Search Results for author:
Found 55 papers, 26 papers with code
Improved motif-scaffolding with SE(3) flow matching
The first is motif amortization, in which FrameFlow is trained with the motif as input using a data augmentation strategy.
Navigating protein landscapes with a machine-learned transferable coarse-grained model
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost.
Fast protein backbone generation with SE(3) flow matching
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching.
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
To further strengthen the applicability of diffusion models to limited training data, we investigate the transferability of EQGAT-diff trained on the large PubChem3D dataset with implicit hydrogen atoms to target different data distributions.
Reaction coordinate flows for model reduction of molecular kinetics
In this work, we introduce a flow based machine learning approach, called reaction coordinate (RC) flow, for discovery of low-dimensional kinetic models of molecular systems.
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems.
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics
A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average.
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated with timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$).
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution.
Rigid Body Flows for Sampling Molecular Crystal Structures
Normalizing flows (NF) are a class of powerful generative models that have gained popularity in recent years due to their ability to model complex distributions with high flexibility and expressiveness.
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems.
Machine learning frontier orbital energies of nanodiamonds
Nanodiamonds have a wide range of applications including catalysis, sensing, tribology and biomedicine.
Ab-initio quantum chemistry with neural-network wavefunctions
Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also play an increasingly important role in scientific discovery.
Model-free optimization of power/efficiency tradeoffs in quantum thermal machines using reinforcement learning
We introduce a general model-free framework based on Reinforcement Learning to identify out-of-equilibrium thermodynamic cycles that are Pareto optimal trade-offs between power and efficiency for quantum heat engines and refrigerators.
Flow-matching -- efficient coarse-graining of molecular dynamics without forces
Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations.
Electronic excited states in deep variational Monte Carlo
Obtaining accurate ground and low-lying excited states of electronic systems is crucial in a multitude of important applications.
Equivariant Graph Attention Networks for Molecular Property Prediction
Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in drug discovery.
Unsupervised Learning of Group Invariant and Equivariant Representations
In this work, we extend group invariant and equivariant representation learning to the field of unsupervised deep learning.
Deeptime: a Python library for machine learning dynamical models from time series data
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics.
Smooth Normalizing Flows
In this work, we introduce a class of smooth mixture transformations working on both compact intervals and hypertori.
Identifying optimal cycles in quantum thermal machines with reinforcement-learning
The optimal control of open quantum systems is a challenging task but has a key role in improving existing quantum information processing technologies.
Progress in deep Markov State Modeling: Coarse graining and experimental data restraints
Recent advances in deep learning frameworks have established valuable tools for analyzing the long-timescale behavior of complex systems such as proteins.
Generating stable molecules using imitation and reinforcement learning
Chemical space is routinely explored by machine learning methods to discover interesting molecules, before time-consuming experimental synthesizing is attempted.
Machine Learning Implicit Solvation for Molecular Dynamics
Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data.
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
In this work we address this issue by proposing a permutation-invariant variational autoencoder for graph structured data.
Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry
We derive symmetric and antisymmetric kernels by symmetrizing and antisymmetrizing conventional kernels and analyze their properties.
Parameterized Hypercomplex Graph Neural Networks for Graph Classification
Despite recent advances in representation learning in hypercomplex (HC) space, this subject is still vastly unexplored in the context of graphs.
Ranked #15 on
Graph Property Prediction
on ogbg-molpcba
Auto-Encoding Molecular Conformations
In this work we introduce an Autoencoder for molecular conformations.
Training Invertible Linear Layers through Rank-One Perturbations
Many types of neural network layers rely on matrix properties such as invertibility or orthogonality.
Convergence to the fixed-node limit in deep variational Monte Carlo
Variational quantum Monte Carlo (QMC) is an ab-initio method for solving the electronic Schr\"odinger equation that is exact in principle, but limited by the flexibility of the available ansatzes in practice.
Deep-neural-network solution of the electronic Schrödinger equation
The electronic Schrödinger equation can only be solved analytically for the hydrogen atom, and the numerically exact full configuration-interaction method is exponentially expensive in the number of electrons.
