10 citations · 18 across the 5 of their papers we have counts for
6 papers
A Machine-Learned Interatomic Potential Free-Energy Surface for the First Step of MIL-101(Cr) Secondary Building Unit Formation
Orlando A. Mendible-Barreto, Yamil J. Colón
Metal-organic framework (MOF) self-assembly mechanisms remain poorly characterized because ab initio molecular dynamics (AIMD) are accurate but too computationally expensive to con…
DynaMate2: runtime registration of expert-defined tools for agentic scientific workflow automation
Orlando A. Mendible-Barreto, Ajay Vallabh, Ubaldo M. Córdova-Figueroa +1
Agentic large-language-model systems can coordinate scientific tools, but many implementations remain difficult for domain scientists to extend without modifying the source orchest…
Hybrid Atomistic-Parametric Decoherence Model for Molecular Spin Qubits
Katy Aruachan, Sanoj Raj, Yamil J. Colón +2
Solid-state molecular qubits with open-shell ground states have great potential for addressability, scalability, and tunability, but understanding the fundamental limits of quantum…
Considerations in the use of ML interaction potentials for free energy calculations
Orlando A. Mendible, Jonathan K. Whitmer, Yamil J. Colón
Machine learning force fields (MLFFs) promise to accurately describe the potential energy surface of molecules at the ab initio level of theory with improved computational efficien…
Semi-Empirical Haken-Strobl Model for Molecular Spin Qubits
Katy Aruachan, Yamil J. Colón, Daniel Aravena +1
Understanding the physical processes that determine the relaxation and dephasing times of molecular spin qubits is critical for envisioned applications in quantum met…
Machine learning identification of organic compounds using visible light
Thulasi Bikku, Rubén A. Fritz, Yamil J. Colón +1
Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical…