jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Karim Ben Hicham | Moreno Ascani | Jan G. Rittig | Alexander Mitsos
Source: arXiv (Cornell University) (preprint, 2026-03-11)
Field: Materials Science — Materials Chemistry

Differentiable Thermodynamic Phase-Equilibria for Machine Learning Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorporate thermodynamic structure into neural networks have recently shown strong performance for activity-coefficient modeling. However, extending such approaches to equilibrium data arising from an extremum principle, such as liquid-liquid equilibria, remains difficult. Here we present DISCOMAX, a differentiable algorithm for phase-equilibrium calculation that guarantees thermodynamic consistency at both training and inference, only subject to a user-specified discretization. The method is rooted in statistical thermodynamics, and works via a discrete enumeration with subsequent masked softmax aggregation of feasible states, and together with a straight-through gradient estimator to enable physics-consistent end-to-end learning of neural $g^{E}$-models. We evaluate the approach on binary liquid-liquid equilibrium data and demonstrate that it outperforms existing surrogate-based methods, while offering a general framework for learning from different kinds of equilibrium data. Artificial neural network ; Differentiable function ; Consistency (knowledge bases) ; Estimator ; Softmax function ; Binary number Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Safa Nouaa | Rachid Aziam | Saadia El Qdhy | Ridouan BENHITI | Gabriela Carja | Amina Soudani | Mohamed Chiban
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Alginate-Based Functional Hydrogels Beads for Nutrient Removal: Experimental Insights and Process Optimization Adsorption ; Endothermic process ; Nitrate ; Composite number ; Self-healing hydrogels ; Aqueous solution ; Phosphate ; Calcination ; Layered double hydroxides Layered Double Hydroxides Synthesis and Applications ; Phosphorus and nutrient management ; Adsorption and biosorption for pollutant removal
Authors: Christopher D. Williams | Neil A. Burton | Richard A. Bryce
Source: Journal of Chemical Information and Modeling (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Prediction of Charged Small Molecule Conformations in Solution Using a Balanced ML/MM Potential Reliably evaluating the most stable conformations of charged small organic molecules in solution poses a major challenge to computational chemistry, due to limitations in the accuracy or efficiency of conventional methodologies. In this paper, we present a hybrid machine learning/molecular mechanics (ML/MM) potential, with dynamic, conformationally dependent charges on the atoms in the ML region, that can be used to address this important challenge in drug design. By way of a case study, metadynamics-enhanced molecular dynamics simulations were used to compare the performance of several intermolecular potentials in evaluating the solution phase conformational free energy differences of pharmaceutically relevant ligands based on the protonated 2-phenylethylamine scaffold. A straightforward approach to an ML/MM potential, in which the solute's intramolecular interactions are substituted by an ML model and making no other modifications to the force field, yields results inferior to a conventional fixed-charge MM potential, due to an imbalance created in the intra- and intermolecular interactions. To remedy this shortcoming, we present a new ML/MM potential that combines an accurate, trained-for-purpose ML model (PairFEQ-Net) of the charged ligand, with the polarizable SWM3 MM model of water. Crucially, by employing an empirical parameter to scale gas phase charges, the ML model predicts dynamic, solvent-polarized charges that embed the ligand in the solvent in a more balanced way. This ML/MM potential results in mean absolute errors in the prediction of conformational free energies of just 0.5 kcal mol<sup>-1</sup>, hence furnishing a possible route to the chemically accurate prediction of the shapes of charged organic molecules in aqueous solution. Intramolecular force ; Intermolecular force ; Molecule ; Molecular dynamics ; Polarizable continuum model ; Small molecule ; Polarizability ; Protonation ; Potential of mean force Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Y. N. Wang | pengfei Dang | Cheng Li | Bofeng Shi | Yuehui Xian | Yumei Zhou | Xiangdong Ding | Jun Sun | Dezhen Xue
Source: Acta Materialia (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Phase crossover enables large work output and low thermal hysteresis in shape memory actuation Shape-memory alloy ; Hysteresis ; Thermal ; Crossover ; Actuator ; Work (physics) ; Diffusionless transformation ; Thermal hysteresis Shape Memory Alloy Transformations ; Advanced Materials and Mechanics ; Piezoelectric Actuators and Control
Authors: Yufei Yang | Leqi Qiu | Zhuocheng Gan | Zhiqi Wen | Wenhao Zhang | Bo Wei | Chunyou Mao | Xiangbin Zeng
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Excimer Laser-Enabled Photochemical Synthesis of WS2 Thin Films Excimer laser ; Fabrication ; Thin film ; Thin-film transistor ; Laser ; Transistor ; Transition metal ; Flexible electronics 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Ishna Satyarth | Eric C. Larson | Devin A. Matthews
Source: Journal of Computational Chemistry (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Tensor Hypercontraction Error Correction Using Regression Wavefunction-based quantum methods are some of the most accurate tools for predicting and analyzing the electronic structure of molecules, in particular for accounting for dynamical electron correlation. However, most methods of including dynamical correlation beyond the simple second-order Møller-Plesset perturbation theory (MP2) level are too computationally expensive to apply to large molecules. Approximations which reduce scaling with system size are a potential remedy, such as the tensor hyper-contraction (THC) technique of Hohenstein et al., but also result in additional sources of error. In this work, we correct errors in THC-approximated methods using machine learning. Specifically, we apply THC to third-order Møller-Plesset theory (MP3) as a simplified model for coupled cluster with single and double excitations (CCSD), and train several regression models on observed THC errors from the Main Group Chemistry Database (MGCDB84). We compare performance of multiple linear regression models and nonlinear Kernel Ridge regression models. We also investigate correlation procedures using absolute and relative corrections and evaluate the corrections for both molecule and reaction energies. We discuss the potential for using regression techniques to correct THC-MP3 errors by comparing it to the "canonical" MP3 reference values and find the optimum technique based on accuracy. We find that nonlinear regression models reduced root mean squared errors between THC- and canonical MP3 by a factor of 6-9× for total molecular energies and 2-3× for reaction energies. Regression ; Linear regression ; Nonlinear system ; Scaling ; Nonlinear regression ; Coupled cluster ; Perturbation theory (quantum mechanics) ; Kernel (algebra) ; Tensor (intrinsic definition) Machine Learning in Materials Science ; Tensor decomposition and applications ; Quantum many-body systems
Authors: Chenxi Liu | Yongfeng Gu | Yongjie Ren | Mingyi Ding | Tao Deng | Harald Fuchs | Deyang Ji | Wenping Hu
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Organic neuromorphic vision devices with multilevel memory for palmprint identification Neuromorphic visual devices have emerged as a critical strategy to address the limitation of the von Neumann bottleneck. However, the role of interfacial molecular engineering-specifically the modulation of polar groups in polymer gate dielectrics-in shaping the performance of neuromorphic vision systems remains insufficiently explored. Herein, we report polarity-engineered hafnium oxide (HfO<sub>2</sub>)-based phototransistors that synergistically achieve ultrahigh photodetection sensitivity and multilevel nonvolatile memory. By strategically tuning polar functional groups in polymer gate dielectrics [polyphenylene ether (PPO) and poly(4-vinylphenol) (PVP)] combined with HfO<sub>2</sub>, we demonstrate an enhancement in photoresponsivity compared to traditional low-polarity dielectrics, alongside realistic emulation of synaptic plasticity. The optimized devices exhibit exceptional comprehensive performance, including an ON/OFF ratio exceeding 10<sup>5</sup>, cycling endurance over 700 program/erase (P/E) cycles, retention time greater than 3 × 10<sup>4</sup> s, and 256 distinct conductance states (8-bit resolution), thus setting a new benchmark for multilevel memory capacity in memory devices. When integrated