jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sobin Alosious | Meng Jiang | Tengfei Luo
Source: MRS Bulletin (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

Computation and machine learning for materials: Past, present, and future perspectives Abstract Computational methods and machine learning (ML) are reshaping materials science by accelerating their discovery, design, and optimization. Traditional approaches such as density functional theory and molecular dynamics have been instrumental in studying materials at the atomic level. However, their high computational cost and, in certain cases, limited accuracy can restrict the scope of in silico exploration. ML promises to accelerate material property prediction and design. However, in many areas, the volume and fidelity of the data are critical barriers. Active learning can reduce the reliance on large data sets, and simulation has emerged as a critical tool for generating data on the fly. Despite these advances, challenges remain, particularly in data quality, model interpretability, and bridging the gap between computational predictions and experimental validation. Future research should develop automated frameworks capable of designing and testing materials for specific applications, and integrating ML with traditional simulations and experiments can contribute to this goal. Graphic abstract Bridging (networking) ; Scope (computer science) ; Computation ; Fidelity ; High fidelity ; Property (philosophy) ; Computational model Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Markus Orsi | Angelo Frei
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

ELECTRUM: an electron configuration-based universal metal fingerprint for transition metal compounds Machine learning has experienced a drastic rise in interest and applications in all fields of chemistry, enabling researchers to leverage large chemical datasets to gain novel insights. The success of machine learning-driven projects in chemistry hinges on three key factors: access to robust and comprehensive datasets, a well-defined objective, and effective molecular representations that convert chemical structures into machine-readable formats. Transition metal complexes have lagged behind their organic counterparts on all three of these avenues. The large diversity of structures, coordination numbers and modes have made its translation to a machine-readable format an ongoing challenge. Here we introduce ELECTRUM, an electron configuration-based universal metal fingerprint for transition metal compounds. Its lightweight implementation enables the straightforward conversion of any transition metal complex into a simple fingerprint. Utilising a novel dataset generated from the Cambridge Structural Database (CSD), we demonstrate that ELECTRUM effectively captures the structural diversity of transition metal complexes. By plotting nearest-neighbor relationships in ELECTRUM space, we reveal meaningful clustering in two-dimensional representations. Furthermore, we use the ELECTRUM encoding to train machine learning models on the prediction of metal complex coordination numbers from ligand structures and metal identity alone. We show that on a subset of this data, we can train models to predict the oxidation state of metal complexes. These case studies showcase the potential of ELECTRUM as an easy-to-implement fingerprint for metal complexes. We rely on the community to further test, validate, and improve it. Transition metal ; Leverage (statistics) ; Fingerprint (computing) ; Cluster analysis ; Metal ; Metal K-edge ; Identity (music) Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Yizhe Chen | Shomik Verma | Kevin P. Greenman | Haoyu Yin | Zhihao Wang | Lanjing Wang | Jiali Li | Rafael Gómez‐Bombarelli | Aron Walsh | Xiaonan Wang
Source: Chemical Science (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

A unified active learning framework for photosensitizer design The design of high-performance photosensitizers for next-generation photovoltaic and clean energy applications remains a formidable challenge due to the vast chemical space, competing photophysical trade-offs, and computational limitations of traditional quantum chemistry methods. While machine learning offers potential solutions, existing approaches suffer from data scarcity and inefficient exploration of molecular configurations. This work introduces a unified active learning framework that systematically integrates semi-empirical quantum calculations with adaptive molecular screening strategies to accelerate photosensitizer discovery. Our methodology combines three principal components: (1) A hybrid quantum mechanics/machine learning pipeline generating a chemically diverse molecular dataset while maintaining quantum chemical accuracy at significantly reduced computational costs; (2) a graph neural network architecture and uncertainty quantification; (3) Novel acquisition strategies that dynamically balance broad chemical space exploration with targeted optimization of photophysical objectives. The framework demonstrates superior performance in predicting critical energy levels (<i>T</i> <sub>1</sub>/<i>S</i> <sub>1</sub>) compared to conventional screening approaches, while effectively prioritizing synthetically feasible candidates. By open-sourcing both the curated molecular dataset and implementation tools, this work establishes an extensible platform for data-driven discovery of optoelectronic materials, with immediate applications in solar energy conversion and beyond. Chemical space ; Photosensitizer ; Photovoltaic system ; Pipeline (software) ; Efficient energy use ; Principal (computer security) ; Quantum chemical ; Quantum Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Naruki Yoshikawa | Gun Deniz Akkoc | Sergio Pablo‐García | Yang Cao | Han Hao | Alán Aspuru‐Guzik
Source: Digital Discovery (article, 2025-01-01)
Field: Chemistry — Electrochemistry

