jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hyowon Seo | Difan Zhang | T. Alan Hatton
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Chemistry — Electrochemistry

Experimental and Virtual Screening of Phenazine and Phenothiazine Derivatives in Aqueous Solution for Electrochemical Carbon Capture Molecular redox-active compounds, including quinone and aza-aromatics, have emerged as tools for capturing carbon dioxide using renewable electricity. Given the vast and highly diverse chemical space of the candidate compounds, accessing their electrochemical properties in a rapid way is attractive for both experimental and virtual screening approaches. Here we present a study on the experimental screening of a series of commercial redox-active compounds. Phenazine and phenothiazine dyes have been experimentally tested for applications in electrochemical carbon capture, for which they are finding increasing applications as redox mediators due to their unique redox properties in aqueous solution. Nicotinamide has been used as a redox-inactive solubilizing agent to increase the solubilities of general phenazine and phenothiazine derivatives for rapid experimental screening of commercial compounds without time-consuming synthetic modifications. Cyclic voltammetry in aqueous solutions under nitrogen and carbon dioxide indicated reversible behavior in the pH swing of an aqueous solution. Bulk electrolysis experiments demonstrated the chemical stability of phenazine and phenothiazine derivatives. The redox properties and the chemical stability make phenazine and phenothiazine derivatives interesting candidates for future use as redox-active materials in electrochemical carbon capture. We also present progress towards a virtual screening for the optimization of electrochemical and physicochemical properties of redox-active compounds. Based on our experimental screening of commercial phenazine and phenothiazine compounds identifying neutral red as the lead compound, a virtual screening was performed for optimization of electrochemical and physicochemical properties using machine learning. The virtual screening development steps include (1) chemical library generation using neutral red as a lead compound, (2) molecular property prediction based on quantum chemical calculations, (3) aqueous solubility prediction using machine learning, (4) data processing and database creation, and (5) experimental validation of the optimized compounds for the use of electrochemical carbon capture. Figure 1 Phenazine ; Redox ; Phenothiazine ; Aqueous solution ; Electrochemistry ; Chemistry ; Potassium ferrocyanide ; Combinatorial chemistry ; Inorganic chemistry ; Organic chemistry ; Electrode Electrochemical Analysis and Applications ; Chemistry and Chemical Engineering ; Free Radicals and Antioxidants
Authors: Akshay Krishna Ammothum Kandy | Kevin Rossi | Alexis Raulin-Foissac | Ga ́etan Laurens | Julien Lam
Source: ChemRxiv (preprint, 2023-03-21)
Field: Materials Science — Materials Chemistry

Comparing transferability in neural network approaches and linear models formachine-learning interaction potentials Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training-set poses a significant challenge. Here, we compare the transferability of three MLIP approaches: i) Neural Network Potentials (NNP), ii) Physical LassoLars Interactions Potential (PLIP) and iii) Linear Potentials with Belher-Parrinello descriptors, trained over a small but diverse configuration of zinc oxide polymorphs. We obtained good agreement between each MLIP models and density functional theory reference results for physical properties including bulk lattice parameters, surface energies, and vibrational density of states. However, the NNP model performed poorly when compared to the other two linear models for the structural optimization of nanoparticles and molecular dynamics simulation of liquid phases, which are systems outside the training-set. While providing less accurate prediction for solid Zinc Oxides phases, both linear models appear more transferable than NNP when testing for nanoscale systems and liquid phases Transferability ; Interatomic potential ; Artificial neural network ; Density functional theory ; Lattice (music) ; Computer science ; Set (abstract data type) ; Statistical physics ; Molecular dynamics ; Materials science ; Machine learning ; Chemical physics ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science
Authors: Tal Kiani | Avi Caciularu | Shani Zev | Dan Thomas Major | Jacob Goldberger
Source:  (article, 2023-09-17)
Field: Materials Science — Materials Chemistry

Utilizing Perturbation of Atoms’ Positions for Equivariant Pre-Training in 3D Molecular Analysis Over the past few years, a number of Graph Neural Network (GNN) architectures have been effectively employed for molecular analysis. However, generating annotated molecular data usually requires molecular dynamics or quantum chemistry calculations, which can be extremely time-consuming. To address this challenge, we introduce a predictive equivariant self-supervision technique that is founded on perturbing the 3D positions of the atoms. This method is ideal for 3D molecular data and allows the network to initially learn general structural information before fine-tuning it for specific tasks. We demonstrate that these pre-training procedures can also be utilized to fine-tune the network for learning molecular properties on a different dataset. Our pre-training method is demonstrated to surpass previously proposed solutions via extensive experiments on different standard molecular datasets. Equivariant map ; Computer science ; Artificial neural network ; Molecular dynamics ; Perturbation (astronomy) ; Training set ; Graph ; Molecular graph ; Ideal (ethics) ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Algorithm ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tobias Heinrich | Hung-Tzu Chang | Sergey Zayko | Murat Sivis | Claus Ropers
Source: arXiv (Cornell University) (preprint, 2023-05-03)
Field: Chemistry — Spectroscopy

High-sensitivity extreme-ultraviolet transient absorption spectroscopy enabled by machine learning We introduce a machine-learning-based approach to enhance the sensitivity of optical-extreme ultraviolet (XUV) transient absorption spectroscopy. A reference spectrum is used as input to a three-layer feed-forward neural network, allowing for an efficient elimination of source noise from measurement data. In pump-probe experiments using high-harmonic radiation, we show a more than tenfold improvement in noise suppression in XUV transient absorption spectra compared to conventional referencing. Utilizing strong spectral correlations in the source fluctuations, the network facilitates a pixel-wise noise reduction without the need for wavelength calibration of the reference spectrum. The presented method can be adapted to a wide range of beam lines and enables the investigation of subtle electron and lattice dynamics in the weak excitation regime, relevant for the study of photovoltaics and photoinduced phase transitions of strongly correlated materials. Extreme ultraviolet ; Spectroscopy ; Optics ; Materials science ; Noise (video) ; Avalanche photodiode ; Optoelectronics ; Absorption spectroscopy ; Sensitivity (control systems) ; Ultraviolet ; Physics ; Computer science ; Electronic engineering ; Laser ; Artificial intelligence ; Engineering Spectroscopy and Laser Applications ; Photoacoustic and Ultrasonic Imaging ; Advanced Fluorescence Microscopy Techniques
Authors: Jon Lopez-Zorrilla | Xabier M. Aretxabaleta | Inwon Yue | Inigo Etxebarria | Hegoi Manzano | Nongnuch Artrith
Source: arXiv (Cornell University) (preprint, 2023-02-18)
Field: Materials Science — Materials Chemistry

ænet-PyTorch: a GPU-supported implementation for machine learning atomic potentials training In this work, we present ænet-PyTorch, a PyTorch-based implementation for training artificial neural network-based machine learning interatomic potentials. Developed as an extension of the atomic energy network (ænet), ænet-PyTorch provides access to all the tools included in ænet for the application and usage of the potentials. The package has been designed as an alternative to the internal training capabilities of ænet, leveraging the power of graphic processing units to facilitate direct training on forces in addition to energies. This leads to a substantial reduction of the training time by one to two orders of magnitude compared to the CPU implementation, enabling direct training on forces for systems beyond small molecules. Here we demonstrate the main features of ænet-PyTorch and show its performance on open databases. Our results show that training on all the force information within a data set is not necessary, and including between 10% to 20% of the force information is sufficient to achieve optimally accurate interatomic potentials with the least computational resources. Computer science ; Training (meteorology) ; Artificial intelligence ; Parallel computing ; Computational science ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Bhupendra Sharma | L. N. Chaudhary | Rajendra Adhikari | Madhav Prasad Ghimire
Source: Bibechana (article, 2023-11-30)
Field: Materials Science — Materials Chemistry

Machine learning driven prediction of lattice constants in transition metal dichalcogenides Machine learning represents an emerging branch of artificial intelligence, centering on the enhancement of algorithms in computer programs through the utilization of data and the accumulation of research-driven knowledge. The requirement for artificial intelligence in materials science is essential due to the significant need for innovative high-performance materials on a large scale. In this report, the gradient boosting regression tree model of machine learning was applied to predict the lattice constants of cubic and trigonal MX2 systems (M=transition metal and X=chalcogen atoms). The theoretical/experimental values of the materials were compared to the predicted values to calculate the standard errors such as RMSE (root mean square error) and MAE (mean absolute error). The features used to predict lattice constants were ionic radius, lattice angles, bandgap, formation energy, total magnetic moment, density and oxidation states. The features versus contribution barplot has been drawn to reveal the contribution level of each parameter in the degree of [0,1] to obtain the predictions. This report provides a precise account of the prediction methodology for lattice parameters of the transition metal dichalcogenides family, a process that was previously not reported. Lattice constant ; Ionic radius ; Lattice (music) ; Lattice energy ; Mean squared error ; Materials science ; Gradient boosting ; Transition metal ; Root mean square ; Machine learning ; Artificial intelligence ; Condensed matter physics ; Computer science ; Crystal structure ; Mathematics ; Chemistry ; Diffraction ; Physics ; Crystallography ; Optics ; Ion ; Statistics ; Random forest ; Quantum mechanics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Boron and Carbon Nanomaterials Research
Authors: Jingqi Wang | Jiapeng Liu | Hongshuai Wang | Guolin Ke | Linfeng Zhang | Jian Wu | Zhifeng Gao | Diannan Lu
Source: ChemRxiv (preprint, 2023-05-08)
Field: Chemistry — Inorganic Chemistry

