jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Patrick Sorrel Mvoto Kongo | Steve Cabrel Teguia Kouam | Jean-Pierre Tchapet Njafa | S. G. Nana Engo
Source: Open MIND (preprint, 2026-03-03)
Field: Materials Science — Materials Chemistry

Computational discovery of bifunctional organic semiconductors for energy and biosensing The discovery of synthetically accessible organic semiconductors with exceptional performance remains a critical bottleneck in materials science. While these materials offer compelling advantages - structural modularity, mechanical flexibility, and cost-effective solution processing - for applications in photovoltaics and biosensors, identifying candidates that balance high efficiency with practical synthesis presents significant challenges. To address this challenge, we developed a high-throughput screening approach using 17 458 molecules from the PubChemQC B3LYP/6-31G*//PM6 dataset. Our strategy employs a composite metric, PCESAScore = PCE - SAScore, which systematically balances power conversion efficiency (PCE) predictions from the Scharber model against synthetic accessibility scores. This approach successfully identified seven multi-functional candidates that demonstrate both exceptional photovoltaic performance (PCE up to 36.1 %) and strong protein-binding affinity for biosensing applications. Notably, molecule 4550 emerged as the optimal candidate, exhibiting a ligand efficiency of 0.340 kcal/mol/heavy atom with 100 % target promiscuity. Our computational framework integrates machine learning, density functional theory, and molecular docking to bridge the gap between theoretical performance and experimental feasibility. These findings establish a systematic pathway for discovering synthetically compatible organic semiconductors that can simultaneously address energy conversion and molecular recognition challenges. Bottleneck ; Bifunctional ; Photovoltaics ; Organic solar cell ; Photovoltaic system ; Organic semiconductor ; Efficient energy use ; Energy conversion efficiency Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Luminescence and Fluorescent Materials
Authors: Linfeng Zhang | Taoyong Cui | Dongzhan Zhou | Lei Bai | Sufei Zhang | Luca Rossi | Mao Su | Wanli Ouyang | Pheng-Ann Heng
Source: ArXiv.org (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

An SO(3)-equivariant reciprocal-space neural potential for long-range interactions Long-range electrostatic and polarization interactions play a central role in molecular and condensed-phase systems, yet remain fundamentally incompatible with locality-based machine-learning interatomic potentials. Although modern SO(3)-equivariant neural potentials achieve high accuracy for short-range chemistry, they cannot represent the anisotropic, slowly decaying multipolar correlations governing realistic materials, while existing long-range extensions either break SO(3) equivariance or fail to maintain energy-force consistency. Here we introduce EquiEwald, a unified neural interatomic potential that embeds an Ewald-inspired reciprocal-space formulation within an irreducible SO(3)-equivariant framework. By performing equivariant message passing in reciprocal space through learned equivariant k-space filters and an equivariant inverse transform, EquiEwald captures anisotropic, tensorial long-range correlations without sacrificing physical consistency. Across periodic and aperiodic benchmarks, EquiEwald captures long-range electrostatic behavior consistent with ab initio reference data and consistently improves energy and force accuracy, data efficiency, and long-range extrapolation. These results establish EquiEwald as a physically principled paradigm for long-range-capable machine-learning interatomic potentials. Equivariant map ; Polarization (electrochemistry) ; Aperiodic graph ; Reciprocal ; Artificial neural network ; Ab initio ; Inversion (geology) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Quantum many-body systems
Authors: Guangfa Li | Luka Malashkhia | Anh Tran | Yan Wang | Dehao Liu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Physics-Constrained Bayesian Neural Networks to Predict Grain Evolution Extrapolation ; Artificial neural network ; Probabilistic logic ; Monte Carlo method ; Bayesian probability ; Bayesian network ; Bayesian statistics ; Bayesian inference Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and mechanical properties
Authors: Xinyu Jiang | Mingan Chen | Chuanlong Zeng | Duanhua Cao | Jie Yu | Runze Zhang | Zunyun Fu | Zhehuan Fan | Jiacheng Xiong | Xutong Li | Xiaomin Luo | Dan Teng | Mingyue Zheng
Source: ChemRxiv (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Deep Residual Learning for Molecular Force Fields Accurate interatomic interaction modeling via force fields is critical for biological studies and drug discovery, but balancing accuracy and generalizability remains challenging. We introduce ResFF (Residual Learning Force Field), a hybrid machine learning force field that employs deep residual learning to integrate physics-based learnable molecular mechanics covalent terms with residual corrections from a lightweight equivariant neural network. Through a three-stage joint optimization, the two components are trained in a complementary manner to achieve optimal performance. Benchmarks show ResFF outperforms classical and neural network force fields in generalization (mean absolute error (MAE): 1.16 kcal/mol on Gen2-Opt, 0.90 kcal/mol on DES370K), torsional profiles (MAE: 0.45/0.50 kcal/mol on TorsionNet-500 and Torison Scan), and intermolecular interactions (MAE: 0.39 kcal/mol on S66×8). It also enables precise energy minima reproduction and stable molecular dynamics of biological systems. ResFF merges physical constraints with neural expressiveness, offering a robust tool for accurate and efficient molecular simulation. Force field (fiction) ; Maxima and minima ; Artificial neural network ; Residual ; Generalization ; Molecular dynamics ; Generalizability theory ; Deep learning Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Raphael T. Husistein | Markus Reiher
Source: arXiv (Cornell University) (preprint, 2026-04-01)
Field: Materials Science — Materials Chemistry

A New Paradigm for Computational Chemistry Computational chemistry has become an indispensable tool for generating data and insights, pervading all branches of experimental chemistry. Its most central concept is the potential energy hypersurface, key to all chemistry and materials science, as it assigns an energy to a molecular structure, the necessary ingredient for reaction mechanism elucidation and reaction rate calculation. Density functional theory (DFT) has been the most important method in practice for obtaining such energies, which is mirrored in the use of high-performance computing hardware. In the last two decades, a new class of surrogate potential energy functions has been evolving with remarkable properties: quantum accuracy combined with force-field speed. Until very recently, their application was hampered by the fact that they needed to be trained on truly large system-specific data sets, generated before a computational chemistry study could be started (in sharp contrast to DFT, which, as a first-principles method, works out of the box, but at a far higher price of computational cost). Very recently, this roadblock has been overcome by so-called foundation machine learning interatomic potentials, which are poised to completely change the way we do computational chemistry, likely prompting us to abandon DFT as the prime method of choice for this purpose in less than a decade. Computational model ; Density functional theory ; Quantum chemistry ; Potential energy ; Computational resource ; Computational complexity theory ; Mechanism (biology) ; Class (philosophy) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Qichao Wei | Pingluo Zhao | Hongmei Zhang | Yangwei Wang | Xingwang Cheng
Source: Results in Engineering (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Closed-loop inverse design of titanium alloys with multi-objective performance via multimodal data-driven modeling • Multimodal titanium alloy data linking process-microstructure-strength. • LSTM predicts full stress-strain; The average stress MAE is 38.4 MPa. The failure strain MAPE is 0.438%. • GA-guided inverse design; The maximum ultimate compressive strength of the five new alloys has increased by 59.7%. This study proposes a high-throughput, automated design framework for high-performance titanium alloys, integrating multimodal data fusion, deep learning prediction, and closed-loop genetic algorithm (GA) optimization. Unlike varying contrast methods that struggle with heterogeneous data, we constructed a standardized multimodal database fusing alloy composition, processing parameters, and ResNet50-extracted microstructural features. A long short-term memory (LSTM) neural network model was developed for full-range non-destructive prediction of 604 experimental stress-strain curves of titanium alloys. The mean absolute error (MAE) of stress prediction was 38.4 MPa, and the mean absolute percentage error (MAPE) of fracture strain was only 0.438%. Furthermore, a closed-loop inverse design framework, integrating the GA with the deep learning model, simultaneously optimizes composition, processing, and microstructure to automatically identify new titanium alloys with superior ultimate performance across diverse service environments. Experimental verification on five representative new alloys shows that under room temperature, high temperature, and dynamic compression conditions, their highest ultimate compressive strength has been increased by up to 59.7% compared with the original reference alloy. These results comprehensively verify the effectiveness of data-driven closed-loop material design in rapidly discovering and optimizing the extreme properties of advanced alloys, providing new theoretical and technical insights for the intelligent design and engineering application of complex alloy systems. Titanium alloy ; Inverse ; Artificial neural network ; Mean absolute percentage error ; Compressive strength ; Compression (physics) ; Approximation error ; Fracture (geology) Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Model Reduction and Neural Networks
Authors: Haibin Wei | Fuyi Chen
