{"total":82,"rows":[{"id":"2410.22570","title":"Orb: A Fast, Scalable Neural Network Potential","created":"2024-10-29","cited_by_count":66,"area":null},{"id":"2306.06482","title":"TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials","created":"2023-06-10","cited_by_count":54,"area":null},{"id":"2211.03263","title":"AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages","created":"2022-11-07","cited_by_count":46,"area":null},{"id":"2207.12858","title":"Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentials","created":"2022-07-25","cited_by_count":37,"area":null},{"id":"2210.07209","title":"Computer-Aided Multi-Objective Optimization in Small Molecule Discovery","created":"2022-10-13","cited_by_count":31,"area":null},{"id":"2503.09814","title":"A practical guide to machine learning interatomic potentials -- Status and future","created":"2025-03-12","cited_by_count":26,"area":null},{"id":"2305.14247","title":"Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science","created":"2023-05-23","cited_by_count":25,"area":null},{"id":"2403.09549","title":"Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields","created":"2024-03-14","cited_by_count":18,"area":null},{"id":"2208.08236","title":"DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation","created":"2022-08-17","cited_by_count":15,"area":null},{"id":"2402.17660","title":"TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations","created":"2024-02-27","cited_by_count":15,"area":null},{"id":"2304.13880","title":"Deep Learning Techniques for Hyperspectral Image Analysis in Agriculture: A Review","created":"2023-04-26","cited_by_count":12,"area":null},{"id":"2308.09142","title":"Accurate machine learning force fields via experimental and simulation data fusion","created":"2023-08-17","cited_by_count":12,"area":null},{"id":"2212.03916","title":"Transfer learning for chemically accurate interatomic neural network potentials","created":"2022-12-07","cited_by_count":11,"area":null},{"id":"2212.07959","title":"Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls","created":"2022-12-15","cited_by_count":11,"area":null},{"id":"2303.02216","title":"Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials","created":"2023-03-03","cited_by_count":11,"area":null},{"id":"2305.16325","title":"Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces","created":"2023-05-10","cited_by_count":10,"area":null},{"id":"2310.20155","title":"MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows","created":"2023-10-31","cited_by_count":10,"area":null},{"id":"2109.09569","title":"Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments","created":"2021-09-20","cited_by_count":9,"area":null},{"id":"2201.08110","title":"NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic","created":"2022-01-20","cited_by_count":8,"area":null},{"id":"2304.03906","title":"Instructor-inspired Machine Learning for Robust Molecular Property Prediction","created":"2023-04-08","cited_by_count":8,"area":null},{"id":"2305.09790","title":"Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation","created":"2023-04-27","cited_by_count":8,"area":null},{"id":"2402.12335","title":"Image Super-resolution Inspired Electron Density Prediction","created":"2024-02-19","cited_by_count":8,"area":null},{"id":"2402.13984","title":"Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators","created":"2024-02-21","cited_by_count":8,"area":null},{"id":"2207.09971","title":"NeuralNEB -- Neural Networks can find Reaction Paths Fast","created":"2022-07-20","cited_by_count":7,"area":null},{"id":"2312.08307","title":"3DReact: Geometric deep learning for chemical reactions","created":"2023-12-13","cited_by_count":7,"area":null},{"id":"2406.14149","title":"QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules","created":"2024-06-20","cited_by_count":7,"area":null},{"id":"2603.02002","title":"MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials","created":"2026-03-02","cited_by_count":7,"area":null},{"id":"2310.10598","title":"Pareto Optimization to Accelerate Multi-Objective Virtual Screening","created":"2023-10-16","cited_by_count":6,"area":null},{"id":"2301.02665","title":"Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space","created":"2023-01-06","cited_by_count":5,"area":null},{"id":"2409.01931","title":"On the design space between molecular mechanics and machine learning force fields","created":"2024-09-03","cited_by_count":4,"area":null},{"id":"2603.04523","title":"Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials","created":"2026-03-04","cited_by_count":4,"area":null},{"id":"2303.05911","title":"Lifelong Machine Learning Potentials","created":"2023-03-10","cited_by_count":3,"area":null},{"id":"2405.11785","title":"Guided Multi-objective Generative AI to Enhance Structure-based Drug Design","created":"2024-05-20","cited_by_count":3,"area":null},{"id":"2410.08024","title":"Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling","created":"2024-10-10","cited_by_count":3,"area":null},{"id":"2507.05559","title":"MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials","created":"2025-07-08","cited_by_count":3,"area":null},{"id":"2605.00640","title":"Knowing when to trust machine-learned interatomic potentials","created":"2026-05-01","cited_by_count":3,"area":null},{"id":"2605.03964","title":"Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs","created":"2026-05-05","cited_by_count":3,"area":null},{"id":"2108.04698","title":"Active Learning for Saddle Point Calculation","created":"2021-08-10","cited_by_count":2,"area":null},{"id":"2205.11117","title":"PyRelationAL: a python library for active learning research and development","created":"2022-05-23","cited_by_count":2,"area":null},{"id":"2409.12015","title":"All-in-one foundational models learning across quantum chemical levels","created":"2024-09-18","cited_by_count":2,"area":null},{"id":"2410.19631","title":"Efficient Biological Data Acquisition through Inference Set