Alessandro Paticchio, Tommaso Scarlatti, Marios Mattheakis, Pavlos Protopapas, and Marco Brambilla. 12/2020. “

Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread.” In 2020 NeurIPS Workshop on Machine Learning and the Physical Sciences. NeurIPS.

Publisher's VersionAbstract
Studying the dynamics of COVID-19 is of paramount importance to understanding the efficiency of restrictive measures and develop strategies to defend against up- coming contagion waves. In this work, we study the spread of COVID-19 using a semi-supervised neural network and assuming a passive part of the population remains isolated from the virus dynamics. We start with an unsupervised neural network that learns solutions of differential equations for different modeling param- eters and initial conditions. A supervised method then solves the inverse problem by estimating the optimal conditions that generate functions to fit the data for those infected by, recovered from, and deceased due to COVID-19. This semi-supervised approach incorporates real data to determine the evolution of the spread, the passive population, and the basic reproduction number for different countries.

2020_covid_2010.05074.pdf Henry Jin, Marios Mattheakis, and Pavlos Protopapas. 12/2020. “

Unsupervised Neural Networks for Quantum Eigenvalue Problems.” In 2020 NeurIPS Workshop on Machine Learning and the Physical Sciences. NeurIPS.

Publisher's VersionAbstractEigenvalue problems are critical to several fields of science and engineering. We present a novel unsupervised neural network for discovering eigenfunctions and eigenvalues for differential eigenvalue problems with solutions that identically satisfy the boundary conditions. A scanning mechanism is embedded allowing the method to find an arbitrary number of solutions. The network optimization is data-free and depends solely on the predictions. The unsupervised method is used to solve the quantum infinite well and quantum oscillator eigenvalue problems.

2020_eigenvalues_2010.05075.pdf Marios Mattheakis. 8/26/2020. “

Riding Waves in Neuromorphic Computing.” APS Physics 12 (132), Pp. 1-3.

Publisher's VersionAbstractAn artificial neural network incorporating nonlinear waves could help reduce energy consumption within a bioinspired (neuromorphic) computing device.

physics.13.132.pdf Yue Luo, Rebecca Engelke, Marios Mattheakis, Michele Tamagnone, Stephen Carr, Kenji Watanabe, Takashi Taniguchi, Efthimios Kaxiras, Philip Kim, and William L. Wilson. 8/2020. “

In-situ nanoscale imaging of moiré superlattices in twisted van der Waals heterostructures.” Nature Communication, 11, 4209, Pp. 1-7.

Publisher's VersionAbstractDirect visualization of nanometer-scale properties of moiré superlattices in van der Waals

heterostructure devices is a critically needed diagnostic tool for study of the electronic and optical phenomena induced by the periodic variation of atomic structure in these complex systems. Conventional imaging methods are destructive and insensitive to the buried device geometries, preventing practical inspection. Here we report a versatile scanning probe microscopy employing infrared light for imaging moiré superlattices of twisted bilayers graphene encapsulated by hexagonal boron nitride. We map the pattern using the scattering dynamics of phonon polaritons launched in hexagonal boron nitride capping layers via its interaction with the buried moiré superlattices. We explore the origin of the double-line features imaged and show the mechanism of the underlying effective phase change of the phonon polariton reflectance at domain walls. The nano-imaging tool developed provides a non-destructive analytical approach to elucidate the complex physics of moiré engineered heterostructures.

2020_tbg_phonons_natcomm.pdf G. A. Tritsaris, S. Carr, Z. Zhu, Y. Xie, S. Torrisi, J. Tang, M.Mattheakis, D. Larson, and E. Kaxiras. 6/2020. “

Electronic structure calculations of twisted multi-layer graphene superlattices.” 2D Materials, 7, Pp. 035028.

Publisher's VersionAbstractQuantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and studied, a systematic investigation of the properties of arbitrarily layered graphene assemblies is still lacking. We introduce theoretical concepts and methods for the processing of materials information, and as a case study, apply them to investigate the electronic structure of multi-layer graphene-based assemblies in a high-throughput fashion. We provide a critical discussion of patterns and trends in tight binding band structures and we identify specific layered assemblies using low-dispersion electronic bands as indicators of potentially interesting physics like strongly correlated behavior. A combination of data-driven models for visualization and prediction is used to intelligently explore the materials space. This work more generally aims to increase confidence in the combined use of physics-based and data-driven modeling for the systematic refinement of knowledge about 2D layered materials, with implications for the development of novel quantum devices.

2001.11633.pdf Georgios A. Tritsaris, Yiqi Xie, Alexander M. Rush, Stephen Carr, Marios Mattheakis, and Efthimios Kaxiras. 6/2020. “

LAN -- A materials notation for 2D layered assemblies.” J. Chem. Inf. Model. .

Publisher's VersionAbstractTwo-dimensional (2D) layered materials offer intriguing possibilities for novel physics and applications. Before any attempt at exploring the materials space in a systematic fashion, or combining insights from theory, computation and experiment, a formal description of information about an assembly of arbitrary composition is required. Here, we introduce a domain-generic notation that is used to describe the space of 2D layered materials from monolayers to twisted assemblies of arbitrary composition, existent or not-yet-fabricated. The notation corresponds to a theoretical materials concept of stepwise assembly of layered structures using a sequence of rotation, vertical stacking, and other operations on individual 2D layers. Its scope is demonstrated with a number of example structures using common single-layer materials as building blocks. This work overall aims to contribute to the systematic codification, capture and transfer of materials knowledge in the area of 2D layered materials.

1910.03413.pdf Feiyu Chen, David Sondak, Pavlos Protopapas, Marios Mattheakis, Shuheng Liu, Devansh Agarwal, and Marco Di Giovanni. 2/2020. “

NeuroDiffEq: A Python package for solving differential equations with neural networks.” Journal of Open Source Software, 5, 46.

Publisher's Version 2020_joss_neurodiffeq.pdf G. Barmparis, G. Neofotistos, M.Mattheakis, J. Hitzanidi, G. P. Tsironis, and E. Kaxiras. 2/2020. “

Robust prediction of complex spatiotemporal states through machine learning with sparse sensing.” Physics Letters A, 384, Pp. 126300.

Publisher's VersionAbstractComplex spatiotemporal states arise frequently in material as well as biological systems consisting of multiple interacting units. A specific, but rather ubiquitous and interesting example is that of “chimeras”, existing in the edge between order and chaos. We use Machine Learning methods involving “observers” to predict the evolution of a system of coupled lasers, comprising turbulent chimera states and of a less chaotic biological one, of modular neuronal networks containing states that are synchronized across the networks. We demonstrated the necessity of using “observers” to improve the performance of Feed-Forward Networks in such complex systems. The robustness of the forecasting capabilities of the “Observer Feed-Forward Networks” versus the distribution of the observers, including equidistant and random, and the motion of them, including stationary and moving was also investigated. We conclude that the method has broader applicability in dynamical system context when partial dynamical information about the system is available.

2020_pla_robustmlpredictions.pdf