jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pol Benítez | Cibrán Rico Lopez | Edgardo Saucedo | Claudio Cazorla
Source: arXiv (Cornell University) (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Machine Learning Modeling of Temperature-Dependent Optoelectronic Properties of Anharmonic Solid Solutions Leveraging strong optoelectronic responses to external stimuli, such as temperature and electric fields, is central to the development of advanced photonic technologies, including adaptive photodetectors and reconfigurable photovoltaic devices. However, only a limited number of semiconducting materials, typically characterized by strong electron-phonon coupling, are known to exhibit such pronounced responsiveness, and their equilibrium optoelectronic properties are often not optimally suited for targeted applications. Chemical engineering strategies, such as doping and solid-solution mixing, are therefore widely employed to fine-tune the electronic and optical properties of semiconductors. Predicting the impact of such modifications, however, remains highly challenging due to the intrinsic complexity of chemically disordered and anharmonic systems, as well as the computational limitations of conventional first-principles approaches. In this work, we introduce a new computational framework that combines ab initio electronic-structure methods with machine-learning techniques to achieve first-principles precision in the prediction of optoelectronic properties of anharmonic solid solutions at finite temperature. We apply this approach to silver chalco-halide solid solutions, an emergent class of optoelectronic materials that have been experimentally shown to exhibit large band-gap tunability and strong responses to thermal excitations. Our results provide quantitative insight into the interplay between chemical disorder, lattice dynamics, and electronic structure in these materials. More broadly, this study establishes a general strategy for the accurate modeling of optoelectronic functionality in chemically disordered semiconductors. Anharmonicity ; Photodetector ; Photonics ; Ab initio ; Thermal ; Photovoltaic system Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Xintian Wang | Junmin Chen | Zhuoying Zhu | Peichen Zhong
Source: arXiv (Cornell University) (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Differentiable hybrid force fields support scalable autonomous electrolyte discovery Autonomous electrolyte discovery demands a computational engine that satisfies a critical trilemma: it must be fast enough for high-throughput screening, accurate enough for quantitative property prediction, and calibratable enough for online refinement. Classical empirical force fields (FFs) are fast but rely heavily on error cancellation, while standard machine learning interatomic potentials (MLIPs) are computationally expensive, lack rigorous long-range physics, and resist gradient-based calibration. In this Perspective, we highlight that differentiable hybrid FFs resolve this trilemma by fusing physically motivated functional forms with neural-network short-range corrections. Grounded in Energy Decomposition Analysis (EDA), state-of-the-art models such as PhyNEO-Electrolyte and ByteFF-Pol achieve zero-shot generalization to bulk phases, delivering throughputs on the order of tens of ns/day (up to $\sim$50 ns/day, depending on model complexity) for 10,000-atom systems. Crucially, their physical skeletons provide a well-conditioned parameter space for differentiable molecular dynamics (dMD). This enables a dual-calibration paradigm: bottom-up \textit{ab initio} parameterization combined with top-down fine-tuning from macroscopic experimental observables. We propose that this architecture meets the requirements of a ``ChemRobot-ready'' digital twin by integrating physics-grounded simulation with experimentally calibratable refinement, thereby enabling closed-loop autonomous electrolyte discovery. Differentiable function ; Generalization ; Property (philosophy) ; Scalability ; Computational model ; Trilemma ; Decomposition ; Basis (linear algebra) ; Realization (probability) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Abderrahmane Semmeq | Andoni Ugartemendia | Alessandro Mossa | Serena Coiai | Giorgia Brancolini | Giacomo Prampolini
Source: Journal of Chemical Theory and Computation (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

From Quantum Mechanics to Coarse-Grained Models: Bridging the Gap toward Polymer Rational Design In the rational design of novel polymers, the role of simulation methods based on classical physics is often hindered by the limited accuracy and transferability of the available models, at both the full-atomistic (FA) and coarse-grained (CG) level. Here, we introduce a first-principles-based, fully modular computational protocol for the generation of accurate and consistent FA and CG force fields, tailored on a specific material, and requiring as the sole input the chemical formula of one repeating monomeric unit of the target polymer. The proposed workflow is aimed to connect, across multiple scales, Quantum Mechanical (QM) calculations, FA and CG quantum-mechanically derived force fields (QMD-FFs), and Molecular Dynamics (MD), integrating them into a single, consistent, and reproducible framework. The protocol is tested on poly(ethylene terephthalate) (PET), a well-known polymeric material, widely used in the packaging industry. MD simulations carried out with our FA and CG QMD-FFs are found to