Advances of machine learning in materials science: Ideas and techniques
In this big data era, the use of large dataset in conjunction with machine learning (ML) has
been increasingly popular in both industry and academia. In recent times, the field of …
been increasingly popular in both industry and academia. In recent times, the field of …
Fluctuations at Metal Halide Perovskite Grain Boundaries Create Transient Trap States: Machine Learning Assisted Ab Initio Analysis
All-inorganic perovskites are promising candidates for solar energy and optoelectronic
applications, despite their polycrystalline nature with a large density of grain boundaries …
applications, despite their polycrystalline nature with a large density of grain boundaries …
Extending the time scales of nonadiabatic molecular dynamics via machine learning in the time domain
AV Akimov - The Journal of Physical Chemistry Letters, 2021 - ACS Publications
A novel methodology for direct modeling of long-time scale nonadiabatic dynamics in
extended nanoscale and solid-state systems is developed. The presented approach …
extended nanoscale and solid-state systems is developed. The presented approach …
Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Bidirectional Long Short-Term Memory Networks
Essential for understanding far-from-equilibrium processes, nonadiabatic (NA) molecular
dynamics (MD) requires expensive calculations of the excitation energies and NA couplings …
dynamics (MD) requires expensive calculations of the excitation energies and NA couplings …
Photocatalytic activity of dual defect modified graphitic carbon nitride is robust to tautomerism: machine learning assisted ab initio quantum dynamics
Two-dimensional graphitic carbon nitride (GCN) is a popular metal-free polymer for
sustainable energy applications due to its unique structure and semiconductor properties …
sustainable energy applications due to its unique structure and semiconductor properties …
Interpolating nonadiabatic molecular dynamics hamiltonian with inverse fast fourier transform
Nonadiabatic (NA) molecular dynamics (MD) allows one to investigate far-from-equilibrium
processes in nanoscale and molecular materials at the atomistic level and in the time …
processes in nanoscale and molecular materials at the atomistic level and in the time …
Solving kernel ridge regression with gradient-based optimization methods
Kernel ridge regression, KRR, is a non-linear generalization of linear ridge regression. Here,
we introduce an equivalent formulation of the objective function of KRR, opening up both for …
we introduce an equivalent formulation of the objective function of KRR, opening up both for …
Recent advances in machine learning for electronic excited state molecular dynamics simulations
B Bachmair, MM Reiner, MX Tiefenbacher… - 2022 - books.rsc.org
Machine learning has proven useful in countless different areas over the past years,
including theoretical and computational chemistry, where various issues can be addressed …
including theoretical and computational chemistry, where various issues can be addressed …
Interpolating Moving Ridge Regression (IMRR): A machine learning algorithm to predict energy gradients for ab initio molecular dynamics simulations
K Fujioka, R Sun - Chemical Physics, 2022 - Elsevier
Ab initio molecular dynamics (AIMD) simulations are a direct way to visualize chemical
reactions and help elucidate non-statistical dynamics that does not follow the intrinsic …
reactions and help elucidate non-statistical dynamics that does not follow the intrinsic …
A Comprehensive Machine Learning Based Modeling of Income Tax Collection.
N Chung, TM Thom, TT Tran… - International …, 2024 - search.ebscohost.com
Income tax is one of the important sources of revenue for each country, income tax
forecasting is thus one of the important tasks of each country. This work presents a machine …
forecasting is thus one of the important tasks of each country. This work presents a machine …