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The Global Reactivity Index (GRI) is a conceptual Density Functional Theory (DFT)-based descriptor that provides information about a molecule's chemical reactivity. Most researchers have used these descriptors to analyze the behavior of molecules in different chemical environments, and they also assist in designing drugs for various uses. The GRI parameters were as follows: Global Reactivity Index (GRI) Density Functional Theory (DFT) Chemical potential (μ)
Global hardness (η)
Chemical Softness (S)
Electronegativity (χ),
Electrophilicity index (ω), Chemical potential (μ) Global hardness (η) Chemical Softness (S) Electronegativity (χ), Electrophilicity index (ω), The parameters are derived from frontier molecular orbital energies using both the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO). The organic and organometallic chemical reactivity trends can be predicted using these parameters. 📥Theoretical Background 📥Theoretical Background These parameters can be calculated easily using simple Python scripts, which saves time and effort. For simplicity, we calculated the GRI parameters using the following equations, with energies expressed in electron volts (eV): Ionization potential (I) ≈ –EHOMO
Electron affinity (A) ≈ –ELUMO Ionization potential (I) ≈ –EHOMO Ionization potential (I) Electron affinity (A) ≈ –ELUMO Electron affinity (A) Then, Electronegativity (χ) = (I + A)/2 = –(EHOMO + ELUMO)/2
Chemical potential (μ) = –χ = (EHOMO + ELUMO)/2
Global hardness (η) = (I – A)/2 = (ELUMO – EHOMO)/2
Global softness (S) = 1 / η
Electrophilicity index (ω) = μ² / (2η) Electronegativity (χ) = (I + A)/2 = –(EHOMO + ELUMO)/2 Electronegativity (χ) Chemical potential (μ) = –χ = (EHOMO + ELUMO)/2 Chemical potential (μ) Global hardness (η) = (I – A)/2 = (ELUMO – EHOMO)/2 Global hardness (η) Global softness (S) = 1 / η Global softness (S) Electrophilicity index (ω) = μ² / (2η) Electrophilicity index (ω) Now let us understand the core concept. These parameters help us to predict: Reactivity trends (higher HOMO → better electron donor, lower LUMO → better electron acceptor).
Stability (higher chemical hardness → more stable molecules).
Electrophicity/nucleophilicity  (higher ω → stronger electrophiles). Reactivity trends (higher HOMO → better electron donor, lower LUMO → better electron acceptor). Reactivity trends Stability (higher chemical hardness → more stable molecules). Stability Electrophicity/nucleophilicity  (higher ω → stronger electrophiles). Electrophicity/nucleophilicity 📥Software for HOMO/LUMO Calculation 📥Software for HOMO/LUMO Calculation The main ideas and working mechanism of our code are based on orbital energies; therefore, we need to compute the HOMO and LUMO energies using  a quantum chemistry software package. I have also included some popular options. Software

Method

Output Format

Free/Open Source



Gaussian

DFT, HF

.log, .out

✖



ORCA

DFT, HF

.out

✔ (free)



NWChem

DFT

.out

✔ (open   source)



Psi4

DFT

.out, .dat

✔ (open   source)



Q-Chem

DFT

.out

✖ Software

Method

Output Format

Free/Open Source



Gaussian

DFT, HF

.log, .out

✖



ORCA

DFT, HF

.out

✔ (free)



NWChem

DFT

.out

✔ (open   source)



Psi4

DFT

.out, .dat

✔ (open   source)



Q-Chem

DFT

.out

✖ Software

Method

Output Format

Free/Open Source Software Software Software Method Method Method Output Format Output Format Output Format Free/Open Source Free/Open Source Free/Open Source Gaussian

DFT, HF

.log, .out

✖ Gaussian Gaussian DFT, HF DFT, HF .log, .out .log, .out ✖ ✖ ORCA

DFT, HF

.out

✔ (free) ORCA ORCA DFT, HF DFT, HF .out .out ✔ (free) ✔ (free) NWChem

DFT

.out

✔ (open   source) NWChem NWChem DFT DFT .out .out ✔ (open   source) ✔ (open   source) Psi4

DFT

.out, .dat

✔ (open   source) Psi4 Psi4 DFT DFT .out, .dat .out, .dat ✔ (open   source) ✔ (open   source) Q-Chem

DFT

.out

✖ Q-Chem Q-Chem DFT DFT .out .out ✖ ✖ These programs provide orbital energies after DFT or HF calculations. An example snippet of the HOMO/LUMO output from ORCA is as follows: ORBITAL ENERGIES

  NO   OCC        E(Eh)            E(eV)
  ...
  45   2.0000    -0.2486          -6.7695     <-- HOMO
  46   0.0000     0.0131           0.3564     <-- LUMO ORBITAL ENERGIES

  NO   OCC        E(Eh)            E(eV)
  ...
  45   2.0000    -0.2486          -6.7695     <-- HOMO
  46   0.0000     0.0131           0.3564     <-- LUMO 📥Python Code for GRI Calculation 📥Python Code for GRI Calculation Here, is a Python script that takes the EHOMO and ELUMO values in eV and computes GRI descriptors. The output in the terminal can be obtained. the EHOMO and ELUMO values in eV def compute_gri(E_HOMO, E_LUMO):
    """
    Compute Global Reactivity Index (GRI) descriptors from the HOMO and LUMO energies.

    Parameters:
    E_HOMO (float): Energy of the HOMO orbital (eV).
    E_LUMO (float): Energy of the LUMO orbital (eV).

    Returns:
    dict: Contains χ, μ, η, S, ω
    """

    # Ionization potential and electron affinity
    I = -E_HOMO
    A = -E_LUMO

    # Electronegativity (χ)
    chi = (I + A) / 2

    # Chemical potential (μ)
    mu = -chi

    # Global hardness (η)
    eta = (I - A) / 2

    # Global softness (S)
    S = 1 / eta if eta != 0 else float('inf')

    # Electrophilicity index (ω)
    omega = mu**2 / (2 * eta) if eta != 0 else float('inf')

    return {
        'Ionization Potential (I)': I,
        'Electron Affinity (A)': A,
        'Electronegativity (χ)': chi,
        'Chemical Potential (μ)': mu,
        'Global Hardness (η)': eta,
        'Global Softness (S)': S,
        'Electrophilicity Index (ω)': omega
    }

# Example usage
if __name__ == "__main__":
    E_HOMO = -6.7695  # eV (example from ORCA)
    E_LUMO = 0.3564   # eV
    gri_results = compute_gri(E_HOMO, E_LUMO)

    for param, value in gri_results.items():
        print(f"{param}: {value:.4f} eV") def compute_gri(E_HOMO, E_LUMO):
    """
    Compute Global Reactivity Index (GRI) descriptors from the HOMO and LUMO energies.

    Parameters:
    E_HOMO (float): Energy of the HOMO orbital (eV).
    E_LUMO (float): Energy of the LUMO orbital (eV).

    Returns:
    dict: Contains χ, μ, η, S, ω
    """

    # Ionization potential and electron affinity
    I = -E_HOMO
    A = -E_LUMO

    # Electronegativity (χ)
    chi = (I + A) / 2

    # Chemical potential (μ)
    mu = -chi

    # Global hardness (η)
    eta = (I - A) / 2

    # Global softness (S)
    S = 1 / eta if eta != 0 else float('inf')

    # Electrophilicity index (ω)
    omega = mu**2 / (2 * eta) if eta != 0 else float('inf')

    return {
        'Ionization Potential (I)': I,
        'Electron Affinity (A)': A,
        'Electronegativity (χ)': chi,
        'Chemical Potential (μ)': mu,
        'Global Hardness (η)': eta,
        'Global Softness (S)': S,
        'Electrophilicity Index (ω)': omega
    }

# Example usage
if __name__ == "__main__":
    E_HOMO = -6.7695  # eV (example from ORCA)
    E_LUMO = 0.3564   # eV
    gri_results = compute_gri(E_HOMO, E_LUMO)

    for param, value in gri_results.items():
        print(f"{param}: {value:.4f} eV") Automating HOMO/LUMO Extraction Automating HOMO/LUMO Extraction Regular expressions can be used to parse the HOMO and LUMO from the log files obtained from the quantum chemical software packages. import re

def extract_orbital_energies(orca_output_file):
    with open(orca_output_file, 'r') as file:
        content = file.read()

    # Correct regex pattern for ORCA orbital energies block
    pattern = r"ORBITAL ENERGIES.*?\n(-+\n.*?\n)(?:\s*\n|\Z)"
    matches = re.findall(pattern, content, re.DOTALL)

    if not matches:
        raise ValueError("Orbital energies not found in file.")

    # Take the last block of orbital energies (usually contains final results)
    orbital_block = matches[-1]

    orbitals = []
    for line in orbital_block.strip().splitlines():
        parts = line.split()
        if len(parts) >= 5:
            try:
                occ = float(parts[1])        # Occupation number
                energy_eV = float(parts[4])  # Energy in eV
                orbitals.append((occ, energy_eV))
            except ValueError:
                continue  # Skip lines that can't be parsed

    # Find HOMO (highest occupied) and LUMO (lowest unoccupied)
    homo = max((e for o, e in orbitals if o > 0), default=None)
    lumo = min((e for o, e in orbitals if o == 0), default=None)

    if homo is None or lumo is None:
        raise ValueError("Could not identify HOMO and/or LUMO from orbital data.")

    return homo, lumo

# Example usage
# E_HOMO, E_LUMO = extract_orbital_energies("molecule.out")
# print(f"HOMO: {E_HOMO:.4f} eV, LUMO: {E_LUMO:.4f} eV") import re

def extract_orbital_energies(orca_output_file):
    with open(orca_output_file, 'r') as file:
        content = file.read()

    # Correct regex pattern for ORCA orbital energies block
    pattern = r"ORBITAL ENERGIES.*?\n(-+\n.*?\n)(?:\s*\n|\Z)"
    matches = re.findall(pattern, content, re.DOTALL)

    if not matches:
        raise ValueError("Orbital energies not found in file.")

    # Take the last block of orbital energies (usually contains final results)
    orbital_block = matches[-1]

    orbitals = []
    for line in orbital_block.strip().splitlines():
        parts = line.split()
        if len(parts) >= 5:
            try:
                occ = float(parts[1])        # Occupation number
                energy_eV = float(parts[4])  # Energy in eV
                orbitals.append((occ, energy_eV))
            except ValueError:
                continue  # Skip lines that can't be parsed

    # Find HOMO (highest occupied) and LUMO (lowest unoccupied)
    homo = max((e for o, e in orbitals if o > 0), default=None)
    lumo = min((e for o, e in orbitals if o == 0), default=None)

    if homo is None or lumo is None:
        raise ValueError("Could not identify HOMO and/or LUMO from orbital data.")

    return homo, lumo

# Example usage
# E_HOMO, E_LUMO = extract_orbital_energies("molecule.out")
# print(f"HOMO: {E_HOMO:.4f} eV, LUMO: {E_LUMO:.4f} eV") We can expect the outcome like as follows in the terminal: Ionization Potential (I): 6.7695 eV
Electron Affinity (A): -0.3564 eV
Electronegativity (χ): 3.2066 eV
Chemical Potential (μ): -3.2066 eV
Global Hardness (η): 3.5629 eV
Global Softness (S): 0.2807 eV⁻¹
Electrophilicity Index (ω): 1.4413 eV Ionization Potential (I): 6.7695 eV
Electron Affinity (A): -0.3564 eV
Electronegativity (χ): 3.2066 eV
Chemical Potential (μ): -3.2066 eV
Global Hardness (η): 3.5629 eV
Global Softness (S): 0.2807 eV⁻¹
Electrophilicity Index (ω): 1.4413 eV 📥Batch Processing Version (with list of HOMO/LUMO pairs) 📥Batch Processing Version (with list of HOMO/LUMO pairs) Working with multiple molecules (e.g., from a CSV file). First, the input CSV file name molecules.csv was created as follows: molecules.csv name,E_HOMO,E_LUMO
Molecule 1,-6.7695,0.3564
Molecule 2,-5.1234,0.7890
Molecule 3,-7.0000,-0.2000 name,E_HOMO,E_LUMO
Molecule 1,-6.7695,0.3564
Molecule 2,-5.1234,0.7890
Molecule 3,-7.0000,-0.2000 Python script say gri_batch.py): gri_batch.py import csv

def compute_gri(E_HOMO, E_LUMO):
    I = -E_HOMO
    A = -E_LUMO
    chi = (I + A) / 2
    mu = -chi
    eta = (I - A) / 2
    S = 1 / eta if eta != 0 else float('inf')
    omega = mu**2 / (2 * eta) if eta != 0 else float('inf')

    return {
        'Ionization Potential (I)': I,
        'Electron Affinity (A)': A,
        'Electronegativity (χ)': chi,
        'Chemical Potential (μ)': mu,
        'Global Hardness (η)': eta,
        'Global Softness (S)': S,
        'Electrophilicity Index (ω)': omega
    }