Does one need to polish electrodes in an eight pattern? Automation provides the answer Automation of electrochemical measurements can accelerate the discovery of new electroactive materials. Automation ; Electrode ; Electrochemistry ; Computer science ; Nanotechnology ; Engineering ; Materials science ; Chemistry ; Mechanical engineering Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Conducting polymers and applications
Authors: Sk Md Ahnaf Akif Alvi | Mrinalini Mulukutla | Nicolás Vicente Flores | Danial Khatamsaz | Jan Janßen | Danny Pérez | Douglas Allaire | Vahid Attari | Raymundo Arróyave
Source: npj Computational Materials (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design. Gaussian process ; Kriging ; Gaussian ; Surrogate model ; Boosting (machine learning) ; Artificial neural network ; Uncertainty quantification ; Regression Machine Learning in Materials Science ; Refrigeration and Air Conditioning Technologies ; Nuclear Materials and Properties
Authors: Yonatan Harnik | Hadas Shalit Peleg | Amit H. Bermano | Anat Milo
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data efficient molecular image representation learning using foundation models Deep learning (DL) in chemistry has seen significant progress, yet its applicability is limited by the scarcity of large, labeled datasets and the difficulty of extracting meaningful molecular features. Molecular representation learning (MRL) has emerged as a powerful approach to address these challenges by decoupling feature extraction and property prediction. In MRL, a deep learning network is first trained to learn molecular features from large, unlabeled datasets and then finetuned for property prediction on smaller specialized data. Whereas MRL methods have been widely applied across chemical applications, these models are typically trained from scratch. Herein, we propose that foundation models can serve as an advantageous starting point for developing MRL models. Foundation models are large models trained on diverse datasets capable of addressing various downstream tasks. For example, large language models like OpenAI's GPT-4 can be finetuned with minimal additional data for tasks considerably different from their training. Based on this premise we leveraged OpenAI's vision foundation model, CLIP, as the backbone for developing MoleCLIP, a molecular image representation learning framework. MoleCLIP requires significantly less molecular pretraining data to match the performance of state-of-the-art models on standard benchmarks. Furthermore, MoleCLIP outperformed existing models on homogeneous catalysis datasets, emphasizing its robustness to distribution shifts, which allows it to adapt effectively to varied tasks and datasets. This successful application of a general foundation model to chemical tasks highlights the potential of innovations in DL research to advance synthetic chemistry and, more broadly, any field where molecular property description is central to discovery. Foundation (evidence) ; Representation (politics) ; Image (mathematics) ; Computer science ; Artificial intelligence ; Geography ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Weiqi Li | Jie Wu | Dongwei Yao | Feng Wu | Lei Wang | Hua Lou | Haibin He
Source: Processes (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Neural Network-Based Control Optimization for NH3 Leakage and NOx Emissions in SCR Systems This study proposes a data-driven optimization framework to enhance emission control performance in diesel engine selective catalytic reduction (SCR) systems under transient operating conditions. A one-dimensional SCR model was constructed in GT-Power, and simulation datasets were generated using experimentally measured inputs from the World Harmonized Transient Cycle (WHTC), with representative emission responses obtained by varying fixed ammonia-to-NOx (A/N) ratios. Building on these datasets, a hybrid prediction model combining Long Short-Term Memory (LSTM) networks and multi-head attention mechanisms was developed to accurately forecast SCR outlet NH3 leakage and NOx emissions. The model exhibited high predictive accuracy, achieving R2 values exceeding 0.977 and low RMSE across training, validation, and test sets. Based on the model predictions, a constrained dynamic multi-objective optimization strategy was implemented to adaptively adjust ammonia dosing, aiming to simultaneously minimize NH3 leakage and NOx emissions. The optimized NH3 injection profiles were validated through reapplication in the GT-Power simulation environment. Compared to the baseline fixed-ratio control strategy, the proposed approach reduced NH3 leakage and NOx emissions by 34.40% and 11.15%, respectively, as determined for the transient segment of the WHTC cycle. These results demonstrate the effectiveness of integrating physics-based simulation, deep learning prediction, and dynamic optimization for improving aftertreatment adaptability and emission compliance in real-world diesel engine applications. All reported values are based on a single simulated WHTC cycle without statistical uncertainty analysis. NOx ; Leakage (economics) ; Environmental science ; Automotive engineering ; Waste management ; Engineering ; Chemistry ; Economics ; Combustion Catalytic Processes in Materials Science ; Water Quality Monitoring and Analysis ; Advanced Combustion Engine Technologies
Authors: Yudong Shi | Zhao Zheng | Yinggan Zhang | Zhou Cui | Peng Lin | Cuilian Wen | Baisheng Sa | Zhimei Sun
Source: Advanced Intelligent Discovery (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Graph Attention Neural Networks for Interpretable and Generalizable Prediction of Janus III–Vi Van Der Waals Heterostructures Graph neural networks (GNNs) have been widely used in materials science due to their ability to process complex graph‐structured data by capturing the relationships between atoms, molecules, or crystal structures within materials. However, due to the lack of interpretability, GNN acts as a “black box” model in most cases. In this work, by introducing the attention mechanism into GNN, a graph attention neural network (GANN) model for 2D materials is proposed, which not only reaches accurate prediction performances but also shows interpretability via attention mechanism analysis. Taking Janus III–VI van der Waals heterostructures as a representative case, the MAE for predicting formation energy, lattice constants, PBE bandgap, and HSE06 bandgap are 0.009 eV, 0.003 Å, 0.087, and 0.123 eV, respectively. Remarkably, the GANN model shows outstanding generalizable ability that can achieve accurate prediction using guessed input structures without fully structural relaxation to reach the ground state for Janus III–VI vdW heterostructures. Furthermore, the attention mechanisms of integration enable qualitative analysis of the contributions of the nearest neighboring atoms, endowing the GNN model with enhanced interpretability. Our findings offer novel perspectives for AI‐driven 2D material design by establishing an optimal balance between predictive accuracy and model interpretability in GNN approaches. Janus ; van der Waals force ; Artificial neural network ; Artificial intelligence ; Deep neural networks ; Computer science ; Graph ; Machine learning ; Psychology ; Theoretical computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Anyang Peng | Chunfang Cai | Mingyu Guo | Duo Zhang | Chengqian Zhang | Wanrun Jiang | Yinan Wang | Antoine Loew | Chengkun Wu | E Weinan | Linfeng Zhang | Han Wang
Source: ArXiv.org (preprint, 2025-04-28)
Field: Materials Science — Materials Chemistry

LAMBench: A Benchmark for Large Atomistic Models Large Atomistic Models (LAMs) have undergone remarkable progress recently, emerging as universal or fundamental representations of the potential energy surface defined by the first-principles calculations of atomistic systems. However, our understanding of the extent to which these models achieve true universality, as well as their comparative performance across different models, remains limited. This gap is largely due to the lack of comprehensive benchmarks capable of evaluating the effectiveness of LAMs as approximations to the universal potential energy surface. In this study, we introduce LAMBench, a benchmarking system designed to evaluate LAMs in terms of their generalizability, adaptability, and applicability. These attributes are crucial for deploying LAMs as ready-to-use tools across a diverse array of scientific discovery contexts. We benchmark ten state-of-the-art LAMs released prior to August 1, 2025, using LAMBench. Our findings reveal a significant gap between the current LAMs and the ideal universal potential energy surface. They also highlight the need for incorporating cross-domain training data, supporting multi-fidelity modeling, and ensuring the models' conservativeness and differentiability. As a dynamic and extensible platform, LAMBench is intended to continuously evolve, thereby facilitating the development of robust and generalizable LAMs capable of significantly advancing scientific research. The LAMBench code is open-sourced at https://github.com/deepmodeling/lambench, and an interactive leaderboard is available at https://www.aissquare.com/openlam?tab=Benchmark. Benchmarking ; Benchmark (surveying) ; Energy (signal processing) ; Ideal (ethics) ; Code (set theory) Machine Learning in Materials Science
Authors: Mahshad Fani | William Chadwell | Nishad Tasnim | Xin Wang | Mehran Araghi | Kun Lü | Zejian Zhou | Tang Gu | Shuozhi Xu
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Constructing surrogates for atomistic simulations via deep learning and generative large language models Abstract Atomistic simulations offer insights into material behavior at the atomic level. However, they can be computationally intensive. In this paper, two deep learning models, deep symbolic optimization (DSO) and deep neural networks (DNN), and one generative large language model, GPT-4o, are employed to construct surrogates for atomistic simulations. Specifically, atomistic simulations are first performed to investigate the collapse of a nanovoid under hydrostatic pressure. We focus on the role of initial void radius and material characteristics, such as intrinsic and unstable stacking fault energies and surface energy (SE). We find that the critical pressure required for void collapse spans from 17.05 to 19.62 GPa, with the highest values corresponding to the maximum USFE. Additionally, an intermediate SE value (1068.13 $$\hbox {mJ/m}^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mtext>mJ/m</mml:mtext> <mml:mn>2</mml:mn> </mml:msup> </mml:math> ) minimizes the critical pressure. Based on the simulation results, surrogate models based on DSO, DNN, and GPT-4o are constructed, concluding that the SE affects the critical pressure the most. Graphical abstract Generative grammar ; Computer science ; Generative model ; Artificial intelligence ; Natural language processing ; Cognitive science ; Psychology Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Tobias Kreiman | Yutong Bai | Fadi Atieh | Elizabeth Weaver | Eric Qu | Aditi S. Krishnapriyan
Source: ArXiv.org (preprint, 2025-10-02)
Field: Chemistry — Physical and Theoretical Chemistry

