Computational Training Class
The computational training class is an initiative to strengthen and expand a year-round hybrid research program aimed at preparing and increasing the number of community college (CC) students in STEM PhD programs.
The long-term goal of this proposal is to develop a sustainable and practical year-round innovative, collaborative research program between partnering UC R1 institutions and geographically distinct California CCs (Sacramento City College, Los Medanos College, Cosumnes River College, American River College, Folsom Lake College, Long Beach City College, and West Los Angeles College) which can later be broadly adapted by other STEM departments across the entire UC system.
In stark contrast to current research opportunities at the CCs, this program will expose and enable CC students, directly from their CCs, to use and apply cutting-edge computational tools including machine learning (ML), artificial intelligence (AI), quantum mechanical calculations (QM), molecular dynamic simulations (MD), qualitative analysis (QA), and data science (DS) methods for chemical research.
Specific objectives of the program are:
(1) Match students from community colleges across the state with remote computational chemistry and chemistry education research projects during the school year.
(2) Invite students to conduct in-person research at UCLA during the summer.
(3) Facilitate a pipeline of research-ready STEM majors transfer students to UCLA.
(4) Support community college chemistry faculty to access research and educational resources.
The Gutierrez group have proven success working with community college students/faculty and lead cutting-edge research programs designed to be fully accessible to remote researchers in CC students in STEM and, in parallel, prepare them to transfer and thrive at R1 institutions.
Computational Training Class Winter/Spring Quarter 2026
Meet The Team
Program Directors of Computational Training Program in Chemistry
University of California Los Angeles
Sacramento City College
West Los Angeles College
Computational Class Coordinators
Main Coordinator
Coordinator
Computational Class Teaching Assistant
Sacramento City College
Mentors
Gutierrez Research Group
UCLA
Gutierrez Research Group
UCLA
Tantillo Research Group
UC Davis
Gutierrez Research Group
UCLA
Tantillo Research Group
UC Davis
Gutierrez Research Group
UCLA
Gutierrez Research Group
UCLA
Gutierrez Research Group
UCLA
Tantillo Research Group
UC Davis
Gutierrez Research Group
UCLA
Undergraduate Students
Isabel Wong
Cosumnes River College
Simreet Josan
Cosumnes River College
Patrick Bernardino
Cosumnes River College
Elijah Pascua
Cosumnes River College
Kimi Do
Cosumnes River College
Brandon Barco
Sacramento City College
Nicholas Froehlich
Long Beach City College
Sequin Thomas
Sacramento City College
Learning Outcomes
Skill #C1: Building a molecule
Level Up Skill #C1: Demonstrate proficiency in GaussView software to build molecules including their chemical characteristic:
Bond types: single, double, triple
Bond length: 1.53Å, 1.33 Å, and 1.18 Å.
Conformations: eclipse, staggered, chair, boat.
Molecular geometry: tetrahedral, trigonal planar, square planar, linear.
Bond rotation: angles, dihedral angles.
Skill #C2: Submiting your first job to the supercomputer
Level Up Skill #C2: Choose appropriate computational methods for specific systems (organic compounds, organometallic reagents, radicals, anions, etc.)
Level Up Skill #C2: Understand and apply principles of density functional theory (empiricaldispersion=gd3, cpcm, sdm, nosymm guess=(mix, always), dispersion, etc.)
Level Up Skill #C2: Demonstrate proficiency in computational software (GaussView, FileZilla, Terminal, Python) and commands to submit, execute, and analyze computational calculations.
Level Up Skill #C2: Monitor your computational calculations.
Skill #3: Building a transition state
Level Up Skill #C3: Read and understand prior literature in the field to create an initial 3D structure that resembles the geometry of your transition state.
Level Up Skill #C3: Perform a partial optimization of the reaction coordinate to find a maximum energy structure.
Level Up Skill #C3: Use your optimized structure with key computational commands (e.g. opt=(ts, noeigen, calcfc) to obtain the real transition state.
Level Up Skill #C3: Verify your transition state by performing vibrational frequency analysis and confirming that there is exactly one imaginary frequency (negative eigenvalue).
Level Up Skill #C3: Visualize the vibration corresponding to the imaginary frequency to ensure it represents the correct bond breaking and bond forming step or refine the initial geometry if necessary and resubmit calculation.
Skill #4: Validating the results
Level Up Skill #C4: Ensure calculation was completed properly.
Level Up Skill #C4: Verify that the results are accurate and compared to experimental data or literature values.
Level Up Skill #C4: Estimate the energies of reactants, transition states, and products.
Level Up Skill #C4: Represent your results in a reaction coordinate diagram using the energies obtained from your computational calculation.