A5085917255- Computational Drug Discovery Methods
- Crystallography and molecular interactions
- Advanced Chemical Physics Studies
- Microbial Natural Products and Biosynthesis
- Chalcogenide Semiconductor Thin Films
- Solid-state spectroscopy and crystallography
- Machine Learning in Materials Science
- High-pressure geophysics and materials
- X-ray Diffraction in Crystallography
- 2D Materials and Applications
- MXene and MAX Phase Materials
- Material Dynamics and Properties
- Phase Equilibria and Thermodynamics
- Nonlinear Optical Materials Research
- Chemical Thermodynamics and Molecular Structure
- Advanced NMR Techniques and Applications
- Perovskite Materials and Applications
- Crystallization and Solubility Studies
- Thermal and Kinetic Analysis
University of California, San Francisco
2022
Gilead Sciences (United States)
2022
University of California, Riverside
2013-2021
University of California System
2017-2020
Riverside
2017-2020
Transition metal dichalcogenides (TMDs) have emerged as a new class of two-dimensional materials that are promising for electronics and photonics. To date, optoelectronic measurements in these shown the conventional behavior expected from photoconductors such linear or sublinear dependence photocurrent on light intensity. Here, we report observation regime operation where depends superlinearly We use spatially resolved devices consisting CVD-grown monolayers TMD alloys spanning MoS2 to MoSe2...
Sputtering of MoS2 films single-layer thickness by low-energy argon ions selectively reduces the sulfur content material without significant depletion molybdenum. X-ray photoelectron spectroscopy shows little modification Mo 3d states during this process, suggesting absence reorganization or damage to overall structure film. Accompanying ab initio molecular dynamics simulations find clusters vacancies in top plane be structurally stable. Measurements photoluminescence at temperatures between...
Molecular crystal structure prediction is increasingly being applied to study the solid form landscapes of larger, more flexible pharmaceutical molecules. Despite many successes in prediction, van der Waals-inclusive density functional theory (DFT) methods exhibit serious failures predicting polymorph stabilities for a number systems exhibiting conformational polymorphism, where changes intramolecular conformation lead different intermolecular packings. Here, polymorphs o-acetamidobenzamide,...
Machine learning-based drug discovery success depends on molecular representation. Yet traditional fingerprints omit both the protein and pointers back to structural information that would enable better model interpretability. Therefore, we propose LUNA, a Python 3 toolkit calculates encodes protein–ligand interactions into new hashed inspired by Extended Connectivity FingerPrint (ECFP): EIFP (Extended Interaction FingerPrint), FIFP (Functional Hybrid (HIFP). LUNA also provides visual...
Hybrid quasi-harmonic electronic structure strategies can predict molecular crystal thermal expansion and thermochemistry in good agreement with experiments at reasonable computational cost.
Accurate electronic structure calculations for the structures and simulated Raman spectra of high-pressure carbon dioxide suggest phases III VII are identical, phase diagram should be revised.
Ab initio nuclear magnetic resonance chemical shift prediction plays an important role in the determination or validation of crystal structures. The ability to predict shifts more accurately can translate increased confidence resulting structural assignments. Standard electronic structure predictions for molecular structures neglect thermal expansion, which lead appreciable underestimation molar volumes. This study examines this volume error and its impact on 68 13C- 28 15N-predicted taken...
Abstract Machine learning-based drug discovery success depends on molecular representation. Yet traditional fingerprints omit both the protein and pointers back to structural information that would enable better model interpretability. Therefore, we propose LUNA, a Python 3 toolkit calculates encodes protein-ligand interactions into new hashed inspired by Extended Connectivity Finger-Print (ECFP): EIFP (Extended Interaction FingerPrint), FIFP (Functional Hybrid FingerPrint (HIFP). LUNA also...
The ability to predict not only what organic crystal structures might occur but also the thermodynamic conditions under which they are most stable would be extremely useful for discovering and designing new materials. present study takes a step in that direction by predicting temperature- pressure-dependent phase boundary between α β polymorphs of resorcinol using density functional theory (DFT) quasi-harmonic approximation. To circumvent major computational bottleneck associated with...