with classical machine learning algorithms, these phototransistors efficiently extract discriminative optoelectronic features from CASIA-palmprint database images, enabling reliable biometric authentication with accuracy above 98%. This work establishes fundamental molecular design principles for neuromorphic electronics and presents an energy-efficient paradigm for vision systems that unify sensing, memory, and <i>in situ</i> processing, paving the way for next-generation intelligent devices. Neuromorphic engineering ; Emulation ; Reconfigurability ; Benchmark (surveying) ; Electronics ; Deep learning ; MNIST database ; Sensitivity (control systems) Luminescence and Fluorescent Materials ; Advanced Memory and Neural Computing ; Forensic Fingerprint Detection Methods
Authors: Ishna Satyarth | Eric C. Larson | Devin A. Matthews
Source: Open MIND (preprint, 2026-02-27)
Field: Materials Science — Materials Chemistry

Tensor Hypercontraction Error Correction Using Regression Wavefunction-based quantum methods are some of the most accurate tools for predicting and analyzing the electronic structure of molecules, in particular for accounting for dynamical electron correlation. However, most methods of including dynamical correlation beyond the simple second-order Møller-Plesset perturbation theory (MP2) level are too computationally expensive to apply to large molecules. Approximations which reduce scaling with system size are a potential remedy, such as the tensor hyper-contraction (THC) technique of Hohenstein et al., but also result in additional sources of error. In this work, we correct errors in THC-approximated methods using machine learning. Specifically, we apply THC to third-order Møller-Plesset theory (MP3) as a simplified model for coupled cluster with single and double excitations (CCSD), and train several regression models on observed THC errors from the Main Group Chemistry Database (MGCDB84). We compare performance of multiple linear regression models and non-linear Kernel Ridge regression models. We also investigate correlation procedures using absolute and relative corrections and evaluate the corrections for both molecule and reaction energies. We discuss the potential for using regression techniques to correct THC-MP3 errors by comparing it to the "canonical" MP3 reference values and find the optimum technique based on accuracy. We find that non-linear regression models reduced root mean squared errors between THC- and canonical MP3 by a factor of 6-9$\times$ for total molecular energies and 2-3$\times$ for reaction energies. Regression ; Linear regression ; Scaling ; Regression analysis ; Tensor (intrinsic definition) ; Perturbation theory (quantum mechanics) ; Kernel (algebra) ; Local regression ; Approximation error Machine Learning in Materials Science ; Tensor decomposition and applications ; Advanced Chemical Physics Studies
Authors: Gui-Xiang Zhou | Cheng Hou
Source: Organic Chemistry Frontiers (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

DFT and machine learning insights into Ru( <scp>ii</scp> ) complex-catalyzed transfer hydrogenation DFT and machine learning reveal the favorable metal–ligand cooperative mechanism in Ru( ii )-catalyzed transfer hydrogenation and identify key descriptors for rational catalyst design. Reduction (mathematics) ; Transfer hydrogenation ; Catalytic hydrogenation ; Transfer of learning ; Transfer (computing) Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods ; Organoboron and organosilicon chemistry
Authors: Luca Thiede | Abdulrahman Aldossary | Andreas Burger | Jorge Arturo Campos-Gonzalez-Angulo | Ning Wang | Alexander Zook | Melisa Alkan | Kouhei Nakaji | Taylor L. Patti | Jérôme F. Gonthier | Vakili, Mohammad Ghazi | Alán Aspuru-Guzik
Source: Open MIND (preprint, 2026-02-23)
Field: Materials Science — Materials Chemistry

Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled-cluster (CC) theory is the most successful method for achieving accuracy beyond DFT and for predicting properties that closely align with experiment. It is known as the ''gold standard'' of quantum chemistry. Unfortunately, the high computational cost of CC limits its widespread applicability. In this work, we present the Molecular Orbital Learning (MōLe) architecture, an equivariant machine learning model that directly predicts CC's core mathematical objects, the excitation amplitudes, from the mean-field Hartree-Fock molecular orbitals as inputs. We test various aspects of our model and demonstrate its remarkable data efficiency and out-of-distribution generalization to larger molecules and off-equilibrium geometries, despite being trained only on small equilibrium geometries. Finally, we also examine its ability to reduce the number of cycles required to converge CC calculations. MōLe can set the foundations for high-accuracy wavefunction-based ML architectures to accelerate molecular design and complement force-field approaches. Generalization ; Atomic orbital ; Complement (music) ; Wave function ; Set (abstract data type) ; Molecular orbital ; Density functional theory ; Cluster (spacecraft) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Ishna Satyarth | Eric C. Larson | Devin A. Matthews
Source: ArXiv.org (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Tensor Hypercontraction Error Correction Using Regression Wavefunction-based quantum methods are some of the most accurate tools for predicting and analyzing the electronic structure of molecules, in particular for accounting for dynamical electron correlation. However, most methods of including dynamical correlation beyond the simple second-order Møller-Plesset perturbation theory (MP2) level are too computationally expensive to apply to large molecules. Approximations which reduce scaling with system size are a potential remedy, such as the tensor hyper-contraction (THC) technique of Hohenstein et al., but also result in additional sources of error. In this work, we correct errors in THC-approximated methods using machine learning. Specifically, we apply THC to third-order Møller-Plesset theory (MP3) as a simplified model for coupled cluster with single and double excitations (CCSD), and train several regression models on observed THC errors from the Main Group Chemistry Database (MGCDB84). We compare performance of multiple linear regression models and non-linear Kernel Ridge regression models. We also investigate correlation procedures using absolute and relative corrections and evaluate the corrections for both molecule and reaction energies. We discuss the potential for using regression techniques to correct THC-MP3 errors by comparing it to the "canonical" MP3 reference values and find the optimum technique based on accuracy. We find that non-linear regression models reduced root mean squared errors between THC- and canonical MP3 by a factor of 6-9$\times$ for total molecular energies and 2-3$\times$ for reaction energies. Regression ; Linear regression ; Scaling ; Regression analysis ; Tensor (intrinsic definition) ; Perturbation theory (quantum mechanics) ; Kernel (algebra) ; Local regression ; Approximation error Machine Learning in Materials Science ; Tensor decomposition and applications ; Advanced Chemical Physics Studies
Authors: Sowjanya Krishna Pinisetty | Joojo Walker | Sk Abdul Moyez | Udayabhaskararao Thumu | Kun She | Ting Zhong
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Comparative Evaluation of Machine and Deep Learning Models for Bandgap Prediction in Mixed-Compositional Halide Perovskites: Identifying hybrid models as the Optimal Framework Band gap ; Photovoltaics ; Deep learning ; Halide ; Key (lock) ; Nonlinear system ; Support vector machine ; Regression analysis Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Marco Bertani | Matilde Benassi | Lorenzo Gottardi | Gianluca Malavasi | Ashutosh Goel | Alfonso Pedone
Source: Acta Materialia (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Integrating artificial intelligence with experimental and computational materials science to predict crystallization in multicomponent glasses Crystallization profoundly influences the performance of glasses, governing their stability in nuclear waste forms, manufacturability in industrial systems, and functionality in glass-ceramics. However, the predictive models remain elusive due to the structural complexity of multicomponent glasses. Here, we establish a data-driven framework that integrates systematic experimental datasets, atomistic simulations and machine learning to predict crystallization in multicomponent alkali aluminoborosilicate glasses designed in the primary crystallization field of nepheline (NaAlSiO 4 ). Molecular dynamics models, validated against multinuclear MAS NMR spectra, reveal medium-range order and cluster motifs that are structurally similar to nepheline. From this, we derived a structural descriptor—the mean cumulative displacement (MCD)—that quantifies structural similarity between the glass and nepheline crystal. When combined with optical basicity, MCD enables robust separation of crystallizing and non-crystallizing glasses. A support vector machine (SVM) classifier trained on MCD and OB achieved ≥94% accuracy across 88 glass compositions, yielding interpretable decision boundaries that link chemistry, structure, and crystallization behavior. While the present study focuses on nepheline crystallization, the methodology provides a structure-based framework that could be extended to other crystalline phases and glass families in future work. Crystallization ; Science, technology and society ; Computational model ; Work (physics) ; Microstructure Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Solidification and crystal growth phenomena
Authors: Mohamed RJILI
Source: Figshare (article, 2026-02-27)
Field: Materials Science — Biomaterials

Supramolecular architectures on a torus: geometrically modulated chiral phases and the impact of elastic anisotropy The control of supramolecular self-assembly on curved manifolds represents a challenge in soft matter. This work investigates chiral nematic phases confined to a toroidal geometry, where intrinsic helical twisting power competes with spatially varying curvature. We employ Physics-Informed Neural Networks (PINNs) to simulate self-assembly of chiral host-guest systems, mapping interactions between molecular chirality (q0), saddle-splay elasticity (K24), and bulk elastic anisotropy (K11, K22, K33). Our simulation results unveil a phase diagram governed by a ”geometric clamp” mechanism. Specifically, the saddle-splay potential – arising from coupling between the director field and negative Gaussian curvature – locally suppresses chiral induction. This yields spatially heterogeneous ground states where curvature-pinned achiral domains coexist with frustrated helical textures. We demonstrate that tuning the material’s elastic anisotropy offers a chemical route to select the orientation of these supramolecular domains, providing design principles for curvature-directed functional materials. Anisotropy ; Supramolecular chemistry ; Gaussian curvature ; Liquid crystal ; Curvature ; Elasticity (physics) ; Chirality (physics) ; Coupling (piping) ; Phase diagram Supramolecular Self-Assembly in Materials ; Surface Chemistry and Catalysis ; Liquid Crystal Research Advancements
Authors: Mohammad Abu-Shams | Qutaiba Altwarah | Sameer Al-Dahidi | Saleem Ramadan | Duha Alkurdi
Source: Next Materials (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Data-driven prediction of tensile strength and energy consumption in MEX using machine learning models Machine Learning (ML) techniques were employed to predict Tensile Strength (TS) and Energy Consumption (EC) in the Material EXtrusion (MEX) process. Four key process parameters were considered: Infill Percentage (IP), Layer Height (LH), Number of Walls (N w ), and Bed Temperature (BT). Several supervised ML models were trained and evaluated, including Linear Regression (LR), Regression Trees (RTs), Ensemble of RTs (ERTs), Support Vector Machine (SVM), Kernel Regression (KR), and Artificial Neural Network (ANN). Each model was assessed using five-fold cross-validation, and a separate test set was used to determine the model’s ability to capture the relationship between input parameters and output responses. Among the models investigated, the ERTs consistently achieved the highest predictive performance, with R² values exceeding 99% for EC and 85% for TS, respectively. The obtained results highlight the potential of ML to enhance prediction of mechanical properties and energy consumption in the MEX process using data obtained through Design Of Experiments (DOE). • Machine Learning (ML) predicts tensile strength and energy use in FDM processes. • Four FDM parameters studied: IP, LH, Nw, and BT. • Six ML models trained with Cross-Validation and test set evaluation. • Ensemble of Regression Trees gave best results: R 2 > 99% (EC), > 85% (TS). • ML shows strong potential for DOE-based FDM prediction. Ultimate tensile strength ; Energy consumption ; Energy (signal processing) ; Artificial neural network ; Feature (linguistics) ; Support vector machine Machine Learning in Materials Science ; Composite Material Mechanics ; Material Selection and Properties