jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Frank Hu | Hanqing He | David Yaron
Source: Journal of Chemical Theory and Computation (article, 2023-09-14)
Field: Materials Science — Materials Chemistry

Treating Semiempirical Hamiltonians as Flexible Machine Learning Models Yields Accurate and Interpretable Results Quantum chemistry provides chemists with invaluable information, but the high computational cost limits the size and type of systems that can be studied. Machine learning (ML) has emerged as a means to dramatically lower the cost while maintaining high accuracy. However, ML models often sacrifice interpretability by using components such as the artificial neural networks of deep learning that function as black boxes. These components impart the flexibility needed to learn from large volumes of data but make it difficult to gain insight into the physical or chemical basis for the predictions. Here, we demonstrate that semiempirical quantum chemical (SEQC) models can learn from large volumes of data without sacrificing interpretability. The SEQC model is that of density-functional-based tight binding (DFTB) with fixed atomic orbital energies and interactions that are one-dimensional functions of the interatomic distance. This model is trained to <i>ab initio</i> data in a manner that is analogous to that used to train deep learning models. Using benchmarks that reflect the accuracy of the training data, we show that the resulting model maintains a physically reasonable functional form while achieving an accuracy, relative to coupled cluster energies with a complete basis set extrapolation (CCSD(T)*/CBS), that is comparable to that of density functional theory (DFT). This suggests that trained SEQC models can achieve a low computational cost and high accuracy without sacrificing interpretability. Use of a physically motivated model form also substantially reduces the amount of <i>ab initio</i> data needed to train the model compared to that required for deep learning models. Interpretability ; Extrapolation ; Computer science ; Flexibility (engineering) ; Artificial intelligence ; Ab initio ; Density functional theory ; Machine learning ; Deep learning ; Artificial neural network ; Function (biology) ; Computational chemistry ; Physics ; Chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Junliang Liu | Jason E. Hein
Source: Nature Synthesis (article, 2023-05-30)
Field: Materials Science — Materials Chemistry

Automation, analytics and artificial intelligence for chemical synthesis Automation ; Analytics ; Identification (biology) ; Computer science ; Data science ; Data analysis ; Artificial intelligence ; Engineering ; Data mining Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Vaidehi Menon | Sambit Das | Vikram Gavini | Liang Qi
Source: Acta Materialia (article, 2023-11-15)
Field: Materials Science — Biomaterials

Atomistic simulations and machine learning of solute grain boundary segregation in Mg alloys at finite temperatures Molecular dynamics ; Materials science ; Grain boundary ; Crystallite ; Thermodynamics ; Spectral line ; Physics ; Microstructure ; Computational chemistry ; Metallurgy ; Chemistry Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Microstructure and mechanical properties
Authors: Xabier García-Andrade | Pablo G. Tahoces | Jesús Pérez‐Ríos | Emilio Martínez‐Núñez
Source: The Journal of Physical Chemistry A (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations Different machine learning (ML) models are proposed in the present work to predict density functional theory-quality barrier heights (BHs) from semiempirical quantum mechanical (SQM) calculations. The ML models include a multitask deep neural network, gradient-boosted trees by means of the XGBoost interface, and Gaussian process regression. The obtained mean absolute errors are similar to those of previous models considering the same number of data points. The ML corrections proposed in this paper could be useful for rapid screening of the large reaction networks that appear in combustion chemistry or in astrochemistry. Finally, our results show that 70% of the features with the highest impact on model output are bespoke predictors. This custom-made set of predictors could be employed by future Δ-ML models to improve the quantitative prediction of other reaction properties. Kriging ; Artificial neural network ; Computer science ; Regression ; Gaussian ; Gaussian process ; Bespoke ; Machine learning ; Set (abstract data type) ; Work (physics) ; Artificial intelligence ; Data set ; Algorithm ; Statistical physics ; Chemistry ; Mathematics ; Computational chemistry ; Physics ; Statistics ; Thermodynamics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Joshua A. Rackers | Lucas Tecot | Mario Geiger | Tess Smidt
Source: Machine Learning Science and Technology (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

