1.6k citations · 4k across the 16 of their papers we have counts for
17 papers
Atoms that Matter: Largest Materials Datasets Are 99% Redundant, and Local Chemistry Can Guide the Required 1%
Evgeny Moerman, Igor Poltavsky, Anton Charkin-Gorbulin +1
General-purpose machine-learning force fields (MLFFs) for materials are now trained on the order of million structures, assuming transferability requires such scale. We test…
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
Accurate quantum Monte Carlo forces for machine-learned force fields: Ethanol as a benchmark
Emiel Slootman, Igor Poltavsky, Ravindra Shinde +4
Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC forces f…
Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields Under the Microscope
Gregory Fonseca, Igor Poltavsky, Alexandre Tkatchenko
As the sophistication of Machine Learning Force Fields (MLFF) increases to match the complexity of extended molecules and materials, so does the need for tools to properly analyze…
Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules
Adil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela +2
Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fract…
Improving Molecular Force Fields Across Configurational Space by Combining Supervised and Unsupervised Machine Learning
Gregory Fonseca, Igor Poltavsky, Valentin Vassilev-Galindo +1
The training set of atomic configurations is key to the performance of any Machine Learning Force Field (MLFF) and, as such, the training set selection determines the applicability…
Challenges for Machine Learning Force Fields in Reproducing Potential Energy Surfaces of Flexible Molecules
Valentin Vassilev-Galindo, Gregory Fonseca, Igor Poltavsky +1
Dynamics of flexible molecules are often determined by an interplay between local chemical bond fluctuations and conformational changes driven by long-range electrostatics and van…
Machine Learning Force Fields
Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda +5
In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditiona…
Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky +2
Highly accurate force fields are a mandatory requirement to generate predictive simulations. Here we present the path for the construction of machine learned molecular force fields…
Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky +2
We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) app…
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky +2
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global pot…
i-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations
Venkat Kapil, Mariana Rossi, Ondrej Marsalek +23
Progress in the atomic-scale modelling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomi…
Machine Learning of Accurate Energy-Conserving Molecular Force Fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda +3
Using conservation of energy - a fundamental property of closed classical and quantum mechanical systems - we develop an efficient gradient-domain machine learning (GDML) approach…
Quantum Tunneling of Thermal Protons Through Pristine Graphene
Igor Poltavsky, Limin Zheng, Majid Mortazavi +1
Atomically thin two-dimensional materials such as graphene and hexagonal boron nitride have recently been found to exhibit appreciable permeability to thermal protons, making these…
Thermal and electronic fluctuations of flexible adsorbed molecules: Azobenzene on Ag(111)
Reinhard J. Maurer, Wei Liu, Igor Poltavsky +4
We investigate the thermal and electronic collective fluctuations that contribute to the finite-temperature adsorption properties of flexible adsorbates on surfaces on the example…
Thermodynamics of low dimensional spin-1/2 Heisenberg ferromagnets in an external magnetic field within Green function formalism
T. N. Antsygina, M. I. Poltavskaya, I. I. Poltavsky +1
The thermodynamics of low dimensional spin-1/2 Heisenberg ferromagnets (HFM) in an external magnetic field is investigated within a second-order two-time Green function formalism i…