Nanoscale Quantum Light-Matter

Welcome to my homepage! I am Sajal K. Giri, currently serving as a postdoctoral fellow in the research group of Prof. George C. Schatz. Our research primarily focuses on the study of classical and quantum light-induced dynamical processes that take place in atoms, molecules, and nanoscale systems by developing theories and implementing computational methods. A significant part of our research deals with the development of machine learning (ML) methods to model the dynamics and spectroscopic aspects of such light-induced processes.

Profile

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

Research Topics

Can ML be used to gain insights into light-matter interactions beyond accelerating predictions?

Quantum light spectroscopy of molecules

Plasmon enhanced photocatalysis and spectroscopy

Charge and energy transfer at metal-molecule interface

Research

Machine Learning Light-Matter

The understanding of light-induced dynamics in molecules and nanoscale systems is often limited due to their inherent complexities. Are all these complexities essential that we take into consideration for solving the dynamics or provide enough transparency to gain insights? If there exists a subspace with minimal complexities encoding the essential dynamics then the problem is simplified significantly. But how do we explore that space if it exists at all? We combine quantum dynamics with machine learning methods to address this point.

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Quantum Light-Matter

We explore quantum advantages in spectroscopic problems of molecules under full quantum description i.e., treating both system 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. Further, we study formation polaritonic states through strong coupling between plasmons and molecules, and their effects on spectroscopy, transport, and photocatalysis. To this end, we develope theoretical methods to explore entangled light induced dynamics in molecules modeling the quantum state of light and their interactions with molecules.

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Plasmon Light-Matter

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. We develope theory and model to elucidate such complex dynamics, including plasmon-driven chemical reactions, surface-enhanced Raman scattering (SERS), and harmonic generation processes.

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Quantum Transport

A nontrivial geometric phase emerges in quantum transport dynamics as a consequence of external periodic drivings. We explore the effect of this phase in transport processes and importantly control them with external drivings through effective modeling of the dynamics.

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Publications

Sort 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]

DOI

BibTeX

Entanglement 2024

@article{Entanglement_2024,

}

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

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},

url = {https://doi.org/10.1063/5.0216887},

eprint = {https://pubs.aip.org/aip/jcp/article-pdf/doi/10.1063/5.0216887/20069728/044703\_1\_5.0216887.pdf}

}

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

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

BibTeX

Plasmon 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

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

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

BibTeX

Transport 2022

@article{Transport_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

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

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

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

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

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)

Nanoscale Quantum Light-Matter

Profile

Research Topics

Research

Machine Learning Light-Matter

Quantum Light-Matter

Plasmon Light-Matter

Quantum Transport

Publications

Sort 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