A recipe for cracking the quantum scaling limit with machine learned electron densities Abstract A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, however, imposes an effective limit of about a few dozen atoms on traditional electronic structure calculations. We present a machine learning (ML) method to break through this scaling limit for electron densities. We show that Euclidean neural networks can be trained to predict molecular electron densities from limited data. By learning the electron density, the model can be trained on small systems and make accurate predictions on large ones. In the context of water clusters, we show that an ML model trained on clusters of just 12 molecules contains all the information needed to make accurate electron density predictions on cluster sizes of 50 or more, beyond the scaling limit of current quantum chemistry methods. Scaling ; Limit (mathematics) ; Context (archaeology) ; Statistical physics ; Quantum chemistry ; Quantum ; Electron ; Electron density ; Cluster (spacecraft) ; Classical limit ; Computer science ; Quantum mechanics ; Physics ; Molecule ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Tianzhixi Yin | Gihan Panapitiya | Elizabeth Coda | Emily Saldanha
Source: Journal of Cheminformatics (article, 2023-11-08)
Field: Materials Science — Materials Chemistry

Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction Deep learning models have proven to be a powerful tool for the prediction of molecular properties for applications including drug design and the development of energy storage materials. However, in order to learn accurate and robust structure-property mappings, these models require large amounts of data which can be a challenge to collect given the time and resource-intensive nature of experimental material characterization efforts. Additionally, such models fail to generalize to new types of molecular structures that were not included in the model training data. The acceleration of material development through uncertainty-guided experimental design has the promise to significantly reduce the data requirements and enable faster generalization to new types of materials. To evaluate the potential of such approaches for electrolyte design applications, we perform comprehensive evaluation of existing uncertainty quantification methods on the prediction of two relevant molecular properties - aqueous solubility and redox potential. We develop novel evaluation methods to probe the utility of the uncertainty estimates for both in-domain and out-of-domain data sets. Finally, we leverage selected uncertainty estimation methods for active learning to evaluate their capacity to support experimental design. Computer science ; Leverage (statistics) ; Generalization ; Property (philosophy) ; Applicability domain ; Machine learning ; Artificial intelligence ; Domain (mathematical analysis) ; Data mining ; Quantitative structure–activity relationship ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Hai‐Chen Wang | Jonathan Schmidt | Miguel A. L. Marques | Ludger Wirtz | A. Romero
Source: 2D Materials (article, 2023-04-12)
Field: Materials Science — Materials Chemistry

Symmetry-based computational search for novel binary and ternary 2D materials Abstract We present a symmetry-based systematic approach to explore the structural and compositional richness of two-dimensional materials. We use a ‘combinatorial engine’ that constructs candidate compounds by occupying all possible Wyckoff positions for a certain space group with combinations of chemical elements. These combinations are restricted by imposing charge neutrality and the Pauling test for electronegativities. The structures are then pre-optimized with a specially crafted universal neural-network force-field, before a final step of geometry optimization using density-functional theory is performed. In this way we unveil an unprecedented variety of two-dimensional materials, covering the whole periodic table in more than 30 different stoichiometries of form A n B m or A n B m C k . Among the discovered structures, we find examples that can be built by decorating nearly all Platonic and Archimedean tessellations as well as their dual Laves or Catalan tilings. We also obtain a rich, and unexpected, polymorphism for some specific compounds. We further accelerate the exploration of the chemical space of two-dimensional materials by employing machine-learning-accelerated prototype search, based on the structural types discovered in the systematic search. In total, we obtain around 6500 compounds, not present in previous available databases of 2D materials, with a distance to the convex hull of thermodynamic stability smaller than 250 meV/atom. Ternary operation ; Periodic table ; Convex hull ; Space group ; Crystal structure prediction ; Tetrahedron ; Density functional theory ; Space (punctuation) ; Binary number ; Combinatorics ; Regular polygon ; Materials science ; Mathematics ; Physics ; Computer science ; Computational chemistry ; Crystallography ; Geometry ; Chemistry ; Crystal structure ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; Inorganic Chemistry and Materials
Authors: Jin‐Xun Liu | Shawn Lu | Shiuan-Bai Ann | Suljo Linic
Source: ACS Catalysis (article, 2023-06-21)
Field: Materials Science — Materials Chemistry