significantly outperform standard general-purpose models in predicting key properties such as density, glass transition temperature, as well as the intra- and supra-molecular structure. Such improvement is traced back to the accuracy of the parent QM description by controlling the adherence of the lower level models to the reference set, monitoring this flow of information at each step of the applied procedure. The performances obtained for PET confirm the reliability of a general and tunable approach, which supports systematic refinement and hence stands as a promising tool for <i>in silico</i> design of novel polymers. Subjected to further automation, the procedure could also be integrated into computational machine-learning-based high-throughput schemes, paving the way toward an efficient data-driven polymer discovery. Bridging (networking) ; Modular design ; Transferability ; Workflow ; Quantum ; Protocol (science) ; Molecular dynamics ; Rational design ; Force field (fiction) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer crystallization and properties
Authors: Naoya Kuroda | Kenji Ishihara | Tomoya Shiota | Wataru Mizukami
Source: ArXiv.org (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications Machine learning interatomic potentials (MLIPs) evaluate potential energy surfaces orders of magnitude faster while maintaining accuracy comparable to first-principles calculations, and universal MLIPs that cover most of the periodic table are becoming increasingly commonplace. However, existing large-scale datasets have limited or no coverage of heavy elements such as minor actinides crucial in the nuclear field, and universal MLIPs are typically limited to 89 elements. Here, we constructed a heavy element dataset HE26 containing minor actinides, based on experimental and computational literature data. By integrating this with existing molecular and crystal datasets, we developed an open-source universal MLIP covering 97 elements, the broadest elemental coverage to date. The resulting model showed strong performance on the inorganic MPtrj and organic OFF23 test sets and promising accuracy on HE26. The dataset and model open a pathway toward the development of energy resources and the design of novel materials, such as actinide-based high-entropy ceramics, in the nuclear field. Periodic table ; Interatomic potential ; Table (database) ; Energy (signal processing) ; Minor (academic) ; Cover (algebra) ; Experimental data Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Nuclear Materials and Properties
Authors: Yi Li | Yewei Ding | Siqi Wang | Yutong Song | Kai Ge | Gabriele Sadowski | Yuanhui Ji
Source: Chemical Engineering Science (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Mechanism-embedded deep learning platform enabling accurate structure–property prediction for complex systems Scalability ; Property (philosophy) ; Deep learning ; Binary number ; Process (computing) ; Key (lock) ; Stability (learning theory) ; Complex system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Ian Chen | Alfredo Alexander-Katz
Source: arXiv (Cornell University) (preprint, 2026-03-18)
Field: Materials Science — Materials Chemistry

Rapid Neural Network Prediction of Linear Block Copolymer Free Energies Free energies are fundamental quantities governing phase behavior and thermodynamic stability in polymer systems, yet their accurate computation often requires extensive simulations and post-processing techniques such as the Bennett Acceptance Ratio (BAR). While BAR provides reliable estimates when applied between closely related thermodynamic states, evaluating free energies across large changes in interaction strength typically requires a sequence of intermediate simulations to maintain sufficient phase-space overlap, substantially increasing computational cost. In this work we develop a machine learning framework for rapidly predicting excess free energies of linear diblock copolymer systems from simulation-derived energetic descriptors. Using dissipative particle dynamics simulations of freely-jointed chain polymers, we construct a dataset of per-chain energetic statistics, including heterogeneous interaction energies, homogeneous interaction energies, and bonded spring energies, and train feed-forward neural networks to learn the relationship between these descriptors and free energies computed using a stratified BAR procedure. The resulting models accurately reproduce the reference free energies across a range of chain lengths, compositions, and densities, including polymer architectures held out from training. In regimes where direct, brute-force BAR estimates become unreliable due to poor phase-space overlap, the neural network predictions remain consistent with the reference values. These results demonstrate that physically informed machine learning models can serve as efficient surrogates for expensive free-energy calculations and provide a promising approach for accelerating thermodynamic analysis of polymer systems. Artificial neural network ; Bar (unit) ; Block (permutation group theory) ; Stability (learning theory) ; Work (physics) ; Dissipative particle dynamics ; Dissipative system ; Range (aeronautics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer crystallization and properties
Authors: Nils Gönnheimer | K. C. Reuter | Venkat Kapil | Johannes T. Margraf
Source: ChemRxiv (article, 2026-03-09)
Field: Chemistry — Physical and Theoretical Chemistry

