Quantum Light-Matter

Welcome to my homepage! I am Sajal Kumar Giri, currently serving as a postdoctoral researcher in the research group of Prof. George C. Schatz at the Northwestern University, USA. We work at the intersection of chemical physics, quantum optics, machine learning, and quantum information. Our research focuses on the fundamental aspects of interactions between light and matter at the quantum level.

Department of Chemistry

Northwestern University

2145 Sheridan Road, IL 60208, USA

Email: sajal.giri@northwestern.edu

2021-Present, Postdoc: Northwestern University, USA with Prof. G. C. Schatz
2021, ML Scientist: Entos Inc. (Iambic), California, USA
2020, Postdoc: Max-Planck Institute PKS, Germany with Prof. J. M. Rost
2020, Ph.D.: Max-Planck Institute PKS, Germany with Prof. J. M. Rost
2015, M.Sc.: IIT Kanpur, India with Prof. S. Keshavamurthy
2013, B.Sc.: Ramakrishna Mission Vidyamandira, Belur Math, India

Research

Quantum Light Meets Nonlinear Spectroscopy

We explore quantum advantages in addressing nonlinear spectroscopic aspects of molecules and materials under full quantum description, treating both molecules and light quantum mechanically. The main motivation behind this is whether we can go beyond the fundamental limits of conventional classical light spectroscopy and control the nonlinear optical processes. Entangled light has demonstrated potential in enhancing two-photon absorption and improving photochemical reaction efficiencies. To this end, we develope theoretical methods to explore entangled light induced dynamics modeling the quantum state of light and their interactions with molecule and material systems.

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AI for Quantum Light-Matter

Understanding of quantum dynamics in complex molecules and nanomaterials is often challenging due to their inherent intricacies. Are all these complexities crucial for capturing the essential dynamics? If a subspace exists that encodes the essential dynamics with minimal complexity, it would significantly simplify the problem. But how can we identify and explore such a subspace, if it exists at all? To address this, we integrate quantum dynamics with machine learning methods. One of the main objectives of this research is to simulate quantum dynamics in an effective subspace that accelerates predictions while improving transparency.

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Harnessing Entanglement in Exciton Dynamics

The generation and control of quantum entanglement in exciton dynamics is a fundamental challenge for realizing quantum information processing with molecular and material systems. Entanglement is a key resource for various quantum technologies including quantum computing, communication, and sensing. By manipulating entangled states, we explore how quantum correlations can be leveraged to enhance information processing capabilities beyond classical limits. Our research focuses on the generation of quantum entanglement in excitonic systems and the investigation of entangled photon scattering, offering insights into the fundamental processes of entanglement transfer and fidelity.

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Plasmon-Molecule Strong Coupling

Nanomaterials exhibit efficient light absorption across a broad spectral range, largely attributed to localized surface plasmon resonance (LSPR). Within this research field, the central focus lies in understanding hot-carrier dynamics generated through the decay of LSPR and their role in potential applications for harnessing solar radiation. Our studies investigate the generation of polaritonic states through strong coupling between plasmons and molecules, and explore their impact on spectroscopic and photocatalytic processes.

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Publications

Filter by Year || Research

Switching of electrochemical selectivity due to plasmonic field-induced dissociation

Author(s): F. M. Alcorn , S. K. Giri, M. Chattoraj , R. Nixon , G. C. Schatz, and P. K. Jain

Journal: Proc. Natl. Acad. Sci. (USA) (2024)

DOI

BibTeX

Plasmon 2024

@article{Plasmon_2024,

}

Impact of classical and quantum light on donor-acceptor-donor molecules

Author(s): H. Mandal^*, S. K. Giri^*, S. Jovanovski, O. Varnavski, M. Zagorska, R. Ganczarczyk, T.-M. Chiang, G. C. Schatz, and T. Goodson III

[^* Equal Contribution]

Journal: J. Phys. Chem. Lett. 15, 9493–9501 (2024)