Transformers Discover Molecular Structure Without Graph Priors Graph Neural Networks (GNNs) are the dominant architecture for molecular machine learning, particularly for molecular property prediction and machine learning interatomic potentials (MLIPs). GNNs perform message passing on predefined graphs often induced by a fixed radius cutoff or k-nearest neighbor scheme. While this design aligns with the locality present in many molecular tasks, a hard-coded graph can limit expressivity due to the fixed receptive field and slows down inference with sparse graph operations. In this work, we investigate whether pure, unmodified Transformers trained directly on Cartesian coordinates$\unicode{x2013}$without predefined graphs or physical priors$\unicode{x2013}$can approximate molecular energies and forces. As a starting point for our analysis, we demonstrate how to train a Transformer to competitive energy and force mean absolute errors under a matched training compute budget, relative to a state-of-the-art equivariant GNN on the OMol25 dataset. We discover that the Transformer learns physically consistent patterns$\unicode{x2013}$such as attention weights that decay inversely with interatomic distance$\unicode{x2013}$and flexibly adapts them across different molecular environments due to the absence of hard-coded biases. The use of a standard Transformer also unlocks predictable improvements with respect to scaling training resources, consistent with empirical scaling laws observed in other domains. Our results demonstrate that many favorable properties of GNNs can emerge adaptively in Transformers, challenging the necessity of hard-coded graph inductive biases and pointing toward standardized, scalable architectures for molecular modeling. Molecular graph ; Transformer ; Inference ; Scaling ; Scalability ; Locality ; Graph ; Topology (electrical circuits) History and advancements in chemistry
Authors: Riin Rebane | Anu Teearu | Irja Helm | Johan Bobacka | Jérôme Randon | Jonas Bergquist | Ivo Leito
Source: Analytical and Bioanalytical Chemistry (article, 2025-01-29)
Field: Chemistry — Physical and Theoretical Chemistry

EACH Erasmus Mundus programme: advancing excellence in analytical chemistry education and industry impact Erasmus+ ; Excellence ; Chemistry ; Medical laboratory ; Analytical Chemistry (journal) ; Engineering ethics ; Nanotechnology ; Engineering ; Environmental chemistry ; Medicine ; The Renaissance ; Philosophy ; Materials science ; Epistemology ; Pathology ; Art history Various Chemistry Research Topics ; Biomedical and Engineering Education ; Chemistry and Chemical Engineering
Authors: Ashutosh Sengar
Source: Preprints.org (preprint, 2025-01-27)
Field: Materials Science — Biomaterials

Next-Generation Liposomal Drug Delivery Systems: Core Principles, Innovations, and Targeting Strategies Modern therapeutics have come to focus attention on liposomal drug delivery systems as innovative and effective vehicles for targeted drug delivery. Lipid-based vesicles may encapsulate drugs that are either hydrophilic or hydrophobic, bringing about unique advantages such as improved bioavailability, protection of the encapsulated drug from degradation, and the potential to provide controlled and sustained release. Liposomal systems have also been formulated to produce different kinds of formulations including surface-modified liposomes, stealth liposomes, and cationic liposomes; each has a different therapeutic application. The introduction of liposomal systems into some newly emerging technologies like nanotechnology helps target drugs better and personalize treatment protocols. This review on the basics in drug delivery as liposomes that covers structural and compositional aspects, mechanisms associated with the encapsulation, and release, also incorporates new developments in the formulations of liposomal, comparisons between liposomes, and other types of delivery systems, and possibilities in the integration of herbal medicines, such as the Tribulus terrestris to enhance therapeutic performance. Further discussed issues are scaling up, stability, and regulatory challenges of liposomal formulation, besides the opportunities for further advancements of liposomal technology in future pharmaceutical applications. Thus, it is expected that the role of liposomes in personalized medicine will continue to increase significantly with a good prospect for the creation of safer, more efficient, and targeted therapies for most diseases. Drug delivery ; Liposome ; Core (optical fiber) ; Targeted drug delivery ; Drug ; Business ; Nanotechnology ; Risk analysis (engineering) ; Pharmacology ; Process management ; Computer science ; Medicine ; Materials science Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Advanced Drug Delivery Systems