Milind Malshe
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Ab initio potential-energy surfaces for complex, multichannel systems using modified novelty sampling and feedforward neural networks
LM Raff, M Malshe, M Hagan, DI Doughan, MG Rockley, R Komanduri
The Journal of chemical physics 122 (8), 2005
Neural networks in chemical reaction dynamics
L Raff
OUP USA, 2012
Dynamic layer rearrangement during growth of layered oxide films by molecular beam epitaxy
JH Lee, G Luo, IC Tung, SH Chang, Z Luo, M Malshe, M Gadre, ...
Nature materials 13 (9), 879-883, 2014
Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks
A Pukrittayakamee, M Malshe, M Hagan, LM Raff, R Narulkar, ...
The Journal of chemical physics 130 (13), 2009
Linear affine transformations between 3-lead (Frank XYZ leads) vectorcardiogram and 12-lead electrocardiogram signals
D Dawson, H Yang, M Malshe, STS Bukkapatnam, B Benjamin, ...
Journal of electrocardiology 42 (6), 622-630, 2009
Development of generalized potential-energy surfaces using many-body expansions, neural networks, and moiety energy approximations
M Malshe, R Narulkar, LM Raff, M Hagan, S Bukkapatnam, PM Agrawal, ...
The Journal of chemical physics 130 (18), 2009
Theoretical investigation of the dissociation dynamics of vibrationally excited vinyl bromide on an ab initio potential-energy surface obtained using modified novelty sampling …
M Malshe, LM Raff, MG Rockley, M Hagan, PM Agrawal, R Komanduri
The Journal of chemical physics 127 (13), 2007
Parametrization of analytic interatomic potential functions using neural networks
M Malshe, R Narulkar, LM Raff, M Hagan, S Bukkapatnam, R Komanduri
The Journal of chemical physics 129 (4), 2008
Parametrization of interatomic potential functions using a genetic algorithm accelerated with a neural network
S Bukkapatnam, M Malshe, PM Agrawal, LM Raff, R Komanduri
Physical Review B—Condensed Matter and Materials Physics 74 (22), 224102, 2006
Classification of atrial fibrillation episodes from sparse electrocardiogram data
S Bukkapatnam, R Komanduri, H Yang, P Rao, WC Lih, M Malshe, ...
Journal of Electrocardiology 41 (4), 292-299, 2008
Accurate prediction of higher-level electronic structure energies for large databases using neural networks, Hartree–Fock energies, and small subsets of the database
M Malshe, A Pukrittayakamee, LM Raff, M Hagan, S Bukkapatnam, ...
The Journal of chemical physics 131 (12), 2009
A self-starting method for obtaining analytic potential-energy surfaces from ab initio electronic structure calculations
PM Agrawal, M Malshe, R Narulkar, LM Raff, M Hagan, S Bukkapatnum, ...
The Journal of Physical Chemistry A 113 (5), 869-877, 2009
Recurrence quantification analysis and principal components in the detection of myocardial infarction from vectorcardiogram signals
H Yang, M Malshe, STS Bukkapatnam, R Komanduri
Proc. of the 3rd INFORMS Workshop on Data Mining and Health Informatics, 2008
Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases
M Malshe, LM Raff, M Hagan, S Bukkapatnam, R Komanduri
The Journal of chemical physics 132 (20), 2010
Self-supervised Learning Approach to Detect Corrosion Products in Biofilm images
MS Vidya Bommanapally, M. Ashaduzzman, Milind Malshe, Parvathi Chundi
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 3555 …, 2021
An AI-based approach for detecting cells and microbial byproducts in low volume scanning electron microscope images of biofilms
MS Dilanga Abeyrathna, Md Ashaduzzaman, Milind Malshe, Jawaharraj Kalimuthu ...
Frontiers in Microbiology 13, 2022
Machine Learning in 2D Materials Science
P Chundi, V Gadhamshetty, BK Jasthi, C Lushbough
CRC Press, 2023
Ab Initio Molecular Dynamics (AIMD)-a New Approach for Development of Accurate Potentials
MM Malshe
Oklahoma State University, 2004
In-situ Surface X-ray Diffraction Study of Ruddlesden-Popper Series Thin Film Growth
JH Lee, SH Chang, Z Luo, I Tung, M Malshe, J Jellinek, J Eastman, ...
APS March Meeting Abstracts 2013, Y11. 011, 2013
Development of Interatomic Potential Functions Based on Ab Initio Methods and Neural Networks for Molecular Dynamics Simulations
MM Malshe
Oklahoma State University, 2009
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