119 citations · 380 across the 26 of their papers we have counts for
26 papers
Finite-temperature Green's function cluster expansion from thermofield doubles: Breakdown of the polaron picture
M. R. Carbone, S. Fomichev, B. Kloss +4
We introduce a method, numerically exact in principle, for computing the momentum- and frequency-resolved single-particle Green's function of a polaron at finite temperature. The m…
Hamiltonian parameter inference from resonant inelastic x-ray scattering with active learning
Marton K. Lajer, Xin Dai, Kipton Barros +3
Identifying model Hamiltonians is a vital step toward creating predictive models of materials. Here, we combine Bayesian optimization with the EDRIXS numerical package to infer Ham…
OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra
Shubha R. Kharel, Fanchen Meng, Xiaohui Qu +2
X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents sig…
Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models
Hyuna Kwon, Tim Hsu, Wenyu Sun +8
The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework b…
Atomic Insights into the Oxidative Degradation Mechanisms of Sulfide Solid Electrolytes
Chuntian Cao, Matthew R. Carbone, Cem Komurcuoglu +15
Electrochemical degradation of solid electrolytes is a major roadblock in the development of solid-state batteries, and the formed solid-solid interphase (SSI) plays a key role in…
Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats
Wei Chen, Yihui Ren, Ai Kagawa +9
Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for develo…
Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design
Matthew R. Carbone, Hyeong Jin Kim, Chandima Fernando +7
The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in v…
Accurate, uncertainty-aware classification of molecular chemical motifs from multi-modal X-ray absorption spectroscopy
Matthew R. Carbone, Phillip M. Maffettone, Xiaohui Qu +2
Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry and biology. In this work, we present neu…
Physically interpretable approximations of many-body spectral functions
Shubhang Goswami, Kipton Barros, Matthew R. Carbone
The rational function approximation provides a natural and interpretable representation of response functions such as the many-body spectral functions. We apply the Vector Fitting…
Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes
Haoyue Guo, Matthew R. Carbone, Chuntian Cao +9
X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of the absorber atom. In t…
Self-driving Multimodal Studies at User Facilities
Phillip M. Maffettone, Daniel B. Allan, Stuart I. Campbell +11
Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days t…
Machine-learning the spectral function of a hole in a quantum antiferromagnet
Jackson Lee, Matthew R. Carbone, Weiguo Yin
Understanding charge motion in a background of interacting quantum spins is a fundamental problem in quantum many-body physics. The most extensively studied model for this problem…
Decoding Structure-Spectrum Relationships with Physically Organized Latent Spaces
Zhu Liang, Matthew R. Carbone, Wei Chen +5
A new semi-supervised machine learning method for the discovery of structure-spectrum relationships is developed and demonstrated using the specific example of interpreting X-ray a…
Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files
Matthew R. Carbone, Fanchen Meng, Christian Vorwerk +7
First-principles computational spectroscopy is a critical tool for interpreting experiment, performing structure refinement, and developing new physical understanding. Systematical…
The Generalized Green's function Cluster Expansion: A Python package for simulating polarons
Matthew R. Carbone, Stepan Fomichev, Andrew J. Millis +3
We present an efficient implementation of the Generalized Green's function Cluster Expansion (GGCE), which is a new method for computing the ground-state properties and dynamics of…
When not to use machine learning: a perspective on potential and limitations
M. R. Carbone
The unparalleled success of artificial intelligence (AI) in the technology sector has catalyzed an enormous amount of research in the scientific community. It has proven to be a po…
Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles
Animesh Ghose, Mikhail Segal, Fanchen Meng +7
As machine learning (ML) methods continue to be applied to a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important…
Competition between Barrier- and Entropy-Driven Activation in Glasses
Matthew R. Carbone, Marco Baity-Jesi
In simplified models of glasses we clarify the existence of two different kinds of activated dynamics, which coexist, with one of the two dominating over the other. One is the ener…
Machine learning of Kondo physics using variational autoencoders and symbolic regression
Cole Miles, Matthew R. Carbone, Erica J. Sturm +4
We employ variational autoencoders to extract physical insight from a dataset of one-particle Anderson impurity model spectral functions. Autoencoders are trained to find a low-dim…
Bond-Peierls polaron: Moderate mass enhancement and current-carrying ground state
Matthew R. Carbone, Andrew J. Millis, David R. Reichman +1
We study polarons in the one-dimensional Bond-Peierls electron-phonon model, in which phonons on bonds of a lattice modulate the hopping of electrons between lattice sites, and con…
Numerically Exact Generalized Green's Function Cluster Expansions for Electron-Phonon Problems
Matthew R. Carbone, David R. Reichman, John Sous
We generalize the family of approximate momentum average methods to formulate a numerically exact, convergent hierarchy of equations whose solution provides an efficient algorithm…
Predicting impurity spectral functions using machine learning
Erica J. Sturm, Matthew R. Carbone, Deyu Lu +2
The Anderson Impurity Model (AIM) is a canonical model of quantum many-body physics. Here we investigate whether machine learning models, both neural networks (NN) and kernel ridge…
Microscopic model of the doping dependence of line widths in monolayer transition metal dichalcogenides
Matthew R. Carbone, Matthew Z. Mayers, David R. Reichman
A fully microscopic model of the doping-dependent exciton and trion line widths in the absorption spectra of monolayer transition metal dichalcogenides in the low temperature and l…
Effective Trap-like Activated Dynamics in a Continuous Landscape
Matthew R. Carbone, Valerio Astuti, Marco Baity-Jesi
We use a simple model to extend network models for activated dynamics to a continuous landscape with a well-defined notion of distance and a direct connection to many-body systems.…