (15 papers), Protein Structure and Dynamics (8 papers) and Computational Drug Discovery Methods (5 papers). Huziel E. Sauceda collaborates with scholars based in Mexico, Germany and South Korea. Huziel E. Sauceda's co-authors include Alexandre Tkatchenko, Kristof T. Schütt, Pieter-Jan Kindermans, K. Müller, Klaus‐Robert Müller, Stefan Chmiela, Igor Poltavsky, Oliver T. Unke, Michael Gastegger and Ignacio L. Garzón and has published in prestigious journals such as Nature Communications, The Journal of Chemical Physics and Nano Letters.

In The Last Decade

Huziel E. Sauceda

22 papers receiving 3.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

SchNet – A deep learning architecture for molecules and materials

2018 1.4k citations Kristof T. Schütt, Huziel E. Sauceda et al. The Journal of Chemical Physics profile →
Machine learning of accurate energy-conserving molecular force fields

2017 810 citations Stefan Chmiela, Alexandre Tkatchenko et al. Science Advances profile →
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

2021 215 citations Oliver T. Unke, Stefan Chmiela et al. Nature Communications profile →
Accurate global machine learning force fields for molecules with hundreds of atoms

2023 112 citations Stefan Chmiela, Valentín Vassilev-Galindo et al. Science Advances profile →

Peers

Countries citing papers authored by Huziel E. Sauceda

Since Specialization

Citations

This map shows the geographic impact of Huziel E. Sauceda's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Huziel E. Sauceda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Huziel E. Sauceda more than expected).

Fields of papers citing papers by Huziel E. Sauceda

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Huziel E. Sauceda. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Huziel E. Sauceda. The network helps show where Huziel E. Sauceda may publish in the future.

Co-authorship network of co-authors of Huziel E. Sauceda

This figure shows the co-authorship network connecting the top 25 collaborators of Huziel E. Sauceda. A scholar is included among the top collaborators of Huziel E. Sauceda based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Huziel E. Sauceda. Huziel E. Sauceda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Atomic orbits in molecules and materials for improving machine learning force fields 2025 ·Machine Learning Science and Technology ·(unknown), А. И. Кокорин, Huziel E. Sauceda, Stefan Chmiela, Claudio Quarti, David Beljonne, Alexandre Tkatchenko, Igor Poltavsky	1
2	Intercavity polariton slows down dynamics in strongly coupled cavities 2024 ·Nature Communications ·(unknown), Brenda Vargas, David Ley, (unknown), Huziel E. Sauceda, (unknown), Hugo A. Lara-García, Arturo Camacho-Guardian, Giuseppe Pirruccio	3
3	Accurate global machine learning force fields for molecules with hundreds of atoms breakdown → 2023 ·Science Advances ·Stefan Chmiela, Valentín Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus‐Robert Müller	112
4	Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Bidirectional Long Short-Term Memory Networks 2023 ·The Journal of Physical Chemistry Letters ·Bipeng Wang, (unknown), Yifan Wu, Klaus‐Robert Müller, Huziel E. Sauceda, Oleg V. Prezhdo	12
5	BIGDML—Towards accurate quantum machine learning force fields for materials 2022 ·Nature Communications ·Huziel E. Sauceda, (unknown), Stefan Chmiela, Lauro Oliver Paz‐Borbón, Klaus‐Robert Müller, Alexandre Tkatchenko	54
6	High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks 2022 ·Machine Learning Science and Technology ·(unknown), Klaus‐Robert Müller, Huziel E. Sauceda	9
7	SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects breakdown → 2021 ·Nature Communications ·Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus‐Robert Müller	215
8	On the forbidden graphene's ZO (out-of-plane optic) phononic band-analog vibrational modes in fullerenes 2021 ·arXiv (Cornell University) ·(unknown), Ignacio L. Garzón, Huziel E. Sauceda	1
9	deepqmc/deepqmc: DeepQMC 0.3.0 2021 ·Zenodo (CERN European Organization for Nuclear Research) ·Jan Hermann, (unknown), Huziel E. Sauceda	0
10	Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields 2020 ·The Journal of Chemical Physics ·Huziel E. Sauceda, Michael Gastegger, Stefan Chmiela, Klaus‐Robert Müller, Alexandre Tkatchenko	34
11	Molecular Force Fields with Gradient-Domain Machine Learning: Dynamics of Small Molecules with Coupled Cluster Forces 2019 ·Bulletin of the American Physical Society ·Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus‐Robert Müller, Alexandre Tkatchenko	1
12	Modeling Molecular Spectra with Interpretable Atomistic Neural Networks 2019 ·Bulletin of the American Physical Society ·Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus‐Robert Müller, Alexandre Tkatchenko	1
13	sGDML: Constructing accurate and data efficient molecular force fields using machine learning 2019 ·Computer Physics Communications ·Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky, Klaus‐Robert Müller, Alexandre Tkatchenko	160
14	Mechanical Vibrations of Atomically Defined Metal Clusters: From Nano- to Molecular-Size Oscillators 2018 ·Nano Letters ·Paolo Maioli, Tatjana Stoll, Huziel E. Sauceda, (unknown), Aude Demessence, Franck Bertorelle, Aurélien Crut, Fabrice Vallée, Ignacio L. Garzón, Giulio Cerullo, Natalia Del Fatti	68
15	Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields 2018 ·RePEc: Research Papers in Economics ·Stefan Chmiela, Huziel E. Sauceda, Klaus‐Robert Müller, Alexandre Tkatchenko	2
16	Machine learning of accurate energy-conserving molecular force fields breakdown → 2017 ·Science Advances ·Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda, Igor Poltavsky, Kristof T. Schütt, Klaus‐Robert Müller	810
17	Structural Determination of Metal Nanoparticles from their Vibrational (Phonon) Density of States 2014 ·The Journal of Physical Chemistry C ·Huziel E. Sauceda, Ignacio L. Garzón	33
18	Size and Shape Dependence of the Vibrational Spectrum and Low-Temperature Specific Heat of Au Nanoparticles 2013 ·The Journal of Physical Chemistry C ·Huziel E. Sauceda, Fernando Salazar, Luis A. Pérez, Ignacio L. Garzón	38
19	Vibrational Spectrum, Caloric Curve, Low-Temperature Heat Capacity, and Debye Temperature of Sodium Clusters: The Na₁₃₉⁺ Case 2013 ·The Journal of Physical Chemistry C ·Huziel E. Sauceda, (unknown), Fernando Salazar, Luis A. Pérez, Ignacio L. Garzón	12
20	Vibrational Properties of Metal Nanoparticles: Atomistic Simulation and Comparison with Time-Resolved Investigation 2012 ·The Journal of Physical Chemistry C ·Huziel E. Sauceda, Denis Mongin, Paolo Maioli, Aurélien Crut, M. Pellarin, Natalia Del Fatti, Fabrice Vallée, Ignacio L. Garzón	60

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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