jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Finkler, Jonas Alexander | Lin, Yan; id_orcid 0000-0002-2320-9777 | Du, Tao | Hu, Jilin; id_orcid 0000-0002-7739-7769 | Smedskjær, Morten Mattrup
Source: VBN Forskningsportal (Aalborg Universitet) (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Multi Element Glass Dataset for Benchmarking Generative Models for Disordered Materials Diffusion generative models originally became popular for image generation but have more recently also found adoption by the materials science community. By conditioning such models on material properties, new materials can be predicted that match a set of given target properties. In the domain of crystalline materials, significant progress has been made with the development of new model architectures, driven by the availability of large databases, such as the Materials Project. Unfortunately, data availability for disordered materials such as glasses is extremely limited.<br/><br/>We therefore developed the Multi Element Glass Dataset (MEGD) to train and benchmark generative models for amorphous materials. The molecular dynamics dataset consists of over 9000 samples of oxide glass structures together with properties including thermal expansion coefficients, heat capacities and elastic constants. As the glass structure depends on the thermal history, we developed a dedicated simulation workflow to generate the structural samples. The workflow is based on the BMP1) interatomic potential and determines a suitable initial temperature for each composition that ensures proper melting while avoiding evaporation events. The MEGD includes a wide range of compositions including up to three glass formers (Si, P, Al) and up to four out of seven modifiers (Li, Be, K, Ca, Ti, Ba, Zn).<br/><br/>We highlight our own benchmarking results based on the MEGD using our recently developed AMDEN (Amorphous Materials DEnoising Network) framework. Workflow ; Benchmarking ; Benchmark (surveying) ; Generative grammar ; Set (abstract data type) ; Amorphous solid ; Domain (mathematical analysis) ; Range (aeronautics) Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys ; Material Dynamics and Properties
Authors: Luo, Yufei | Gu, Xiang | Sun, Jian
Source: arXiv (Cornell University) (preprint, 2025-11-21)
Field: Materials Science — Materials Chemistry

Generating transition states of chemical reactions via distance-geometry-based flow matching Transition states (TSs) are crucial for understanding reaction mechanisms, yet their exploration is limited by the complexity of experimental and computational approaches. Here we propose TS-DFM, a flow matching framework that predicts TSs from reactants and products. By operating in molecular distance geometry space, TS-DFM explicitly captures the dynamic changes of interatomic distances in chemical reactions. A network structure named TSDVNet is designed to learn the velocity field for generating TS geometries accurately. On the benchmark dataset Transition1X, TS-DFM outperforms the previous state-of-the-art method React-OT by 30\% in structural accuracy. These predicted TSs provide high-quality initial structures, accelerating the convergence of CI-NEB optimization. Additionally, TS-DFM can identify alternative reaction paths. In our experiments, even a more favorable TS with lower energy barrier is discovered. Further tests on RGD1 dataset confirm its strong generalization ability on unseen molecules and reaction types, highlighting its potential for facilitating reaction exploration. Generalization ; Matching (statistics) ; Convergence (economics) ; Benchmark (surveying) ; Chemical reaction ; Flow (mathematics) ; Transition state ; Field (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Luo, Yufei | Gu, Xiang | Sun, Jian
Source: ArXiv.org (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Generating transition states of chemical reactions via distance-geometry-based flow matching Transition states (TSs) are crucial for understanding reaction mechanisms, yet their exploration is limited by the complexity of experimental and computational approaches. Here we propose TS-DFM, a flow matching framework that predicts TSs from reactants and products. By operating in molecular distance geometry space, TS-DFM explicitly captures the dynamic changes of interatomic distances in chemical reactions. A network structure named TSDVNet is designed to learn the velocity field for generating TS geometries accurately. On the benchmark dataset Transition1X, TS-DFM outperforms the previous state-of-the-art method React-OT by 30\% in structural accuracy. These predicted TSs provide high-quality initial structures, accelerating the convergence of CI-NEB optimization. Additionally, TS-DFM can identify alternative reaction paths. In our experiments, even a more favorable TS with lower energy barrier is discovered. Further tests on RGD1 dataset confirm its strong generalization ability on unseen molecules and reaction types, highlighting its potential for facilitating reaction exploration. Generalization ; Matching (statistics) ; Convergence (economics) ; Benchmark (surveying) ; Chemical reaction ; Flow (mathematics) ; Transition state ; Field (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ishan Nadkarni | Jhonatam Cordeiro | N. R. Aluru
Source: The Journal of Physical Chemistry Letters (article, 2025-03-18)
Field: Materials Science — Materials Chemistry

