Klaus-Robert Müller
Klaus-Robert Müller has been a professor of computer science at Technische Universität Berlin since 2006; at the same time he is directing rsp. co-directing the Berlin Machine Learning Center and the Berlin Big Data Center and most recently BIFOLD . He studied physics in Karlsruhe from1984 to 1989 and obtained his Ph.D. degree in computer science at Technische Universität Karlsruhe in 1992. After completing a postdoctoral position at GMD FIRST in Berlin, he was a research fellow at the University of Tokyo from 1994 to 1995. In 1995, he founded the Intelligent Data Analysis group at GMD-FIRST (later Fraunhofer FIRST) and directed it until 2008. From 1999 to 2006, he was a professor at the University of Potsdam. From 2012 he has been Distinguished Professor at Korea University in Seoul. In 2020/2021 he spent his sabbatical at Google Brain as a Principal Scientist. Among others, he was awarded the Olympus Prize for Pattern Recognition (1999), the SEL Alcatel Communication Award (2006), the Science Prize of Berlin by the Governing Mayor of Berlin (2014), the Vodafone Innovations Award (2017), Hector Science Award (2024), Pattern Recognition Best Paper award (2020), Digital Signal Processing Best Paper award (2022). In 2012, he was elected member of the German National Academy of Sciences-Leopoldina, in 2017 of the Berlin Brandenburg Academy of Sciences, in 2021 of the German National Academy of Science and Engineering and also in 2017 external scientific member of the Max Planck Society. From 2019 on he became an ISI Highly Cited researcher in the cross-disciplinary area. His research interests are intelligent data analysis and Machine Learning in the sciences (Neuroscience (specifically Brain-Computer Interfaces, Physics, Chemistry) and in industry.
Authored Publications
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Accurate global machine learning force fields for molecules with hundreds of atoms
Stefan Chmiela
Valentin Vassilev Galindo
Adil Kabylda
Huziel E. Sauceda
Alexandre Tkatchenko
Science Advances, vol. 9(2) (2023), eadf0873
Preview abstract
Global machine learning force fields, with the capacity to capture collective interactions in molecular systems, now scale up to a few dozen atoms due to considerable growth of model complexity with system size. For larger molecules, locality assumptions are introduced, with the consequence that nonlocal interactions are not described. Here, we develop an exact iterative approach to train global symmetric gradient domain machine learning (sGDML) force fields (FFs) for several hundred atoms, without resorting to any potentially uncontrolled approximations. All atomic degrees of freedom remain correlated in the global sGDML FF, allowing the accurate description of complex molecules and materials that present phenomena with far-reaching characteristic correlation lengths. We assess the accuracy and efficiency of sGDML on a newly developed MD22 benchmark dataset containing molecules from 42 to 370 atoms. The robustness of our approach is demonstrated in nanosecond path-integral molecular dynamics simulations for supramolecular complexes in the MD22 dataset.
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Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation
Kai J. Miller
Gabriela Ojeda Valencia
Harvey Huang
Nicholas M. Gregg
Gregory A. Worrell
Dora Hermes
Plos Computational Biology, vol. 19(5) (2023), e1011105
Preview abstract
Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to understand how brain regions interact with one another. Voltages are measured from implanted electrodes in one brain area while stimulating another with brief current impulses separated by several seconds. Historically, researchers have tried to understand the significance of evoked voltage polyphasic deflections by visual inspection, but no general-purpose tool has emerged to understand their shapes or describe them mathematically. We describe and illustrate a new technique to parameterize brain stimulation data, where voltage response traces are projected into one another using a semi-normalized dot product. The length of timepoints from stimulation included in the dot product is varied to obtain a temporal profile of structural significance, and the peak of the profile uniquely identifies the duration of the response. Using linear kernel PCA, a canonical response shape is obtained over this duration, and then single-trial traces are parameterized as a projection of this canonical shape with a residual term. Such parameterization allows for dissimilar trace shapes from different brain areas to be directly compared by quantifying cross-projection magnitudes, response duration, canonical shape projection amplitudes, signal-to-noise ratios, explained variance, and statistical significance. Artifactual trials are automatically identified by outliers in sub-distributions of cross-projection magnitude, and rejected. This technique, which we call “Canonical Response Parameterization” (CRP) dramatically simplifies the study of CCEP shapes, and may also be applied in a wide range of other settings involving event-triggered data.