Mechanisms of Ethylene Epoxidation over Silver from Machine Learning-Accelerated First-Principles Modeling and Microkinetic Simulations We employed machine learning-augmented density functional theory (DFT) thermodynamic calculations to assess the stability of different AgOx structures under catalytic ethylene epoxidation reaction conditions. We found that there are multiple AgOx surface motifs that could co-exist under the relevant conditions. These included Ag surface oxides (e.g., AgO_p(4 × 4) and Ag1.83O) and atomic oxygen-covered Ag(111) surfaces. Furthermore, we employed DFT calculations to evaluate the energetics of different reaction mechanisms by which ethylene and oxygen can react on these surfaces. These studies revealed several energetically viable reaction pathways for ethylene epoxidation. Microkinetic modeling analyses, based on the DFT-calculated reaction pathways, showed that ethylene epoxidation can proceed on all surfaces and that multiple pathways, including those involving Langmuir–Hinshelwood and Eley–Rideal mechanisms, could be involved in selective and unselective reactions. The diversity of mechanisms that we discovered in the context of the relatively simple ethylene epoxidation reaction on Ag suggests that the richness and complexity of surface chemistry are most likely a rule rather than an exception in heterogeneous catalytic chemical transformations on metal surfaces and that the concept of a single or even a dominant mechanism and reaction intermediates might need to be revisited for many reactions. Ethylene ; Chemistry ; Catalysis ; Density functional theory ; Reaction mechanism ; Context (archaeology) ; Computational chemistry ; Reaction intermediate ; Heterogeneous catalysis ; Photochemistry ; Chemical physics ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Elena Gelžinytė | Mario Öeren | Matthew Segall | Gábor Cśanyi
Source: Journal of Chemical Theory and Computation (article, 2023-12-18)
Field: Materials Science — Materials Chemistry

Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications to bond dissociation energy (BDE) prediction, for example, in the context of cytochrome P450 (CYP) metabolism. The transferability of the MACE potential was validated on the COMP6 data set, containing only closed-shell molecules, where it reaches better accuracy than the readily available general ANI-2x potential. MACE achieves similar accuracy on two CYP metabolism-specific data sets, which include open- and closed-shell structures. This model enables us to calculate the aliphatic C-H BDE, which allows us to compare reaction energies of hydrogen abstraction, which is the rate-limiting step of the aliphatic hydroxylation reaction catalyzed by CYPs. On the "CYP 3A4" data set, MACE achieves a BDE RMSE of 1.37 kcal/mol and better prediction of BDE ranks than alternatives: the semiempirical AM1 and GFN2-xTB methods and the ALFABET model that directly predicts bond dissociation enthalpies. Finally, we highlight the smoothness of the MACE potential over paths of sp<sup>3</sup>C-H bond elongation and show that a minimal extension is enough for the MACE model to start finding reasonable minimum energy paths of methoxy radical-mediated hydrogen abstraction. Altogether, this work lays the ground for further extensions of scope in terms of chemical elements, (CYP-mediated) reaction classes and modeling the full reaction paths, not only BDEs. Chemistry ; Molecule ; Dissociation (chemistry) ; Transferability ; Bond-dissociation energy ; Computer science ; Mace ; Hydrogen bond ; Computational chemistry ; Biological system ; Physical chemistry ; Machine learning ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Apiphu Chenwittayakhachon | Kulpavee Jitapunkul | Bunyanuch Nakpalad | Phanit Worrayotkovit | Supawadee Namuangruk‬ | Pichamon Sirisinudomkit | Pawin Iamprasertkun
Source: 2D Materials (article, 2023-01-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning approach to understanding the ‘synergistic’ pseudocapacitive effects of heteroatom doped graphene Abstract In recent years, graphene has been widely utilised as a supercapacitor electrode, and doping heteroatom on graphene is reported to enhance the pseudocapacitance of the electrode materials significantly resulting in a high energy density. However, the relationship and charge storage mechanism of a so-called ‘synergistic effect’ between those doped atoms including oxygen-, nitrogen-, and sulphur-doping on supercapacitor performances remain inscrutable. In this study, machine learning models are used to predict the capacitance of heteroatom-doped graphene-based supercapacitors and establish the effects of heteroatom-doping. Trained artificial neural network can accurately predict the capacitance of the electrode, drawing the best synthesis conditions for the heteroatom-doped graphene. Furthermore, we successfully demonstrate the synergistic effect that arises from co-doping nitrogen, sulphur, and locate the optimised region for N/S-co-doping with high capacitance, and high retention rate. Machine learning methods allow us to consider a much larger space of heteroatom-doping combinations to maximise the supercapacitor performances and provide a useful guideline for co-doping graphene-based supercapacitors. Heteroatom ; Supercapacitor ; Graphene ; Pseudocapacitance ; Doping ; Materials science ; Capacitance ; Nanotechnology ; Electrochemistry ; Electrode ; Optoelectronics ; Chemistry ; Organic chemistry ; Physical chemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Konstantin Gubaev | Viktor Zaverkin | Prashanth Srinivasan | Andrew Ian Duff | Johannes Kästner | Blazej Grabowski
Source: npj Computational Materials (article, 2023-07-25)
Field: Materials Science — Materials Chemistry

Performance of two complementary machine-learned potentials in modelling chemically complex systems Abstract Chemically complex multicomponent alloys possess exceptional properties derived from an inexhaustible compositional space. The complexity however makes interatomic potential development challenging. We explore two complementary machine-learned potentials—the moment tensor potential (MTP) and the Gaussian moment neural network (GM-NN)—in simultaneously describing configurational and vibrational degrees of freedom in the Ta-V-Cr-W alloy family. Both models are equally accurate with excellent performance evaluated against density-functional-theory. They achieve root-mean-square-errors (RMSEs) in energies of less than a few meV/atom across 0 K ordered and high-temperature disordered configurations included in the training. Even for compositions not in training, relative energy RMSEs at high temperatures are within a few meV/atom. High-temperature molecular dynamics forces have similarly small RMSEs of about 0.15 eV/Å for the disordered quaternary included in, and ternaries not part of training. MTPs achieve faster convergence with training size; GM-NNs are faster in execution. Active learning is partially beneficial and should be complemented with conventional human-based training set generation. Atom (system on chip) ; Moment (physics) ; Gaussian ; Convergence (economics) ; Molecular dynamics ; Tensor (intrinsic definition) ; Artificial neural network ; Statistical physics ; Computer science ; Materials science ; Degrees of freedom (physics and chemistry) ; Physics ; Machine learning ; Mathematics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: Riping Liu | Xiuyuan Zhu | Jiayao Duan | Junxin Chen | Zhuang Xie | Chaoyue Chen | Xi Xie | Yanxi Zhang | Wan Yue
Source: Angewandte Chemie International Edition (article, 2023-12-12)
Field: Materials Science — Polymers and Plastics