Molecular Denoising Using Diffusion Models with Physics-Informed Priors Denoising Diffusion Probabilistic Models (DDPMs) are powerful generative models that have demonstrated superior performance in a variety of tasks and applications in material science and molecular graph modeling. Inspired by nonequilibrium statistical mechanics, these models iteratively degrade data through a forward diffusion process and then restore it by learning the time-reversal of the forward process. Despite their success, a significant drawback of DDPMs is their reliance on numerous iterations to generate high-quality samples, resulting in slow sampling. In this Letter, we introduce a strategy to improve DDPMs for atomistic systems by leveraging the thermodynamics of the data by deriving physics-informed priors. Drawing on principles from statistical mechanics, we derive physics-informed parameters for the prior distribution to initialize the Markov chain closer to the true data distribution. This strategy shortens the Markov chain, thereby improving the model's training efficiency and accelerating the sampling process. We demonstrate the effectiveness of our method in denoising noisy radial distribution functions obtained from a single atomic configuration of diverse Lennard-Jones and multiatomic liquids. Prior probability ; Statistical physics ; Diffusion ; Noise reduction ; Computational biology ; Computer science ; Physics ; Artificial intelligence ; Biology ; Bayesian probability ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics ; Advanced Electron Microscopy Techniques and Applications
Authors: Soheil Jamali | Jiahui Chen | Fauzia Haque | Mahmoud Moradi
Source: Biophysical Journal (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

BPS2025 - Free energy calculations using generative models trained based on molecular dynamics trajectories: A diffusion model approach Generative grammar ; Molecular dynamics ; Statistical physics ; Dynamics (music) ; Generative model ; Diffusion ; Computer science ; Energy (signal processing) ; Biological system ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry ; Thermodynamics ; Biology Machine Learning in Materials Science
Authors: Edgar Olehnovics | Yifei Michelle Liu | Nada Mehio | Ahmad Y. Sheikh | Michael R. Shirts | Matteo Salvalaglio
Source: ChemRxiv (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Lattice Free Energies of Molecular Crystals Using Normalizing Flow Free energy perturbation ; Molecular dynamics ; Energy flow ; Lattice (music) ; Thermodynamic integration ; Scalability ; Artificial neural network ; Degrees of freedom (physics and chemistry) ; Crystal structure prediction Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems
Authors: Edgar Olehnovics | Yifei Michelle Liu | Nada Mehio | Ahmad Y. Sheikh | Michael R. Shirts | Matteo Salvalaglio
Source: ChemRxiv (preprint, 2025-09-23)
Field: Chemistry — Analytical Chemistry

Lattice free energies of molecular crystals using normalizing flow Free energy perturbation ; Molecular dynamics ; Crystal structure prediction ; Crystal (programming language) ; Lattice (music) ; Potential energy ; Energy landscape ; Perturbation (astronomy) Petroleum Processing and Analysis
Authors: Haoke Qiu | Haozhe Huang | Hong-Li Yang | Alán Aspuru‐Guzik | Zhao‐Yan Sun
Source: ChemRxiv (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

A Robust and Versatile Generative Model for Inverse Design of Polymers Heuristics ; Inverse ; Generator (circuit theory) ; Polymer ; Generative Design ; Generative grammar Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Long Xu | Yongcai Chen | Fengshuo Liu
Source:  (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

RUMC: Reward-Guided Monte Carlo Sampling with Uncertainty Awareness for De Novo Molecular Generation The design of novel small molecules is crucial for advancing drug discovery, materials science, and chemical biology. Recent advances in deep generative modeling have driven notable progress in molecular generation and property optimization. However, methods such as Monte Carlo sampling still face substantial challenges in thoroughly exploring the vast chemical space and further improving the diversity, validity, and uniqueness of generated molecules. To address these limitations, we introduce RUMC-a reward-guided, uncertainty-aware Monte Carlo sampling framework for de novo molecular generation. RUMC is built upon a transformer-based generative adversarial network and incorporates advanced reinforcement learning strategies. Specifically, RUMC employs a reward-guided sampling mechanism with a deduplicated experience replay buffer to prioritize unique, high-reward molecules and applies robust reward normalization for stable training. Additionally, RUMC utilizes an uncertainty-aware exploration strategy based on Monte Carlo Dropout and the Upper Confidence Bound (UCB) criterion to balance exploration and exploitation during molecular generation. Extensive experiments on the QM9 and ZINC datasets demonstrate that RUMC significantly improves the validity, novelty, and diversity of generated molecules compared to existing methods, while ensuring excellent drug-like properties. Code and data are available at https://github.com/xulong0826/RUMC. Monte Carlo method ; Chemical space ; Sampling (signal processing) ; Importance sampling ; Probabilistic logic ; Dropout (neural networks) ; Thompson sampling Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Adversarial Robustness in Machine Learning
Authors: K.-H. Wang | Jon Paul Janet | Alessandro Tibo
Source: ChemRxiv (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Controllable Molecular Generation with Fine-tuned Flow-matching Model Set (abstract data type) ; Generative grammar ; Reinforcement learning ; Generative model ; Contrast (vision) ; Field (mathematics) ; Joint (building) ; Space (punctuation) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Z.X. Qi | Zijing Liu | Jiying Zhang | He Cao | Xiaohua Xu | Yu Li
Source:  (article, 2025-03-12)
Field: Chemistry — Spectroscopy

ControlMol: Adding Substructure Control To Molecule Diffusion Models Due to the vast design space of molecules, generating molecules conditioned on a specific sub-structure relevant to a particular function or therapeutic target is a crucial task in computer-aided drug design. Existing works mainly focus on specific tasks, such as linker design or scaffold hopping, each task requires training a model from scratch, and many well-pretrained De Novo molecule generation model parameters are not effectively utilized. To this end, we propose a two-stage training approach, consisting of condition learning and condition optimization. In the condition learning stage, we adopt the idea of ControlNet and design some meaningful adjustments to make the unconditional generative model learn sub-structure conditioned generation. In the condition optimization stage, by using human preference learning, we further enhance the stability and robustness of sub-structure control. In our experiments, only trained on randomly partitioned sub-structure data, the proposed method outperforms previous techniques by generating more valid and diverse molecules. Our method is easy to implement and can be quickly applied to various pre-trained molecule generation models. Substructure ; Diffusion ; Molecule ; Control (management) ; Computer science ; Chemistry ; Physics ; Thermodynamics ; Artificial intelligence ; Engineering ; Structural engineering Analytical Chemistry and Chromatography
Authors: Viejay Ordillo | Mikiya Fujii | Harashima Yosuke | Takayama, Tomoaki | Takasuka, Shogo
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Generative Modeling for Materials Design and Discovery using Invertible String-based Materials Representation Representation (politics) ; Invertible matrix ; Generative grammar ; Generative model ; Generative Design ; Feature (linguistics) Machine Learning in Materials Science ; Material Selection and Properties ; Block Copolymer Self-Assembly
Authors: Liam Harcombe | Timothy T. Duignan
Source: ArXiv.org (preprint, 2025-04-04)
Field: Materials Science — Materials Chemistry

On the Connection Between Diffusion Models and Molecular Dynamics Neural Network Potentials (NNPs) have emerged as a powerful tool for modelling atomic interactions with high accuracy and computational efficiency. Recently, denoising diffusion models have shown promise in NNPs by training networks to remove noise added to stable configurations, eliminating the need for force data during training. In this work, we explore the connection between noise and forces by providing a new, simplified mathematical derivation of their relationship. We also demonstrate how a denoising model can be implemented using a conventional MD software package interfaced with a standard NNP architecture. We demonstrate the approach by training a diffusion-based NNP to simulate a coarse-grained lithium chloride solution and employ data duplication to enhance model performance. Connection (principal bundle) ; Noise (video) ; Noise reduction ; Diffusion ; Artificial neural network ; Software ; Software package ; Lithium chloride Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Physical and Chemical Molecular Interactions
Authors: C.S. Campbell | A. Romero | Kamal Choudhary
Source: ArXiv.org (preprint, 2025-10-17)
Field: Materials Science — Materials Chemistry

AtomBench: A Benchmark for Generative Atomic Structure Models using GPT, Diffusion, and Flow Architectures Generative models have become significant assets in the exploration and identification of new materials, enabling the rapid proposal of candidate crystal structures that satisfy target properties. Despite the increasing adoption of diverse architectures, a rigorous comparative evaluation of their performance on materials datasets is lacking. In this work, we present a systematic benchmark of three representative generative models- AtomGPT (a transformer-based model), Crystal Diffusion Variational Autoencoder (CDVAE), and FlowMM (a Riemannian flow matching model). These models were trained to reconstruct crystal structures from subsets of two publicly available superconductivity datasets- JARVIS Supercon 3D and DS A/B from the Alexandria database. Performance was assessed using the Kullback-Leibler (KL) divergence between predicted and reference distributions of lattice parameters, as well as the mean absolute error (MAE) of individual lattice constants. For the computed KLD and MAE scores, CDVAE performs most favorably, followed by AtomGPT, and then FlowMM. All benchmarking code and model configurations will be made publicly available at https://github.com/atomgptlab/atombench_inverse. Autoencoder ; Benchmark (surveying) ; Benchmarking ; Matching (statistics) ; Generative grammar ; Lattice (music) ; Generative model ; Divergence (linguistics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Jun Dong | Ruina Si | Menghan Zhang | Xuhai Pan | Ziqi Shang | Wen Duan Yan
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Characteristics of Constant Volume Hydrogenation and Construction of Linear Diffusion Model for Solid Hydrogen Storage Materials: A Case Study of Lani5 Alloy Hydrogen storage ; Diffusion ; Alloy ; Volume (thermodynamics) ; Constant (computer programming) ; Materials science ; Hydrogen ; Thermodynamics ; Metallurgy ; Chemistry ; Computer science ; Physics ; Organic chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Nuclear Materials and Properties
Authors: Huidong Tang | Chen Li | Sayaka Kamei | Yoshihiro Yamanishi | Yasuhiko Morimoto
Source:  (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

InstGAN: Instant Actor-Critic-Driven GAN for De Novo Molecule Generation and Property Optimization Deep generative models, such as generative adversarial networks (GANs), have been employed for de~novo molecular generation in drug discovery. Most prior studies have utilized reinforcement learning (RL) algorithms, particularly Monte Carlo tree search (MCTS), to handle the discrete nature of molecular representations in GANs. However, due to the inherent instability in training GANs and RL models, along with the high computational cost associated with MCTS sampling, MCTS RL-based GANs struggle to scale to large chemical databases. To tackle these challenges, this study introduces a novel GAN based on actor-critic RL with instant and global rewards, called InstGAN, to generate molecules at the token-level with multi-property optimization. Furthermore, maximized information entropy is leveraged to alleviate the mode collapse. The experimental results demonstrate that InstGAN outperforms other baselines, achieves comparable performance to state-of-the-art models, and efficiently generates molecules with multi-property optimization. The code is available at: https://github.com/tang777777/InstGAN. Tree (set theory) ; Reinforcement learning ; Generative grammar ; Entropy (arrow of time) ; Property (philosophy) ; Molecule ; Cross entropy ; Shuffling Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Sirine Ayadi | Leon Hetzel | Johanna Sommer | Fabian J. Theis | Stephan Günnemann
Source: arXiv (Cornell University) (preprint, 2025-01-05)
Field: Chemistry — Physical and Theoretical Chemistry

Unified Guidance for Geometry-Conditioned Molecular Generation Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain, which has experienced great attention through the success of generative models and, in particular, diffusion models. However, current molecular diffusion models are tailored towards a specific downstream task and lack adaptability. We introduce UniGuide, a framework for controlled geometric guidance of unconditional diffusion models that allows flexible conditioning during inference without the requirement of extra training or networks. We show how applications such as structure-based, fragment-based, and ligand-based drug design are formulated in the UniGuide framework and demonstrate on-par or superior performance compared to specialised models. Offering a more versatile approach, UniGuide has the potential to streamline the development of molecular generative models, allowing them to be readily used in diverse application scenarios. Geometry ; Computer science ; Mathematics Various Chemistry Research Topics ; Molecular Junctions and Nanostructures ; Monoclonal and Polyclonal Antibodies Research
Authors: Odin Zhang | Jieyu Jin | Zhenxing Wu | Jintu Zhang | Ping Yuan | Yuntao Yu | Haitao Lin | Haiyang Zhong | Xujun Zhang | Chenqing Hua | Weibo Zhao | Zhengshuo Zhang | Kejun Ying | Yufei Huang | Huifeng Zhao | Yu Kang | Peichen Pan | Jike Wang | Dong Guo | Shuangjia Zheng | Chang‐Yu Hsieh | Tingjun Hou
Source: Nature Computational Science (article, 2025-10-15)
Field: Chemistry — Organic Chemistry

ECloudGen: leveraging electron clouds as a latent variable to scale up structure-based molecular design Latent variable ; Benchmark (surveying) ; Chemical space ; Latent variable model ; Generative grammar ; Variable (mathematics) ; Scale (ratio) ; Generative model Click Chemistry and Applications ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Mei Ma | Xiujuan Lei
Source: IEEE Transactions on Computational Biology and Bioinformatics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

BiAtt-GVAE: Molecular design for specific target via graph variational autoencoder based on bi-channel interactive attention network Designing bioactive molecules with desired properties for specific targets is a longstanding challenge in drug design. We introduce a model called BiAtt-GVAE, which incorporates a conditional to more effectively constrain and enhance the generated molecules. To capture interaction sites between ligand atoms and protein amino acids, as well as the structure-properties relationship of ligands, we designed a bi-channel interactive attention network. The interactive attention block operates on the protein-ligand graph, facilitating the interactive learning of ligand atoms and protein amino acid features. We consider various properties to learn the ligand structure-properties relationship through a multi-head cross-attention block. Testing on EGFR and CDK2 targets, as well as through a dedicated case study for designing SARS-CoV-2 Mpro inhibitors demonstrates the effectiveness of BiAtt-GVAE. It has been verified through subsequent investigation of the molecular structure and molecular docking of Mpro inhibitors that BiAtt-GVAE can produce compounds with high ratings for both novelty and affinity. Autoencoder ; Computer science ; Graph ; Channel (broadcasting) ; Theoretical computer science ; Topology (electrical circuits) ; Artificial intelligence ; Computer network ; Mathematics ; Artificial neural network ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods