1 code implementation • 11 Mar 2024 • Nima Karimitari, William J. Baldwin, Evan W. Muller, Zachary J. L. Bare, W. Joshua Kennedy, Gábor Csányi, Christopher Sutton

Our model is then combined with a simple random structure search algorithm to predict the structure of hypothetical HOIPs given only the proposed composition.

2 code implementations • 13 Feb 2024 • Rokas Elijošius, Fabian Zills, Ilyes Batatia, Sam Walton Norwood, Dávid Péter Kovács, Christian Holm, Gábor Csányi

Using insights from the trained model, we present Similarity-based Molecular Generation (SiMGen), a new method for zero shot molecular generation.

1 code implementation • 30 Jan 2024 • Ivan Grega, Ilyes Batatia, Gábor Csányi, Sri Karlapati, Vikram S. Deshpande

In this work, we generate a big dataset of structure-property relationships for strut-based lattices.

no code implementations • 16 Oct 2023 • Ilyes Batatia, Lars L. Schaaf, Huajie Chen, Gábor Csányi, Christoph Ortner, Felix A. Faber

Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications.

Ranked #5 on Graph Regression on ZINC-500k

no code implementations • 9 Oct 2022 • Cas van der Oord, Matthias Sachs, Dávid Péter Kovács, Christoph Ortner, Gábor Csányi

Data-driven interatomic potentials have emerged as a powerful class of surrogate models for {\it ab initio} potential energy surfaces that are able to reliably predict macroscopic properties with experimental accuracy.

no code implementations • 2 Oct 2022 • James P. Darby, Dávid P. Kovács, Ilyes Batatia, Miguel A. Caro, Gus L. W. Hart, Christoph Ortner, Gábor Csányi

Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modelling and the visualisation and analysis of materials datasets. The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them.

2 code implementations • 15 Jun 2022 • Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, Gábor Csányi

In particular, we show that using four-body messages reduces the required number of message passing iterations to just two, resulting in a fast and highly parallelizable model, reaching or exceeding state-of-the-art accuracy on the rMD17, 3BPA, and AcAc benchmark tasks.

2 code implementations • 13 May 2022 • Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi

The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures.

no code implementations • 12 Jan 2021 • Felix Musil, Andrea Grisafi, Albert P. Bartók, Christoph Ortner, Gábor Csányi, Michele Ceriotti

The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation.

Chemical Physics

1 code implementation • ICLR 2021 • Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández-Lobato

Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials.

no code implementations • 21 Jun 2020 • Chiheb Ben Mahmoud, Andrea Anelli, Gábor Csányi, Michele Ceriotti

The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture, and is central to modern electronic structure theory.

1 code implementation • 28 Dec 2015 • Sandip De, Albert P. Bartók, Gábor Csányi, Michele Ceriotti

For instance, a structural similarity metric is crucial for classifying structures, searching chemical space for better compounds and materials, and driving the next generation of machine-learning techniques for predicting the stability and properties of molecules and materials.

Materials Science Chemical Physics

2 code implementations • 14 Sep 2012 • Albert P. Bartók, Risi Kondor, Gábor Csányi

We review some recently published methods to represent atomic neighbourhood environments, and analyse their relative merits in terms of their faithfulness and suitability for fitting potential energy surfaces.

Computational Physics Materials Science

2 code implementations • 12 Dec 2009 • Brendon J. Brewer, Livia B. Pártay, Gábor Csányi

We introduce a general Monte Carlo method based on Nested Sampling (NS), for sampling complex probability distributions and estimating the normalising constant.

Computation Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability

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