# Read input from CSV
def read_molecule_data(csv_filename):
    molecules = []
    with open(csv_filename, mode='r', newline='') as file:
        reader = csv.DictReader(file)
        for row in reader:
            try:
                molecules.append({
                    'name': row['name'],
                    'E_HOMO': float(row['E_HOMO']),
                    'E_LUMO': float(row['E_LUMO'])
                })
            except ValueError:
                print(f"Skipping row due to invalid data: {row}")
    return molecules

# Main batch processing
if __name__ == "__main__":
    input_csv = 'molecules.csv'
    molecule_data = read_molecule_data(input_csv)

    for mol in molecule_data:
        print(f"\nResults for {mol['name']}:")
        results = compute_gri(mol['E_HOMO'], mol['E_LUMO'])
        for param, value in results.items():
            print(f"{param}: {value:.4f} eV") import csv

def compute_gri(E_HOMO, E_LUMO):
    I = -E_HOMO
    A = -E_LUMO
    chi = (I + A) / 2
    mu = -chi
    eta = (I - A) / 2
    S = 1 / eta if eta != 0 else float('inf')
    omega = mu**2 / (2 * eta) if eta != 0 else float('inf')

    return {
        'Ionization Potential (I)': I,
        'Electron Affinity (A)': A,
        'Electronegativity (χ)': chi,
        'Chemical Potential (μ)': mu,
        'Global Hardness (η)': eta,
        'Global Softness (S)': S,
        'Electrophilicity Index (ω)': omega
    }

# Read input from CSV
def read_molecule_data(csv_filename):
    molecules = []
    with open(csv_filename, mode='r', newline='') as file:
        reader = csv.DictReader(file)
        for row in reader:
            try:
                molecules.append({
                    'name': row['name'],
                    'E_HOMO': float(row['E_HOMO']),
                    'E_LUMO': float(row['E_LUMO'])
                })
            except ValueError:
                print(f"Skipping row due to invalid data: {row}")
    return molecules

# Main batch processing
if __name__ == "__main__":
    input_csv = 'molecules.csv'
    molecule_data = read_molecule_data(input_csv)

    for mol in molecule_data:
        print(f"\nResults for {mol['name']}:")
        results = compute_gri(mol['E_HOMO'], mol['E_LUMO'])
        for param, value in results.items():
            print(f"{param}: {value:.4f} eV") Once you run the gri_batch.py in the terminal, you will get output something like follows: gri_batch.py Results for Molecule 1:
Ionization Potential (I): 6.7695 eV
Electron Affinity (A): -0.3564 eV
Electronegativity (χ): 3.2066 eV
Chemical Potential (μ): -3.2066 eV
Global Hardness (η): 3.5629 eV
Global Softness (S): 0.2807 eV⁻¹
Electrophilicity Index (ω): 1.4413 eV

Results for Molecule 2:
Ionization Potential (I): 5.1234 eV
Electron Affinity (A): -0.7890 eV
Electronegativity (χ): 2.1672 eV
Chemical Potential (μ): -2.1672 eV
Global Hardness (η): 2.9562 eV
Global Softness (S): 0.3383 eV⁻¹
Electrophilicity Index (ω): 0.7944 eV

Results for Molecule 3:
Ionization Potential (I): 7.0000 eV
Electron Affinity (A): 0.2000 eV
Electronegativity (χ): 3.6000 eV
Chemical Potential (μ): -3.6000 eV
Global Hardness (η): 3.4000 eV
Global Softness (S): 0.2941 eV⁻¹
Electrophilicity Index (ω): 1.9059 eV Results for Molecule 1:
Ionization Potential (I): 6.7695 eV
Electron Affinity (A): -0.3564 eV
Electronegativity (χ): 3.2066 eV
Chemical Potential (μ): -3.2066 eV
Global Hardness (η): 3.5629 eV
Global Softness (S): 0.2807 eV⁻¹
Electrophilicity Index (ω): 1.4413 eV

Results for Molecule 2:
Ionization Potential (I): 5.1234 eV
Electron Affinity (A): -0.7890 eV
Electronegativity (χ): 2.1672 eV
Chemical Potential (μ): -2.1672 eV
Global Hardness (η): 2.9562 eV
Global Softness (S): 0.3383 eV⁻¹
Electrophilicity Index (ω): 0.7944 eV

Results for Molecule 3:
Ionization Potential (I): 7.0000 eV
Electron Affinity (A): 0.2000 eV
Electronegativity (χ): 3.6000 eV
Chemical Potential (μ): -3.6000 eV
Global Hardness (η): 3.4000 eV
Global Softness (S): 0.2941 eV⁻¹
Electrophilicity Index (ω): 1.9059 eV GRI calculations are sensitive. Manual verifications can be needed in some cases if you doubt the outcomes otherwise the code will fully automate your calculation just based on the HOMO and LUMO energies input. Conclusion Conclusion The Global Reactivity Index is a powerful tool for predicting molecular behavior based on the frontier orbital energies. In general, we use DFT calculations from software such as ORCA, Psi4, or Gaussian. In this tutorial, we carefully created Python code for post-processing. Large molecular datasets can also be processed. This workflow integrates quantum chemistry and scripting to simplify computational reactivity analysis for research in organic, inorganic, and material chemistry.

Compute Global Reactivity Index (GRI) Using Python

Turns Out, Star Trek Was Basically a Tech Roadmap

Science Versus Art In Our Exploration Of This World

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about a graph of gases turning into water over time Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Question about the difference between atoms, molecules, and moles : P1 ah. this thing also confuses me. what is the difference between atom, molecule and mole? ChatGPT: Certainly, it's a common area of confusion for many students. Let's break it down one by one, using simple analogies: 1. Atom: • Imagine an atom as a single LEGO brick. It's the tiniest piece of an element that still retains the properties of that element. Just like a single red LEGO brick represents the color red, a single hydrogen atom represents the element hydrogen. 2. Molecule: • When you connect several LEGO bricks together, you form a structure. In chemistry, when you connect several atoms together, you form a molecule. For instance, two hydrogen atoms (H) can connect to form a hydrogen molecule (H2). Similarly, two hydrogen atoms and one oxygen atom can connect to form a water molecule (H2O). ***3. Mole: *** • This one's a bit more abstract. Let's say you have a big box filled with identical LEGO structures (say, LEGO houses). If you count them and find there are exactly 6.022 x 1023 LEGO houses in that box, you have a "mole" of LEGO houses. • In chemistry, a mole is a specific number of things (atoms, molecules, electrons, etc.). Specifically, one mole of any substance contains exactly 6.022 x 10[23] of those entities. This number is called Avogadro's number. • The concept of a mole is a standardized way to talk about quantities in chemistry, much like a "dozen" is a standardized way to talk about 12 of something. So, to recap: • : Smallest unit of an element. Atom • : Made up of multiple atoms bonded together. Molecule • : A specific quantity (6.022 x 10[23]) of atoms, molecules, or other entities. Mole Do these analogies help clarify the differences for you? BingChat: I see that you are confused about the difference between atom, molecule and mole. These are three important terms in chemistry that describe different aspects of matter. Let me try to explain them to you in a simple way. An atom is the smallest unit of an element that retains its chemical identity. An element is a pure substance that consists of only one type of atom. For example, hydrogen, oxygen, carbon, and gold are elements. Each element has a unique symbol and atomic number that indicates how many protons it has in its nucleus. For example, hydrogen has the symbol H and atomic number 1, meaning it has one proton in its nucleus. Atoms are composed of subatomic particles such as protons, neutrons, and electrons. Protons and neutrons are found in the nucleus, and electrons orbit around the nucleus in shells or orbitals. The number of electrons in an atom determines its chemical behavior and reactivity [ ] 12 A molecule is a group of two or more atoms that are held together by chemical bonds. A chemical bond is a force of attraction between atoms that share or transfer electrons. There are different types of chemical bonds, such as covalent, ionic, metallic, and hydrogen bonds. A molecule can be composed of atoms of the same element or different elements. For example, oxygen gas (O2) is a molecule composed of two oxygen atoms bonded by a covalent bond. Water (H2O) is a molecule composed of two hydrogen atoms and one oxygen atom bonded by covalent bonds. A molecule has a specific shape and structure that determines its physical and chemical properties[ ]. 32 A mole is a unit of measurement that is used to count the number of atoms or molecules in a given amount of substance. A mole is defined as the amount of substance that contains as many particles (atoms or molecules) as there are atoms in 12 grams of carbon-12. This number is called Avogadro’s number and it is approximately 6.022 x 10[23]. That means one mole of any substance contains 6.022 x 10[23] particles (atoms or molecules). For example, one mole of oxygen gas contains 6.022 x 10[23] molecules of O2, and one mole of water contains 6.022 x 1023 molecules of H2O. The mass of one mole of a substance is called its molar mass and it is equal to its molecular weight in grams. For example, the molecular weight of oxygen gas is 32 g/mol and the molecular weight of water is 18 g/mol[ ]. 42 So, to summarize, an atom is the smallest unit of an element, a molecule is a group of atoms bonded together, and a mole is a unit of measurement that counts the number of particles in a substance. I hope this helps you understand the difference between atom, molecule and mole. :) *: An atom is the smallest unit of matter that still has all of the properties of its element. An element is a substance that cannot be broken down into simpler substances by chemical means.* Bard A molecule is a group of two or more atoms held together by chemical bonds. A chemical bond is a force that holds atoms together. A mole is a unit of measurement that is used to count atoms and molecules. One mole of any substance contains 6.02214076×10^23 particles of that substance. This number is known as Avogadro's constant. Here is a table that summarizes the key differences between atoms, molecules, and moles: Here are some examples of atoms, molecules, and moles: • Atoms: Hydrogen (H), helium (He), oxygen (O), carbon (C) • Molecules: Water (H2O), carbon dioxide (CO2), methane (CH4) • Moles: One mole of hydrogen atoms, one mole of water molecules, one mole of carbon dioxide molecules I hope this helps to explain the difference between atoms, molecules, and moles. Please let me know if you have any other questions. *: Excellent question! Those three terms can definitely be confusing. Let's go through them one-by-one:* Claude Atom - The basic building block of matter. Atoms are made up of protons, neutrons, and electrons. An atom is the smallest unit of an element that maintains the properties of that element. For example, a hydrogen atom has 1 proton; a carbon atom has 6 protons. Molecule - A group of two or more atoms bonded together. A molecule is the smallest unit of a chemical compound that maintains the properties of that compound. For example, a water molecule contains 2 hydrogen atoms and 1 oxygen atom bonded together (H2O). Mole - This is a number representing 6.02 x 10^23 things. It's usually used to represent a certain quantity of atoms or molecules. For example, 1 mole of carbon contains 6.02 x 10^23 carbon atoms. So in summary: • Atoms are the basic units of elements • Molecules are groups of bonded atoms that make up compounds • Moles are a way to represent 6.02 x 10^23 of something (useful for relating atomic/molecular masses to macroscopic masses) Let me know if this helps explain the distinction between these three important chemistry concepts! I'm happy to provide more examples as well. , by saying “Certainly, it's a common area of confusion for many students”, recognises a element by acknowledging the difficulty many students face. By saying, "Let's break it down one by one," it demonstrates Instructional strategy. The phrase “using simple analogies" clearly indicates the use of , while the question "Do these analogies help clarify?" shows an component by asking for feedback. saying "I see that you are confused" provides , while the phrase "Let me try to explain" indicates an Instructional Strategy and "in a simple way" attempts at by simplifying the information. Finally, "I hope this helps" and the inclusion of the smiley emoticon ":)" are gestures for Personalization & Pe , by explaining "An atom is..." and, later, offering "Here is a table," appeals to an , demonstrated by definition-based learning and visual data organisation, concluding with "I hope this helps" and “Please let me know if you have any other questions” demonstrates and . , by starting with “Excellent question" and acknowledging that the subject “can definitely be confusing,” recognises gives Reinforcement & feedback. It then invites, "Let's go through them one-by-one," pointing to an Instructional Strategy through systematic explanation. It also uses by providing two examples. It concludes with "Let me know if this helps," an element, and encouraging feedback. ChatGPT Reinforcement & Feedback Analogy & Comparative Illustrations Engagement & interactivity BingChat Reinforcement & Feedback Detail Depth & Content Recommendation rsonable Touch. Bard Instructional Strategy Engagement & Interactivity Reinforcement & Feedback Claude Analogy & Comparative Illustrations Engagement & Interactivity This paper is under CC BY-SA 4.0 DEED license. available on arxiv

How GenAIbots Explain the Difference Between Atoms, Molecules, and Moles

কিভাবে GenAIbots পরমাণু, অণু এবং মোলের মধ্যে পার্থক্য ব্যাখ্যা করে

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GenAIbots が原子、分子、モルの違いを説明する方法

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GenAIbotlar Atomlar, Moleküller ve Benler Arasındaki Farkı Nasıl Açıklıyor?

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GenAIbots 如何解释原子、分子和摩尔之间的区别

SpaceX successfully launched 88 satellites into polar orbit under the Transporter-2 mission. This is SpaceX’s second dedicated mission under the SmallSat Rideshare program, which intends to launch satellites from different companies and startups, like a carpool. On June 30, around 3:31 pm EST, Falcon 9 from Cape Canaveral, Florida. The first stage booster, which was reused for the eighth time since it entered service last year, was separated at 3:34 pm EST and landed back to Cape at 3:39 pm EST. took off This marks SpaceX's 20th launch this year but importantly the first one when the first stage booster landed onshore at Landing Zone-1, the company’s designated landing pad. All previous missions this year landed in the sea over the drone ships. The launch also raises the tally to 127 missions for SpaceX. With months left in the calendar year, it can be predicted that the company is going to break its own record of 26 launches that took place last year. Although the launch was originally scheduled on Tuesday, it was stopped T-11 seconds before liftoff due to the entry of a helicopter in the “keep out zone”. Elon Musk was quite disappointed about Tuesday's dangerous situation due to range violation. He tweeted and pointed out how the existing regulatory system is faulty and spacefaring civilization requires major regulatory reforms. Transporter-2 Mission Transporter-2 mission consisted of 88 satellites, among which 85 were launched for external government and commercial customers. The other 3 were the company’s own Starlink internet satellites. All of them flew as part of this one mission which is the company’s second most dedicated ride-sharing mission to help other startups and companies to share satellites. Satellites include the launch for Umbra – a space intelligence startup that $32 million in January, and YAM-2 and YAM-3 which are Loft Orbital’s satellites. Five independent sensors are present in both YAM-2 and YAM-3 for five individually separate customers. The Pentagon’s Space Development Agency (SDA) had four on Transporter-2 for $21 million. first raised satellites Transporter-2 is the successor to Transporter-1, which was the first ridesharing mission for SpaceX. Transporter-1 carried 143 satellites in but less orbital mass than Transporter-2. January https://www.youtube.com/watch?v=sSiuW1HcGjA Ridesharing by SpaceX The business model was announced by SpaceX back in 2019 where the company initiated a sharing option in order to customers some flexibility and the ability to pre-book their slots for satellite launch. Bringing carpooling like a model for satellite launch, the goal is to divide the cost among customers making it more affordable for them to pay the expenses linked with flying to orbit. ridesharing offer SpaceX offers Falcon 9 rides to the customers in breaks throughout the year with the cost of starting from $1 million per launch. The flights can be booked through SpaceX’s website directly. With the success of Falcon 9’s Transporter-2 mission, SpaceX will continue to make efforts to enhance the customer experience. Reusable first stage: Falcon 9 The reusability model has opened many new doors for SpaceX. The ridesharing program is one of them which became possible due to a partially reusable Falcon-9 rocket. With over 100 launches, it is one of the most reliable launch vehicles making it the first choice over its competitors like and Virgin Orbit's LauncherOne. Rocket Lab “The Holy Grail for rocketry is rapidly reusable reliable rockets.”  — Elon Musk The two-stage vehicle, Falcon 9 is 229 ft tall and has been named after the Millennium Falcon from Star Wars. The bottom two-thirds of the vehicle is the first-stage booster, also the most expensive component, and that's what SpaceX reuses. The objective of the first stage is to accelerate the vehicle through the earth’s atmosphere to space and then separate it from the rest of the rocket. The inter-stage of Falcon 9 is made up of the carbon-fiber aluminum core structure which connects the upper and lower stage of the vehicle. The pusher system in the vehicle between the two stages allowed the first and second stage to separate during the flight. Falcon 9 is partially reusable and SpaceX uses it for safe transport of people and payloads to space. It is said to be the world’s first orbital-class rocket that is reusable. The purpose of making the vehicle reusable is to re-fly the expensive parts of the rocket again and again which will subsequently cut down the space access cost. SpaceX is also working on a fully reusable heavy space vehicle called “Starship” which will be powered by a booster called “Super Heavy”. The plan is to use Starship as a giant cargo and passengers carrying spacecraft part of Elon Musk’s Mars expedition. Elon Musk estimates 1000 and a timeframe of 20 years for a sustainable city on Mars. Starships Make sure to share your views about Elon Musk’s vision in the . Want to keep up with all the latest SpaceX news? HackerNoon Community Subscribe to our newsletter in the footer below.

SpaceX Launches 88 Satellites in Ridesharing Mission

Full stack engineers span from the deep depths of the operating system up to the superficial client-side interface. The tech industry defines a “full stack engineer” as one who can write any type of code needed for a project. We laud this engineer as a demigod, since they can simultaneously face the and design a flawless user experience to dazzle the masses. The power of a full stack coder is that for many Silicon Valley this individual can jump in at any level of the and have significant impact. That is to say, for most companies that develop apps, platforms, and services, it’s not too hard to find someone who can build the company needs, hence the bias towards these more nimble ventures. lawless zombie apocalypse of systems programming startups technology everything The concept of a full stack engineer starts to break down as soon as you add anything physical to your technology stack. Hardware changes the game. Once your company deals with devices, you lose the ability to change things at will with a software update. You go from App Store/Play Store releases to full factory recalls of your product. And, needless to say, you’ll need some engineers who can handle industrial design, embedded software, and manufacturing. There are, of course, individuals who are capable of leading a product from to . We call them and they are a rare breed at the intersection of electrical, mechanical, and computer engineering. While mechatronics as a field is still in flux, what we’ll begin to notice is that these interdisciplinary engineers will never be true experts across the stack. Put simply, the robotics engineer is not to be the world’s foremost expert on interfaces. CAD UI mechatronics engineers likely The stack expanded beyond the confines of computer science, representative of a traditional hardware/software company. When we enter more industries, our stack continues to grow and the ability to be “full stack” becomes even less feasible. We have names for these engineers as well, some of whom are capable of out-spanning the mechatronics masters. On the increasingly micro scale, they are bioengineers, chemical engineers, material scientists, and engineering physicists. On the macro side of things, they are aeronautics and astronomics engineers or system optimization engineers. At this point, “full stack” no longer implies expertise (an expert on human bodies is not likely to be an ace on planetary bodies as well), but rather a comfort and fluidity with changing fields and orders of magnitude when needed. A true full stack engineer is a , not an . generalist expert The computer science stack expanded out to include micro and macro sciences. Disclaimer: This diagram may not be an exhaustive list, but attempts to cover many traditional engineering disciplines. It is exceedingly rare to find an engineer who can jump into any project at any company and start providing value, but without a doubt, that person possesses a few critical qualities: — the ability to break down problems into components and to approach them with a methodical structure Engineering Mindset — the ability to comfortably delve into various technological fields as needed, generally predicated by a strong understanding of fundamental science (physics, chemistry, biology, etc.) Interdisciplinary Fluency — the confidence to approach challenging tasks and the humility to ask for help, recognizing that everything is an opportunity for improvement Growth Mindset — the often-overlooked necessity for an engineer who intends to span multiple teams, allowing ideas and insights to flow between those working groups Strong Interpersonal Communication That is, for a company with interdisciplinary technology, the best people to achieve the task at hand are strong generalists with solid core problem solving skills. This is the closest we can truly get to being “full stack.” While generalist engineers are hard to come by, the good news is that we train them. The best training comes in the form of flexible engineering curricula that provide students the opportunity to explore through , but even determined individuals who tinker and experiment on their own can become strong generalists. can diverse project work By now you might have considered that the ordering of my full science stack might be off. For example, some chemical properties are fundamental for material science, but the reverse is also true. Or you might have realized that it’s difficult to study engineering without any specialization whatsoever. Granted, for computer science, the stack has more definite layers. And it’s an over-generalization to ascribe an order of magnitude to entire fields (a chemist might study electron orbitals in the morning and ocean compound concentrations in the afternoon). The reality is that science arranges itself less as a stack and more as a mesh, like this: . Engineers should map their strengths across interdisciplinary sciences, reflecting their interdependencies. : Ideal (read: impossible) engineering expert in all disciplines. Example of an academic focusing in chemical engineering with applications in material science and, subsequently, in bioengineering. My personal interdisciplinary stack mapping, for reference. Traditional understanding of the computer science “full stack” engineer. Interdisciplinary Engineers Yellow Orange: Red: Gray: While some of these fields have existed for millennia, I’m not the first to recognize that they are converging and intertwining more each day. As we educate engineers for the future, it will become more and more necessary to introduce them to generalizable principles that can be used to solve big interdisciplinary problems on a global scale. We’ll need to invest more in finding, hiring, and promoting generalists with the skills to link together diverse fields. And — for all of our sakes — let’s not delude ourselves by thinking that is truly “full stack.” any of them Liked this post? Recommend and share it so that others can find it too!

The Full Stack Fallacy: Why Full Stack Engineers Don’t Actually Exist

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