A high-performance, GPU-accelerated PyTorch testbed for ML-MD algorithms featuring JIT-compiled analytical Jacobian force kernels achieving 3-10x speedup over autograd, robust Langevin dynamics with Velocity-Verlet integration, and modular architecture designed as ground-truth validation for novel machine learning approaches in molecular dynamics.
Experience rare transition events between energy basins in this extended Müller-Brown simulation. Watch as particles overcome energy barriers to explore different regions of the potential energy landscape.
Explore the molecular dynamics of liquid argon in this fundamental LAMMPS simulation. This classic system demonstrates liquid-state behavior and serves as a benchmark for molecular dynamics methods.
MOSES introduces a comprehensive benchmarking platform for molecular generative models, offering standardized datasets, evaluation metrics, and baselines.
ChemBERTa-3 provides a unified, scalable infrastructure for pretraining and benchmarking chemical foundation models, addressing reproducibility gaps in previous studies like MoLFormer through standardized scaffold splitting and open-source tooling.
ChemDFM-R is a 14B-parameter chemical reasoning model that integrates a 101B-token dataset of atomized chemical knowledge. Using a novel mix-sourced distillation strategy and domain-specific reinforcement learning, it achieves state-of-the-art performance on chemical benchmarks.
This work investigates the scaling hypothesis for molecular transformers, training RoBERTa models on 77M SMILES from PubChem. It compares Masked Language Modeling (MLM) against Multi-Task Regression (MTR) pretraining, finding that MTR yields better downstream performance but is computationally heavier.
This methodological paper proposes a linear-attention transformer decoder trained on 1.1 billion molecules. It introduces pair-tuning for efficient property optimization and establishes empirical scaling laws relating inference compute to generation novelty.
This paper introduces ChemBERTa, a RoBERTa-based model pretrained on 77M SMILES strings. It systematically evaluates the impact of pretraining dataset size, tokenization strategies, and input representations (SMILES vs. SELFIES) on downstream MoleculeNet tasks, finding that performance scales positively with data size.
This paper introduces Chemformer, a BART-based sequence-to-sequence model pre-trained on 100M molecules using a novel ‘combined’ masking and augmentation task. It achieves state-of-the-art top-1 accuracy on reaction prediction benchmarks while significantly reducing training time through transfer learning.
ChemDFM-X is a multimodal chemical foundation model that integrates five non-text modalities (2D graphs, 3D conformations, images, MS2 spectra, IR spectra) into a single LLM decoder. It overcomes data scarcity by generating a 7.6M instruction-tuning dataset through approximate calculations and model predictions, achieving state-of-the-art generalist performance.
Deep dive into 24 image-to-sequence OCSR methods (2019-2025), comparing encoder-decoder architectures, molecular string representations, training scale, and hardware requirements.