jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cong Fu | Yuchao Lin | Zachary Krueger | Wei-Kuan Yu | Xiaoning Qian | Byung-Jun Yoon | Raymundo Arróyave | Xiaofeng Qian | Toshiyuki Maeda | Maho Nakata | Shuiwang Ji
Source: PubMed (preprint, 2025-07-08)
Field: Chemistry — Physical and Theoretical Chemistry

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials. Computational quantum chemistry plays a critical role in drug discovery, chemical synthesis, and materials science. While first-principles methods, such as density functional theory (DFT), provide high accuracy in modeling electronic structures and predicting molecular properties, they are computationally expensive. Machine learning interatomic potentials (MLIPs) have emerged as promising surrogate models that aim to achieve DFT-level accuracy while enabling efficient large-scale atomistic simulations. The development of accurate and transferable MLIPs requires large-scale, high-quality datasets with both energy and force labels. Critically, MLIPs must generalize not only to stable geometries but also to intermediate, non-equilibrium conformations encountered during atomistic simulations. In this work, we introduce PubChemQCR, a large-scale dataset of molecular relaxation trajectories curated from the raw geometry optimization outputs of the PubChemQC project. PubChemQCR is the largest publicly available dataset of DFT-based relaxation trajectories for small organic molecules, comprising approximately 3.5 million trajectories and over 300 million molecular conformations computed at various levels of theory. Each conformation is labeled with both total energy and atomic forces, making the dataset suitable for training and evaluating MLIPs. To provide baselines for future developments, we benchmark nine representative MLIP models on the dataset. Our resources are publicly available at https://huggingface.co/divelab. Benchmark (surveying) ; Quantum chemistry ; Quantum ; Quantum chemical ; Interatomic potential ; Chemical physics ; Computer science ; Statistical physics ; Computational chemistry ; Chemistry ; Materials science ; Physics ; Quantum mechanics ; Molecular dynamics ; Molecule ; Geography ; Cartography Various Chemistry Research Topics
Authors: Shigenobu Ogata | Shuhei Shinzato | Hideki Mori
Source: Journal of the Japan Institute of Metals and Materials (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

An Overview of Machine Learning Potentials for Structural Materials Machine-learning interatomic potentials (MLIPs) are emerging as a practical route to bridge the accuracy-cost gap between empirical force fields and first-principles methods for atomistic modeling of structural materials.This article reviews two major families-descriptor-based models (e.g., BPNN, GAP/SOAP, SNAP, DeePMD) and graph neural network approaches (e.g., SchNet, NequIP, MACE, Allegro)-highlighting differences in symmetry handling, scalability, and data requirements.We outline dataset construction workflows, including active-learning loops, uncertainty estimation, and transfer learning, that reduce labeling cost while improving coverage of phases, defects, and compositions.Representative applications to metals and ceramics are summarized, such as screw-dislocation core energetics in bcc Fe, diffusion and chemical short-range order in CrCoNi medium-entropy alloys, and large-scale plasticity in functional ceramics.Practical guidelines are discussed for choosing between descriptor-based and equivariant GNN models, balancing accuracy and throughput, leveraging GPU acceleration, and validating models via thermo-mechanical properties and dynamical simulations. Feature (linguistics) ; Artificial neural network ; Support vector machine ; Active learning (machine learning) Machine Learning in Materials Science ; Material Selection and Properties ; Topology Optimization in Engineering
Authors: Sandip Mane | Dhanabal S. Palaniswamy
Source: Polymers for Advanced Technologies (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Graph‐Based Deep Learning for Optimized Design of <scp>HSV</scp> Using Advanced Composite Materials ABSTRACT Hydrogen storage vessels (HSVs) demand sophisticated composites with excellent strength‐to‐weight values to withstand extreme pressure while maintaining safety and durability. Accurate prediction of the mechanical properties of these composites is critical for optimizing their performance. However, traditional empirical models, which involve complex, non‐linear interactions, are frequently difficult for computers to portray between various composite constituents. To overcome this limitation, this study introduces a Sparse Spectral Graph Convolutional Network (SGCN)‐based framework for predicting the mechanical behavior of material composites for purposes involving hydrogen storage. The approach leverages graph‐based deep learning to model the relationships among key material features such as matrix type, reinforcement type, fiber orientation, and mechanical strength. By encoding these properties into a structured graph, the SGCN effectively learns spatial and relational dependencies within the data. The system is developed in Python and validated using an open‐source dataset of composite materials. Comparative experiments with Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Artificial Neural Networks (ANN) are examples of conventional machine learning models that demonstrate that the SGCN achieves superior accuracy in capturing complex material characteristics. This research presents a scalable, data‐driven solution for enhancing the creation and improvement of composite hydrogen storage containers. Python (programming language) ; Deep learning ; Convolutional neural network ; Artificial neural network ; Composite number ; Computation ; Graph ; Reinforcement learning Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Fiber-reinforced polymer composites
Authors: Nikhil K. Barua | Holger Kleinke
Source: ACS Applied Energy Materials (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Machine Learning Predictions of Thermopower for Thermoelectric Material Screening Machine or deep learning studies to predict the electronic properties, particularly the Seebeck coefficient, of thermoelectric (TE) materials remain relatively few. Considering the benefits and limitations of these studies, we developed a data-driven, structure-independent machine learning script. The script can produce Seebeck coefficient predictions by using a terminal-executed batch file. Our model within the script was trained with the latest version of the Starry data set, with experimentally derived thermoelectric data. The model was then tested across multiple sets of test sets. Postprediction, we use SHapley Additive exPlanations to analyze the interpretability of our model with chemical insights into features influencing our models’ decisions. Interpretability ; Thermoelectric effect ; Seebeck coefficient ; Thermoelectric materials ; Power (physics) ; Test data ; Experimental data Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Raúl Pérez‐Soto | Mihai V. Popescu | Sabari Kumar | Leticia Adao Gomes | Changyeob Lee | Elijah Shore | Steven R. López | Robert S. Paton | Seonah Kim
Source: ChemRxiv (preprint, 2025-04-30)
Field: Materials Science — Materials Chemistry

