'In silico' discovery of materials with targeted characteristics

Discovering materials with targeted properties 'in silico' (all candidate identification, simulation, and protyping done in computers) is a rapidly evolving field with immense potential. It holds the promise of accelerating materials science by bypassing the laborious and expensive trial-and-error approach of traditional methods. Here's an overview of how it works and the exciting possibilities it offers:

Key Approaches:

• Density Functional Theory (DFT): A quantum mechanical method for simulating electron behavior in materials, predicting their electronic and structural properties with high accuracy.
• Machine Learning (ML): Algorithmic analysis of large datasets to identify relationships between material properties and their structure or composition.
• Molecular Dynamics (MD): Simulating the movement of atoms within a material over time, providing insights into its behavior under different conditions.
• High-Throughput Screening (HTS): Automated exploration of vast databases of potential materials, filtering them based on desired properties.

Workflow:

1. Define target properties: Identify the desired characteristics of the material, such as high electrical conductivity, specific optical bandgap, or exceptional strength.
2. Generate candidate materials: Use DFT, ML, or other methods to create a pool of potential materials with varying compositions and structures.
3. Virtual screening: Employ HTS and ML models to filter the candidates based on their predicted properties, quickly discarding those unlikely to meet the target criteria.
4. Refinement and validation: Further analyze the shortlisted materials with more sophisticated simulations and theoretical calculations to refine their properties and confirm their feasibility.
5. Experimental validation: Finally, synthesize and test the most promising candidates in the lab to experimentally verify their predicted properties and performance.

Benefits:

• Reduced time and cost: In silico methods can dramatically accelerate materials discovery compared to traditional trial-and-error, saving years and millions of dollars.
• Access to unexplored materials: Computational tools can explore vast spaces of possibilities beyond what's practical to synthesize, leading to the discovery of novel materials with unprecedented properties.
• Targeted design: By focusing on specific properties, in silico methods can tailor materials to specific applications, leading to higher efficiency and performance.
• Discovery of fundamentally new materials: In silico tools could lead to the creation of materials with entirely new properties and functionalities never seen before.
• Improved understanding: Simulations provide insights into the fundamental mechanisms behind material behavior, enabling better optimization and design.

IBM Z16

1. High-performance computing: With its powerful Telum chips and parallel processing architecture, the z16 can handle complex scientific simulations and calculations much faster than conventional computers. This enables researchers to tackle problems like modeling protein folding, designing new materials, and simulating climate change scenarios with greater accuracy and detail.

2. Advanced cryptography and data security: The z16 is the industry's first quantum-safe system, featuring built-in encryption algorithms resistant to potential future attacks from quantum computers. This secure architecture ensures the integrity of sensitive scientific data, protects intellectual property, and facilitates secure collaboration across research institutions.

3. Artificial intelligence and machine learning: The z16 supports AI and ML applications by providing a platform for training and deploying complex models. This can be used for analyzing large datasets of scientific data, identifying patterns and correlations, and making predictions with significantly improved accuracy.

4. High availability and reliability: The z16 is known for its exceptional uptime and resilience to faults, making it ideal for running critical scientific applications that require consistent and reliable access to data and computing resources. This reduces downtime and ensures continuous progress in research projects.

5. Scalability and flexibility: The z16 can be easily scaled to meet the evolving needs of scientific research. This allows organizations to adjust computing power and storage capacity as required, supporting large-scale projects and collaborative endeavors.

6. Quantum-safe: With the new Crypto Express8S (CEX8C), IBM z16 helps deliver quantum-safe APIs that position businesses to begin the usage of quantum-safe cryptography along with classical cryptography as they begin modernizing applications and building applications. Discovering where and what kind of cryptography is being used is a key first step along the journey to quantum safety. IBM z16 provides instrumentation that can be used to track cryptographic instruction execution in the CP Assist for Cryptographic Functions (CPACF).[4]

The on-chip Integrated Accelerator for AI is designed for high-speed, real-time inferencing at scale. It is designed to add more than six TFLOPS of processing power shared by all cores on the chip. This centralized AI design is intended to provide high performance and consistent low-latency inferencing for processing a mix of transactional and AI workloads at speed and scale. Now, complex neural network inferencing that uses real-time data can be run and delivers insights within high-throughput enterprise workloads in real time while still meeting stringent service-level agreements (SLAs). A robust ecosystem of frameworks and open-source tools, when combined with the IBM Deep Learning Compiler that generates inferencing programs that are highly optimized for the IBM Z architecture and the Integrated Accelerator for AI, help enable rapid development and deployment of deep learning and machine learning models on IBM Z to accelerate time to market.[4]

An example of a scientific field benefiting from IBM z16:

Materials science: The z16 can be used to design and test new materials with specific properties, leading to advancements in battery technology, solar energy, and other fields.