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Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields
Niklas Schmitz
Stefan Chmiela
The Journal of Physical Chemistry Letters, vol. 13(43) (2022), pp. 10183-10189
Preview abstract
Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive. Here, machine learning (ML) models can help to be data economic as they can be successfully constrained using the underlying symmetry and conservation laws of physics. However, so far, every descriptor newly proposed for an ML model has required a cumbersome and mathematically tedious remodeling. We therefore propose using modern techniques from algorithmic differentiation within the ML modeling process, effectively enabling the usage of novel descriptors or models fully automatically at an order of magnitude higher computational efficiency. This paradigmatic approach enables not only a versatile usage of novel representations and the efficient computation of larger systems─all of high value to the FF community─but also the simple inclusion of further physical knowledge, such as higher-order information (e.g., Hessians, more complex partial differential equations constraints etc.), even beyond the presented FF domain.
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Harmoni: a Method for Eliminating Spurious Interactions due to the Harmonic Components in Neuronal Data
Mina Jamshidi Idaji
Juanli Zhang
Tilman Stephani
Guido Nolte
Arno Villringer
Vadim Nikulin
Neuroimage, vol. 252 (2022), pp. 119053
Preview abstract
Cross-frequency synchronization (CFS) has been proposed as a mechanism for integrating spatially and spectrally distributed information in the brain. However, investigating CFS in Magneto- and Electroencephalography (MEG/EEG) is hampered by the presence of spurious neuronal interactions due to the non-sinusoidal waveshape of brain oscillations. Such waveshape gives rise to the presence of oscillatory harmonics mimicking genuine neuronal oscillations. Until recently, however, there has been no methodology for removing these harmonics from neuronal data. In order to address this long-standing challenge, we introduce a novel method (called HARMOnic miNImization - Harmoni) that removes the signal components which can be harmonics of a non-sinusoidal signal. Harmoni’s working principle is based on the presence of CFS between harmonic components and the fundamental component of a non-sinusoidal signal. We extensively tested Harmoni in realistic EEG simulations. The simulated couplings between the source signals represented genuine and spurious CFS and within-frequency phase synchronization. Using diverse evaluation criteria, including ROC analyses, we showed that the within- and cross-frequency spurious interactions are suppressed significantly, while the genuine activities are not affected. Additionally, we applied Harmoni to real resting-state EEG data revealing intricate remote connectivity patterns which are usually masked by the spurious connections. Given the ubiquity of non-sinusoidal neuronal oscillations in electrophysiological recordings, Harmoni is expected to facilitate novel insights into genuine neuronal interactions in various research fields, and can also serve as a steppingstone towards the development of further signal processing methods aiming at refining within- and cross-frequency synchronization in electrophysiological recordings.
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Efficient Computation of Higher-Order Subgraph Attribution via Message Passing
Ping Xiong
Thomas Schnake
Gregoire Montavon
Shin Nakajima
ICML (2022) (to appear)
Preview abstract
Explaining graph neural networks (GNNs) has become more and more important recently. Higherorder interpretation schemes, such as GNNLRP (layer-wise relevance propagation for GNN),
emerged as powerful tools for unraveling how
different features interact thereby contributing to
explaining GNNs. Methods such as GNN-LRP
perform walks between nodes at each layer, and
there are exponentially many such walks. In this
work, we demonstrate that such exponential complexity can be avoided, in particular, we propose
novel linear-time (w.r.t. depth) algorithms that
enable to efficiently perform GNN-LRP for subgraphs. Our algorithms are derived via message
passing techniques that make use of the distributive property, thereby directly computing quantities for higher-order explanations. We further
adapt our efficient algorithms to compute a generalization of subgraph attributions that also takes
into account the neighboring graph features. Experimental results show significant acceleration of
the proposed algorithms and demonstrate a high
usefulness and scalability of our novel generalized
subgraph attribution.