DOI

BibTeX

Entanglement 2024

@article{Entanglement_2024,

author = {Mandal, Haraprasad and Giri, Sajal Kumar and Jovanovski, Sara and Varnavski, Oleg and Zagorska, Malgorzata and Ganczarczyk, Roman and Chiang, Tse-Min and Schatz, George C. and Goodson, Theodore},

title = {Impact of classical and quantum light on donor-acceptor-donor molecules},

journal = {The Journal of Physical Chemistry Letters},

volume = {0},

number = {0},

pages = {9493-9501},

year = {2024},

doi = {10.1021/acs.jpclett.4c01948}

}

Laser pulse induced second- and third-harmonic generation of gold nanorods with real-time time-dependent density functional tight binding (RT-TDDFTB) method

Journal: J. Chem. Phys. 161, 044703 (2024)

Author(s): S. K. Giri, and G. C. Schatz

DOI

arXiv

BibTeX

Plasmon 2024

@article{Plasmon_2024,

author = {Giri, Sajal Kumar and Schatz, George C.},

title = {Laser pulse induced second- and third-harmonic generation of gold nanorods with real-time time-dependent density functional tight binding (RT-TDDFTB) method},

journal = {The Journal of Chemical Physics},

volume = {161},

number = {4},

pages = {044703},

month = {07},

year = {2024},

doi = {10.1063/5.0216887}

}

Roadmap on data-centric materials science

Journal: Modelling Simul. Mater. Sci. Eng. 32 063301 (2024)

Author(s): S. Baue, ..., S. K. Giri , ..., and M. Scheffler

DOI

arXiv

BibTeX

BiGmax Research ML 2024

@article{ML_2024,

author = {Bauer, Stefan and Benner, Peter and Bereau, Tristan and Blum, Volker and Boley, Mario and Carbogno, Christian and Catlow, C. Richard A. and Dehm, Gerhard and Eibl, Sebastian and Ernstorfer, Ralph and Fekete, {\'A}d{\'a}m and Foppa, Lucas and Fratzl, Peter and Freysoldt, Christoph and Gault, Baptiste and Ghiringhelli, Luca M. and Giri, Sajal K. and Gladyshev, Anton and Goyal, Pawan and Hattrick-Simpers, Jason and Kabalan, Lara and Karpov, Petr and Khorrami, Mohammad S. and Koch, Christoph T. and Kokott, Sebastian and Kosch, Thomas and Kowalec, Igor and Kremer, Kurt and Leitherer, Andreas and Li, Yue and Liebscher, Christian H. and Logsdail, Andrew J. and Lu, Zhongwei and Luong, Felix and Marek, Andreas and Merz, Florian and Mianroodi, Jaber R. and Neugebauer, J{\"o}rg and Pei, Zongrui and Purcell, Thomas A. R. and Raabe, Dierk and Rampp, Markus and Rossi, Mariana and Rost, Jan-Michael and Saal, James and Saalmann, Ulf and Sasidhar, Kasturi Narasimha and Saxena, Alaukik and Sbail{\`o}, Luigi and Scheidgen, Markus and Schloz, Marcel and Schmidt, Daniel F. and Teshuva, Simon and Trunschke, Annette and Wei, Ye and Weikum, Gerhard and Xian, R. Patrick and Yao, Yi and Yin, Junqi and Zhao, Meng and Scheffler, Matthias},

title = {Roadmap on data-centric materials science},

journal = {Modelling and Simulation in Materials Science and Engineering},

volume = {32},

number = {6},

pages = {063301},

month = {jul},

year = {2024},

publisher = {IOP Publishing}

doi = {10.1088/1361-651X/ad4d0d},

url = {https://dx.doi.org/10.1088/1361-651X/ad4d0d},

}

Plasmon-enhanced spectroscopy and photocatalysis

Journal: arXiv:2402.13478 (2024)

Author(s): S. K. Giri , and G. C. Schatz

DOI

arXiv

BibTeX

Spectroscopy 2024

@article{Plasmon_2024,

Journal = {arXiv:2402.13478}

}

Colors of entangled two-photon absorption

Journal: Proc. Natl. Acad. Sci. (USA) 120, e2307719120 (2023)

Author(s): O. Varnavski, S. K. Giri , T. Chiang, C. J. Zeman IV, G. C. Schatz, and T. Goodson III

DOI

BibTeX

In the News Entanglement 2023

@article{Entanglement_2023

author = {O. Varnavski; S. K. Giri; T.-M. Chiang; C. J. Zeman; G. C. Schatz; T. Goodson},

title = {Colors of entangled two-photon absorption},

journal = {Proceedings of the National Academy of Sciences (USA)},

volume = {120},

number = {35},

pages = {e2307719120},

year = {2023},

doi = {10.1073/pnas.2307719120},

URL = {https://www.pnas.org/doi/abs/10.1073/pnas.2307719120},

eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.2307719120}

}

Photodissociation of H2 on Ag and Au nanoparticles: Effect of size and plasmon versus interband transitions on threshold intensities for dissociation

Journal: J. Phys. Chem. C 127, 4115 (2023)

Author(s): S. K. Giri , and G. C. Schatz

DOI

arXiv

BibTeX

Plasmon 2023

@article{Plasmon_2023,

author = {S. K. Giri and G. C. Schatz},

title = {Photodissociation of H2 on Ag and Au Nanoparticles: Effect of Size and Plasmon versus Interband Transitions on Threshold Intensities for Dissociation},

journal = {The Journal of Physical Chemistry C},

volume = {127},

number = {8},

pages = {4115-4123},

year = {2023},

doi = {10.1021/acs.jpcc.3c00006}

}

Manipulating two-photon absorption of molecules through efficient optimization of entangled light

Journal: J. Phys. Chem. Lett. 13, 10140 (2022)

Author(s): S. K. Giri , and G. C. Schatz

DOI

arXiv

BibTeX

Entanglement 2022

@article{Entanglement_2022,

author = {S. K. Giri and G. C. Schatz},

title = {Manipulating Two-Photon Absorption of Molecules through Efficient Optimization of Entangled Light},

journal = {The Journal of Physical Chemistry Letters},

volume = {13},

number = {43},

pages = {10140-10146},

year = {2022},

doi = {10.1021/acs.jpclett.2c02842}

}

Controlling thermodynamics of a quantum heat engine with modulated amplitude drivings

Journal: Phys. Rev. E 106, 024131 (2022)

Author(s): S. K. Giri , and H. P. Goswami

DOI

arXiv

BibTeX

Spectroscopy 2022

@article{Spectroscopy_2022,

author = {Giri, Sajal Kumar and Goswami, Himangshu Prabal},

title = {Controlling thermodynamics of a quantum heat engine with modulated amplitude drivings},

journal = {Phys. Rev. E},

volume = {106},

issue = {2},

pages = {024131},

numpages = {8},

year = {2022},

month = {Aug},

publisher = {American Physical Society},

doi = {10.1103/PhysRevE.106.024131}

}

Perspectives for analyzing non-linear photo ionization spectra with deep neural networks trained with synthetic Hamilton matrices

Journal: Faraday Discuss. 228, 502 (2021)

Author(s): S. K. Giri , L. Alonso, U. Saalmann, and J. M. Rost

DOI

arXiv

BibTeX

ML 2021

@article{ML_2021,

author = {Giri, Sajal Kumar and Alonso, Lazaro and Saalmann, Ulf and Rost, Jan Michael},

title = {Perspectives for analyzing non-linear photo-ionization spectra with deep neural networks trained with synthetic Hamilton matrices},

journal = {Faraday Discuss.},

volume = {228},

issue = {0},

pages = {502-518},

year = {2021},

publisher = {The Royal Society of Chemistry},

doi = {10.1039/D0FD00117A}

}

Purifying electron spectra from noisy pulses with machine learning using synthetic Hamilton matrices

Journal: Phys. Rev. Lett. 124, 113201 (2020)

Author(s): S. K. Giri , U. Saalmann, and J. M. Rost

DOI

arXiv

BibTeX

ML 2020

@article{ML_2020,

author = {Giri, Sajal Kumar and Saalmann, Ulf and Rost, Jan M.},

title = {Purifying electron spectra from noisy pulses with machine learning using synthetic Hamilton matrices},

journal = {Phys. Rev. Lett.},

volume = {124},

issue = {11},

pages = {113201},

numpages = {6},

year = {2020},

month = {Mar},

publisher = {American Physical Society},

doi = {10.1103/PhysRevLett.124.113201}

}

Nonequilibrium fluctuations of a driven quantum heat engine via machine learning

Journal: Phys. Rev. E 99, 022104 (2019)

Author(s): S. K. Giri , and H. P. Goswami

DOI

arXiv

BibTeX

ML 2019

@article{ML_2019,

author = {Giri, Sajal Kumar and Goswami, Himangshu Prabal},

title = {Nonequilibrium fluctuations of a driven quantum heat engine via machine learning},

journal = {Phys. Rev. E},

volume = {99},

issue = {2},

pages = {022104},

numpages = {8},

year = {2019},

month = {Feb},

publisher = {American Physical Society},

doi = {10.1103/PhysRevE.99.022104}

}

Adiabatic passage to the continuum: Controlling ionization with chirped laser pulses

Journal: Phys. Rev. Lett. 121, 153203 (2018)

Author(s): U. Saalmann, S. K. Giri and J. M. Rost

DOI

arXiv

BibTeX

Spectroscopy 2018

@article{Spectroscopy_2018,

author = {Saalmann, Ulf and Giri, Sajal Kumar and Rost, Jan M.},

title = {Adiabatic passage to the continuum: Controlling ionization with chirped laser pulses},

journal = {Phys. Rev. Lett.},

volume = {121},

issue = {15},

pages = {153203},

numpages = {5},

year = {2018},

month = {Oct},

publisher = {American Physical Society},

doi = {10.1103/PhysRevLett.121.153203}

}

Geometric phaselike effects in a quantum heat engine

Journal: Phys. Rev. E 96, 052129 (2017)

Author(s): S. K. Giri , and H. P. Goswami

DOI

arXiv

BibTeX

Spectroscopy 2017

@article{Spectroscopy_2017,

author = {Giri, Sajal Kumar and Goswami, Himangshu Prabal},

title = {Geometric phaselike effects in a quantum heat engine},

journal = {Phys. Rev. E},

volume = {96},

issue = {5},

pages = {052129},

numpages = {7},

year = {2017},

month = {Nov},

publisher = {American Physical Society},

doi = {10.1103/PhysRevE.96.052129}

}

Single-photon ionization in intense, fluctuating pulses

Journal: J. Mod. Opt. 64, 1004 (2017)

Author(s): S. K. Giri , U. Saalmann, and J. M. Rost

DOI

Teaching

Machine Learning for Chemistry

Content

1. Introduction and Math Overview [2]

Introduction to ML Chemistry, Linear Algebra, Probability (Baye's Rule, Normal Distribution, and Likelihood Function)

2. Basics of Machine Learning [3]

Learning Algorithms, Linear Regression (Normal Equations, and LMS Algorithm)

3. Bias-Variance Tradeoff and Regularization [3]

Over/Under-ﬁtting, Bias-Variance Decomposition, Regularized Least Squares (LASSO and Ridge)

4. Neural Network [10]

Perceptron, Feedforward Network, Network Training, Error Backpropagation, Stochastic Gradient Descent (SGD)/Adam Algorithms, Regularization in Neural Networks and Generalization, Examples from Chemistry

5. Convolutional Neural Network (CNN) [5]

Basic Ideas, Convolution, Pooling, CNN Structure and Training, CNN in Chemistry

6. Graph Neural Network (GNN) for Molecules [5]

Graph Representation of Molecules, GNN Structure and Training, GNN in chemistry

7. High-Dimensional Neural Network Potentials [5]

Feature Extraction, Symmetry Functions, Energy and Force Calculations, Construction of High-Dimensional Neural Network Potentials

8. Generative Models for Molecules [5]

Inverse Design of Molecules, Variational Autoencoders, Generative Adversarial Networks

9. Kernal Methods for Quantum Chemistry [4]

Representations of Molecular Systems, Kernal Ridge Regression, Applications in Quantum Chemistry (Potential Energy Surface, Molecular Property Prediction)

Quantum Light-Matter

Recent News

Research

Quantum Light Meets Nonlinear Spectroscopy

AI for Quantum Light-Matter

Harnessing Entanglement in Exciton Dynamics

Plasmon-Molecule Strong Coupling

Publications

Filter by Year || Research

Switching of electrochemical selectivity due to plasmonic field-induced dissociation

Impact of classical and quantum light on donor-acceptor-donor molecules

Laser pulse induced second- and third-harmonic generation of gold nanorods with real-time time-dependent density functional tight binding (RT-TDDFTB) method

Roadmap on data-centric materials science

Plasmon-enhanced spectroscopy and photocatalysis

Colors of entangled two-photon absorption

Photodissociation of H2 on Ag and Au nanoparticles: Effect of size and plasmon versus interband transitions on threshold intensities for dissociation

Manipulating two-photon absorption of molecules through efficient optimization of entangled light

Controlling thermodynamics of a quantum heat engine with modulated amplitude drivings

Perspectives for analyzing non-linear photo ionization spectra with deep neural networks trained with synthetic Hamilton matrices

Purifying electron spectra from noisy pulses with machine learning using synthetic Hamilton matrices

Nonequilibrium fluctuations of a driven quantum heat engine via machine learning

Adiabatic passage to the continuum: Controlling ionization with chirped laser pulses

Geometric phaselike effects in a quantum heat engine

Single-photon ionization in intense, fluctuating pulses

Teaching

Machine Learning for Chemistry

Content

1. Introduction and Math Overview [2]

Introduction to ML Chemistry, Linear Algebra, Probability (Baye's Rule, Normal Distribution, and Likelihood Function)

2. Basics of Machine Learning [3]

Learning Algorithms, Linear Regression (Normal Equations, and LMS Algorithm)

3. Bias-Variance Tradeoff and Regularization [3]

Over/Under-ﬁtting, Bias-Variance Decomposition, Regularized Least Squares (LASSO and Ridge)

4. Neural Network [10]

Perceptron, Feedforward Network, Network Training, Error Backpropagation, Stochastic Gradient Descent (SGD)/Adam Algorithms, Regularization in Neural Networks and Generalization, Examples from Chemistry

5. Convolutional Neural Network (CNN) [5]

Basic Ideas, Convolution, Pooling, CNN Structure and Training, CNN in Chemistry

6. Graph Neural Network (GNN) for Molecules [5]

Graph Representation of Molecules, GNN Structure and Training, GNN in chemistry

7. High-Dimensional Neural Network Potentials [5]

Feature Extraction, Symmetry Functions, Energy and Force Calculations, Construction of High-Dimensional Neural Network Potentials

8. Generative Models for Molecules [5]

Inverse Design of Molecules, Variational Autoencoders, Generative Adversarial Networks

9. Kernal Methods for Quantum Chemistry [4]

Representations of Molecular Systems, Kernal Ridge Regression, Applications in Quantum Chemistry (Potential Energy Surface, Molecular Property Prediction)

References

1. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006

2. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016