Siriusitzy

Electrolyte System Modeling and Classical MD Workflow

This tutorial provides a step-by-step guide for modeling electrolyte solutions and running classical molecular dynamics (MD) simulations. It is aimed at beginners and covers environment setup, required software installation, molecular modeling, simulation script preparation, job submission, and data analysis. Please follow each step closely and refer to documentation or community resources when needed.

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Table of Contents

1. Environment Setup and Software Installation
2. Detailed Modeling Workflow

iiLaurens

window.scrollTo(0, 0)
var bodyRect = document.body.getBoundingClientRect();

var items = Array.prototype.slice.call(
  document.querySelectorAll('*')
).map(function(element) {
  var rect=element.getBoundingClientRect();
  return {
    element: element,
    include: (element.tagName === "BUTTON" || element.tagName === "A" || (element.onclick != null) || window.getComputedStyle(element).cursor == "pointer"),

ruizb

The following section is not part of monet.js documentation, but I think it's worth showing how we can compose readers using fp-ts.

Reader composition

Let's say we have the following piece of code:

interface Dependencies {
  logger: { log: (message: string) => void }
 env: 'development' | 'production'

hkucuk

import webview
import base64

def open_file_dialog(window):
    file_types = ('Image Files (*.bmp;*.jpg;*.gif;*.png)', 
                  'All files (*.*)')
    
    path = window.create_file_dialog(webview.OPEN_DIALOG,
                                     allow_multiple=False,
                                     file_types=file_types)

PARC6502

Backend as a Service

Note on sizes: a lot of those might be inaccurate as there might be many microservices required aside from the core release/docker image. I haven't tested these recently so I'm not sure

Supabase - ~94.9K stars

• Designed explicitly as an open source Firebase alternative
• Typescript based
• Docker support
• Full Postgres database with Realtime subscriptions

PocketBase - ~54.5K stars

michaelchughes

Goal

Install working tensorflow or pytorch via standard conda environment workflow.

Basic Setup : Install pytorch in a fresh conda environment

The recommended conda-based install process works smoothly:

$ # Create a fresh environment

socool

// Enable component-scanning and auto-configuration with @SpringBootApplication Annotation
// It combines @Configuration + @ComponentScan + @EnableAutoConfiguration
@SpringBootApplication
public class FooApplication {
  public static void main(String[] args) {
    // Bootstrap the application
    SpringApplication.run(FooApplication.class, args);
  }
}

endolith

"""
Train a neural network to implement the discrete Fourier transform
"""
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential

N = 32
batch = 10000

oifland

// Related to https://issues.jenkins-ci.org/browse/JENKINS-26481

abcs = ['a', 'b', 'c']

node('master') {
    stage('Test 1: loop of echo statements') {
        echo_all(abcs)
    }
    
    stage('Test 2: loop of sh commands') {

jonico

#!groovy
import groovy.json.JsonOutput
import groovy.json.JsonSlurper

def label = "mypod-${UUID.randomUUID().toString()}"
podTemplate(label: label, yaml: """
spec:
      containers:
      - name: mvn
        image: maven:3.3.9-jdk-8