Metal-organic frameworks meet Uni-MOF: a transformer-based gas adsorption detector Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks(MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for universal multi-gas prediction. Specifically, Uni-MOF serves as a versatile "gas adsorption detector" for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Impressively, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties. Overfitting ; Metal-organic framework ; Adsorption ; Computer science ; Representation (politics) ; Materials science ; Process engineering ; Nanotechnology ; Artificial intelligence ; Artificial neural network ; Chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Machine Learning in Materials Science
Authors: Anders S. Larsen | Toms Rekis | Anders Ø. Madsen
Source: ChemRxiv (preprint, 2023-11-09)
Field: Materials Science — Materials Chemistry

PhAI: A deep learning approach to solve the crystallographic phase problem For more than 100 years, X-ray crystallography has provided a unique view on the three-dimensional structure of atoms and molecules in crystals. However, to determine even the simplest structures now and a hundred years ago, one needs to overcome a mathematical hurdle for which the solution is not known even to this day. To reconstruct the 3-dimensional electron density map, from which the structure can be inferred, the complex structure factors F = |F| exp(iφ) of a sufficiently large number of diffracted reflections must be known. In a conventional diffraction experiment, only the amplitudes |F| are obtained, while the phases φ are lost. This is the crystallographic phase problem. Seventy years of research has established successful ab initio phasing methods such as direct methods and charge flipping. However, these methods are limited to atomic- resolution data, complicating structure determination from weakly-scattering crystals. Here, we show that a neural network can solve the crystallographic phase problem at a resolution of only 2 Å. We have developed an approach to generate millions of artificial structures and respective diffraction data for training of a neural network. We demonstrate that ab initio phasing based on this neural network is possible using 10 % to 20 % of the data needed for present-day methods, breaking the paradigm that atomic resolution is necessary for ab initio structure solution. The current neural network works in common centrosymmetric space groups and for modest unit cell dimensions, and suggests that neural networks can be used to solve the phase problem in the general case. This approach will enable structure solution for weakly-scattering crystals such as metal-organic frameworks or nanometer-sized crystals investigated using electron diffraction. Phaser ; Phase problem ; Ab initio ; Artificial neural network ; Diffraction ; Phase (matter) ; Resolution (logic) ; Crystallography ; Computer science ; Physics ; Amplitude ; Algorithm ; Artificial intelligence ; Chemistry ; Quantum mechanics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Richard Jana | Miguel A. Caro
Source: arXiv (Cornell University) (preprint, 2023-02-27)
Field: Materials Science — Materials Chemistry

Searching for iron nanoparticles with a general-purpose Gaussian approximation potential We present a general-purpose machine learning Gaussian approximation potential (GAP) for iron that is applicable to all bulk crystal structures found experimentally under diverse thermodynamic conditions, as well as surfaces and nanoparticles (NPs). By studying its phase diagram, we show that our GAP remains stable at extreme conditions, including those found in the Earth's core. The new GAP is particularly accurate for the description of NPs. We use it to identify new low-energy NPs, whose stability is verified by performing density functional theory calculations on the GAP structures. Many of these NPs are lower in energy than those previously available in the literature up to $N_\text{atoms}=100$. We further extend the convex hull of available stable structures to $N_\text{atoms}=200$. For these NPs, we study characteristic surface atomic motifs using data clustering and low-dimensional embedding techniques. With a few exceptions, e.g., at magic numbers $N_\text{atoms}=59$, $65$, $76$ and $78$, we find that iron tends to form irregularly shaped NPs without a dominant surface character or characteristic atomic motif, and no reminiscence of crystalline features. We hypothesize that the observed disorder stems from an intricate balance and competition between the stable bulk motif formation, with bcc structure, and the stable surface motif formation, with fcc structure. We expect these results to improve our understanding of the fundamental properties and structure of low-dimensional forms of iron, and to facilitate future work in the field of iron-based catalysis. Nanoparticle ; Chemical physics ; Density functional theory ; Materials science ; Band gap ; Crystal structure ; Gaussian ; Convex hull ; Phase diagram ; Nanotechnology ; Physics ; Statistical physics ; Crystallography ; Condensed matter physics ; Chemistry ; Regular polygon ; Quantum mechanics ; Phase (matter) ; Mathematics ; Geometry Machine Learning in Materials Science ; Topic Modeling
Authors: Jack Richter-Powell | Luca Thiede | Alán Asparu-Guzik | David Duvenaud
Source: arXiv (Cornell University) (preprint, 2023-11-09)
Field: Materials Science — Materials Chemistry

