Accepted Work

All accepted work 🌟 denotes spotlight Work

• Learning the Language of NMR: Structure Elucidation from NMR spectra using Transformer Models

• Automated Diffraction Pattern Analysis for Identifying Crystal Systems Using Multiview Opinion Fusion Machine Learning

• CLCS : Contrastive Learning between Compositions and Structures for practical Li-ion battery electrodes design

• Search strategies for asynchronous parallel self-driving laboratories with pending points

• Data Distillation for Neural Network Potentials toward Foundational Dataset

• 🌟Capturing Formulation Design of Battery Electrolytes with Chemical Large Language Model

• Connectivity Optimized Nested Graph Networks for Crystal Structures

• Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction

• 🌟Graph-to-String Variational Autoencoder for Synthetic Polymer Design

• Haldane Bundles: A Dataset for Learning to Predict the Chern Number of Line Bundles on the Torus

• 🌟Message Passing Neural Network for Predictig Dipole Moment Dependent Core Electron Excitation Spectra

• Impacts of Data and Models on Unsupervised Pre-training for Molecular Property Prediction

• rvesimulator: An automated representative volume element simulator for data-driven material discovery

• MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network

• Symbolic Learning for Material Discovery

• Combinatorial Optimization via Memory Metropolis: Template Networks for Proposal Distributions in Simulated Annealing applied to Nanophotonic Inverse Design

• 🌟Enhancing Extrapolation in Materials Science through Contrastive Learning of Chemical Compositions

• Multi-modal Foundation Model for Material Design

• Automatic Generation of Mechanistic Pathways of Organic Reactions with Dual Templates

• CURATOR: Autonomous Batch Active-Learning Workflow for Catalysts

• 🌟Discovery of Novel Reticular Materials for Carbon Dioxide Capture using GFlowNets

• Data Efficient Training for Materials Property Prediction Using Active Learning Querying

• 🌟Scalable Diffusion for Materials Generation

• High throughput decomposition of spectra

• 🌟Learning Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

• Demonstrating ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry

• Scaling Sodium-ion Battery Development with NLP

• Accurate Prediction of Experimental Band Gaps from Large Language Model-Based Data Extraction

• A Bayesian Approach to Designing Microstructures and Processing Pathways for Tailored Material Properties

• Gotta be SAFE: A new Framework for Molecular Design

• 🌟Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

• Evaluating AI-guided Design for Scientific Discovery

• Searching for High-Value Molecules Using Reinforcement Learning and Transformers

• Rapid Fitting of Band-Excitation Piezoresponse Force Microscopy Using Physics Constrained Unsupervised Neural Networks

• Distributed Reinforcement Learning for Molecular Design: Antioxidant case

• Phonon predictions with E(3)-equivariant graph neural networks

• Understanding Experimental Data by Identifying Symmetries with Deep Learning

• Extremely Noisy 4D-TEM Strain Mapping Using Cycle Consistent Spatial Transforming Autoencoders

• A Generative Model for Accelerated Inverse Modelling Using a Novel Embedding for Continuous Variables

• MatKG-2: Unveiling precise material science ontology through autonomous committees of LLMs

• Active Causal Machine Learning for Molecular Property Prediction

• 🌟HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science

• 🌟MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling

• 🌟EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations

• Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning

• Unveiling the Secrets of $^1$H-NMR Spectroscopy: A Novel Approach Utilizing Attention Mechanisms

• Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction

• Eco-Comp: Towards Responsible Computing in Materials Science

• Multi-objective Evolutionary Design of Microstructures using Diffusion Autoencoders

• AdsorbRL: Deep Reinforcement Learning for Inverse Catalyst Design

• Active learning for excited states dynamics simulations to discover molecular degradation pathways

• 🌟ExPT: Synthetic Pretraining for Few-Shot Experimental Design

• MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design

• Deep inverse design of hydrophobic patches on DNA origami for mesoscale assembly of superlattices

• Out of Domain Stress Prediction on a Dataset of Simulated 3D Polycrystalline Microstructures

• Aniso-GNN: physics-informed graph neural networks that generalize to anisotropic properties of polycrystals

• Hierarchical GFlowNet for Crystal Structure Generation

• 🌟Crystal-GFlowNet: sampling materials with desirable properties and constraints

• 🌟Exploring Organic Syntheses through Natural Language

• On the importance of catalyst-adsorbate geometric relative information when predicting relaxed energy

• Self-supervised Crack Detection in X-ray Computed Tomography Data of Additive Manufacturing Parts

• 🌟Learning Interatomic Potentials at Multiple Scales

• 🌟Accelerated Sampling of Rare Events using a Neural Network Bias Potential

• Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction

• 🌟Machine learning force field ranking of candidate solid electrolyte interphase structures in Li-ion batteries

• BroGNet: Momentum-Conserving Graph Neural Stochastic Differential Equation for Learning Brownian Dynamics

• Towards equilibrium molecular conformation generation with GFlowNets

• Accelerated Modelling of Interfaces for Electronic Devices using Graph Neural Networks

• Tree-based Quantile Active Learning for automated discovery of MOFs

• MTENCODER: A Multi-task Pretrained Transformer Encoder for Materials Representation Learning

• CoNO: Complex Neural Operator for Continuous Dynamical Systems

• CoDBench: A Critical Evaluation of Data-driven Models for Continuous Dynamical Systems

• LLM Drug Discovery Challenge: A Contest as a Feasibility Study on the Utilization of Large Language Models in Medicinal Chemistry

• Inverse-design of organometallic catalysts with guided equivariant diffusion

• 🌟KREED: Denoising Diffusion for 3D Structure Determination from Isotopologue Rotational Spectra in Natural Abundance

• Tokenizer Effect on Functional Material Prediction: Investigating Contextual Word Embeddings for Knowledge Discovery

• PIHLoRA: Physics-informed hypernetworks for low-ranked adaptation

• Retrieval of synthesis parameters of polymer nanocomposites using LLMs

• HEPOM: A predictive framework for accelerated Hydrolysis Energy Predictions of Organic Molecules

• Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks