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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

Trend · papers per month

count% of corpus
1122 · Jul 202019922001200920172026
3 results for “photochemistry”
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamicsphysics.chem-ph

SchNarc combines SchNet and SHARC for efficient photodynamics simulations.

problem Efficiently simulate excited-state dynamics of complex molecules.
method Combines SchNet for multiple electronic states with SHARC for molecular dynamics, learning energies, forces, and couplings.
result Paves the way for efficient photodynamics simulations of complex systems.
SchNetSHARCdeep learningexcited-state dynamicsphotochemistry
2020-02-17abs ↗pdf ↗
Machine learning for electronically excited states of moleculesphysics.chem-ph

Machine learning speeds up quantum chemical calculations of excited states.

problem Accurate quantum chemical calculations of excited states are computationally expensive.
method Employing machine learning to speed up and advance excited-state simulations in various fields.
result Machine learning techniques can significantly reduce computational time for excited-state simulations.
machine learningexcited statesquantum chemistryphotochemistryphotophysics
2020-07-10abs ↗pdf ↗
Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Spacephysics.chem-ph

Paper explores ML for UV spectra, showing transferability in chemical space.

problem Modeling excited states and predicting properties of unseen molecules.
method Adapting charge model for excited states, using SchNarc approach.
result ML models can predict properties of unseen molecules and different excited states.
machine learningexcited statestransferabilitychemical spaceUV absorption
2020-07-15abs ↗pdf ↗