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Academia

Comparison between CE-TinNet and LCNN November 2, 2021 5 minute read

Overview

The origin of cluster expansions can be traced back to the early 1950s, when Kikuchi developed an Ising model-based cluster variation method to study order-disorder phenomena. In 1984, Sanchez et al. developed a general formalism for the description of configurational cluster expansions in terms of a complete basis set expansion. In simple terms, CE decomposes the energy of a configuration into one-body, two-body, and higher-order interaction terms (‘clusters’), and each term has a corresponding weight called effective cluster interaction (ECI) analogous to the interaction strength. The energy can be exactly reproduced only if all clusters are included in... read more

CO2 electroreduction on Bismuth with Ionic Liquids October 1, 2021 2 minute read

Introduction

Electrochemical reduction of CO2 has attracted researchers’ attention as it has the potential to utilize the abundant greenhouse gas in the Earth’s atmosphere and store intermittent energy from solar panels and wind turbines in chemical bonds. Many metals show activities in reducing CO2 in aqueous phase. However, with the competition of the hydrogen evolution reaction, the selectivity is poor towards valued chemicals. Also, the high overpotential needed to drive the reactions causes low efficiencies that inhibits the practical applications.

CO2 cycle

Many metals show CO22 reduction activity

read more
CE-TinNet September 1, 2021 3 minute read

Dependencies

  • TinNet
  • The Alloy-Theoretic Automated Toolkit (ATAT)
  • PyTorch (v1.3.1)
  • Pymatgen
  • Python3
  • Numpy

Background

Surface properties of materials are important for catalysis applications. Recent advances in computing power make density functional theory (DFT) to be possible to gain insights into the reactions happening on the catalyst surfaces. However, the surface environments are way more complex and adsorbates on surfaces can have many configurations. One possible way to solve this issue is by employing the cluster expansion to consider the adsorbate-adsorbate interactions.

Cluster expansion

Cluster expansion is developed based on the Ising... read more

Ammonia

Prospective and Economic Analysis for solar fertilizers April 28, 2021 6 minute read

Ammonia has an annual global production of more than 170 million metric tons, 85% of which are further processed to Nitrogen based fertilizers (Pattabathula and Richardson 2016). N fertilizers are estimated to feed half of the global population (Zhang et al. 2015). Thus, ammonia plays a key role in feeding the sustaining growing human population and helps solve the world hunger issue. In the early 1900s, Haber and Bosch invented a thermocatalytic process to synthesize ammonia from nitrogen and hydrogen, which is well known as the Haber-Bosch process right now, and it is currently the only industrialized process to produce... read more

Aspen Plus

Prospective and Economic Analysis for solar fertilizers April 28, 2021 6 minute read

Ammonia has an annual global production of more than 170 million metric tons, 85% of which are further processed to Nitrogen based fertilizers (Pattabathula and Richardson 2016). N fertilizers are estimated to feed half of the global population (Zhang et al. 2015). Thus, ammonia plays a key role in feeding the sustaining growing human population and helps solve the world hunger issue. In the early 1900s, Haber and Bosch invented a thermocatalytic process to synthesize ammonia from nitrogen and hydrogen, which is well known as the Haber-Bosch process right now, and it is currently the only industrialized process to produce... read more

Bash

Bash tools February 1, 2021 4 minute read

This is a tool to query the current running jobs in the server’s queue. It has the function that you can directly obtain more info than the ‘squeue’ command, which is the popular command for the slurm workload manager.

Key features:

  • Show the RunTime and TimeLimit as the same time, which helps you to get your job status.
  • Show the job’s running directory, which can be directly accessed. It can save lots of time to find the job’s directory when you are running multiple jobs
#!/usr/bin/env bash if [ “$SYSNAME”... read more

Catalysis

CO2 electroreduction on Bismuth with Ionic Liquids October 1, 2021 2 minute read

Introduction

Electrochemical reduction of CO2 has attracted researchers’ attention as it has the potential to utilize the abundant greenhouse gas in the Earth’s atmosphere and store intermittent energy from solar panels and wind turbines in chemical bonds. Many metals show activities in reducing CO2 in aqueous phase. However, with the competition of the hydrogen evolution reaction, the selectivity is poor towards valued chemicals. Also, the high overpotential needed to drive the reactions causes low efficiencies that inhibits the practical applications.

CO2 cycle

Many metals show CO22 reduction activity

read more
Data Mining for Catalyst Materials August 1, 2021 6 minute read

Overview

Catalyst materials are playing important roles in the whole world. Catalysts are composed of different chemical species, leading to the different catalytic performances. While catalysts are often composed of a single kind of metal, they can also be a combination of several or even more than five kinds of different metals. So the whole possible catalyst space is huge, which means it’s extremely hard to directly synthesize each of them and tell which one performs better for a specific case. Thus, machine learning methods step in to help screen the catalysts, but ML methods usually require large data... read more

Prospective and Economic Analysis for solar fertilizers April 28, 2021 6 minute read

Ammonia has an annual global production of more than 170 million metric tons, 85% of which are further processed to Nitrogen based fertilizers (Pattabathula and Richardson 2016). N fertilizers are estimated to feed half of the global population (Zhang et al. 2015). Thus, ammonia plays a key role in feeding the sustaining growing human population and helps solve the world hunger issue. In the early 1900s, Haber and Bosch invented a thermocatalytic process to synthesize ammonia from nitrogen and hydrogen, which is well known as the Haber-Bosch process right now, and it is currently the only industrialized process to produce... read more

Cluster Expansion

Comparison between CE-TinNet and LCNN November 2, 2021 5 minute read

Overview

The origin of cluster expansions can be traced back to the early 1950s, when Kikuchi developed an Ising model-based cluster variation method to study order-disorder phenomena. In 1984, Sanchez et al. developed a general formalism for the description of configurational cluster expansions in terms of a complete basis set expansion. In simple terms, CE decomposes the energy of a configuration into one-body, two-body, and higher-order interaction terms (‘clusters’), and each term has a corresponding weight called effective cluster interaction (ECI) analogous to the interaction strength. The energy can be exactly reproduced only if all clusters are included in... read more

Data

Data Mining for Catalyst Materials August 1, 2021 6 minute read

Overview

Catalyst materials are playing important roles in the whole world. Catalysts are composed of different chemical species, leading to the different catalytic performances. While catalysts are often composed of a single kind of metal, they can also be a combination of several or even more than five kinds of different metals. So the whole possible catalyst space is huge, which means it’s extremely hard to directly synthesize each of them and tell which one performs better for a specific case. Thus, machine learning methods step in to help screen the catalysts, but ML methods usually require large data... read more

Economic analysis

Prospective and Economic Analysis for solar fertilizers April 28, 2021 6 minute read

Ammonia has an annual global production of more than 170 million metric tons, 85% of which are further processed to Nitrogen based fertilizers (Pattabathula and Richardson 2016). N fertilizers are estimated to feed half of the global population (Zhang et al. 2015). Thus, ammonia plays a key role in feeding the sustaining growing human population and helps solve the world hunger issue. In the early 1900s, Haber and Bosch invented a thermocatalytic process to synthesize ammonia from nitrogen and hydrogen, which is well known as the Haber-Bosch process right now, and it is currently the only industrialized process to produce... read more

Hartree Fock

Calculating Hartree Fock Energy of Hydrogen Using Python October 2, 2020 12 minute read

Overview

The lattice convolutional neural network (LCNN) is a Python library for deep learning of lattice system developed by the Vlachos group at the University of Delaware and Jung group at KAIST. The model has been built in hope to improve upon cluster expansion methods, linear regression based on the clusters in the lattice. The LCNN performs better than the cluster expansion and cluster expansion based machine learning methods with sufficient number of data points.

I employed this LCNN to train the Cluster expansion model and then compare with the CE-TinNet model.

Import needed libraries

read more

Kinetics

Kinetic Monte Carlo and Copper surface evolution April 1, 2019 2 minute read

Introduction

Electrochemical reduction of CO_$2$ has attracted researchers’ attention as it has the potential to utilize the abundant greenhouse gas in the Earth’s atmosphere and store intermittent energy from solar panels and wind turbines in chemical bonds. A commercially viable catalyst for CO2 electroreduction should meet the cost-effective requirement while possessing high efficiency and selectivity. Experimental studies showed that Copper (Cu) can catalyze direct electrochemical reduction of CO2 to hydrocarbons in an aqueous bicarbonate solution with a high current density. Some Studies also showed there was a surface evolution of Cu surface under the reducing potential.

read more

Machine Learning

Comparison between CE-TinNet and LCNN November 2, 2021 5 minute read

Overview

The origin of cluster expansions can be traced back to the early 1950s, when Kikuchi developed an Ising model-based cluster variation method to study order-disorder phenomena. In 1984, Sanchez et al. developed a general formalism for the description of configurational cluster expansions in terms of a complete basis set expansion. In simple terms, CE decomposes the energy of a configuration into one-body, two-body, and higher-order interaction terms (‘clusters’), and each term has a corresponding weight called effective cluster interaction (ECI) analogous to the interaction strength. The energy can be exactly reproduced only if all clusters are included in... read more

CE-TinNet September 1, 2021 3 minute read

Dependencies

  • TinNet
  • The Alloy-Theoretic Automated Toolkit (ATAT)
  • PyTorch (v1.3.1)
  • Pymatgen
  • Python3
  • Numpy

Background

Surface properties of materials are important for catalysis applications. Recent advances in computing power make density functional theory (DFT) to be possible to gain insights into the reactions happening on the catalyst surfaces. However, the surface environments are way more complex and adsorbates on surfaces can have many configurations. One possible way to solve this issue is by employing the cluster expansion to consider the adsorbate-adsorbate interactions.

Cluster expansion

Cluster expansion is developed based on the Ising... read more

Predicting material properties using transfer learning February 1, 2020 4 minute read

Background

The evolution of the research workflow in computational chemistry is about 3 generations. The first-generation approach is to calculate the physical properties of an input structure, which is often performed via an approximation to the Schrödinger equation 
For example, Density functional theory. In the second-generation approach, by using global optimization (for example, an evolutionary algorithm), an input of chemical composition is mapped to an output that contains predictions of the structure or ensemble of structures that the combination of elements are likely to adopt. The emerging third-generation approach is to use machine-learning techniques with the ability to predict... read more

Neural networks

CE-TinNet September 1, 2021 3 minute read

Dependencies

  • TinNet
  • The Alloy-Theoretic Automated Toolkit (ATAT)
  • PyTorch (v1.3.1)
  • Pymatgen
  • Python3
  • Numpy

Background

Surface properties of materials are important for catalysis applications. Recent advances in computing power make density functional theory (DFT) to be possible to gain insights into the reactions happening on the catalyst surfaces. However, the surface environments are way more complex and adsorbates on surfaces can have many configurations. One possible way to solve this issue is by employing the cluster expansion to consider the adsorbate-adsorbate interactions.

Cluster expansion

Cluster expansion is developed based on the Ising... read more

Python

Data Mining for Catalyst Materials August 1, 2021 6 minute read

Overview

Catalyst materials are playing important roles in the whole world. Catalysts are composed of different chemical species, leading to the different catalytic performances. While catalysts are often composed of a single kind of metal, they can also be a combination of several or even more than five kinds of different metals. So the whole possible catalyst space is huge, which means it’s extremely hard to directly synthesize each of them and tell which one performs better for a specific case. Thus, machine learning methods step in to help screen the catalysts, but ML methods usually require large data... read more

Calculating Hartree Fock Energy of Hydrogen Using Python October 2, 2020 12 minute read

Overview

The lattice convolutional neural network (LCNN) is a Python library for deep learning of lattice system developed by the Vlachos group at the University of Delaware and Jung group at KAIST. The model has been built in hope to improve upon cluster expansion methods, linear regression based on the clusters in the lattice. The LCNN performs better than the cluster expansion and cluster expansion based machine learning methods with sufficient number of data points.

I employed this LCNN to train the Cluster expansion model and then compare with the CE-TinNet model.

Import needed libraries

read more
Predicting material properties using transfer learning February 1, 2020 4 minute read

Background

The evolution of the research workflow in computational chemistry is about 3 generations. The first-generation approach is to calculate the physical properties of an input structure, which is often performed via an approximation to the Schrödinger equation 
For example, Density functional theory. In the second-generation approach, by using global optimization (for example, an evolutionary algorithm), an input of chemical composition is mapped to an output that contains predictions of the structure or ensemble of structures that the combination of elements are likely to adopt. The emerging third-generation approach is to use machine-learning techniques with the ability to predict... read more

Quantum Chemistry

Calculating Hartree Fock Energy of Hydrogen Using Python October 2, 2020 12 minute read

Overview

The lattice convolutional neural network (LCNN) is a Python library for deep learning of lattice system developed by the Vlachos group at the University of Delaware and Jung group at KAIST. The model has been built in hope to improve upon cluster expansion methods, linear regression based on the clusters in the lattice. The LCNN performs better than the cluster expansion and cluster expansion based machine learning methods with sufficient number of data points.

I employed this LCNN to train the Cluster expansion model and then compare with the CE-TinNet model.

Import needed libraries

read more

Tutorial

Setting up your working environment April 2, 2021 10 minute read

How to log onto the ARC computing system

1.Install VPN

For users that want to use the ARC system off campus, a VPN is needed. Check out how to install and use the software through this website (they provide very detailed instructions and videos): VT4help. All different systems (Linux, MacOS and Windows) are supported. Make sure you connect to the VPN before logging onto the computing system when you are off campus.

2.Mac users login

Open your terminal software (in launchpad → other).

Login by ssh commands listed below. Our university’s supercomputing system has... read more

Tips to Mount Remote directories June 3, 2020 1 minute read

Mount a local directory to servers

For a MacOS user: Go to https://osxfuse.github.io and download FUSE for MacOS and SSHFS softwares (you can download the latest versions of them) and install both following the instructions.

After installation, go to terminal and 3 steps to setup your folder:

cd ~ mkdir newriver mkdir newriver_home

Tip 1: Using alias for quick usage

Open ~/.bash_profile, add two mount commands:

alias mount_newriver="sshfs #yourid#@newriver1.arc.vt.edu:/work/common/hxin_lab/#yourid# /Users/#yourmacusername#/newriver” alias mount_newriver_home="sshfs  read more
High Performance Clusters Usage Guide June 2, 2020 2 minute read

New Server TinkerCliffs Quick Guide

  1. Login in:

ssh -Y (yourID)@tinkercliffs1.arc.vt.edu or ssh -Y (yourID)@tinkercliffs2.arc.vt.edu

For quick login, edit your local .bash_profile by: emacs ~/.bash_profile Adding this part:

alias tinkercliffs="ssh -Y (yourID)@tinkercliffs1.arc.vt.edu"

Then you can login by just type tinkercliffs locally.

  1. After logging in, setup your home directory .bashrc file:
emacs /home/$(whoami)/.bashrc

Add this part to the end:

if [ $SYSNAME ==  read more

Working Environment

Setting up your working environment April 2, 2021 10 minute read

How to log onto the ARC computing system

1.Install VPN

For users that want to use the ARC system off campus, a VPN is needed. Check out how to install and use the software through this website (they provide very detailed instructions and videos): VT4help. All different systems (Linux, MacOS and Windows) are supported. Make sure you connect to the VPN before logging onto the computing system when you are off campus.

2.Mac users login

Open your terminal software (in launchpad → other).

Login by ssh commands listed below. Our university’s supercomputing system has... read more

Bash tools February 1, 2021 4 minute read

This is a tool to query the current running jobs in the server’s queue. It has the function that you can directly obtain more info than the ‘squeue’ command, which is the popular command for the slurm workload manager.

Key features:

  • Show the RunTime and TimeLimit as the same time, which helps you to get your job status.
  • Show the job’s running directory, which can be directly accessed. It can save lots of time to find the job’s directory when you are running multiple jobs
#!/usr/bin/env bash if [ “$SYSNAME”... read more
Tips to Mount Remote directories June 3, 2020 1 minute read

Mount a local directory to servers

For a MacOS user: Go to https://osxfuse.github.io and download FUSE for MacOS and SSHFS softwares (you can download the latest versions of them) and install both following the instructions.

After installation, go to terminal and 3 steps to setup your folder:

cd ~ mkdir newriver mkdir newriver_home

Tip 1: Using alias for quick usage

Open ~/.bash_profile, add two mount commands:

alias mount_newriver="sshfs #yourid#@newriver1.arc.vt.edu:/work/common/hxin_lab/#yourid# /Users/#yourmacusername#/newriver” alias mount_newriver_home="sshfs  read more

python

Recommendation Systems January 2, 2022 10 minute read

What is a recommendation system

Recommendation engines are a subclass of machine learning which generally deal with ranking or rating products/users. Recommender systems are algorithms aim at suggesting relevant items to users. They have been widely applied by many big name companies like Youtube, Amazon, Netflix and others to increase the engagement with users and the platform. Recommendation systems are employed since they can generate more profit when companies use efficient systems and help them stand out from competitors. There are many real life examples, YouTube uses recommendations to suggest you other videos similar to the ones you liked... read more

recommendator

Recommendation Systems January 2, 2022 10 minute read

What is a recommendation system

Recommendation engines are a subclass of machine learning which generally deal with ranking or rating products/users. Recommender systems are algorithms aim at suggesting relevant items to users. They have been widely applied by many big name companies like Youtube, Amazon, Netflix and others to increase the engagement with users and the platform. Recommendation systems are employed since they can generate more profit when companies use efficient systems and help them stand out from competitors. There are many real life examples, YouTube uses recommendations to suggest you other videos similar to the ones you liked... read more

  • Academia (4)
  • Ammonia (1)
  • Aspen Plus (1)
  • Bash (1)
  • Catalysis (3)
  • Cluster Expansion (1)
  • Data (1)
  • Economic analysis (1)
  • Hartree Fock (1)
  • Kinetics (1)
  • Machine Learning (3)
  • Neural networks (1)
  • Python (3)
  • Quantum Chemistry (1)
  • Tutorial (3)
  • Working Environment (4)
  • python (1)
  • recommendator (1)

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