# Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting

@article{Thaler2021LearningNN, title={Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting}, author={Stephan Thaler and Julija Zavadlav}, journal={Nature Communications}, year={2021}, volume={12} }

In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently. Top-down approaches that learn NN potentials directly from experimental data have received less attention, typically facing numerical and computational challenges when backpropagating through MD simulations. We present the Differentiable Trajectory Reweighting (DiffTRe) method, which bypasses differentiation through the MD simulation for time-independent…

## 10 Citations

### Deep coarse-grained potentials via relative entropy minimization.

- Computer ScienceThe Journal of chemical physics
- 2022

This work demonstrates that RE training is more data efficient, due to accessing the CG distribution during training, resulting in improved free energy surfaces and reduced sensitivity to prior potentials, and supports the use of training objectives beyond FM, as a promising direction for improving CG NN potential's accuracy and reliability.

### Learning Pair Potentials using Differentiable Simulations

- Computer ScienceThe Journal of chemical physics
- 2023

This work proposes a general stochastic method for learning pair interactions from data using differentiable simulations (DiffSim), and demonstrates the approach by recovering simple pair potentials, such as Lennard-Jones systems, from radial distribution functions.

### Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls

- Computer ScienceArXiv
- 2022

It is demonstrated here that scalable Bayesian UQ via stochastic gradient MCMC (SG-MCMC) yields reliable uncertainty estimates for MD observables and that cold posteriors can reduce the required training data size and that for reliable UQ, multiple Markov chains are needed.

### Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

- Computer ScienceArXiv
- 2022

A novel benchmark suite for ML MD simulation is introduced, identifying stability as a key metric for ML models to improve and illustrating, in particular, how the commonly benchmarked force accuracy is not well aligned with relevant simulation metrics.

### Machine Learning Coarse-Grained Potentials of Protein Thermodynamics

- Computer ScienceArXiv
- 2022

It is shown that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins, indicating that machine learning coarse- grained potentials could provide a feasible approach to simulate and understand protein dynamics.

### Coarse-grained molecular dynamics study based on TorchMD

- Computer ScienceChinese Journal of Chemical Physics
- 2021

The workflow in this work provides another option to study the protein folding and other relative processes with the deep learning CG model and shows that the main phenomenon of protein folding with TorchMD CG model is the same as the all-atom simulations, but with a less simulating time scale.

### Is there a one-to-one correspondence between interparticle interactions and physical properties of liquid?

- PhysicsPhysica A: Statistical Mechanics and its Applications
- 2022

### Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy.

- Materials ScienceThe journal of physical chemistry letters
- 2022

Novel optoelectronic materials have the potential to revolutionize the ongoing green transition by both providing more efficient photovoltaic (PV) devices and lowering energy consumption of devices…

### Machine learned coarse-grained protein force-fields: Are we there yet?

- Computer ScienceCurrent opinion in structural biology
- 2023

### Emerging Era of Biomolecular Membrane Simulations: Automated Physically-Justified Force Field Development and Quality-Evaluated Databanks

- The Journal of Physical Chemistry B
- 2022

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