Transfer learning discovery of molecular modulators for perovskite solar cells

• URL: http://arxiv.org/abs/2511.00204v1
• Date: Fri, 31 Oct 2025 19:04:48 GMT
• Title: Transfer learning discovery of molecular modulators for perovskite solar cells
• Authors: Haoming Yan, Xinyu Chen, Yanran Wang, Zhengchao Luo, Weizheng Huang, Hongshuai Wang, Peng Chen, Yuzhi Zhang, Weijie Sun, Jinzhuo Wang, Qihuang Gong, Rui Zhu, Lichen Zhao,
• Abstract summary: Machine learning offers potential for accelerating materials discovery.<n>Applying ML to PSCs remains a major challenge due to data scarcity and limitations of traditional quantitative structure-property relationship (QSPR) models.<n>Here, we apply a chemical informed transfer learning framework based on pre-trained deep neural networks, which achieves high accuracy in predicting the molecular modulator's effect on the power conversion efficiency (PCE) of PSCs.
• Score: 12.261047353532591
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The discovery of effective molecular modulators is essential for advancing perovskite solar cells (PSCs), but the research process is hindered by the vastness of chemical space and the time-consuming and expensive trial-and-error experimental screening. Concurrently, machine learning (ML) offers significant potential for accelerating materials discovery. However, applying ML to PSCs remains a major challenge due to data scarcity and limitations of traditional quantitative structure-property relationship (QSPR) models. Here, we apply a chemical informed transfer learning framework based on pre-trained deep neural networks, which achieves high accuracy in predicting the molecular modulator's effect on the power conversion efficiency (PCE) of PSCs. This framework is established through systematical benchmarking of diverse molecular representations, enabling lowcost and high-throughput virtual screening over 79,043 commercially available molecules. Furthermore, we leverage interpretability techniques to visualize the learned chemical representation and experimentally characterize the resulting modulator-perovskite interactions. The top molecular modulators identified by the framework are subsequently validated experimentally, delivering a remarkably improved champion PCE of 26.91% in PSCs.

Related papers

• Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators [10.025462072265706]
  We show that incorporating auxiliary sources of information can improve the data efficiency and generalization of implicit transfer operators for molecular dynamics.<n>Our approach, PLaTITO, achieves state-of-the-art performance on equilibrium sampling benchmarks for out-of-distribution protein systems.
  arXiv  Detail & Related papers  (2026-02-11T09:26:12Z)
• Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis [51.83339196548892]
  ChemCraft is a novel framework that decouples chemical reasoning from knowledge storage.<n>ChemCraft achieves superior performance with minimal inference costs.<n>This work establishes a cost-effective and privacy-preserving paradigm for AI-aided chemistry.
  arXiv  Detail & Related papers  (2026-01-25T04:23:34Z)
• Quantum-centric machine learning for molecular dynamics [9.147935750042352]
  ab initio methods require self-consistent optimization of electronic states at every nuclear configuration.<n>We introduce a quantum-centric machine learning (QCML) model-a hybrid quantum-classical framework.<n>By pretraining the Transformer on a diverse dataset of molecules, QCML learns transferable mappings between molecular descriptors and PQC parameters.
  arXiv  Detail & Related papers  (2025-11-11T02:41:31Z)
• Foundation Models for Discovery and Exploration in Chemical Space [57.97784111110166]
  MIST is a family of molecular foundation models trained on large unlabeled datasets.<n>We demonstrate the ability of these models to solve real-world problems across chemical space.
  arXiv  Detail & Related papers  (2025-10-20T17:56:01Z)
• DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra [60.39311767532607]
  We present DiffMS, a formula-restricted encoder-decoder generative network that achieves state-of-the-art performance on this task.<n>To develop a robust decoder that bridges latent embeddings and molecular structures, we pretrain the diffusion decoder with fingerprint-structure pairs.<n>Experiments on established benchmarks show that DiffMS outperforms existing models on de novo molecule generation.
  arXiv  Detail & Related papers  (2025-02-13T18:29:48Z)
• Machine Learning Co-pilot for Screening of Organic Molecular Additives for Perovskite Solar Cells [12.969955836781773]
  Co-Pilot for Perovskite Additive Screener (Co-PAS) is an ML-driven framework designed to accelerate additive screening for perovskite solar cells.<n>Co-PAS overcomes predictive biases by integrating scaffold-based pre-screening and latent Junction Tree Variational Autoencoder (JTVAE)<n>We identify several promising passivating molecules, including the novel Boc-L-threonine N-hydroxysuccin ester (BTN)
  arXiv  Detail & Related papers  (2024-12-18T17:52:45Z)
• Enhanced sampling of robust molecular datasets with uncertainty-based collective variables [0.0]
  We propose a method that leverages uncertainty as the collective variable (CV) to guide the acquisition of chemically-relevant data points. This approach employs a Gaussian Mixture Model-based uncertainty metric from a single model as the CV for biased molecular dynamics simulations.
  arXiv  Detail & Related papers  (2024-02-06T06:42:51Z)
• PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction [41.631010796783706]
  We propose PACIA, a parameter-efficient GNN adapter for few-shot MPP. We then adopt a hierarchical adaptation mechanism to adapt the encoder at task-level and the predictor at query-level.
  arXiv  Detail & Related papers  (2023-10-01T08:28:04Z)
• MolCAP: Molecular Chemical reActivity pretraining and prompted-finetuning enhanced molecular representation learning [3.179128580341411]
  MolCAP is a graph pretraining Transformer based on chemical reactivity (IMR) knowledge with prompted finetuning. Prompted by MolCAP, even basic graph neural networks are capable of achieving surprising performance that outperforms previous models.
  arXiv  Detail & Related papers  (2023-06-13T13:48:06Z)
• Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning [60.02391969049972]
  We introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system.
  arXiv  Detail & Related papers  (2023-06-08T17:12:08Z)
• 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)
• Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport [49.237577649802034]
  We develop a novel optimal transport-based algorithm termed MROT to enhance their generalization capability for molecular regression problems. MROT significantly outperforms state-of-the-art models, showing promising potential in accelerating the discovery of new substances.
  arXiv  Detail & Related papers  (2022-02-13T04:56:18Z)
• Do Large Scale Molecular Language Representations Capture Important Structural Information? [31.76876206167457]
  We present molecular embeddings obtained by training an efficient transformer encoder model, referred to as MoLFormer. Experiments show that the learned molecular representation performs competitively, when compared to graph-based and fingerprint-based supervised learning baselines.
  arXiv  Detail & Related papers  (2021-06-17T14:33:55Z)

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.