In silico material design on the IBM HPC cloud is an exciting realm with immense potential to revolutionize materials science. It leverages the cloud's immense computing power and advanced tools to:

1. Accelerate materials discovery: Traditional trial-and-error methods for discovering new materials are slow and expensive. The HPC cloud allows researchers to virtually screen millions of candidate materials, simulating their properties and performance under various conditions, rapidly identifying promising candidates for further exploration.

2. Design materials with targeted properties: By inputting desired properties like high electrical conductivity, specific optical bandgap, or exceptional strength, the HPC cloud enables researchers to tailor materials for specific applications with greater precision and efficiency.

3. Reduce costs and risks: Virtual experimentation in the cloud eliminates the need for expensive lab equipment and synthesis costs, minimizing risks associated with failed experiments. This allows researchers to focus resources on the most promising candidates with higher confidence.

4. Explore uncharted territories: Computational tools on the HPC cloud can access vast chemical spaces beyond laboratory limitations, opening doors to entirely new classes of materials with previously unimagined properties.

5. Deepen understanding of material behavior: Simulating materials on the atomic and molecular level provides insights into their fundamental mechanisms, enabling researchers to refine their designs and optimize performance.

Here are some specific examples of 'in silico' material design on the IBM HPC cloud:

• Developing high-performance batteries: Researchers are using the cloud to design new electrode materials for lithium-ion batteries with higher energy density and faster charging times, potentially revolutionizing electric vehicle technology.
• Discovering novel catalysts: By simulating catalytic reactions at the atomic level, researchers are identifying new catalysts for clean energy production and sustainable chemical synthesis.

Conclusion:

The process of finding candidate molecular and atomic combinations is very time consuming. Using IBM Z16 mainframe and AI both in software and on the chip, the world can now accelerate scientific discovery and create new combinations of materials. This is a not an attempt to play God - just accelerate process of discovering new materials for cost effective living.

Caveat: This is not legal advise and should be construed as such. This article is written by me as an individual and does not represent guidance from IBM Corporation where I work.

References:

1. https://arxiv.org/pdf/2401.04070.pdf
2. https://encyclopedia.pub/entry/26218
3. https://www.sciencedirect.com/science/article/pii/S2542435120302798
4. https://www.redbooks.ibm.com/redbooks/pdfs/sg248950.pdf
5. Images from: https://www.redbooks.ibm.com/redbooks/pdfs/sg248952.pdf
6. https://www.redbooks.ibm.com/redpapers/pdfs/redp5679.pdf
7. Sodium intercalation in layered oxides: "High-Rate Sodium Intercalation Enabled by Tuning Oxygen Vacancies in Na2Fe2Ni3Mn4O2 Cathode" by Xiao et al. (2023) explores using layered oxides for sodium-ion batteries with faster charging and discharge rates.
8. Silicon nanowires with carbon coating: "Ultralong Graphite Sheath Protected Si Nanowire/CNT Hybrid Anodes for High-Performance Lithium-Ion Batteries" by Wang et al. (2023) presents a silicon nanowire anode design with a protective carbon coating, addressing the stability and volume expansion challenges of pure silicon.
9. Lithium metal anode with solid electrolyte: "Highly Stable Lithium Metal Anode Enabled by Interfacial Engineering with a Polymer Solid Electrolyte" by Li et al. (2023) utilizes a solid electrolyte interface to improve the safety and cycling life of lithium metal anodes.

• Solid-state polymer electrolyte: "Stable and High-Conductive Polymer Electrolyte Based on Poly(ethylene oxide) and Ionic Liquids for High-Performance Lithium-Ion Batteries" by Zhang et al. (2023) reports a new polymer electrolyte with enhanced ionic conductivity and stability for solid-state batteries.