A data-driven interpretation of the stability of molecular crystals
- URL: http://arxiv.org/abs/2209.10709v1
- Date: Wed, 21 Sep 2022 23:32:53 GMT
- Title: A data-driven interpretation of the stability of molecular crystals
- Authors: Rose K. Cersonsky, Maria Pakhnova, Edgar A. Engel, Michele Ceriotti
- Abstract summary: Predicting the stability of crystal structures formed from molecular building blocks is a non-trivial scientific problem.
We introduce a structural descriptor tailored to the prediction of the binding energy for a curated dataset of organic crystals.
We then interpret this library using a low-dimensional representation of the structure-energy landscape.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Due to the subtle balance of intermolecular interactions that govern
structure-property relations, predicting the stability of crystal structures
formed from molecular building blocks is a highly non-trivial scientific
problem. A particularly active and fruitful approach involves classifying the
different combinations of interacting chemical moieties, as understanding the
relative energetics of different interactions enables the design of molecular
crystals and fine-tuning their stabilities. While this is usually performed
based on the empirical observation of the most commonly encountered motifs in
known crystal structures, we propose to apply a combination of supervised and
unsupervised machine-learning techniques to automate the construction of an
extensive library of molecular building blocks. We introduce a structural
descriptor tailored to the prediction of the binding energy for a curated
dataset of organic crystals and exploit its atom-centered nature to obtain a
data-driven assessment of the contribution of different chemical groups to the
lattice energy of the crystal. We then interpret this library using a
low-dimensional representation of the structure-energy landscape and discuss
selected examples of the insights that can be extracted from this analysis,
providing a complete database to guide the design of molecular materials.
Related papers
- UniIF: Unified Molecule Inverse Folding [67.60267592514381]
We propose a unified model UniIF for inverse folding of all molecules.
Our proposed method surpasses state-of-the-art methods on all tasks.
arXiv Detail & Related papers (2024-05-29T10:26:16Z) - AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning [4.437756445215657]
We present AlphaCrystal-II, a novel knowledge-based solution that exploits the abundant inter-atomic interaction patterns found in existing crystal structures.
By leveraging the wealth of inter-atomic relationships of known crystal structures, our approach demonstrates remarkable effectiveness and reliability in structure prediction.
arXiv Detail & Related papers (2024-04-07T05:17:43Z) - Stoichiometry Representation Learning with Polymorphic Crystal
Structures [54.65985356122883]
Stoichiometry descriptors can reveal the ratio between elements involved to form a certain compound without any structural information.
We propose PolySRL, which learns the probabilistic representation of stoichiometry by utilizing the readily available structural information.
arXiv Detail & Related papers (2023-11-17T20:34:28Z) - From molecules to scaffolds to functional groups: building context-dependent molecular representation via multi-channel learning [10.025809630976065]
This paper introduces a novel pre-training framework that learns robust and generalizable chemical knowledge.
Our approach demonstrates competitive performance across various molecular property benchmarks.
arXiv Detail & Related papers (2023-11-05T23:47:52Z) - Scalable Diffusion for Materials Generation [99.71001883652211]
We develop a unified crystal representation that can represent any crystal structure (UniMat)
UniMat can generate high fidelity crystal structures from larger and more complex chemical systems.
We propose additional metrics for evaluating generative models of materials.
arXiv Detail & Related papers (2023-10-18T15:49:39Z) - Data-Driven Score-Based Models for Generating Stable Structures with
Adaptive Crystal Cells [1.515687944002438]
This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical composition.
The novelty of the presented approach resides in the fact that the lattice of the crystal cell is not fixed.
A multigraph crystal representation is introduced that respects symmetry constraints, yielding computational advantages.
arXiv Detail & Related papers (2023-10-16T02:53:24Z) - Crystal-GFN: sampling crystals with desirable properties and constraints [103.79058968784163]
We introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials.
In this paper, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench.
The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.
arXiv Detail & Related papers (2023-10-07T21:36:55Z) - State-specific protein-ligand complex structure prediction with a
multi-scale deep generative model [68.28309982199902]
We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures.
Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
arXiv Detail & Related papers (2022-09-30T01:46:38Z) - A graph representation of molecular ensembles for polymer property
prediction [3.032184156362992]
In contrast to organic molecules, polymers are often not well-defined single structures but an ensemble of similar molecules.
We introduce a graph representation of molecular ensembles and an associated graph neural network architecture that is tailored to polymer property prediction.
arXiv Detail & Related papers (2022-05-17T20:31:43Z) - A silicon qubit platform for in situ single molecule structure
determination [0.7187911114620571]
Imaging individual conformational instances of generic, inhomogeneous, transient or intrinsically disordered protein systems at the single molecule level in situ is one of the notable challenges in structural biology.
Here we tackle the problem by designing a single molecule imaging platform technology embracing the advantages silicon-based spin qubits.
We demonstrate through detailed simulation, that this platform enables scalable atomic-level structure-determination of individual molecular systems in native environments.
arXiv Detail & Related papers (2021-12-07T10:42:09Z) - Learning Neural Generative Dynamics for Molecular Conformation
Generation [89.03173504444415]
We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph.
We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
arXiv Detail & Related papers (2021-02-20T03:17:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.