9 research outputs found
MOESM1 of Role of linkage structures in supply chain for managing greenhouse gas emissions
Additional file 1. Numerical examples with Leontief inverse and List of sector names
Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models-0
<p><b>Copyright information:</b></p><p>Taken from "Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models"</p><p>http://www.biomedcentral.com/1752-0509/1/51</p><p>BMC Systems Biology 2007;1():51-51.</p><p>Published online 16 Nov 2007</p><p>PMCID:PMC2233642.</p><p></p>non-zeros in , perturbation size 1%, SNR 100. Three initial random experiments, to reduce memory requirements in [3] method. : [3], experimental design. : [3], random experiments. : Our method, Laplace prior, experimental design. : Our method, Laplace prior, random experiments
Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models-1
<p><b>Copyright information:</b></p><p>Taken from "Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models"</p><p>http://www.biomedcentral.com/1752-0509/1/51</p><p>BMC Systems Biology 2007;1():51-51.</p><p>Published online 16 Nov 2007</p><p>PMCID:PMC2233642.</p><p></p>non-zeros in , perturbation size 1%, SNR 100. Three initial random experiments, to reduce memory requirements in [3] method. : [3], experimental design. : [3], random experiments. : Our method, Laplace prior, experimental design. : Our method, Laplace prior, random experiments
QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization
To obtain observable
physical or molecular properties
such as ionization
potential and fluorescent wavelength with quantum chemical (QC) computation,
multi-step computation manipulated by a human is required. Hence,
automating the multi-step computational process and making it a black
box that can be handled by anybody are important for effective database
construction and fast realistic material design through the framework
of black-box optimization where machine learning algorithms are introduced
as a predictor. Here, we propose a Python library, QCforever, to automate
the computation of some molecular properties and chemical phenomena
induced by molecules. This tool just requires a molecule file for
providing its observable properties, automating the computation process
of molecular properties (for ionization potential, fluorescence, etc.)
and output analysis for providing their multi-values for evaluating
a molecule. Incorporating the tool in black-box optimization, we can
explore molecules that have properties we desired within the limitation
of QC computation
Dataset for Crystal Structure Prediction
This dataset has initial structures and results of local optimization steps for crystalline systems Si, NaCl, Y2Co17, Al2O3, and GaAs
Quantum Annealing Designs Nonhemolytic Antimicrobial Peptides in a Discrete Latent Space
Increasing the variety
of antimicrobial peptides is crucial in
meeting the global challenge of multi-drug-resistant bacterial pathogens.
While several deep-learning-based peptide design pipelines are reported,
they may not be optimal in data efficiency. High efficiency requires
a well-compressed latent space, where optimization is likely to fail
due to numerous local minima. We present a multi-objective peptide
design pipeline based on a discrete latent space and D-Wave quantum
annealer with the aim of solving the local minima problem. To achieve
multi-objective optimization, multiple peptide properties are encoded
into a score using non-dominated sorting. Our pipeline is applied
to design therapeutic peptides that are antimicrobial and non-hemolytic
at the same time. From 200 000 peptides designed by our pipeline,
four peptides proceeded to wet-lab validation. Three of them showed
high anti-microbial activity, and two are non-hemolytic. Our results
demonstrate how quantum-based optimizers can be taken advantage of
in real-world medical studies
MDTS: automatic complex materials design using Monte Carlo tree search
<p>Complex materials design is often represented as a black-box combinatorial optimization problem. In this paper, we present a novel python library called MDTS (Materials Design using Tree Search). Our algorithm employs a Monte Carlo tree search approach, which has shown exceptional performance in computer Go game. Unlike evolutionary algorithms that require user intervention to set parameters appropriately, MDTS has no tuning parameters and works autonomously in various problems. In comparison to a Bayesian optimization package, our algorithm showed competitive search efficiency and superior scalability. We succeeded in designing large Silicon-Germanium (Si-Ge) alloy structures that Bayesian optimization could not deal with due to excessive computational cost. MDTS is available at <a href="https://github.com/tsudalab/MDTS" target="_blank">https://github.com/tsudalab/MDTS</a>.</p
Koopmans’ Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules
Density functional theory (DFT) is a significant computational
tool that has substantially influenced chemistry, physics, and materials
science. DFT necessitates parametrized approximation for determining
an expected value. Hence, to predict the properties of a given molecule
using DFT, appropriate parameters of the functional should be set
for each molecule. Herein, we optimize the parameters of range-separated
functionals (LC-BLYP and CAM-B3LYP) via Bayesian optimization (BO)
to satisfy Koopmans’ theorem. Our results demonstrate the effectiveness
of the BO in optimizing functional parameters. Particularly, Koopmans’
theorem-compliant LC-BLYP (KTLC-BLYP) shows results comparable to
the experimental UV-absorption values. Furthermore, we prepared an
optimized parameter dataset of KTLC-BLYP for over 3000 molecules through
BO for satisfying Koopmans’ theorem. We have developed a machine
learning model on this dataset to predict the parameters of the LC-BLYP
functional for a given molecule. The prediction model automatically
predicts the appropriate parameters for a given molecule and calculates
the corresponding values. The approach in this paper would be useful
to develop new functionals and to update the previously developed
functionals
Koopmans’ Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules
Density functional theory (DFT) is a significant computational
tool that has substantially influenced chemistry, physics, and materials
science. DFT necessitates parametrized approximation for determining
an expected value. Hence, to predict the properties of a given molecule
using DFT, appropriate parameters of the functional should be set
for each molecule. Herein, we optimize the parameters of range-separated
functionals (LC-BLYP and CAM-B3LYP) via Bayesian optimization (BO)
to satisfy Koopmans’ theorem. Our results demonstrate the effectiveness
of the BO in optimizing functional parameters. Particularly, Koopmans’
theorem-compliant LC-BLYP (KTLC-BLYP) shows results comparable to
the experimental UV-absorption values. Furthermore, we prepared an
optimized parameter dataset of KTLC-BLYP for over 3000 molecules through
BO for satisfying Koopmans’ theorem. We have developed a machine
learning model on this dataset to predict the parameters of the LC-BLYP
functional for a given molecule. The prediction model automatically
predicts the appropriate parameters for a given molecule and calculates
the corresponding values. The approach in this paper would be useful
to develop new functionals and to update the previously developed
functionals