Sorting Out Quantum Monte Carlo Molecular modeling at the quantum level requires choosing a parameterization of the wavefunction that both respects the required particle symmetries, and is scalable to systems of many particles. For the simulation of fermions, valid parameterizations must be antisymmetric with respect to the exchange of particles. Typically, antisymmetry is enforced by leveraging the anti-symmetry of determinants with respect to the exchange of matrix rows, but this involves computing a full determinant each time the wavefunction is evaluated. Instead, we introduce a new antisymmetrization layer derived from sorting, the $\textit{sortlet}$, which scales as $O(N \log N)$ with regards to the number of particles -- in contrast to $O(N^3)$ for the determinant. We show numerically that applying this anti-symmeterization layer on top of an attention based neural-network backbone yields a flexible wavefunction parameterization capable of reaching chemical accuracy when approximating the ground state of first-row atoms and small molecules. Wave function ; Antisymmetry ; Antisymmetric relation ; Statistical physics ; Physics ; Symmetry (geometry) ; Quantum Monte Carlo ; Identical particles ; Sorting ; Quantum ; Diffusion Monte Carlo ; Function (biology) ; Fermion ; Monte Carlo method ; Quantum mechanics ; Mathematics ; Algorithm ; Monte Carlo molecular modeling ; Mathematical physics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced NMR Techniques and Applications
Authors: Weizhong Fu | Weiluo Ren | Ji Chen
Source: arXiv (Cornell University) (preprint, 2023-08-04)
Field: Materials Science — Materials Chemistry

Variance extrapolation method for neural-network variational Monte Carlo Constructing more expressive ansatz has been a primary focus for quantum Monte Carlo, aimed at more accurate \textit{ab initio} calculations. However, with more powerful ansatz, e.g. various recent developed models based on neural-network architectures, the training becomes more difficult and expensive, which may have a counterproductive effect on the accuracy of calculation. In this work, we propose to make use of the training data to perform variance extrapolation when using neural-network ansatz in variational Monte Carlo. We show that this approach can speed up the convergence and surpass the ansatz limitation to obtain an improved estimation of the energy. Moreover, variance extrapolation greatly enhances the error cancellation capability, resulting in significantly improved relative energy outcomes, which are the keys to chemistry and physics problems. Ansatz ; Extrapolation ; Monte Carlo method ; Quantum Monte Carlo ; Artificial neural network ; Variance (accounting) ; Convergence (economics) ; Statistical physics ; Computer science ; Applied mathematics ; Variance reduction ; Hybrid Monte Carlo ; Monte Carlo method in statistical physics ; Mathematical optimization ; Algorithm ; Physics ; Mathematics ; Artificial intelligence ; Statistics ; Quantum mechanics ; Markov chain Monte Carlo Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Chemical Physics Studies
Authors: Xiang Fu | Albert Musaelian | Anders Johansson | Tommi Jaakkola | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2023-10-20)
Field: Materials Science — Materials Chemistry

Learning Interatomic Potentials at Multiple Scales The need to use a short time step is a key limit on the speed of molecular dynamics (MD) simulations. Simulations governed by classical potentials are often accelerated by using a multiple-time-step (MTS) integrator that evaluates certain potential energy terms that vary more slowly than others less frequently. This approach is enabled by the simple but limiting analytic forms of classical potentials. Machine learning interatomic potentials (MLIPs), in particular recent equivariant neural networks, are much more broadly applicable than classical potentials and can faithfully reproduce the expensive but accurate reference electronic structure calculations used to train them. They still, however, require the use of a single short time step, as they lack the inherent term-by-term scale separation of classical potentials. This work introduces a method to learn a scale separation in complex interatomic interactions by co-training two MLIPs. Initially, a small and efficient model is trained to reproduce short-time-scale interactions. Subsequently, a large and expressive model is trained jointly to capture the remaining interactions not captured by the small model. When running MD, the MTS integrator then evaluates the smaller model for every time step and the larger model less frequently, accelerating simulation. Compared to a conventionally trained MLIP, our approach can achieve a significant speedup (~3x in our experiments) without a loss of accuracy on the potential energy or simulation-derived quantities. Integrator ; Computer science ; Speedup ; Molecular dynamics ; Limit (mathematics) ; Statistical physics ; Simple (philosophy) ; Scale (ratio) ; Limiting ; Energy (signal processing) ; Work (physics) ; Interatomic potential ; Algorithm ; Simulation ; Physics ; Mathematics ; Quantum mechanics ; Parallel computing Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems