Adriana Ladera

MIT Department: Chemical Engineering
Faculty Mentor: Prof. Heather Kulik
Undergraduate Institution: University of South Florida, Tampa
Website: LinkedIn, Instagram
Research Poster
Lightning Talk

Biography


Hello friends! My name is Adriana Ladera (she/her), and I’m a rising senior at the University of South Florida, USF, pursuing a Bachelor’s degree in Computer Science with a minor in Physics. My current research interests are scientific computing, numerical analysis, and algorithm design, which were heavily inspired by my previous work in computational modeling with the USF Computational Nanoscience Group and the 2019 Material Science/Physics REU at Penn State. Gratefully, these experiences have resulted in my co-authorship of three papers on the computational studies of ferroelectric and relaxor ferroelectric materials, which were published in Acta Materialia, Physical Review B, and the Journal of Applied Physics.

I plan to obtain my Ph.D in Computational Science and Engineering and eventually pursue a career as a research professor with an emphasis on strongly promoting diversity in STEM, especially for women, people of color, and the LGBTQ+ community. Outside of the lab (aka my computer), I enjoy playing piano, running, rock climbing, and creating meme art!

2021 Abstract


Exploring Transition Metal Complex Space with Computation and Artificial Neural Networks

Adriana Ladera1,2, Chenru Duan2,3, Vyshnavi Vennelakanti2,3, Heather J. Kulik2
1 Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA
2 Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
3 Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Transition metal complexes (TMCs) are promising molecular systems of interest due to their broad applications in catalysis, sensing, and energy storage. However, due to their complex electronic structure, TMCs present unique challenges for existing computational electronic structure methods. Density functional theory (DFT), one of the most widely used electronic structure methods, is prone to be inaccurate in predicting properties of TMCs, and property predictions can disagree significantly depending on the choice of density functional approximation (DFA). Given the difficulties in their property evaluation, TMCs could be of interest for benchmarking electronic structure method development beyond DFT. We evaluated the total atomization energy (TAE) of selected TMCs using 23 DFAs that had varying levels of accuracy. We then trained artificial neural networks (ANNs) to learn the TAEs of these TMCs for each of the 23 DFAs separately, targeting TMCs in which relative DFA disagreement on TAE was large, indicating complex electronic structure. We can then build a workflow that would identify the TMCs with the most complex electronic structure via targeting TMCs with large disagreement across DFAs, and yield a benchmark set for the development of new electronic structure methods.