Source: Journal of Applied Physics (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Surface reconstruction of AgPd alloy catalysts via PPO-based deep reinforcement learning AgPd alloy catalysts frequently undergo structural evolution during catalytic reactions. Understanding the mechanisms driving this restructuring requires experimental in situ techniques, which are often expensive, as well as computationally intensive approaches such as machine learning potentials and molecular dynamics simulations. To address these challenges, this study develops a deep reinforcement learning (DRL) framework that integrates proximal policy optimization (PPO) with hierarchical hybrid action spaces and atomic-level state observations, enabling the prediction of surface dynamics beyond the reach of conventional methods. Compared to trust region policy optimization, the PPO-based DRL framework exhibits enhanced stability, accelerated convergence kinetics, and superior asymptotic performance. The DRL framework identifies a transition state with an energy barrier of 1.61 eV along the reconstruction pathway, while nudged elastic band calculations yield a comparable barrier of 1.59 eV. Notably, the DRL framework successfully identifies six distinct surface configurations in AgPd alloys, surpassing the four configurations found using traditional minima hopping methods. Analysis reveals that the final reconstructed surface undergoes facet slip accompanied by atomic substitution, and energy barrier during reconstruction pathway is 1.51 eV at 600 K. The DRL framework uncovers a series of Pd–Ag swap actions wherein Ag preferentially migrates to the surface, driven by surface energy minimization (with Ag surface energy of 1.246 J/m2 vs Pd surface energy of 2.003 J/m2) and strain release. This work represents the first application of a PPO-based DRL approach to predict AgPd surface reconstruction dynamics. Surface reconstruction ; Reinforcement learning ; Energy minimization ; Potential energy surface ; Convergence (economics) ; Maxima and minima ; Molecular dynamics ; Surface (topology) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Model Reduction and Neural Networks
Authors: Olalekan C. Olatunde | Damian C. Onwudiwe | Seshibe Makgato
Source: FlatChem (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Recent advances in the design and application of graphitic carbon nitride-based dual Z-scheme heterojunctions Heterojunction ; Dual (grammatical number) ; Carbon fibers ; Dual role ; Graphite Graphene research and applications ; Advanced Photocatalysis Techniques ; Diamond and Carbon-based Materials Research
Authors: Nils van Staalduinen | Christoph Bannwarth
Source: ChemRxiv (article, 2026-03-08)
Field: Chemistry — Organic Chemistry

When Bonds Resist Rotation: Automated Classification and Representation of Atropisomers in the MolBar Identifier Atropisomerism, a form of chirality arising from restricted rotation around single bonds, holds significant pharmacological importance. Yet, the automated identification and representation of atropisomerism in molecular databases and cheminformatics tools remain limited. Identifying bonds that lead to stable atropisomers typically involves labor-intensive experiments or costly quantum chemical scans. In this work, we present an extension of the molecule identifier MolBar that automatically classifies each rotatable bond in a three-dimensional molecular structure as either rapidly convertible or configurationally stable, specifying the absolute configuration for the latter. Our methodology uses two distinct classifiers, a physically motivated classifier for steric hindrance and a message-passing graph neural network for electronic effects. Integrated into MolBar, this module enables unsupervised, largescale annotation of atropisomeric bonds along with their absolute configurations. This advancement facilitates the representation of atropisomerism in databases and supports atropisomerism-aware machine learning in drug discovery. A preliminary version of MolBar 1.2.0 is available on the Python Package Index (PyPI) and can be installed using the pip command: pip install-pre molbar. Atropisomer ; Identifier ; Artificial neural network ; Representation (politics) ; Classifier (UML) ; Axial chirality ; Graph Axial and Atropisomeric Chirality Synthesis ; Molecular spectroscopy and chirality ; Supramolecular Chemistry and Complexes
Authors: Shin Maeda | Masashi Kishimoto | Ren Matsukawa | Szymon Buchaniec | Yuting Guo | Hiroshi Iwai
Source: International Journal of Heat and Mass Transfer (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Application of physics-informed neural networks for coupled transport-reaction problem in solid oxide fuel cell anode Anode ; Solid oxide fuel cell ; Fuel cells ; Artificial neural network ; Oxide ; Solid fuel Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning and ELM
Authors: Yinqi Zeng | Ruizhong Qiu | Yuanchen Bei | Hanghang Tong | Renjie Li
Source: ChemRxiv (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

PolymerArena: Benchmarking Multi-Task Graph Learning Models on Polymer Property Prediction Graph neural networks (GNNs) are widely used for molecular property prediction, yet their effectiveness across application specific chemical targets, especially polymers, remains highly underexplored. In particular, for photo-sensitive polymers commonly used in photolithography, predictive accuracy depends on capturing subtle structure-property relationships involving nonlinear optical response, excited-state energetics, and physicochemical constraints. To this end, we present a systematic benchmark, called Polymer-Arena, of representative multi-task graph learning models for photo-sensitive polymer property prediction, covering classical message-passing, geometry-aware, and transformer-based architectures under a unified evaluation protocol. By controlling the dataset composition, molecular featurization, and hyperparameter tuning, we isolate architectural inductive biases that govern predictive accuracy, generalization, and stability. Our results reveal clear trade-offs between messagepassing depth, geometric encoding, and attention mechanisms, with no single architecture consistently dominating across tasks. Models incorporating directional or continuous-filter information excel on excited-state and nonlinear optical targets, while simpler message-passing architectures remain competitive on scalar physicochemical properties. These findings provide actionable guidance for model selection in datadriven polymer discovery and establish a reproducible baseline for future graph-based molecular learning. Dataset is available at https: //zenodo.org/records/18228299. Hyperparameter ; Toolbox ; Graph ; Property (philosophy) ; Benchmarking ; Nonlinear system ; Artificial neural network ; Baseline (sea) ; Selection (genetic algorithm) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Xingliang Li | Wen Ren | Guangwei Liu
Source: International Journal of Information and Communication Technology (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Multi-objective optimisation of cigarette tobacco leaf blend formulation based on sensory-chemical correlation and machine learning Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas. Correlation ; Support vector machine ; Information and Communications Technology ; Sustainable development Spectroscopy and Chemometric Analyses ; Sensory Analysis and Statistical Methods ; Machine Learning and Data Classification
Authors: Francesco Calcagno | Luca Serfilippi | Giorgio Franceschelli | Marco Garavelli | Mirco Musolesi | Ivan Rivalta
Source: Journal of Chemical Theory and Computation (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Quantum Chemistry-Driven Molecular Inverse Design of Stable Isomers with Data-Free Reinforcement Learning The inverse design (ID) of molecules remains one of the greatest challenges in chemistry. Machine learning and artificial intelligence (AI) methods are increasingly employed to generate candidate molecules with tailored properties but mostly rely on pretraining over large data sets, which introduces bias. Here, we present a data-free generative AI model called PROTEUS that integrates reinforcement learning with on-the-fly quantum mechanical calculations to enable the <i>de novo</i> design of molecules from first-principles. The AI tool uses a custom syntax and hierarchical learning architecture to navigate the chemical space without prior knowledge, optimizing the desired chemical property. We demonstrate the efficiency of our software by solving complex molecular design tasks related to the maximization of isomerization energy gaps for styrene derivatives. By solving ID problems for which the exact solutions are known, PROTEUS proved to be robust and flexible enough to perform a broad exploration of different chemical spaces while successfully exploiting chemical rewards. This framework opens new avenues for quantum chemistry-driven unbiased molecular design, offering a flexible and scalable strategy to address design challenges in chemistry. Reinforcement learning ; Scalability ; Quantum ; Quantum chemical ; Inverse ; Software ; Chemical space ; Flexibility (engineering) Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Tomoaki Yazaki | Keisuke Arimoto | Junji Yamanaka | Kosuke O. Hara
Source: Science and Technology of Advanced Materials Methods (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Computational material screening for electrode materials of BaSi <sub>2</sub> solar cells In this study, we developed a computational material screening workflow for metallic electrodes of BaSi[Formula: see text] solar cells. Elemental and binary metallic materials in the Materials Project database were screened for four device models with different charge transport layer materials contacting the electrode. The screening criteria included chemical stability, melting point, and work function. In contrast to conventional screening approaches that rely solely on materials descriptors, the present workflow explicitly incorporates device-level performance constraints by using the relationship between the work function and the simulated power conversion efficiency. For melting point evaluation, a linear regression estimation from the cohesive energy and a machine learning model were compared to assess their accuracy, which revealed the higher accuracy of the machine learning model. For work function evaluation, first-principles calculation and another machine learning model were compared, which showed similar accuracies. Considering the computational cost, the machine learning model was used for screening. The threshold of work function screening was determined by device simulations. As a result, promising materials for metallic electrodes were successfully identified. Moreover, the developed screening workflow with high versatility will be applicable to material discovery for other solar cells and semiconductor devices. Workflow ; Work (physics) ; Work function ; Electrode ; Function (biology) ; Characterization (materials science) ; Solar cell ; Material properties ; Semiconductor Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Chalcogenide Semiconductor Thin Films
Authors: Meiru Liu | Yifei Xie | Shuang Li | Shiying Yang | Dezhi Yang | Zhengzheng Zhou | Li Zhang | Yang Lu | Guanhua Du
Source: Chinese Chemical Letters (article, 2026-03-01)
Field: Chemistry — Physical and Theoretical Chemistry

Multi-modal feature fusion graph neural networks for intelligent classification of pharmaceutical solid forms: Cocrystals, salts, and physical mixtures Artificial neural network ; Feature (linguistics) ; Graph ; Generalizability theory ; Quantum chemical ; Fusion ; Granularity ; Molecular graph Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Machine Learning in Materials Science