MACE-MDP: A Foundation Model for Molecular Dipole Moments and Polarizabilities Pre-trained foundation models for machine-learning interatomic potentials (MLIPs) have recently transformed atomistic modeling by enabling efficient zero-shot sampling of potential energy surfaces at near Density-functional theory (DFT) accuracy.However, such models are largely restricted to predicting energies and forces, while largescale models for electronic response properties remain scarce, despite their importance for predicting the spectroscopic and electrostatic behavior of molecules and materials.Here, we introduce MACE-MDP, a foundation model that jointly predicts molecular dipole moments and fully anisotropic polarizability tensors and can be seamlessly combined with existing MLIPs.To enable this, we extend the SPICE dataset with high-accuracy first-principles polarizabilities, creating the SPICE- dataset, and further develop an equivariant message-passing architecture based on MACE to learn both response properties simultaneously.MACE-MDP achieves near first-principles accuracy for dipoles and polarizabilities of organic molecules while substantially reducing computational cost.We further demonstrate transferability from molecules and dimers to small oligomers, like trimers and molecular crystals through harmonic and anharmonic IR and Raman spectroscopy benchmarks, including polymorph identification in molecular crystals through comparison with experimental Raman spectra.These results establish the reliable zero-shot prediction of dielectric response properties and enable rapid first-principles-accurate vibrational spectroscopy, opening the door to applications such as molecular discovery and crystal polymorph identification. Foundation (evidence) ; Dipole ; Moment (physics) ; Field (mathematics) Advanced Physical and Chemical Molecular Interactions ; Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality
Authors: Frederik L. Johansen | Adam F. Sapnik | Erik B. Dam | Raghavendra Selvan | Kirsten M. Ø Jensen
Source: ChemRxiv (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Tackling Real-World Crystal Structure Prediction from Powder X-ray Diffraction Data Crystal structure prediction (CSP) from powder diffraction data is a central challenge in materials chemistry. Machine learning (ML) models show promise, but most are trained on idealized simulated data, limiting reliability on real experiments. Here, we assess real-world behaviour using deCIFer as a representative case. deCIFer is an autoregressive transformer that conditions each step of structure generation on encoded PXRD data, guiding token-wise predictions of space group, lattice parameters, and atomic positions. Using controlled robustness tests, we quantify performance under realistic artefacts (noise, background, peak asymmetry, and Scherrer broadening) and introduce metrics for accuracy and predictive uncertainty. deCIFer adapts smoothly to signal distortions and improves over unconditioned baselines when diffraction features remain informative, while expressing appropriate uncertainty as the PXRD pattern becomes underdetermined. Tests on experimental PXRD show recovery of known structures but also highlight limitations for lower-symmetry and nanocrystalline samples. Overall, ML-based CSP is fundamentally limited by the information content of PXRD, but can accelerate expert workflows by rapidly generating chemically plausible candidates and quantifying uncertainty, making such models valuable human-in-the-loop tools for real-world structure determination. Powder diffraction ; Robustness (evolution) ; Nanocrystalline material ; Diffraction ; Crystal structure ; Autoregressive model ; Direct methods ; X-ray crystallography X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Matthias Kellner | Ruben Rodriguez-Madrid | Jacob B. Holmes | Victor Paul Principe | Lyndon Emsley | Michele Ceriotti
Source: Open MIND (preprint, 2026-03-06)
Field: Chemistry — Spectroscopy

Quantum-corrected NMR crystallography at scale Structure determination by chemical-shift-driven NMR crystallography relies on comparing chemical shieldings measured in solid-state NMR experiments with simulations. However, computational cost limits the accuracy of shielding predictions, that usually rely on low-level electronic-structure approximations and neglect thermal and quantum mechanical nuclear motion, leading to large errors, especially for highly informative hydrogen-bonded protons. To address these limitations, we introduce a quantum-nuclei-corrected (QNC-NMR) approach. We generate inexpensively quantum ensembles using PET-MOLS, a novel machine-learning learning model of the interatomic potential transferable across molecular crystals. Using them as inputs to a chemical-shift model results in a two-fold improvement of the agreement with experiments for hydrogen-bonded protons, without the need for empirical corrections. The ability to sample structures consistent with the experimental conditions enables further refinement of the shielding model by finetuning it against measured shieldings. The favorable scaling with system size allows similar improvements for amorphous materials that are otherwise inaccessible to explicit DFT simulations. Scaling ; Electromagnetic shielding ; Scale (ratio) ; Quantum ; Quantum chemical ; Sample (material) ; Thermal Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography ; Solid-state spectroscopy and crystallography
Authors: Shiru Yang | Jiangtao Cao | Tiantian Zhang | Bin Shao | Xu Zuo
Source: The Journal of Physical Chemistry A (article, 2026-01-08)
