Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search

• URL: http://arxiv.org/abs/2410.08833v2
• Date: Thu, 12 Dec 2024 16:22:24 GMT
• Title: Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search
• Authors: Akshay Subramanian, James Damewood, Juno Nam, Kevin P. Greenman, Avni P. Singhal, Rafael Gómez-Bombarelli,
• Abstract summary: Near-infrared (NIR) sensitive molecules have unique applications in night-vision equipment and biomedical imaging.<n>We leverage structural priors from domain-focused, patent-mined datasets of organic electronic molecules.<n>Our approach generates candidates that retain symmetry constraints from the patent dataset, while also exhibiting red-shifted absorption.
• Score: 0.7893073641122971
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Organic optoelectronic materials are a promising avenue for next-generation electronic devices due to their solution processability, mechanical flexibility, and tunable electronic properties. In particular, near-infrared (NIR) sensitive molecules have unique applications in night-vision equipment and biomedical imaging. Molecular engineering has played a crucial role in developing non-fullerene acceptors (NFAs) such as the Y-series molecules, which have significantly improved the power conversion efficiency (PCE) of solar cells and enhanced spectral coverage in the NIR region. However, systematically designing molecules with targeted optoelectronic properties while ensuring synthetic accessibility remains a challenge. To address this, we leverage structural priors from domain-focused, patent-mined datasets of organic electronic molecules using a symmetry-aware fragment decomposition algorithm and a fragment-constrained Monte Carlo Tree Search (MCTS) generator. Our approach generates candidates that retain symmetry constraints from the patent dataset, while also exhibiting red-shifted absorption, as validated by TD-DFT calculations.

Related papers

• Electron-Electron Interactions in Device Simulation via Non-equilibrium Green's Functions and the GW Approximation [71.63026504030766]
  electron-electron (e-e) interactions must be explicitly incorporated in quantum transport simulation. This study is the first one reporting large-scale atomistic quantum transport simulations of nano-devices under non-equilibrium conditions.
  arXiv  Detail & Related papers  (2024-12-17T15:05:33Z)
• Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond [33.54862439531144]
  Development of reliable and molecular mechanics (MM) force fields is indispensable for biomolecular simulation and computer-aided drug design. We introduce a generalized and machine-learned MM force field, ttexttespaloma-0.3, and an end-to-end differentiable framework using graph neural networks. The force field reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids.
  arXiv  Detail & Related papers  (2023-07-13T23:00:22Z)
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations [51.68332623405432]
  Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations.
  arXiv  Detail & Related papers  (2022-05-17T13:08:28Z)
• A2I Transformer: Permutation-equivariant attention network for pairwise and many-body interactions with minimal featurization [0.1469945565246172]
  In this work, we suggest an end-to-end model which directly predicts per-atom energy from the coordinates of particles. We tested our model against several challenges in molecular simulation problems, including periodic boundary condition (PBC), $n$-body interaction, and binary composition.
  arXiv  Detail & Related papers  (2021-10-27T12:18:25Z)
• Geometric Transformer for End-to-End Molecule Properties Prediction [92.28929858529679]
  We introduce a Transformer-based architecture for molecule property prediction, which is able to capture the geometry of the molecule. We modify the classical positional encoder by an initial encoding of the molecule geometry, as well as a learned gated self-attention mechanism.
  arXiv  Detail & Related papers  (2021-10-26T14:14:40Z)
• Demonstration of electron-nuclear decoupling at a spin clock transition [54.088309058031705]
  Clock transitions protect molecular spin qubits from magnetic noise. linear coupling to nuclear degrees of freedom causes a modulation and decay of electronic coherence. An absence of quantum information leakage to the nuclear bath provides opportunities to characterize other decoherence sources.
  arXiv  Detail & Related papers  (2021-06-09T16:23:47Z)
• Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations [21.237758981760784]
  We present a SMILES Variational Auto-Encoder (VAE) augmented with topological data analysis (TDA) representations of molecules. Our experiments show that this TDA augmentation enables a SMILES VAE to capture the complex relation between 3D geometry and electronic properties.
  arXiv  Detail & Related papers  (2021-06-08T15:49:21Z)
• BIGDML: Towards Exact Machine Learning Force Fields for Materials [55.944221055171276]
  Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 atoms.
  arXiv  Detail & Related papers  (2021-06-08T10:14:57Z)
• Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics [4.276697874428501]
  We present a novel framework to advance simulation by up to three orders of magnitude. LED learns the effective dynamics of molecular systems. We demonstrate the effectiveness of LED in the M"ueller-Brown potential, the Trp Cage protein, and the alanine dipeptide.
  arXiv  Detail & Related papers  (2021-02-17T15:15:37Z)
• Optimizing Molecules using Efficient Queries from Property Evaluations [66.66290256377376]
  We propose QMO, a generic query-based molecule optimization framework. QMO improves the desired properties of an input molecule based on efficient queries. We show that QMO outperforms existing methods in the benchmark tasks of optimizing small organic molecules.
  arXiv  Detail & Related papers  (2020-11-03T18:51:18Z)
• Machine learning of solvent effects on molecular spectra and reactions [3.4376560669160394]
  We introduce the deep neural network FieldSchNet for modeling the interaction of molecules with arbitrary external fields. FieldSchNet offers access to a wealth of molecular response properties, enabling it to simulate a wide range of molecular spectra. We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction.
  arXiv  Detail & Related papers  (2020-10-28T12:50:30Z)
• Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes [51.17758371472664]
  Photoswitchable molecules display two or more isomeric forms that may be accessed using light. We present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning. We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules.
  arXiv  Detail & Related papers  (2020-06-28T20:59:03Z)

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.