Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals.

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials.

Correction: Accelerating copolymer inverse design using monte carlo tree search.

Correction for 'Accelerating copolymer inverse design using monte carlo tree search' by Tarak K. Patra , , 2020, , 23653-23662, https://doi.org/10.

Machine learning overcomes human bias in the discovery of self-assembling peptides.

Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias.

Machine learning the metastable phase diagram of covalently bonded carbon.

Conventional phase diagram generation involves experimentation to provide an initial estimate of the set of thermodynamically accessible phases and their boundaries, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to experimentally inaccessible regions. Such an approach, combined with high throughput first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases (e.g.

Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene.

We introduce a multi-reward reinforcement learning (RL) approach to train a flexible bond-order potential (BOP) for 2D phosphorene based on ab initio training data sets. Our approach is based on a continuous action space Monte Carlo tree search algorithm that is general and scalable and presents an efficient multiobjective optimization scheme for high-dimensional materials design problems. As a proof-of-concept, we deploy this scheme to parametrize multiple structural and dynamical properties of 2D phosphorene polymorphs.

Learning in continuous action space for developing high dimensional potential energy models.

Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example.

Learning with Delayed Rewards-A Case Study on Inverse Defect Design in 2D Materials.

Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends strongly on the nature and organization of defects-their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches.

Accelerating copolymer inverse design using monte carlo tree search.

There exists a broad class of sequencing problems in soft materials such as proteins and polymers that can be formulated as a heuristic search that involves decision making akin to a computer game. AI gaming algorithms such as Monte Carlo tree search (MCTS) gained prominence after their exemplary performance in the computer Go game and are decision trees aimed at identifying the path (moves) that should be taken by the policy to reach the final winning or optimal solution. Major challenges in inverse sequencing problems are that the materials search space is extremely vast and property evaluation for each sequence is computationally demanding.

Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection.

Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial properties of granular or heterophase systems. Traditionally, the identification of the interface between, for precipitate and matrix phases, in APT data has been obtained either by extracting iso-concentration surfaces based on a user-supplied concentration value or by manually perturbing the concentration value until the iso-concentration surface qualitatively matches the interface.

Promoting Noncovalent Intermolecular Interactions Using a C Core Particle in Aqueous PC60s-Covered Colloids for Ultraefficient Photoinduced Particle Activity.

Noncovalent intermolecular interactions in nanomaterials, such as van der Waals effects, allow adjustment of the nanoscopic size of compounds and their conformation in molecular crystal regimes. These strong interactions permit small particle sizes to be maintained as the crystals grow. In particular, these effects can be leveraged in the confined/reinforcing phase of molecules.

"Teamwork Makes the Dream Work": Tribal Competition Evolutionary Search as a Surrogate for Free-Energy-Based Structural Predictions.

Crystal structure prediction has been a grand challenge in material science owing to the large configurational space that one must explore. Evolutionary (genetic) algorithms coupled with first principles calculations are commonly used in crystal structure prediction to sample the ground and metastable states of materials based on configurational energies. However, crystal structure predictions at finite temperature ( T), pressure ( P), and composition ( X) require a free-energy-based search that is often computationally expensive and tedious.

Machine learning coarse grained models for water.

An accurate and computationally efficient molecular level description of mesoscopic behavior of ice-water systems remains a major challenge. Here, we introduce a set of machine-learned coarse-grained (CG) models (ML-BOP, ML-BOP, and ML-mW) that accurately describe the structure and thermodynamic anomalies of both water and ice at mesoscopic scales, all at two orders of magnitude cheaper computational cost than existing atomistic models. In a significant departure from conventional force-field fitting, we use a multilevel evolutionary strategy that trains CG models against not just energetics from first-principles and experiments but also temperature-dependent properties inferred from on-the-fly molecular dynamics (~ 10's of milliseconds of overall trajectories).

Configurational-Bias Monte Carlo Back-Mapping Algorithm for Efficient and Rapid Conversion of Coarse-Grained Water Structures into Atomistic Models.

Coarse-grained molecular dynamics (MD) simulations represent a powerful approach to simulate longer time scale and larger length scale phenomena than those accessible to all-atom models. The gain in efficiency, however, comes at the cost of atomistic details. The reverse transformation, also known as back mapping, of coarse-grained beads into their atomistic constituents represents a major challenge.

Improving the Efficiency of Configurational-Bias Monte Carlo: Extension of the Jacobian-Gaussian Scheme to Interior Sections of Cyclic and Polymeric Molecules.

The Jacobian-Gaussian method, which has recently been developed for generating bending angle trials, is extended to the conformational sampling of inner segments of a long chain or cyclic molecule where regular configurational-bias Monte Carlo was found to be very inefficient or simply incapable (i.e., for the cyclic case).

Improving the Efficiency of Configurational-Bias Monte Carlo: A Jacobian-Gaussian Scheme for Generating Bending Angle Trials for Linear and Branched Molecules.

A new method, called Jacobian-Gaussian scheme, has been developed to overcome the challenge of bending angle generation for linear and branched molecules in configurational-bias Monte Carlo. This method is simple, general, fast, and robust which can yield high acceptance rates. Since there are several bending angles in a branched point and their energies are coupled to each other, generating one trial that is acceptable for all energetic terms is a difficult problem.

Improved Monte Carlo Scheme for Efficient Particle Transfer in Heterogeneous Systems in the Grand Canonical Ensemble: Application to Vapor-Liquid Nucleation.

Reformulation of existing Monte Carlo algorithms used in the study of grand canonical systems has yielded massive improvements in efficiency. Here we present an energy biasing scheme designed to address targeting issues encountered in particle swap moves using sophisticated algorithms such as the Aggregation-Volume-Bias and Unbonding-Bonding methods. Specifically, this energy biasing scheme allows a particle to be inserted to (or removed from) a region that is more acceptable.

Improving the efficiency of configurational-bias Monte Carlo: a density-guided method for generating bending angle trials for linear and branched molecules.

A new method has been developed to generate bending angle trials to improve the acceptance rate and the speed of configurational-bias Monte Carlo. Whereas traditionally the trial geometries are generated from a uniform distribution, in this method we attempt to use the exact probability density function so that each geometry generated is likely to be accepted. In actual practice, due to the complexity of this probability density function, a numerical representation of this distribution function would be required.

Surface induced nucleation of a Lennard-Jones system on an implicit surface at sub-freezing temperatures: a comparison with the classical nucleation theory.

The aggregation-volume-bias Monte Carlo method was employed to study surface-induced nucleation of Lennard-Jonesium on an implicit surface below the melting point. It was found that surfaces catalyze not only the formation of the droplets (where the nucleation free energy barriers were shown to decrease with increasing surface interaction strength), but also the transition of these droplets into crystal structures due to the surface-induced layering effects. However, this only occurs under suitable interaction strength.

Vapor-liquid nucleation in two dimensions: on the intriguing sign switch of the errors of the classical nucleation theory.

A nucleation study of a two-dimensional (2D) Lennard-Jones (LJ) system is done using the aggregation-volume-bias Monte Carlo with umbrella sampling method. The results obtained from this simulation study was compared to those predicted by the classical nucleation theory (CNT). It was found that the nucleation free energy obtained for this 2D LJ system was underestimated by CNT; however, this result is significantly different from that found for the 3D LJ system where CNT overestimates the free energy.