Design","created":"2024-10-25","cited_by_count":2,"area":null},{"id":"2502.13522","title":"Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements","created":"2025-02-19","cited_by_count":2,"area":null},{"id":"2502.18314","title":"Learning atomic forces from uncertainty-calibrated adversarial attacks","created":"2025-02-25","cited_by_count":2,"area":null},{"id":"2505.16301","title":"Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space","created":"2025-05-22","cited_by_count":2,"area":null},{"id":"2505.23354","title":"Representing local protein environments with machine learning force fields","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2605.13788","title":"Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs","created":"2026-05-13","cited_by_count":2,"area":null},{"id":"2109.08612","title":"Active Learning for the Optimal Design of Multinomial Classification in Physics","created":"2021-09-17","cited_by_count":1,"area":null},{"id":"2111.09502","title":"Docking-based Virtual Screening with Multi-Task Learning","created":"2021-11-18","cited_by_count":1,"area":null},{"id":"2209.08376","title":"Unveil the unseen: Exploit information hidden in noise","created":"2022-09-17","cited_by_count":1,"area":null},{"id":"2209.11481","title":"Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings","created":"2022-09-23","cited_by_count":1,"area":null},{"id":"2210.06622","title":"When does deep learning fail and how to tackle it? A critical analysis on polymer sequence-property surrogate models","created":"2022-10-12","cited_by_count":1,"area":null},{"id":"2302.00098","title":"Does Deep Active Learning Work in the Wild?","created":"2023-01-31","cited_by_count":1,"area":null},{"id":"2310.02373","title":"SelectFormer: Private and Practical Data Selection for Transformers","created":"2023-10-03","cited_by_count":1,"area":null},{"id":"2404.11811","title":"Physics-informed active learning for accelerating quantum chemical simulations","created":"2024-04-18","cited_by_count":1,"area":null},{"id":"2503.23515","title":"Optimal Invariant Bases for Atomistic Machine Learning","created":"2025-03-30","cited_by_count":1,"area":null},{"id":"2603.12183","title":"Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials","created":"2026-03-12","cited_by_count":1,"area":null},{"id":"2606.02662","title":"Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning","created":"2026-06-01","cited_by_count":1,"area":null},{"id":"2109.03769","title":"Training Algorithm Matters for the Performance of Neural Network Potential: A Case Study of Adam and the Kalman Filter Optimizers","created":"2021-09-08","cited_by_count":0,"area":null},{"id":"2110.10077","title":"Deep Learning to Estimate Permeability using Geophysical Data","created":"2021-10-08","cited_by_count":0,"area":null},{"id":"2112.09466","title":"Fair Active Learning: Solving the Labeling Problem in Insurance","created":"2021-12-17","cited_by_count":0,"area":null},{"id":"2201.12632","title":"Towards Robust Deep Active Learning for Scientific Computing","created":"2022-01-29","cited_by_count":0,"area":null},{"id":"2205.04547","title":"Machine Learning Diffusion Monte Carlo Energies","created":"2022-05-09","cited_by_count":0,"area":null},{"id":"2208.05341","title":"Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning","created":"2022-08-10","cited_by_count":0,"area":null},{"id":"2310.02904","title":"Spline-based neural network interatomic potentials: blending classical and machine learning models","created":"2023-10-04","cited_by_count":0,"area":null},{"id":"2312.01414","title":"Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water","created":"2023-12-03","cited_by_count":0,"area":null},{"id":"2312.04174","title":"Coherent energy and force uncertainty in deep learning force fields","created":"2023-12-07","cited_by_count":0,"area":null},{"id":"2403.00259","title":"Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO","created":"2024-03-01","cited_by_count":0,"area":null},{"id":"2407.15175","title":"${\\it Asparagus}$: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces","created":"2024-07-21","cited_by_count":0,"area":null},{"id":"2410.17917","title":"regAL: Python Package for Active Learning of Regression Problems","created":"2024-10-23","cited_by_count":0,"area":null},{"id":"2411.19629","title":"OpenQDC: Open Quantum Data Commons","created":"2024-11-29","cited_by_count":0,"area":null},{"id":"2412.06064","title":"Implicit Delta Learning of High Fidelity Neural Network Potentials","created":"2024-12-08","cited_by_count":0,"area":null},{"id":"2501.16398","title":"Data-Efficient Machine Learning Potentials via Difference Vectors Based on Local Atomic Environments","created":"2025-01-27","cited_by_count":0,"area":null},{"id":"2503.14293","title":"Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials","created":"2025-03-18","cited_by_count":0,"area":null},{"id":"2507.09001","title":"Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality","created":"2025-07-11","cited_by_count":0,"area":null},{"id":"2601.05946","title":"A Critical Examination of Active Learning Workflows in Materials Science","created":"2026-01-09","cited_by_count":0,"area":null},{"id":"2603.18864","title":"Data-driven construction of machine-learning-based interatomic potentials for gas-surface scattering dynamics: the case of NO on graphite","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2606.01595","title":"Uncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2607.14486","title":"Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields","created":"2026-07-16","cited_by_count":0,"area":null},{"id":"2609.00488","title":"AdaptNTK: Adaptive Uncertainty Quantification and Active Learning for Neural Network Potentials","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.08399","title":"MLIP Detective: Active Failure Mode Discovery Beyond Benchmark Scores for Machine-Learning Interatomic Potentials","created":"2026-09-08","cited_by_count":0,"area":null},{"id":"2610.05544","title":"Soft Strategy Selection for Batch-Mode Active Learning","created":"2026-10-04","cited_by_count":0,"area":null},{"id":"2610.19630","title":"Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}