A Fragment Based Approach Towards Curating, Comparing and Developing Machine Learning Models Applied in Photo-chemistry The development of Graph Neural Networks for the task of predicting molecular properties has gained a great deal of attention as it typically allows the correlation of quick to compute atomic and bond descriptors with overall molecular properties. With the raising interest in photochemistry and photocatalysis as sustainable alternatives to thermal reactions, curation of virtual databases of computed photophysical properties for training of machine learning models has become popular. Unfortunately, current efforts fail to consider the exciton localization onto different chromophores of the same molecule, leading to potentially large prediction errors. Here we describe a molecular fragmentation strategy that can be used to overcome this limitation, while also providing a way to compare structural diversity between different libraries. Using a newly generated a database of 46,432 adiabatic S0-T1 energy gaps (ALFAST-DB), we compare its diversity against two datasets from the literature and demonstrate that a fragment-based delta learning approach improves model generalizability while achieving accuracies matching those of traditional message passing graph neural network architectures (MPGNN) Fragment (logic) ; Computer science ; Artificial intelligence ; Chemistry ; Programming language Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Sohang Kundu | Diana Chamaki | Hong‐Zhou Ye | Garvit Agarwal | Timothy C. Berkelbach
Source: ArXiv.org (preprint, 2025-09-17)
Field: Materials Science — Materials Chemistry

Reaction dynamics of lithium-mediated electrolyte decomposition using machine learning potentials We study the ring-opening decomposition of ethylene carbonate in the presence of a single lithium atom and on the surface of lithium metal. Combining accurate electronic structure theory, enhanced sampling, and machine learning, we fine-tune the MACE-MP0 foundation model and apply the resulting machine learning potentials to obtain statistically converged free energy profiles and reaction rates. We confirm that the level of electronic structure theory is important, and inaccurate density functionals can overestimate the reaction rate by up to nine orders of magnitude. We also find that harmonic transition state theory underestimates reaction rates by about one order of magnitude. For the surface reaction, we find and characterize a new, ultrafast decomposition pathway wherein the carbonyl is deeply inserted into the lithium surface and bent by about 70$^\circ$. This reaction, which occurs in a few tens of picoseconds, generates a ring-opened intermediate that is a precursor for CO or CO$_2$ formation; by contrast, an alternative pathway that yields CO$_3^{2-}$ and ethylene is found to be non-competitive, occurring on a timescale of tens of nanoseconds. Lithium (medication) ; Decomposition ; Ethylene carbonate ; Density functional theory ; Potential energy surface ; Electronic structure ; Electrolyte ; Surface (topology) ; Ethylene Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced Battery Materials and Technologies
Authors: Joseph Pugar | Calvin Gang | I. Millán | Karl W. Haider | Newell R. Washburn
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning of polyurethane prepolymer viscosity: a comparison of chemical and physicochemical approaches A dual machine learning framework predicts polyurethane prepolymer viscosity using either chemical composition or physicochemical descriptors, balancing accuracy for known chemistries with generalizability to new formulations. Prepolymer ; Polyurethane ; Viscosity ; Generalizability theory ; Rheology Machine Learning in Materials Science ; Polymer composites and self-healing ; Catalysis and Oxidation Reactions
Authors: Khan Raqib Mahmud | Lingxiao Wang | Jinyuan Chen | Xiyuan Liu | Sunzid Hassan
Source:  (article, 2025-03-22)
Field: Materials Science — Polymers and Plastics