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Towards Robust Explanations for Deep Neural Networks
Ann-Kathrin Dombrowski
Christopher Johannes Anders
Pan Kessel
Pattern Recognition, vol. 121 (2022), pp. 108194
Preview abstract
Explanation methods shed light on the decision process of black-box classifiers such as deep neural networks. But their usefulness can be compromised because they are susceptible to manipulations. With this work, we aim to enhance the resilience of explanations. We develop a unified theoretical framework for deriving bounds on the maximal manipulability of a model. Based on these theoretical insights, we present three different techniques to boost robustness against manipulation: training with weight decay, smoothing activation functions, and minimizing the Hessian of the network. Our experimental results confirm the effectiveness of these approaches.
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So3krates - Self-attention for higher-order geometric interactions on arbitrary length-scales
Thorben Frank
Advances in Neural Information Processing Systems (2022) (to appear)
Preview abstract
The application of machine learning (ML) methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab initio methods. However, some quantum mechanical properties of molecules and materials depend on non-local electronic effects, which are often neglected due to the difficulty of modelling them efficiently. This work proposes a modified attention mechanism adapted to the underlying physics, which allows to recover the relevant non-local effects. Namely, we introduce spherical harmonic coordinates (SPHCs) to reflect higher order geometric information for each atom in a molecule, enabling a non-local formulation of attention in the SPHC space. Our proposed model So3krates -- a self-attention based message passing neural network (MPNN) -- uncouples geometric information from atomic features, making them independently amenable to attention mechanisms. We show that in contrast to other published methods, So3krates is able to describe quantum mechanical effects due to orbital overlap over arbitrary length scales. Further, So3krates is shown to match or exceed state-of-the-art performance on the popular MD-17 and QM-7X benchmarks, notably, requiring a significantly lower number of parameters while at the same time giving a substantial speedup compared to other models.
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Higher-Order Explanations of Graph Neural Networks via Relevant Walks
Thomas Schnake
Oliver Eberle
Jonas Lederer
Shin Nakajima
Kristof T. Schütt
Gregoire Montavon
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44(11) (2022), pp. 7581 - 7596
Preview abstract
Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. As GNNs tightly entangle the input graph into the neural network structure, common explainable AI approaches are not applicable. To a large extent, GNNs have remained black-boxes for the user so far. In this paper, we show that GNNs can in fact be naturally explained using higher-order expansions, i.e., by identifying groups of edges that jointly contribute to the prediction. Practically, we find that such explanations can be extracted using a nested attribution scheme, where existing techniques such as layer-wise relevance propagation (LRP) can be applied at each step. The output is a collection of walks into the input graph that are relevant for the prediction. Our novel explanation method, which we denote by GNN-LRP, is applicable to a broad range of graph neural networks and lets us extract practically relevant insights on sentiment analysis of text data, structure-property relationships in quantum chemistry, and image classification.
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Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective
Simon Letzgus
Jonas Lederer
Wojciech Samek
Gregoire Montavon
IEEE Signal Processing Magazine, vol. 39 (4) (2022), 40–58
Preview abstract
In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex nonlinear learning models, such as deep neural networks. Gaining a better understanding is especially important, e.g., for safety-critical ML applications or medical diagnostics and so on. Although such explainable artificial intelligence (XAI) techniques have reached significant popularity for classifiers, thus far, little attention has been devoted to XAI for regression models (XAIR). In this review, we clarify the fundamental conceptual differences of XAI for regression and classification tasks, establish novel theoretical insights and analysis for XAIR, provide demonstrations of XAIR on genuine practical regression problems, and finally, discuss challenges remaining for the field.
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BIGDML—Towards accurate quantum machine learning force fields for materials
Huziel Sauceda
Luis Gálvez-González
Stefan Chmiela
Lauro Oliver Paz Borbon
Alexandre Tkatchenko
Nature Communications, vol. 13 (2022), pp. 3733
Preview abstract
Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive datasets for training. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning (BIGDML) approach and demonstrate its ability to construct reliable force fields using a training set with just 10–200 geometries for materials including pristine and defect-containing 2D and 3D semiconductors and metals, as well as chemisorbed and physisorbed atomic and molecular adsorbates on surfaces. The BIGDML model employs the full relevant symmetry group for a given material, does not assume artificial atom types or localization of atomic interactions and exhibits high data efficiency and state-of-the-art energy accuracies (errors substantially below 1 meV per atom) for an extended set of materials. Extensive path-integral molecular dynamics carried out with BIGDML models demonstrate the counterintuitive localization of benzene–graphene dynamics induced by nuclear quantum effects and their strong contributions to the hydrogen diffusion coefficient in a Pd crystal for a wide range of temperatures.
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Artificial Intelligence and Pathology: from Principles to Practice and Future Applications in Histomorphology and Molecular Profiling
Albrecht Stenzinger
Max Alber
Michael Allgäuer
Phillip Jurmeister
Michael Bockmayr
Jan Budczies
Jochen Lennerz
Johannes Eschrich
Daniel Kazdal
Peter Schirmacher
Alex H Wagner
Frank Tacke
David Capper
Frederick Klauschen
Seminars in Cancer Biology, vol. 84 (2022), pp. 129-143
Preview abstract
The complexity of diagnostic (surgical) pathology has increased substantially over the last
decades with respect to histomorphological and molecular profiling and has steadily expanded
its role in tumor diagnostics and beyond from disease entity identification via prognosis
estimation to precision therapy prediction. It is therefore not surprising that pathology is among
the disciplines in medicine with high expectations in the application of artificial intelligence (AI)
or machine learning approaches given its capabilities to analyse complex data in a quantitative
and standardised manner to further enhance scope and precision of diagnostics. While an
obvious application is the analysis of histological images, recent applications for the analysis
of molecular profiling data from different sources and clinical data support the notion that AI
will support both histopathology and molecular pathology in the future. At the same time,
current literature should not be misunderstood in a way that pathologists will likely be replaced
by AI applications in the foreseeable future. Although AI will likely transform pathology in the
coming years, recent studies reporting AI algorithms to diagnose cancer or predict certain
molecular properties deal with relatively simple diagnostic problems that fall short of the
diagnostic complexity pathologists face in clinical routine. Here, we review the pertinent
literature of AI methods and their applications to pathology, and put the current achievements
and what can be expected in the future in the context of the requirements for research and
routine diagnostics.
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Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks
Paul Ludwig Winkler
Huziel Saucceda
Machine Learning: Science and Technology, vol. 3 (2022), pp. 025011
Preview abstract
Molecular dynamics (MD) simulations are a cornerstone in science, enabling the investigation of a
system’s thermodynamics all the way to analyzing intricate molecular interactions. In general,
creating extended molecular trajectories can be a computationally expensive process, for example,
when running ab-initio simulations. Hence, repeating such calculations to either obtain more
accurate thermodynamics or to get a higher resolution in the dynamics generated by a fine-grained
quantum interaction can be time- and computational resource-consuming. In this work, we
explore different machine learning methodologies to increase the resolution of MD trajectories
on-demand within a post-processing step. As a proof of concept, we analyse the performance of
bi-directional neural networks (NNs) such as neural ODEs, Hamiltonian networks, recurrent NNs
and long short-term memories, as well as the uni-directional variants as a reference, for MD
simulations (here: the MD17 dataset). We have found that Bi-LSTMs are the best performing
models; by utilizing the local time-symmetry of thermostated trajectories they can even learn
long-range correlations and display high robustness to noisy dynamics across molecular
complexity. Our models can reach accuracies of up to 10−4 Å in trajectory interpolation, which
leads to the faithful reconstruction of several unseen high-frequency molecular vibration cycles.
This renders the comparison between the learned and reference trajectories indistinguishable. The
results reported in this work can serve (1) as a baseline for larger systems, as well as (2) for the
construction of better MD integrators.
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Machine Learning Force Fields
Oliver Unke
Stefan Chmiela
Huziel Saucceda
Michael Gastegger
Igor Poltavsky
Kristof T. Schütt
Alexandre Tkatchenko
Chemical Reviews, vol. 121 (16) (2021), 10142-10186, https://pubs.acs.org/doi/pdf/10.1021/acs.chemrev.0c01111
Preview abstract
In recent years, the use of Machine Learning
(ML) in computational chemistry has enabled
numerous advances previously out of reach due
to the computational complexity of traditional
electronic-structure methods. One of the most
promising applications is the construction of
ML-based force fields (FFs), with the aim to
narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs.
The key idea is to learn the statistical relation
between chemical structure and potential energy
without relying on a preconceived notion of fixed
chemical bonds or knowledge about the relevant
interactions. Such universal ML approximations
are in principle only limited by the quality and
quantity of the reference data used to train them.
This review gives an overview of applications
of ML-FFs and the chemical insights that can
be obtained from them. The core concepts underlying ML-FFs are described in detail and a
step-by-step guide for constructing and testing
them from scratch is given. The text concludes
with a discussion of the challenges that remain
to be overcome by the next generation of MLFFs.
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SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
Mihail Bogojeski
Michael Gastegger
Mario Geiger
Tess Smidt
Advances in Neural Information Processing Systems (2021)
Preview abstract
Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Instead of training on a fixed set of properties, more recent approaches attempt to learn the electronic wavefunction (or density) as a central quantity of atomistic systems, from which all other observables can be derived. This is complicated by the fact that wavefunctions transform non-trivially under molecular rotations, which makes them a challenging prediction target. To solve this issue, we introduce general SE(3)-equivariant operations and building blocks for constructing deep learning architectures for geometric point cloud data and apply them to reconstruct wavefunctions of atomistic systems with unprecedented accuracy. Our model reduces prediction errors by up to two orders of magnitude compared to the previous state-of-the-art and makes it possible to derive properties such as energies and forces directly from the wavefunction in an end-to-end manner. We demonstrate the potential of our approach in a transfer learning application, where a model trained on low accuracy reference wavefunctions implicitly learns to correct for electronic many-body interactions from observables computed at a higher level of theory. Such machine-learned wavefunction surrogates pave the way towards novel semi-empirical methods, offering resolution at an electronic level while drastically decreasing computational cost. While we focus on physics applications in this contribution, the proposed equivariant framework for deep learning on point clouds is promising also beyond, say, in computer vision or graphics.
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Explainable Deep One-Class Classification
Philipp Liznerski
Lukas Ruff
Robert Vandermeulen
Billy Joe Franks
Marius Kloft
ICLR 2021 (2021) (to appear)
Preview abstract
Deep one-class classification variants for anomaly detection learn a mapping that
concentrates nominal samples in feature space causing anomalies to be mapped
away. Because this transformation is highly non-linear, finding interpretations poses
a significant challenge. In this paper we present an explainable deep one-class
classification method, Fully Convolutional Data Description (FCDD), where the
mapped samples are themselves also an explanation heatmap. FCDD yields competitive detection performance and provides reasonable explanations on common
anomaly detection benchmarks with CIFAR-10 and ImageNet. On MVTec-AD, a
recent manufacturing dataset offering ground-truth anomaly maps, FCDD sets a
new state of the art in the unsupervised setting. Our method can incorporate groundtruth anomaly maps during training and using even a few of these (∼ 5) improves
performance significantly. Finally, using FCDD’s explanations we demonstrate the
vulnerability of deep one-class classification models to spurious image features
such as image watermarks
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A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff
Jacob Reinhard Kauffmann
Robert Vandermeulen
Gregoire Montavon
Wojciech Samek
Marius Kloft
Thomas G. Dietterich
Proc of the IEEE, vol. 109(5) (2021), pp. 756-795 (to appear)
Preview abstract
Deep learning approaches to anomaly detection have
recently improved the state of the art in detection performance
on complex datasets such as large collections of images or text.
These results have sparked a renewed interest in the anomaly
detection problem and led to the introduction of a great variety
of new methods. With the emergence of numerous such methods
that include approaches based on generative models, one-class
classification, and reconstruction, there is a growing need to bring
methods of this field into a systematic and unified perspective. In
this review, we therefore aim to identify the common underlying
principles as well as the assumptions that are often made
implicitly by various methods. In particular, we draw connections
between classic ‘shallow’ and novel deep approaches and show
how they exactly relate and moreover how this relation might
cross-fertilize or extend both directions. We further provide an
empirical assessment of major existing methods that is enriched
by the use of recent explainability techniques, and present specific
worked-through examples together with practical advice. Finally,
we outline critical open challenges and identify specific paths for
future research in anomaly detection.
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SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Stefan Chmiela
Michael Gastegger
Kristof T. Schütt
Huziel Saucceda
Nature Communications, vol. 12 (2021), pp. 7273
Preview abstract
Machine-learned force fields combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current machine-learned force fields typically ignore electronic degrees of freedom, such as the total charge or spin state, and assume chemical locality, which is problematic when molecules have inconsistent electronic states, or when nonlocal effects play a significant role. This work introduces SpookyNet, a deep neural network for constructing machine-learned force fields with explicit treatment of electronic degrees of freedom and nonlocality, modeled via self-attention in a transformer architecture. Chemically meaningful inductive biases and analytical corrections built into the network architecture allow it to properly model physical limits. SpookyNet improves upon the current state-of-the-art (or achieves similar performance) on popular quantum chemistry data sets. Notably, it is able to generalize across chemical and conformational space and can leverage the learned chemical insights, e.g. by predicting unknown spin states, thus helping to close a further important remaining gap for today’s machine learning models in quantum chemistry.
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Basis profile curve identification to understand electrical stimulation effects in human brain networks
Kai Joshua Miller
Dora Hermes
Plos Computational Biology, vol. 17(9) (2021), e1008710, https://doi.org/10.1371/journal.pcbi.1008710
Preview abstract
Brain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique “basis profile curves” (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome.
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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
John A Keith
Valentin Vassilev Galindo
Bingqing Cheng
Stefan Chmiela
Michael Gastegger
Alexandre Tkatchenko
Chemical Reviews, vol. 121 (16) (2021), 9816-9872, https://pubs.acs.org/doi/pdf/10.1021/acs.chemrev.1c00107
Preview abstract
Machine learning models are poised to make transformative impact in the chemical
sciences by dramatically accelerating computational algorithms and amplifying insights
available from computational chemistry methods. However, achieving this requires a
confluence and coaction of expertise in computer science and physical sciences. This
review is written for new and experienced researchers working at the intersection of
both fields. We first provide concise tutorials of computational chemistry, machine
learning methods, and how insights involving both can be achieved. We then follow
with a critical review of noteworthy applications that demonstrate how computational
quantum chemistry and machine learning can be used together to provide insightful
(and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis,
and drug design.
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Dynamical Strengthening of Covalent and Non-Covalent Molecular Interactions by Nuclear Quantum Effects at Finite Temperature
Huziel Saucceda
Stefan Chmiela
Valentin Vassilev Galindo
Alexandre Tkatchenko
Nature Communications, vol. 12 (2021), pp. 442
Preview abstract
Nuclear quantum effects (NQE) tend to generate delocalized molecular dynamics due to the anharmonicity of interatomic interactions. Here, we present evidence that NQE often enhance electronic
interactions and, in turn, can result in dynamical molecular stabilization at finite temperature. The
underlying physical mechanism promoted by NQE depends on the particular interaction under consideration. First, the effective reduction of interatomic distances between functional groups within
a molecule enhances the n → π
∗
interaction by increasing the overlap between molecular orbitals or
by strengthening electrostatic interactions between neighboring charge densities. Second, NQE can
localize methyl rotors by temporarily changing molecular bond orders and leading to the emergence
of localized transient rotor states. Third, for noncovalent interactions the strengthening comes from
the increase of the polarizability given the expanded average interatomic distances induced by NQE.
The implications of these boosted interactions include counterintuitive hydroxyl–hydroxyl bonding,
hindered methyl rotor dynamics, and molecular stiffening which generates smoother free-energy surfaces. These results challenge the general assumption that NQE tend to mainly generate delocalized
dynamics and reveal that NQE also play an active role in dynamical strengthening of molecular
interactions. Our findings yield new insights into the versatile role of nuclear quantum fluctuations
in molecules and materials
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Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
Huziel Saucceda
Michael Gastegger
Stefan Chmiela
Alexandre Tkatchenko
Journal of Chemical Physics, vol. 153 (2020), pp. 124109
Preview abstract
The goal of the present work is to perform a detailed investigation of the differences between both systems based
on a set of small molecules exhibiting different quantum
mechanical phenomena. Based on these results, different
alternatives are explored for improving the data generation process and their applicability context for expediting the force-field learning procedure. Furthermore,
improvement of the accuracy for MM-FFs is studied by
reparameterising them based on more accurate reference
data and test their limits and functional form flexibility. For this task, we use the recently published sGDML
framework[25, 26] as ML-FF of choice, as it is able to efficiently reconstruct the potential energy surfaces (PES)
of medium sized molecules. The investigated systems are
the molecules ethanol, the keto and enol forms of malondialdehyde (keto-MDA and enol-MDA, respectively) as
well as salicylic and acetylsalicylic acid (Aspirin). In the
context of these systems, we study the performance of
MM-FFs and sGDML derived FFs based on the overall
reliability of the generated PESs, as well as effects arising from chemical phenomena such as hydrogen transfer
and orbital interactions. Although we restrict ourselves
to the sGDML approach, it can nevertheless be expected
that the results found here are equally valid for ML-FFs
in general.
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Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance
Carmen Vidaurre
Stefan Haufe
Tania Jorajuría Gómez
Vadim Nikulin
Frontiers in Neuroscience, vol. 14 (2020), pp. 575081
Preview abstract
Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. For BCIs based on the modulation of sensorimotor rhythms
as measured by means of electroencephalography (EEG), about 20\% of potential users do not obtain enough accuracy to gain reliable control of the system. This lack of efficiency of BCI systems to decode user's intentions necessitates identification of neurophysiological factors determining `good' and `poor' BCI performers. Given that the neuronal oscillations, used in BCI, demonstrate rich a repertoire of spatial interactions, we hypothesized that neuronal activity in sensorimotor areas would define some aspects of BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in calibration and an online feedback session in the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recordings is significantly correlated to online feedback performance in $\mu$ and feedback frequency bands. Importantly,
the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study shows that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.
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Single-cell gene regulatory network prediction by explainable AI
Philipp Keyl
Philip Bischoff
Gabriel Dernbach
Michael Bockmayr
Rebecca Fritz
David Horst
Nils Blüthgen
Grégoire Montavon
Frederick Klauschen
Nucleic Acids Research (2023), gkac1212
Analysing Cerebrospinal Fluid with Explainable Deep Learning: from Diagnostics to Insights
Leonille Schweizer
Philipp Seegerer
Hee‐yeong Kim
René Saitenmacher
Amos Muench
Liane Barnick
Anja Osterloh
Carsten Dittmayer
Ruben Jödicke
Debora Pehl
Annekathrin Reinhardt
Klemens Ruprecht
Werner Stenzel
Annika K Wefers
Patrick N Harter
Ulrich Schüller
Frank L Heppner
Maximilian Alber
Frederick Klauschen
Neuropathology and Applied Neurobiology, vol. 49(1) (2023), e12866
Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings
Carmen Vidaurre
Kshipra Gurunandand
Mina Jamshidi Idaji
Guido Nolte
Marisol Gómez
Arno Villringer
Vadim Nikulin
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