From the Automated Calculation of Potential Energy Surfaces to Accurate Infrared Spectra
B Schröder, G Rauhut - The Journal of Physical Chemistry …, 2024 - ACS Publications
Advances in the development of quantum chemical methods and progress in multicore
architectures in computer science made the simulation of infrared spectra of isolated …
architectures in computer science made the simulation of infrared spectra of isolated …
[HTML][HTML] Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and …
To design new materials and understand their novel phenomena, it is imperative to predict
the structure and properties of materials that often rely on first-principles theory. However …
the structure and properties of materials that often rely on first-principles theory. However …
Improved decision making with similarity based machine learning: applications in chemistry
Despite the fundamental progress in autonomous molecular and materials discovery, data
scarcity throughout chemical compound space still severely hampers the use of modern …
scarcity throughout chemical compound space still severely hampers the use of modern …
Adapting hybrid density functionals with machine learning
Exact exchange contributions significantly affect electronic states, influencing covalent bond
formation and breaking. Hybrid density functional approximations, which average exact …
formation and breaking. Hybrid density functional approximations, which average exact …
All-in-one foundational models learning across quantum chemical levels
Y Chen, PO Dral - arxiv preprint arxiv:2409.12015, 2024 - arxiv.org
Machine learning (ML) potentials typically target a single quantum chemical (QC) level while
the ML models developed for multi-fidelity learning have not been shown to provide scalable …
the ML models developed for multi-fidelity learning have not been shown to provide scalable …
Accurate Neural Network Fine-Tuning Approach for Transferable Ab Initio Energy Prediction across Varying Molecular and Crystalline Scales
WP Ng, Z Zhang, J Yang - Journal of Chemical Theory and …, 2025 - ACS Publications
Existing machine learning models attempt to predict the energies of large molecules by
training small molecules, but eventually fail to retain high accuracy as the errors increase …
training small molecules, but eventually fail to retain high accuracy as the errors increase …
Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning …
This work proposes several machine learning models that predict B3LYP-D4/def-TZVP
outputs from HF-3c outputs for supramolecular structures. The data set consists of 1031 …
outputs from HF-3c outputs for supramolecular structures. The data set consists of 1031 …
Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for Δ-machine learning
We introduce the alchemical harmonic approximation (AHA) of the absolute electronic
energy for charge-neutral iso-electronic diatomics at fixed interatomic distance d 0. To …
energy for charge-neutral iso-electronic diatomics at fixed interatomic distance d 0. To …
Predicting Molecular Energies of Small Organic Molecules with Multifidelity Methods
V Vinod, D Lyu, M Ruth, U Kleinekathöfer… - 2025 - chemrxiv.org
Multifidelity methods in machine learning (ML) have seen an increasing usage for the
prediction of quantum chemical properties. These methods, such as∆-ML and multifidelity …
prediction of quantum chemical properties. These methods, such as∆-ML and multifidelity …
[PDF][PDF] Relative and systematic methods in computational alchemy
SL Krug - 2024 - depositonce.tu-berlin.de
The prediction of properties is among the central pursuits of computational chemistry,
condensed matter, and materials design. However, the mathematical and computational …
condensed matter, and materials design. However, the mathematical and computational …