Versatile Neuromorphic Modulation and Biosensing based on N‐type Small‐molecule Organic Mixed Ionic‐Electronic Conductors Abstract The ion/chemical‐based modulation feature of organic mixed ionic‐electronic conductors (OMIECs) are critical to advancing next generation bio‐integrated neuromorphic hardware. Despite achievements with polymeric OMIECs in organic electrochemical neuronal synapse (OENS). However, small molecule OMIECs based OENS has not yet been realized. Here, for the first time, we demonstrate an effective materials design concept of combining n‐type fused all‐acceptor small molecule OMIECs with subtle side chain optimization that enables robustly and flexibly modulating versatile synaptic behavior and sensing neurotransmitter in solid or aqueous electrolyte, operating in accumulation modes. By judicious tuning the ending side chains, the linear oligoether and butyl chain derivative gNR‐Bu exhibits higher recognition accuracy for a model artificial neural network (ANN) simulation, higher steady conductance states and more outstanding ambient stability, which is superior to the state‐of‐art n‐type OMIECs based OENS. These superior artificial synapse characteristics of gNR‐Bu can be attributed to its higher crystallinity with stronger ion bonding capacities. More impressively, we unprecedentedly realized n‐type small‐molecule OMIECs based OENS as a neuromorphic biosensor enabling to respond synaptic communication signals of dopamine even at sub‐μM level in aqueous electrolyte. This work may open a new path of small‐molecule ion‐electron conductors for next‐generation ANN and bioelectronics. Neuromorphic engineering ; Materials science ; Bioelectronics ; Ionic bonding ; Electrolyte ; Nanotechnology ; Molecule ; Biosensor ; Ion ; Artificial neural network ; Computer science ; Chemistry ; Electrode ; Artificial intelligence ; Organic chemistry Conducting polymers and applications ; Advanced Memory and Neural Computing ; Luminescence and Fluorescent Materials
Authors: Abhiroop Mishra | Dipobrato Sarbapalli | Oliver Rodríguez | Joaquín Rodríguez‐López
Source: Annual Review of Analytical Chemistry (review, 2023-04-17)
Field: Materials Science — Polymers and Plastics

Electrochemical Imaging of Interfaces in Energy Storage via Scanning Probe Methods: Techniques, Applications, and Prospects Developing a deeper understanding of dynamic chemical, electronic, and morphological changes at interfaces is key to solving practical issues in electrochemical energy storage systems (EESSs). To unravel this complexity, an assortment of tools with distinct capabilities and spatiotemporal resolutions have been used to creatively visualize interfacial processes as they occur. This review highlights how electrochemical scanning probe techniques (ESPTs) such as electrochemical atomic force microscopy, scanning electrochemical microscopy, scanning ion conductance microscopy, and scanning electrochemical cell microscopy are uniquely positioned to address these challenges in EESSs. We describe the operating principles of ESPTs, focusing on the inspection of interfacial structure and chemical processes involved in Li-ion batteries and beyond. We discuss current examples, performance limitations, and complementary ESPTs. Finally, we discuss prospects for imaging improvements and deep learning for automation. We foresee that ESPTs will play an enabling role in advancing EESSs as we transition to renewable energies. Scanning electrochemical microscopy ; Scanning probe microscopy ; Nanotechnology ; Scanning ion-conductance microscopy ; Microscopy ; Materials science ; Electrochemistry ; Electrochemical energy conversion ; Computer science ; Scanning electron microscope ; Scanning confocal electron microscopy ; Electrode ; Chemistry ; Optics ; Physics Conducting polymers and applications ; Advancements in Battery Materials ; Electrochemical Analysis and Applications
Authors: Peichen Wu | Ashif Sikandar Iquebal | Kumar Ankit
Source: Computational Materials Science (article, 2023-04-12)
Field: Materials Science — Materials Chemistry

Emulating microstructural evolution during spinodal decomposition using a tensor decomposed convolutional and recurrent neural network Emulation ; Robustness (evolution) ; Computer science ; Convolutional neural network ; Context (archaeology) ; Deep learning ; Artificial neural network ; Artificial intelligence ; Robustification ; Spinodal decomposition ; Algorithm ; Computational science ; Phase (matter) ; Physics ; Geology Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Solidification and crystal growth phenomena