Field: Chemistry — Organic Chemistry

Geometry-Driven Spin-State Switching in Stacked Phenalenyl Dimers A central challenge in molecular magnetism is achieving scalable magnetic moments as molecular systems increase in size. Phenalenyl, a stable radical with an <i>S</i> = 1/2 spin state, serves as an appealing building block for molecular magnetic materials. However, synthesizing high-spin-state stacked dimers has proven difficult due to strong tendencies toward antiferromagnetic pairing. Here, we combine density functional theory with convolutional neural networks to investigate how the stacking geometry governs spin alignment in phenalenyl dimers. Our machine-learning-assisted approach enables efficient mapping of the high-dimensional configurational space, revealing that lateral in-plane displacement can dramatically alter the spin state. Notably, even eclipsed α-carbon stackings, which typically favor low-spin states, can be converted into high-spin configurations through sliding. These results uncover a stacking-dependent mechanism of spin-state control and offer a practical strategy for designing high-spin radicals relevant to quantum information science and molecular spintronics. Stacking ; Magnetism ; Antiferromagnetism ; Density functional theory ; Spin (aerodynamics) ; Block (permutation group theory) Synthesis and Properties of Aromatic Compounds ; Magnetism in coordination complexes ; Machine Learning in Materials Science
Authors: L. Klochko | Mathieu d’Aquin
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Two-stage transfer learning for deep learning-based prediction of lattice thermal conductivity Machine learning promises to accelerate material discovery by enabling high-throughput prediction of desirable macro-properties from atomic-level descriptors or structures. However, the limited data available about precise values of these properties has been a barrier, leading to predictive models with limited precision or ability to generalize. This is particularly true of lattice thermal conductivity (LTC): existing datasets of precise (<i>ab initio</i>, DFT-based) computed values are limited to a few dozen materials with little variability. Based on such datasets, we study the impact of <i>transfer learning</i> on both the precision and robustness of a deep learning model (ParAIsite). We start from an existing model (MEGNet <sup>1</sup>) and show that significant improvements in predicting high-quality approximations of LTC are obtained through applying transfer learning twice: once on the basis of a pre-training of the model on a large number of materials for a different task (predicting formation energy), and a second time using a medium size dataset (a few thousand materials) of low-quality approximations of LTC (based on the AGL workflow). In other words, greater precision and robustness is obtained after a final training (fine-tuning) of the twice pre-trained model with our high-quality, smaller-scale dataset. We also analyze results obtained from using this higher-precision deep-learning model to scan large numbers of materials from the Material Project Database, in search of low-thermal-conductivity compounds. Robustness (evolution) ; Thermal conductivity ; Transfer of learning ; Lattice (music) ; Deep learning ; Thermal ; Training set ; Experimental data Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Aljoscha Felix Baumann | Daniel Mutter | Daniel F. Urban | Christian Elsässer
Source: arXiv (Cornell University) (preprint, 2026-03-11)
Field: Materials Science — Materials Chemistry

High-Throughput-Screening Workflow for Predicting Volume Changes by Ion Intercalation in Battery Materials Mechanical stresses and strains developing locally within the microstructure of active ion-battery-electrode materials during charge-discharge cycles can compromise their long-term stability. In this context, crystalline compounds exhibiting low volume changes are of particular interest. Atomistic simulations can be employed to quantify the volume change of the crystal structure upon intercalation and deintercalation of ions and to elucidate the local mechanisms underlying the global structural response. While density functional theory (DFT) offers a robust and accurate framework for such calculations, its computational cost limits its applicability for large-scale screening of diverse intercalation structures and sites. In this work, we present a workflow designed to prioritize candidate materials for subsequent detailed characterization. The workflow calculates the volume change upon intercalation using atomic-level features and a machine-learning model for bond-length prediction. The bond-length predictions are based on the assumption that bonds between the same ionic species in similar local coordination environments exhibit comparable lengths across different crystallographic structures. The model was trained on a DFT-generated dataset, which inherently defines the chemical space in which reliable predictions can be expected. We demonstrate the workflow's utility by screening approximately 1,175,000 transition-metal oxides and fluorides, followed by DFT validation of the most promising candidates. The proposed workflow enables filtering of large candidate sets and accelerates the potential discovery of low volume change intercalation materials for batteries. Intercalation (chemistry) ; Workflow ; Volume (thermodynamics) ; Ionic bonding ; Ion ; Microstructure Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography