Sergei Manzhos

About Sergei Manzhos

Sergei Manzhos, With an exceptional h-index of 46 and a recent h-index of 36 (since 2020), a distinguished researcher at Tokyo Institute of Technology, specializes in the field of machine learning, solar cells, fuel cells, computational spectroscopy, large-scale ab initio.

His recent articles reflect a diverse array of research interests and contributions to the field:

Natural-like generation of grain boundary models and the combined effects of microstructural elements and lithiation on the plastic behavior of TiO2: A computational study

N‐DMBI Doping of Carbon Nanotube Yarns for Achieving High n‐Type Thermoelectric Power Factor and Figure of Merit

Machine learning of properties of lead-free perovskites with a neural network with additive kernel regression-based neuron activation functions

Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension

Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization

Atoms, dimers, and nanoparticles from orbital-free density-potential-functional theory

Rectangularization of Gaussian process regression for optimization of hyperparameters

Neural network with optimal neuron activation functions based on additive Gaussian process regression

Sergei Manzhos Information

University

Position

Associate Professor

Citations(all)

7305

Citations(since 2020)

4869

Cited By

4285

hIndex(all)

46

hIndex(since 2020)

36

i10Index(all)

155

i10Index(since 2020)

127

Email

University Profile Page

Google Scholar

Sergei Manzhos Skills & Research Interests

machine learning

solar cells

fuel cells

computational spectroscopy

large-scale ab initio

Top articles of Sergei Manzhos

Natural-like generation of grain boundary models and the combined effects of microstructural elements and lithiation on the plastic behavior of TiO2: A computational study

Computational Materials Science

2024/4/25

Hao Wang
Hao Wang

H-Index: 29

Sergei Manzhos
Sergei Manzhos

H-Index: 31

N‐DMBI Doping of Carbon Nanotube Yarns for Achieving High n‐Type Thermoelectric Power Factor and Figure of Merit

Small Methods

2024/3/12

Machine learning of properties of lead-free perovskites with a neural network with additive kernel regression-based neuron activation functions

MRS Advances

2024/1/17

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension

The Journal of Chemical Physics

2024/1/14

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization

Artificial Intelligence Chemistry

2023/12/1

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Atoms, dimers, and nanoparticles from orbital-free density-potential-functional theory

Physical Review A

2023/12/1

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Rectangularization of Gaussian process regression for optimization of hyperparameters

Machine Learning with Applications

2023/9/15

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Neural network with optimal neuron activation functions based on additive Gaussian process regression

The Journal of Physical Chemistry A

2023/9/12

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Orders of coupling representations as a versatile framework for machine learning from sparse data in high-dimensional spaces

2023/7/17

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Hybrid Density Functional Tight Binding (DFTB)─ Molecular Mechanics Approach for a Low-Cost Expansion of DFTB Applicability

Journal of Chemical Theory and Computation

2023/7/14

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Tetramethylammonium Iodide Additive for Enhancing the Charge Carrier Mobilities of Diketopyrrolopyrrole‐Based Conjugated Polymer in Ambipolar Organic Field‐Effect Transistors

Chinese Journal of Chemistry

2023

Factors affecting the techno-economic and environmental performance of on-grid distributed hydrogen energy storage systems with solar panels

Energy

2023/4/15

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Liquid‐State Dithiocarbonate‐Based Polymeric Additives with Monodispersity Rendering Perovskite Solar Cells with Exceptionally High Certified Photocurrent and Fill Factor

Advanced Energy Materials

2023/4

Using Collocation to Solve the Schrödinger Equation

2023/3/8

Sergei Manzhos
Sergei Manzhos

H-Index: 31

A local Gaussian Processes method for fitting potential surfaces that obviates the need to invert large matrices

Journal of Molecular Spectroscopy

2023/3/1

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Non-invasive improvement of machining by reversible electrochemical doping: A proof of principle with computational modeling on the example of lithiation of TiO2

Materials Chemistry and Physics

2023/2/1

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Hao Wang
Hao Wang

H-Index: 29

The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces

The Journal of Chemical Physics

2023/1/28

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Nano-scale smooth surface of the compact-TiO 2 layer via spray pyrolysis for controlling the grain size of the perovskite layer in perovskite solar cells

RSC advances

2023

Sergei Manzhos
Sergei Manzhos

H-Index: 31

Surface engineering of two-dimensional hexagonal boron-nitride for optoelectronic devices

Nanoscale

2023

Hybrid models combining neural networks (NN), Gaussian process regressions (GPR), and high-dimensional model representations (HDMR) for more powerful machine learning We show …

2023

Sergei Manzhos
Sergei Manzhos

H-Index: 31

See List of Professors in Sergei Manzhos University(Tokyo Institute of Technology)

Co-Authors

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