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties
Equivariant neural networks (ENNs) are graph neural networks embedded in $\mathbb{R}^3$ and are well suited for predicting molecular properties.
Coarse Graining Molecular Dynamics with Graph Neural Networks
5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space.
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
We provide a theoretical sufficient criterion showing that the distribution generated by \textit{equivariant} normalizing flows is invariant with respect to these symmetries by design.
Coupling particle-based reaction-diffusion simulations with reservoirs mediated by reaction-diffusion PDEs
In this work, we develop modeling and numerical schemes for particle-based reaction-diffusion in an open setting, where the reservoirs are mediated by reaction-diffusion PDEs.
Quantitative Methods Chemical Physics Computational Physics 92C40, 92C45, 60J70, 60Gxx, 70Lxx
Stochastic Normalizing Flows
The sampling of probability distributions specified up to a normalization constant is an important problem in both machine learning and statistical mechanics.
Deep learning Markov and Koopman models with physical constraints
Here we develop theory and methods for deep learning Markov and Koopman models that can bear such physical constraints.
Computational Physics
Machine learning for protein folding and dynamics
Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning.
Machine learning for molecular simulation
Machine learning (ML) is transforming all areas of science.
Generating valid Euclidean distance matrices
Generating point clouds, e. g., molecular structures, in arbitrary rotations, translations, and enumerations remains a challenging task.
Equivariant Flows: sampling configurations for multi-body systems with symmetric energies
Flows are exact-likelihood generative neural networks that transform samples from a simple prior distribution to the samples of the probability distribution of interest.
Deep neural network solution of the electronic Schrödinger equation
The electronic Schr\"odinger equation describes fundamental properties of molecules and materials, but can only be solved analytically for the hydrogen atom.
Hydrodynamic coupling for particle-based solvent-free membrane models
The great challenge with biological membrane systems is the wide range of scales involved, from nanometers and picoseconds for individual lipids, to the micrometers and beyond millisecond for cellular signalling processes.
Computational Physics Biological Physics Fluid Dynamics
Kernel methods for detecting coherent structures in dynamical data
In particular, we show that kernel canonical correlation analysis (CCA) can be interpreted in terms of kernel transfer operators and that it can be obtained by optimizing the variational approach for Markov processes (VAMP) score.
Machine Learning for Molecular Dynamics on Long Timescales
Molecular Dynamics (MD) simulation is widely used to analyze the properties of molecules and materials.
Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning
Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge.
The mechanism of RNA base fraying: molecular dynamics simulations analyzed with core-set Markov state models
We here use molecular dynamics simulations and Markov state models to characterize the kinetics of RNA fraying and its sequence and direction dependence.
Computational Physics Statistical Mechanics Biological Physics Chemical Physics Biomolecules
Variational Selection of Features for Molecular Kinetics
The modeling of atomistic biomolecular simulations using kinetic models such as Markov state models (MSMs) has had many notable algorithmic advances in recent years.
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data.
An efficient multi-scale Green's Functions Reaction Dynamics scheme
The algorithm is shown to be more efficient than brute-force Brownian dynamics simulations up to a molar concentration of $10^{3}\mu M$ and is up to an order of magnitude more efficient compared with previous MD-GFRD schemes.
Chemical Physics Computational Physics
VAMPnets: Deep learning of molecular kinetics
There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations.
Variational approach for learning Markov processes from time series data
This leads to the definition of a family of score functions called VAMP-r which can be calculated from data, and can be employed to optimize a Markovian model.
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations
Recently, a powerful generalization of MSMs has been introduced, the variational approach (VA) of molecular kinetics and its special case the time-lagged independent component analysis (TICA), which allow us to approximate slow collective variables and molecular kinetics by linear combinations of smooth basis functions or order parameters.
Spectral learning of dynamic systems from nonequilibrium data
Observable operator models (OOMs) and related models are one of the most important and powerful tools for modeling and analyzing stochastic systems.
Bayesian hidden Markov model analysis of single-molecule force spectroscopy: Characterizing kinetics under measurement uncertainty
Single-molecule force spectroscopy has proven to be a powerful tool for studying the kinetic behavior of biomolecules.
Statistical Mechanics Biological Physics Biomolecules
