117 citations · 1.1k across the 40 of their papers we have counts for
40 papers
Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension
Sergei Manzhos, Manabu Ihara
Kernel methods such as kernel ridge regression and Gaussian process regressions with Matern type kernels have been increasingly used, in particular, to fit potential energy surface…
Machine learning of kinetic energy densities with target and feature averaging: better results with fewer training data
Sergei Manzhos, Johann Lüder, Manabu Ihara
Machine learning of kinetic energy functionals (KEF), in particular kinetic energy density (KED) functionals, has recently attracted attention as a promising way to construct KEFs…
Atoms, dimers, and nanoparticles from orbital-free density-potential functional theory
Martin-Isbjörn Trappe, William C. Witt, Sergei Manzhos
Density-potential functional theory (DPFT) is an alternative formulation of orbital-free density functional theory that may be suitable for modeling the electronic structure of lar…
Orders-of-coupling representation with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization
Sergei Manzhos, Manabu Ihara
Representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates (corresponding of different orders of coupling) are useful i…
Neural network with optimal neuron activation functions based on additive Gaussian process regression
Sergei Manzhos, Manabu Ihara
Feed-forward neural networks (NN) are a staple machine learning method widely used in many areas of science and technology. While even a single-hidden layer NN is a universal appro…
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
Sergei Manzhos, Manabu Ihara
Kernel based methods including Gaussian process regression (GPR) and generally kernel ridge regression (KRR) have been finding increasing use in computational chemistry, including…
Non-invasive improvement of machining by reversible electrochemical doping: a proof of principle with computational modeling
Anastassia Sorkin, Yunfa Guo, Manabu Ihara +2
We propose that the machinability of hard ceramics can be improved by reversible electrochemical doping. On the example of TiO2, we show in a combined density functional theory-mol…
On the optimization of hyperparameters in Gaussian process regression with the help of low-order high-dimensional model representation
Sergei Manzhos, Manabu Ihara
When the data are sparse, optimization of hyperparameters of the kernel in Gaussian process regression by the commonly used maximum likelihood estimation (MLE) criterion often lead…
Rectangularization of Gaussian process regression for optimization of hyperparameters
Sergei Manzhos, Manabu Ihara
Gaussian process regression (GPR) is a powerful machine learning method which has recently enjoyed wider use, in particular in physical sciences. In its original formulation, GPR u…
Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data
Eita Sasaki, Manabu Ihara, Sergei Manzhos
We show that Gaussian process regression (GPR) allows representing multivariate functions with low-dimensional terms via kernel design. When using a kernel built with HDMR (High-di…
Effect of a weak magnetic field on ductile-brittle transition in micro-cutting of single-crystal calcium fluoride
Yunfa Guo, Yan Jin Lee, Yu Zhang +3
Magneto-plasticity occurs when a weak magnetic field alters material plasticity and offers a viable solution to enhance ductile-mode cutting of brittle materials. This study demons…
Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method
Owen Ren, Mohamed Ali Boussaidi, Dmitry Voytsekhovsky +2
We present a Python implementation for RS-HDMR-GPR (Random Sampling High Dimensional Model Representation Gaussian Process Regression). The method builds representations of multiva…
Non-parametric Local Pseudopotentials with Machine Learning: a Tin Pseudopotential Built Using Gaussian Process Regression
Johann Lueder, Sergei Manzhos
We present novel non-parametric representation math for local pseudopotentials (PP) based on Gaussian Process Regression (GPR). Local pseudopotentials are needed for materials simu…
Data-driven kinetic energy density fitting for orbital-free DFT: linear vs Gaussian process regression
Sergei Manzhos, Pavlo Golub
We study the dependence of kinetic energy densities (KED) on density-dependent variables that have been suggested in previous works on kinetic energy functionals (KEF) for orbital-…
Lithium attachment to C60 and nitrogen- and boron-doped C60: a mechanistic study
Yingqian Chen, Chae-Ryong Cho, Sergei Manzhos
Fullerene-based materials including C60 and doped C60 have previously been proposed as anodes for lithium ion batteries. It was also shown earlier that n- and p-doping of small mol…
Revisiting Backbonding: The Influence of Orbitals on Metal-CO Bonds and Ligand Red Shifts
Daniel Koch, Yingqian Chen, Pavlo Golub +1
The concept of backbonding is widely used to explain the complex stabilities and CO stretch frequency red shifts of transition metal carbonyls. We theoretically investigate a n…
Machine learning optimization of the collocation point set for solving the Kohn-Sham equation
Jonas Ku, Aditya Kamath, Tucker Carrington +1
The rectangular collocation approach makes it possible to solve the Schrödinger equation with basis functions that do not have amplitude in all regions in which wavefunctions have…
A Scheme for Ultrasensitive Detection of Molecules by Using Vibrational Spectroscopy in Combination with Signal Processing
Tay Yong Boon, Ian Tay Rongde, Loy Liang Yi +3
We show that combining vibrational spectroscopy with signal processing can result in a scheme for ultrasensitive detection of molecules. We consider the vibrational spectrum as a s…
Using rectangular collocation with finite difference derivatives to solve electronic Schrodinger equation
Sergei Manzhos, Tucker Carrington
We show that a rectangular collocation method, equivalent to evaluating all matrix elements with a quadrature-like scheme and using more points than basis functions, is an effectiv…
Stability of charges in titanium compounds and charge transfer to oxygen in titanium dioxide
Daniel Koch, Pavlo Golub, Sergei Manzhos
We investigate the charge density distribution in titanium dioxide, molecular titanium complexes and a variety of periodic titanium compounds using delocalization indices and Bader…
Comparison of three methods to compute optical absorption spectra of organic molecules and solids
Ang Siong Tuan, Amrita Pal, Sergei Manzhos
We compare the performance of an approach using real frequency dependent polarizability to compute optical absorption spectra to linear-response time-dependent density functional t…
Kinetic energy densities based on the fourth order gradient expansion: performance in different classes of materials and improvement via machine learning
Pavlo Golub, Sergei Manzhos
We study the performance of fourth-order gradient expansions of the kinetic energy density (KED) in semi-local kinetic energy functionals depending on the density-dependent variabl…
Influence of the aggregate state on band structure and optical properties of C60 computed with different methods
Amrita Pal, Saeid Arabnejad, Koichi Yamashita +1
C60 and C60 based molecules are efficient acceptor and electron transport layers for planar perovskite solar cells. While properties of these molecules are well studied by ab initi…
Dopant-Dopant Interactions in Beryllium doped Indium Gallium Arsenide: an Ab Initio Study
Vadym Kulish, Wenyuan Liu, Francis Benistant +1
We present an ab initio study of dopant-dopant interactions in beryllium-doped InGaAs. We consider defect formation energies of various interstitial and substitutional defects and…