Comparative Analysis of Deep Learning Approaches for Predicting Thermomechanical Behavior of Shape Memory Polymers Thermomechanical constitutive modeling is crucial for understanding and designing shape memory polymers (SMPs) for advanced engineering applications. Traditional approaches are often time-consuming and computationally expensive which requires the development of more efficient and accurate methods. In this paper, we propose a Transformer-based deep learning model to predict the thermomechanical behavior of semicrys-talline two-way shape memory polymers (2W-SMPs) under thermomechanical cycles. By leveraging its ability to capture complex, time-dependent patterns, the framework accurately models the intricate relationships between polymer strain, time, temperature, and stress. A comparative analysis with other deep learning models demonstrates that the Transformer excels in accuracy and robustness by capturing intricate dependencies and non-linearities in the data. The test results demonstrate that our proposed Transformer model achieved a root mean squared error (RMSE) of 0.3365, outperforms the traditional deep learning models such as the feedforward neural network (FNN), convolutional neural network (CNN), and long short-term memory (LSTM) models which achieved RMSE values of 6.78, 17.35, and 13.85, respectively. This study highlights the potential of the Transformer model as a powerful tool for predicting material behavior and reducing the time and resources required for constitutive modeling. Shape-memory polymer ; Computer science ; Artificial intelligence ; Polymer ; Materials science ; Shape-memory alloy ; Composite material Polymer composites and self-healing
Authors: Ming Yang | Hao Cheng | Tong Yang | Minggang Zeng | Hao Wu | Z N Wang | Haokai Hong | Cheng Chen | Bing Li | Zhongrui Wang | Shu Ping Lau | Wanyu Lin | Kay Chen Tan
Source: Research Square (preprint, 2025-02-06)
Field: Materials Science — Materials Chemistry

A physics-informed cluster graph neural network enables generalizable and interpretable prediction for material discovery Cluster (spacecraft) ; Artificial neural network ; Graph ; Deep neural networks ; Computer science ; Data science ; Artificial intelligence ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and ELM
Authors: Kai‐Le Jiang | Huai‐Qian Wang | Huifang Li | Zi-Xin Wen
Source: The Journal of Chemical Physics (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

A scalable machine learning multi-local regression framework for potential energy surface fitting across diverse chemical landscapes The accurate characterization of the potential energy surface (PES) is fundamental to understanding molecular structures and chemical reaction mechanisms. Traditional approaches, such as ab initio calculations and empirical force fields, struggle to balance computational efficiency and accuracy, particularly for high-dimensional systems. Machine learning methods have demonstrated remarkable efficacy in addressing chemical problems, yet two critical challenges remain: (1) Inadequate representation of high-energy structures (e.g., transition states) in training data due to their scarcity and inherent complexity, leading to biased PES predictions; and (2) insufficient adaptability of data-driven models to dynamic chemical scenarios, as they rely on static benchmark datasets and lack explicit integration with mechanistic knowledge. This study proposes a Clustering and Local Regression Network (CLRNet), a chemical-principle-guided hierarchical framework for PES construction, which integrates data-driven modeling with quantum mechanical insights. CLRNet employs graph neural networks to extract molecular features, integrating unsupervised clustering of features with local potential energy surface regression. CLRNet has outstanding advantages in accommodating high-energy structures and has achieved a balance between model capacity and computing power. This work not only offers a new approach to PES analysis but also bridges the gap between data-driven modeling and chemical intuition. It has great application prospects in downstream tasks such as transition state energy calculation and PES fitting of catalysis and chemical kinetics in physical chemistry. Regression ; Scalability ; Surface (topology) ; Energy (signal processing) ; Computer science ; Machine learning ; Regression analysis ; Artificial intelligence ; Statistics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Gregor N. C. Simm | Jean Hélie | Hannes Schulz | Yicheng Chen | Guillem Simeon | A. N. Kuzina | Ernesto Martinez-Baez | Piero Gasparotto | Gabriele Tocci | Chi Chen | Yatao Li | Li Cheng | Zhenchang Wang | Bichlien H. Nguyen | Jake A. Smith | Lixin Sun
Source: ArXiv.org (preprint, 2025-10-15)
Field: Materials Science — Materials Chemistry

SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles Polymers are a versatile class of materials with widespread industrial applications. Advanced computational tools could revolutionize their design, but their complex, multi-scale nature poses significant modeling challenges. Conventional force fields often lack the accuracy and transferability required to capture the intricate interactions governing polymer behavior. Conversely, quantum-chemical methods are computationally prohibitive for the large systems and long timescales required to simulate relevant polymer phenomena. Here, we overcome these limitations with a machine learning force field (MLFF) approach. We demonstrate that macroscopic properties for a broad range of polymers can be predicted ab initio, without fitting to experimental data. Specifically, we develop a fast and scalable MLFF to accurately predict polymer densities, outperforming established classical force fields. Our MLFF also captures second-order phase transitions, enabling the prediction of glass transition temperatures. To accelerate progress in this domain, we introduce a benchmark of experimental bulk properties for 130 polymers and an accompanying quantum-chemical dataset. This work lays the foundation for a fully in silico design pipeline for next-generation polymeric materials. Scalability ; Pipeline (software) ; Force field (fiction) ; Benchmark (surveying) ; Field (mathematics) ; Polymer ; Range (aeronautics) Machine Learning in Materials Science
Authors: Thomas Kraska
Source: Journal of Chemical Education (article, 2025-10-07)
Field: Chemistry — Physical and Theoretical Chemistry

Classification of NaCl and CsCl Crystal Structures: Establishing an Integrated Didactic Approach for Machine Learning and Chemistry in Upper Secondary Education An example for an integrated approach to machine learning and chemical education in upper secondary education is presented. This activity focuses on the crystal-structure classification of alkali halides. Based on this training set, the crystal structures of alkaline earth metal oxides including RaO were predicted. In the first step, students worked out the chemical atomic and ionic properties that can be used for the classification of crystal structures. Based on this knowledge, students performed an unplugged classification by placing the substances and a classification line in a diagram of property variables equivalent to the machine learning representation. In the second step, machine learning was introduced by a further unplugged activity representing a numerical learning algorithm. Hence, machine learning was employed as a teaching method that allows students to engage deeply in chemical topics as well as an example that shows its potential for chemical applications. The three selected algorithms, Perceptron, Adaline, and logistic classification are fundamental, timeless, and mathematically reduced to be comprehensible in schools. The project was conducted in grade 11 with 16-year-old students in a total lesson time of 315 min over 3 weeks. Crystal (programming language) ; Chemistry education ; Alkali metal ; Crystal chemistry ; Concept learning ; Ionic bonding Various Chemistry Research Topics
Authors: Sree Harsha Bhimineni | Supriti Dutta | Andrea Zambotti | Nicolos Leport | Gugulethu Nkala | Johanna Nelson Weker | Laurent Pilon | Bruce Dunn | Philippe Sautet
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning for the Screening of Fast Energy Storage Pseudocapacitive Materials Pseudocapacitance represents an alternative mechanism for capacitive energy storage, complementing electrical double-layer capacitance (EDLC). By relying on fast, battery-like redox reactions, pseudocapacitors achieve battery-like energy densities while maintaining the high power density and long cycle life of EDLCs. This study aims to discover novel high-performance pseudocapacitive materials by integrating first-principles calculations with machine learning. Specifically, it focuses on lithium intercalation-based pseudocapacitance. The initial dataset comprises a combination of existing battery and pseudocapacitive transitional metal oxides and sulfides. A descriptor-performance model is developed using machine learning and discriminative descriptors are identified using feature engineering. The descriptors encompass compositional, structural, electronic, and lithium intercalation attributes, derived from existing literature and density functional theory (DFT) calculations. This computational framework enables rapid screening of an extensive materials database, systematically narrowing the pool of potential candidates. Promising materials are experimentally validated and incorporated into the model to enhance predictive accuracy, offering a scalable pathway for optimizing the search for pseudocapacitive materials. Figure 1 Energy storage ; Materials science ; Nanotechnology ; Computer science ; Physics Supercapacitor Materials and Fabrication
Authors: Sandip Giri | Anakuthil Anoop
Source: Journal of Computational Chemistry (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Exploring the Chemical Space of Noncovalent Molecular Clusters Using Automated Cluster Building Algorithm and Neural Network Potential Global minima of molecular clusters are one of the acute and long-standing problems in the computational chemistry domain. It is important to understand the lowest energy geometry isomer of a given compound for further investigation of physical and chemical properties. The exponential growth of local minima with increasing cluster size makes the problem more relevant. The existing ab initio methods are accurate but very expensive in terms of computational resources. It opens the gap to find more efficient and reliable solutions for rapid exploration. On the other side, neural network potentials are the rising alternatives, with numerically accurate and reliable results for the molecular cluster problem. We integrate AIMNET2, a pretrained model, into our TABU-based PyAR interface. Originally, this model was trained mostly on organic molecules, and we are using it for the PES explorations of molecular clusters, which is a neighboring domain but does not explicitly use any training input. We demonstrate the effectiveness of our methodology by exploring standard molecular clusters of water, ammonia, hydrogen peroxide, methanol, and acetic acid, with aggregation numbers ranging from 1-10. This work represents a significant step toward fully automated computational molecular cluster generation, paving the way for accelerated exploration and discovery in this field. Maxima and minima ; Cluster (spacecraft) ; Artificial neural network ; Coupled cluster ; Molecular dynamics ; Ab initio ; Energy (signal processing) ; Space (punctuation) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions