AIBullisharXiv – CS AI · Jun 237/10
🧠Researchers introduced UltraNMR, a foundation model trained on 158 million simulated nuclear magnetic resonance spectra that successfully bridges the gap between simulation and real-world molecular analysis. The model demonstrates state-of-the-art performance on experimental NMR tasks and has been applied to identify previously unknown natural products from Chinese herbal medicines, suggesting large-scale simulation pre-training can enable robust generalization in spectroscopy.
AIBullisharXiv – CS AI · Jun 87/10
🧠Researchers introduce E2Former-V2, a more scalable architecture for Equivariant Graph Neural Networks that models 3D molecular systems. By combining algebraic sparsity with hardware-optimized execution, the model achieves 20× computational improvements while maintaining competitive accuracy on molecular datasets.
AIBullisharXiv – CS AI · Jun 87/10
🧠Researchers introduce CatDT, a self-evolving multi-agent AI system that autonomously discovers heterogeneous catalysts by building digital twins of working catalytic systems. The system achieves predictions within 0.5-2x of experimental results across diverse catalyst types and independently identifies non-precious catalyst candidates for propane dehydrogenation that rival industrial platinum-based benchmarks.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers have developed a hybrid framework combining Large Language Models with physics-based simulations to improve synthesis planning for inorganic crystalline materials. Testing on the niobium-oxygen system shows LLMs generate more viable synthesis routes than classical algorithmic approaches by leveraging implicit priors about chemical processes.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers introduce FTDiff, a reinforcement learning framework that fine-tunes diffusion models for molecular generation in drug design by combining group relative policy optimization with fast sampling techniques. The approach eliminates costly post-hoc processing and complex data curation while balancing multiple drug design objectives more effectively than existing methods.
AIBullisharXiv – CS AI · May 277/10
🧠AutoDFT is a closed-loop multi-agent framework that automates density functional theory (DFT) calculations by embedding LLM reasoning throughout the entire computational lifecycle, rather than just the planning phase. The system achieves 94.1% success on a 34-task benchmark and enables non-experts to obtain reliable computational chemistry results by dynamically adapting to failures and unexpected outcomes.
🧠 GPT-5
AIBullisharXiv – CS AI · May 127/10
🧠Researchers introduce MolWorld, a novel AI framework that optimizes molecular structures for drug discovery by modeling actionable pathways between molecules. Unlike existing methods, MolWorld ensures discovered candidates are chemically reachable from known compounds through valid intermediate steps, making them practically viable for lead optimization.
AIBullisharXiv – CS AI · May 127/10
🧠Researchers propose a non-autoregressive machine learning framework that predicts ionic transport properties—critical for battery and energy materials—200 times faster than existing methods while maintaining accuracy. The approach treats atomic trajectories as optional training data, enabling the model to learn dynamic behavior without sequential inference, addressing a major bottleneck in computational materials science.
AIBearisharXiv – CS AI · May 127/10
🧠Researchers have created a benchmark to test whether machine learning interatomic potentials can generalize to unseen molecules by learning underlying chemical principles. The study reveals that state-of-the-art models, including foundation models trained on millions of molecules, fail significantly on out-of-distribution examples, with errors often 10x higher than on training data.
AIBearisharXiv – CS AI · May 127/10
🧠Researchers introduced MDGYM, a benchmark testing AI agents' ability to autonomously execute molecular dynamics simulations, finding that even the strongest systems solve only 21% of easy tasks. The poor performance reveals that advanced code generation does not translate to physical reasoning, exposing a critical gap between general software engineering competence and domain-specific scientific workflows.
🧠 Claude
AIBullisharXiv – CS AI · May 117/10
🧠FlashMol represents a major breakthrough in computational drug discovery by generating high-quality 3D molecular conformations in just 4 steps, compared to hundreds required by traditional diffusion models. The technique achieves 250x acceleration in sampling speed while matching or exceeding the quality of slower teacher models, potentially transforming the economics of large-scale in silico screening.
AIBullisharXiv – CS AI · May 117/10
🧠Researchers introduce DualLGD, a novel dual-stream diffusion architecture for generating molecular structures from mass spectra data. The method achieves 3x improvement over previous state-of-the-art by separating atom-level and bond-level reasoning into dedicated computation streams, addressing a fundamental circular dependency problem in molecular generation.
AIBullisharXiv – CS AI · May 77/10
🧠Researchers have developed a neural network architecture inspired by large language models to predict high-dimensional molecular potential energy surfaces, successfully computing accurate predictions for a 186-dimensional system representing a protonated 21-water cluster—a significant advance in computational chemistry that could accelerate reaction rate predictions.
AIBullisharXiv – CS AI · Apr 207/10
🧠Researchers have developed an exascale workflow using graph foundation models trained on 544+ million atomistic structures to accelerate materials discovery. The system can screen 1.1 billion structures in 50 seconds—a task requiring years of traditional computation—and demonstrates strong transfer learning capabilities across diverse chemical applications.
AIBullisharXiv – CS AI · Apr 137/10
🧠EquiformerV3, an advanced SE(3)-equivariant graph neural network, achieves significant improvements in efficiency, expressivity, and generality for 3D atomistic modeling. The new version delivers 1.75x speedup, introduces architectural innovations like SwiGLU-S² activations and smooth-cutoff attention, and achieves state-of-the-art results on major molecular modeling benchmarks including OC20 and OMat24.
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AIBullisharXiv – CS AI · Mar 56/10
🧠Researchers developed a new AI-powered framework for crystal structure prediction that uses large language models and symmetry-driven generation to overcome computational bottlenecks. The approach achieves state-of-the-art performance in discovering new materials without relying on existing databases, potentially accelerating materials science research.
AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers introduce MMGNN (Multi-level, Multi-color Graph Neural Networks), a novel neural network architecture that decomposes molecular graphs into interaction-specific subgraphs to improve molecular property prediction. The framework demonstrates competitive performance across multiple benchmarks, with variants optimized for topological and geometric molecular representations.
AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers introduced HACO, a Human-AI co-discovery system that identified MaskGIT, a vision-based masked generative model, as an effective framework for crystal structure prediction. The resulting MaskGXT model achieved 79.06% accuracy on MP-20 benchmarks, outperforming previous baselines by 8.19 percentage points, demonstrating how AI systems can transfer learning across scientific domains when guided by human expertise.
AIBullisharXiv – CS AI · Jun 236/10
🧠Researchers developed an explainable graph neural network framework that uses group lasso regularization to predict compound-protein affinity and identify critical molecular substructures in drug discovery. The approach leverages activity-cliff molecule pairs to improve predictions for tyrosine-protein kinases and other targets, demonstrating enhanced interpretability and accuracy in molecular property prediction.
AIBullisharXiv – CS AI · Jun 236/10
🧠Researchers propose a training-free caching strategy that accelerates molecular geometry generation in flow matching models by predicting intermediate hidden states, achieving 2-7x speedups without quality degradation. The method is compatible with pretrained models and compounds with existing optimizations, addressing a critical inference bottleneck in computational chemistry workflows.
AINeutralarXiv – CS AI · Jun 116/10
🧠Researchers introduce MolGram, a neural architecture that enhances transformer-based language models for molecular SMILES strings by integrating a conditional n-gram memory module. This approach addresses the locality gap in character-level tokenization, enabling models to better capture chemical motifs while improving performance across molecule generation, reaction prediction, and retrosynthesis tasks with significantly fewer parameters than baseline models.
AINeutralarXiv – CS AI · Jun 106/10
🧠Researchers introduce ReAlignFit, a machine learning framework that enhances molecular relational learning by incorporating chemical knowledge through induced fit principles to improve prediction stability across different molecular datasets. The method addresses limitations in attention-based alignment mechanisms by using bias correction functions and information bottleneck optimization to better predict molecular binding compatibility.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers introduce an agent-guided multi-fidelity machine learning framework that corrects numerical instabilities in GW-Bethe-Salpeter calculations for simulating electronic and optical properties of strained MoS2-WS2 bilayers. The approach uses confidence-weighted structural agents and Gaussian process corrections to improve accuracy of quasiparticle gaps and exciton binding energies while preserving physical strain dependence.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers demonstrate that large language models can design molecules with chemist-level precision by replacing simple numerical feedback with detailed physicochemical analysis. The approach couples retrieval-augmented generation with self-reflection modules that feed orbital energies and atomic charges back into design iterations, achieving near-perfect accuracy on HOMO-LUMO gap targets and 100% success rates on moderate molecular design tasks.
AINeutralarXiv – CS AI · Jun 86/10
🧠RETROSPECT introduces a modular retrosynthesis system combining a Transformer-based proposal model with LambdaMART reranking to improve chemical synthesis prediction. The system achieves 55% top-1 accuracy on USPTO-50K benchmarks, demonstrating that decomposing retrosynthesis into proposal generation and learned selection improves both ranking quality and candidate diversity.
